Journal of Political Economy

What does new micro price evidence tell us about inflation dynamics and monetary policy transmission?

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To understand inflation dynamics, it is necessary to analyse how often and by how much individual prices change.

Key Points: 
  • To understand inflation dynamics, it is necessary to analyse how often and by how much individual prices change.
  • This article discusses what micro price data gathered by the European System of Central Banks’ Price-setting Microdata Analysis Network (PRISMA) tell us about the way firms set their prices.

Decomposing systemic risk: the roles of contagion and common exposures

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Tuesday, April 23, 2024
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Abstract

Key Points: 
    • Abstract
      We evaluate the effects of contagion and common exposure on banks? capital through
      a regression design inspired by the structural VAR literature and derived from the balance
      sheet identity.
    • Contagion can occur through direct exposures, fire sales, and market-based
      sentiment, while common exposures result from portfolio overlaps.
    • First, we document that contagion varies in time, with the highest levels
      around the Great Financial Crisis and lowest levels during the pandemic.
    • Our new framework complements
      traditional stress-tests focused on single institutions by providing a holistic view of systemic risk.
    • While existing literature presents various contagion narratives, empirical findings on
      distress propagation - a precursor to defaults - remain scarce.
    • We decompose systemic risk into three elements: contagion, common exposures, and idiosyncratic risk, all derived from banks? balance sheet identities.
    • The contagion factor encompasses both sentiment- and contractual-based elements, common exposures consider systemic
      aspects, while idiosyncratic risk encapsulates unique bank-specific risk sources.
    • Our empirical analysis of the Canadian banking system reveals the dynamic nature of contagion, with elevated levels observed during the Global Financial Crisis.
    • In conclusion, our model offers a comprehensive lens for policy intervention analysis and
      scenario evaluations on contagion and systemic risk in banking.
    • This
      notion of systemic risk implies two key components: first, systematic risks (e.g., risks related
      to common exposures) and second, contagion (i.e., an initially idiosyncratic problem becoming
      more widespread throughout the financial system) (see Caruana, 2010).
    • In this paper, we decompose systemic risk into three components: contagion, common exposures, and idiosyncratic risk.
    • First, we include contagion in three forms: sentiment-based contagion, contractual-based
      contagion, and price-mediated contagion.
    • In this context,
      portfolio overlaps create common exposures, implying that bigger overlaps make systematic
      shocks more systemic.
    • With the COVID-19 pandemic starting
      in 2020, contagion drops to all time lows, potentially related to strong fiscal and monetary
      supports.
    • That is, our
      structural model provides a framework for analyzing the impact of policy interventions and
      scenarios on different levels of contagion and systemic risk in the banking system.
    • This provides a complementary approach to
      seminal papers that took a structural approach to contagion, such as DebtRank Battiston et al.
    • More generally, the literature on networks and systemic risk started with Allen and Gale
      (2001) and Eisenberg and Noe (2001).
    • The matrix is structured as follows:
      1

      In our model, we do not distinguish between interbank liabilities and other types of liabilities.

    • In other words, we can and aim to estimate different degrees
      of contagion per asset class, i.e., potentially distinct parameters ?Ga .
    • For that, we build three major
      metrics to check: average contagion, average common exposure, and average idiosyncratic risk.
    • N i j

      et ,
      Further, we define the (N ?K) common exposure matrix as Commt = [A

      (20)

      et ]diag (?C
      ?L

      such that average common exposure reads,
      average common exposure =

      1 XX
      Commik,t .

    • N i j

      (22)

      20

      ? c ),

      The three metrics?average contagion, average common exposure, and average idiosyncratic risk?provide a comprehensive framework for understanding banking dynamics.

    • Figure 4 depicts the average level of risks per systemic risk channel: contagion risk, common exposure, and idiosyncratic risk.
    • Figure 4: Average levels of contagion (Equation (20)), common exposure (Equation (21)), and idiosyncratic risk
      (Equation (22)).
    • The market-based contagion is the contagion due to
      investors? sentiment, and the network is an estimate FEVD on volatility data.
    • For most of
      the sample, we find that contagion had a bigger impact on the variance than common exposures.

Nowcasting consumer price inflation using high-frequency scanner data: evidence from Germany

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Tuesday, April 23, 2024
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Key Points: 

    Monetary asmmetries without (and with) price stickiness

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    Friday, April 19, 2024
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    Key Points: 

      Is home bias biased? New evidence from the investment fund sector

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      Key Points: 

        Transactional demand for central bank digital currency

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        Thursday, April 18, 2024
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        Key Points: 

          As interest rates surge: flighty deposits and lending

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          Friday, April 5, 2024
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          Key Points: 

            US monetary policy is more powerful in low economic growth regimes

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            Tuesday, April 2, 2024
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            Key Points: 

              The unequal impact of the 2021-22 inflation surge on euro area households

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              Tuesday, April 2, 2024
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              The 2021-22 surprise inflation surge had a major impact on households in the euro area.

              Key Points: 
              • The 2021-22 surprise inflation surge had a major impact on households in the euro area.
              • Indeed, not everyone was a net loser: while about 70% of households suffered a loss, the rest enjoyed moderate gains.

              Demographics, labor market power and the spatial equilibrium

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              Tuesday, February 13, 2024
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              Abstract

              Key Points: 
                • Abstract
                  This paper studies how demographics affect aggregate labor market power, the urban wage
                  premium and the spatial concentration of population.
                • I develop a quantitative spatial model
                  in which labor market competitiveness depends on the demographic composition of the local
                  workforce.
                • If these factors differ across workers, labor market power has a role to
                  play in explaining wage inequality.
                • This paper contributes to the literature on differences in labor market power by analyzing a
                  new dimension of heterogeneity: demographics.
                • Since older workers are less mobile in terms of
                  switching workplaces, firms have more labor market power over older workers.
                • I start by estimating labor market power by measuring the sensitivity of worker turnover to
                  the wage paid.
                • I find a strong
                  role of demographics in determining the degree of labor market power enjoyed by firms.
                • Next, I provide evidence of the importance of differences in labor market power for spatial
                  wage inequality.
                • To explore the consequences of labor market sorting, I build a spatial general equilibrium
                  model in which labor market competitiveness depends on the demographic composition of the

                  ECB Working Paper Series No 2906

                  2

                  local workforce.

                • If these factors differ across workers, labor market power has a role to
                  play in explaining wage inequality.
                • In
                  the model, geographic sorting by age matters and leads to higher labor market power in rural
                  areas, which implies an urban wage premium that is 4% larger than with uniform labor supply
                  elasticities.
                • I follow Manning (2013) and estimate labor market power by measuring the sensitivity of worker
                  turnover to the wage paid.
                • Bachmann et al., 2021; Ahlfeldt et al., 2022a; Berger et al.,
                  2022) that nest a monopsonistic labor market in a spatial general equilibrium model (Redding
                  and Rossi-Hansberg, 2017).
                • As firms have more labor market power
                  over older workers, they face an upward-sloping labor supply curve that is less elastic in regions
                  with an older workforce.
                • Firms choose in which labor market to operate in the sense that there is free
                  entry at fixed costs into all locations.
                • How are differences in labor market competitiveness across space sustained in spatial equilibrium?
                • I use the model to quantify the importance of heterogeneity
                  in labor market power for the urban wage premium and the spatial concentration of population.
                • My work is complementary to but quite different
                  from this paper since I argue that population aging increases labor market power rather than
                  product market power.
                • By analyzing the effects of a changing age composition of the workforce in the context
                  of labor market power, I relate to literature on the labor market effects of population aging.
                • ECB Working Paper Series No 2906

                  7

                  after controlling for age, differences in labor market power between East and West Germany
                  vanish.

                • They conclude that higher
                  concentration is associated with higher labor market power (as in the model of Jarosch et al.,
                  forthcoming).
                • I offer an alternative explanation why labor market power differs across regions:
                  Since denser regions have a younger workforce, workers are more mobile in terms of switching
                  jobs which implies lower labor market power of firms.
                • In this case, I infer a
                  high labor supply elasticity and low labor market power of firms.
                • I contribute to this growing debate by
                  quantifying differences in labor market power across worker groups and their effects on regional
                  inequality.
                • While the model shows how demographics affect labor market power, the urban wage premium and agglomeration, one fundamental question remains open for future research: What
                  are the policy implications of (differences in) labor market power?