Monetary Policy and Resource Mobility 2th Anniversary of the Bank of Finland Carl E. Walsh University of California, Santa Cruz May 5-6, 211 C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 1 / 29
Resource mobility Understanding costly resource mobility is important: In U.S. for understanding whether recent high unemployment is structural in nature because of the inability of labor resources to shift quickly between uses. In EU for understanding how the flow of resources among member countries affects EU-wide developments and inflation DSGE policy models: Costly to adjust prices but labor and capital can move between firms without cost. Prominent examples: Smets and Wouter (23, 27), Christiano, Eichenbaum and Evans (25). Models of currency union: Benigno (24), Galí and Monacelli (28) Perfectly integrated financial markets, perfect mobility of labor within members but absolute immobility across member states. C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 2 / 29
1.8 1.6 CONSTRUCTION MANUFACTURING PROFESSIONAL EDUCATION LEISURE GOVERNMENT 1.4 1.2 1..8.6.4 1985 1988 1991 1994 1997 2 23 26 29 Figure: Employment shares in construction, manufacturing, professional and business services, educational and health services, leisure and hospitality services, and government (1985:1 = 1). These sectors account for just under 7% of U.S. total employment. Shaded regions denote NBER business cycle recessions. C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 3 / 29
Key questions 1 How important is resource mobility for the transmission mechanism of monetary policy? 2 How important is resource mobility for the objectives of monetary policy? Resource mobility will matter for both. Focus will be on labor mobility to illustrate this conclusion. C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 4 / 29
Outline of talk Evidence on sectoral reallocation and unemployment: Revisit Lilien (1982), Abraham and Katz (1986); JOLTS data on vacancies. Role of costly labor adjustment in four illustrative models: Quadratic costs; Search model with one sector; Search with skill heterogeneity; Search model with two sectors. Implications for policy. C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 5 / 29
Sectoral dispersion and unemployment Does increased sectoral dispersion lead to a raise in average unemployment? If it does, does this mean some of the rise in U.S. unemployment represents a rise in structural unemployment i.e., a rise in the natural rate? Or, does a cyclical rise in unemployment lead to an increase in sectoral dispersion? C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 6 / 29
Sectoral dispersion and unemployment Sectoral dispersion and unemployment was a topic of debate in the 198s. Lilien (1982) Abraham and Katz (1986) Lilien s index of dispersion: σ t = [ K i=1 ( ei,t e t ) ( log e i,t log e t ) 2 ] 1/2. C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 7 / 29
Sectoral dispersion, unemployment, and vacancies (U.S.) 12 1 UNRATE VACRATE LILIEN_SIGMA 5.5 5. 4.5 8 6 4 2 1985 1988 1991 1994 1997 2 23 26 29 4. 3.5 3. 2.5 2. 1.5 1. Figure: The civilian unemployment rate, the vacancy rate, and sectorial dispersion (right scale); monthly, U.S. data, 1985:1-21:1. The dispersion measure is a 12-month moving average. C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 8 / 29
Sectoral dispersion, unemployment, and vacancies Abraham and Katz (1986) regressions Table 1A U.S.: Monthly 2:12-21:9 z t = c + a σ t +a 1 σ t 1 +b 1 z t 1 + 4 i=1 c i ip t i Unemployment rate Vacancy rate a.31.16 a 1.29.9 b 1 1.1.82 4 i=1 c i.1. Significant at the 5% level; Significant at the 1% level. C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 9 / 29
Sectoral dispersion, unemployment and vacancies Abraham and Katz (1986) regressions Table 1B U.S.: Monthly 2:12-21:9 z t = c + a σ t + 4 i=1 c i ip t i Unemployment rate Vacancy rate a.24.4 a 1 b 1 4 i=1 c i.17.6 Significant at the 5% level; Significant at the 1% level. C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 1 / 29
Sectoral dispersion Using JOLTS data, sectoral dispersion is associated with higher unemployment, consistent with Lilien s earlier findings. Vacancies are negatively (but not statistically significantly) related to sectoral dispersion; This is evidence that the sectoral dispersion index is just reflecting cyclical factors; But, weaker evidence against Lillien s hyposthesis than found by Abraham and Katz. C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 11 / 29
Models of costly labor allocation Role of costly labor adjustment in four illustrative models: 1 Quadratic costs of adjusting employment; 2 Search model with one sector; 3 Search with skill heterogeneity composition effects; 4 Search model with two sectors costly sectoral reallocation. C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 12 / 29
Example 1: Quadratic costs of adjusting labor Lechthaler and Snower (211) Costs of employment adjustment: Inflation is ( σ + ϕ π t = βe t π t+1 + Φ Ψ 2 ( ) 2 Lt 1 Y t L t 1 ) x t + ( ) ( ) Ψ 1 ( x t βe Φ t x t+1 ) + µ Φ t ; q t is real marginal cost, µ t is a markup shock, Φ measures the cost of price adjustment, and Ψ is the cost of adjusting employment. Welfare is maximized if the central bank minimizes ( ) ( ) σ + ϕ Ψ L t π 2 t + xt 2 + l 2 t Φ Φ C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 13 / 29
Response to a markup shock: optimal commitment Output gap (commitment).5 1 1.5 2 4 6 8 1 12 14 16.8.6.4.2 Inflation (commitment) Ψ= Ψ=1.85 Ψ=4.2 2 4 6 8 1 12 14 16 Figure: Optimal response under commitment to a markup shock in the quadratic costs of adjustment model. Discretion C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 14 / 29
Outcomes under Taylor rule and optimal policy Table 3: Effects of Ψ: Welfare loss Taylor rule Discretion Commitment Ψ σ x σ π σ x σ π σ x σ π 4.57 1.73 3.44 3.44 4.74.49 1.85 2.35.89 2.21 1.18 2.37.17 4. 1.5.56 1.47.46 1.49.9 More costly labor adjustment reduces output and inflation volatility, but inflation volatility declines more. Fixed loss function C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 15 / 29
Example 2: Costly labor market search Ravenna and Walsh (211) Mortensen and Pissarides search and matching model. Phillips curve takes the form π t = βe t π t+1 α 1 ũ t + α 2 r t + ε t. IRF Interest rate channel because labor costs depend on the PDV of a match. Social loss is L t = π 2 t + λ x 2 t + λ 1 θ 2 t where θ is labor market tightness (all variables expressed relative to their effi cient levels). Weight on labor market tightness is smaller when labor market is characterized by less turnover. C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 16 / 29
Vacancy rate (%) Shifting Beveridge curve 4 3.5 3 The Beveridge Curve shifted out in the Great Recession o 2:12 27:12 + 28:1 29:12 x 21:1 211:1 2.5 2 1.5 3 4 5 6 7 8 9 1 11 Civilian unemployment rate (%) Figure: The U. S. Beveridge Curve C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 17 / 29
Decline in vacancy yield 12 1 8 6 U V YIELD 1.9 1.8 1.7 1.6 1.5 4 2 21 22 23 24 25 26 27 28 29 21 1.4 1.3 1.2 1.1 Figure: The U.S. unemployment rate, the vacancy rate, and the hiring yield (right scale). C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 18 / 29
Decline in vacancy yield 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1. 21 22 23 24 25 26 27 28 29 21 Figure: The hiring yield and forecasted yield based on labor market tightness (V/U). Forecast obtain from an OLS regreesion of the yield on a constant and V/U, 2:12-29:12. C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 19 / 29
Example 3: Skill heterogeneity Ravenna and Walsh (21) IRF Low skill and high skill workers. Low skill worker more likely to experience job separation. In a recessions, the skill mix of the unemployed shifts towards low skill workers: Reduces the vacancy yield rate as firms see more job applicants they don t want to hire. Reduces incentive for firms to post vacancies C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 2 / 29
Example 4: Sector heterogeneity Two sectors, hiring costs are higher if worker previously employed in the other sector. Matches depend on composition of unemployed: M s t = (v s t ) a u 1 a t g(λ s t ); g where λ s t is the fraction of the unemployment who last worked in sector s. Hiring costs also depend on λ s t. C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 21 / 29
Sector heterogeneity and costly labor search A common productivity shock.2 y h.15.2.1.4.5.6 5 1 15 2.8 5 1 15 2 n v.1.2.3 2 4.4 5 1 15 2 6 5 1 15 2 2 u infl 1.5.5 1.1.5.15 5 1 15 2.2 5 1 15 2 Figure: Impulse responses to a serially correlated productivity shock to both sectors. C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 22 / 29
Sector heterogeneity and costly labor search A sector specific productivity shock.8 h1.12 h2.6.1.4.2.8.6.4.2.2 5 1 15 2 5 1 15 2.35 n1.1 n2.3.25.5.2.15.1.5.5 5 1 15 2.1 5 1 15 2 Figure: Impulse responses of hours and employment to a negative productivity shock only to sector 1. C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 23 / 29
Summary and implications : Current DSGE policy models minimize costs of labor reallocation. The Great Recession in the U.S. does not overturn earlier conclusions about the link between sectoral dispersion and unemployment. Evidence from Beveridge Curve and decline in vacancy yield suggests mismatch of workers and job openings may have increased. When labor reallocation is costly, the economy s dynamics and the cost of fluctuations are affected. Role for labor market objectives. Low turnover in labor markets can raise the importance of inflation stability. Composition effects may be important for macro dynamics and therefore for policy objectives and for designing monetary policy. These general conclusions will apply to other factors of production and to other situations in which there are costs of adjustment that reflect the imperfect mobility of resources. C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 24 / 29
C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 25 / 29
Output gap (discretion).5 1 Ψ= Ψ=1.85 Ψ=4 1.5 2 4 6 8 1 12 14 16 Inflation (discretion).8.6.4.2.2 2 4 6 8 1 12 14 16 Figure: Optimal response under discretion to a markup shock in the quadratic costs of adjustment model Return C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 26 / 29
Outcomes with loss function fixed Table 2: Effects of Ψ: Fixed loss Taylor rule Discretion Commitment Ψ σ x σ π σ x σ π σ x σ π 4.57 1.73 3.44 3.44 4.74.49 1.85 2.35.89 2.8 1.76 2.38.16 4. 1.5.56 1.44.8 1.5.8 Return C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 27 / 29
.2.1.3.2 5 1 15 2 inflation: us inflation: eu unemployment: us unemployment: eu.1 2 4 6 5 1 15 2 5 1 15 2 θ: us θ: eu Figure: Responses to a one standard deviation bargaining shock for U.S. (solid line) and EU (dotted line) calibrations. (π and θ scaled in percentage point deviations from steady state; unemployment scaled as percentage points of total labor force). Return C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 28 / 29
Negative productivity shock 1 2 3 4 5 Job finding probability log change EU unconditional high skill low skill 1 2 3 4 5 8 6 Unconditional screening out rate log change EU baseline without composition effect 4 2 1 2 3 4 5 Figure: Skill heterogeneity: response to a negative productivity shock: Job finding and screening rates Return C. E. Walsh (UCSC) Bank of Finland 2th Anniversary May 5-6, 211 29 / 29