Nick Bloom, Econ 247, 2015 Nick Bloom Productivity and Reallocation.

Nick Bloom, Econ 247, 2015

Nick Bloom

Productivity and Reallocation


Big Overview

Economists started looking at establishment data in the 1990s (Haltiwanger, Davis, Bartelsman, Bailey etc.)

There was surprise over:

• High levels of turnover

• Heterogeneity within industries

• The lumpiness of micro-economic activity

• The importance of reallocation in driving productivity


Why should you be interested in this?

Important to understanding growth – e.g. 3/4 productivity growth is reallocation, unemployment driven by churn etc

Second, this is a fertile area of research:• It is new – many open questions• It is hard – typically needs mix of empirics, simulation

and modeling, so barriers to entry high

Third, Stanford has a Census node. Census data is painful to access, but this also deters – so still low-hanging fruit (like my Grandma’s attic – some amazing stuff in there)


High levels of turnover

Heterogeneity within industries

The lumpiness of micro-economic activity

The importance of reallocation in driving productivity


Turnover

About 15% of jobs are destroyed and 20% created in the private sector every year. About 80% of this turnover occurs within the same SIC-4 digit industry

This is robust across countries (US, Europe, Asia and SA)

But, before I show data a couple of point on definitions:

• This is turnover in “jobs”, defined in terms of establishment employment changes, e.g. CES

• A linked (but distinct concept) is turnover in “employment” – which is two to three times higher – defined in terms of workers changes, e.g. CPS


Turnover in “Jobs” versus “Employment” – Expanding Firm example

Source: John Haltiwanger Note: Worker flow=14, Job flow=4

Nick Bloom, Econ 247, 2015Source: John Haltiwanger

Turnover in “Jobs” versus “Employment” – Contracting Firm example

Note: Worker flow=15, Job flow=9


Quarterly Job Flows in Private Sector, 1990-2005, BED data

Source: John Haltiwanger (2005)

(1) Net jobs flows equal change in employment ≈ change in unemployment(2) Gross flows are much bigger than net flows(3) Reduction in job churn that (in manufacturing) part of a longer trend(4) Job destruction does not necessarily mean firing – could be not hiring a replacement for a separation.

Updated: quarterly job flows continued falling in the Great Recession, particularly the creation margin

Source: Business Employment Dynamics (BED) and CPS

Source: Grimm, Haltiwanger and Foster (2013), “Reallocation in the Great Recession: Cleansing or Not?”

Change in unemployment rate

Young/Small plants have much higher flows

Source: Business Dynamics Statistics (BDS)



Current recession – challenge is falling labor force participation (mainly low skilled men)


Job Flows and Employment Flows, total private (% of total)

Source: John Haltiwanger


This is very much in the spirit of Schumpeter


This is very much in the spirit of Schumpeter

Although probably his most famous quote was:“Early in life I had three ambitions. I wanted to be the greatest economist in the world, the greatest horseman in Austria, and the best lover in Vienna. Well, I never became the greatest horseman in Austria“

To which the (un-attributed) response was:“Those we knew Schumpeter as an Economist, Lover or a Horseman presumed his skills were in the other two fields”

“The fundamental impulse that keeps the capital engine in motion comes from the new consumers’ goods, the new methods of production and transportation, the new markets... [The process] incessantly revolutionizes from within, incessantly destroying the old one, incessantly creating a new one. This process of Creative Destruction is the essential fact of capitalism.”Schumpeter (p. 83, 1942)


Heterogeneity basic facts

Typical gap between 10th and 90th percentiles of productivity within same industry is 200% (Syverson, 2004)

These spreads are very persistent:• About 70% to 80% annual job-flows are persistent• About 60% to 70% annual productivity growth is

persistent

Nick Bloom, Econ 247, 2015 18

Big TFP dispersion across firms: for example, US ready mix concrete plants:

Source: Syverson (2004)

High competitionLow competition


What could cause this heterogeneity?

One possibility is pure measurement error, but:

• Productivity is strongly linked with exit and LR growth

• When looking at micro-industries where we measure plant prices (e.g. boxes, bread, block ice, concrete, plywood, carbon black etc.) still see this spread (Foster, Haltiwanger and Syverson, 2008 AER)


Explanations of this heterogeneity?

Several possible economic models of the spread are:

• Mistakes/learning (Jovanovic, 1982 Econometrica)

• Mis-measurement:• “Hard” technology (e.g. R&D)• Skills• Other inputs (computers) or utilization

• Management and managers


Lumpiness of growth

The share of employment growth generated by large adjustments is big (Davis and Haltiwanger, 1992 QJE)

• More than 2/3 manufacturing job creation/destruction accounted for by +25% changes

• For non-manufacturing even greater

Same is true, but more extreme, for investment (Doms and Dunne, 1998 RED).

Suggests substantial adjustment-costs in factor changes


Lumpiness of employment growth

Source: John Haltiwanger, annual data manufacturing


Measuring productivity (ωi,t)

Labor Productivity:

tititi lvaLP ,,,

Three factor TFP:

timtiktiltiti mklyTFP ,,,,3,

Five factor TFP:

tictietimtiktiltiti cemklyTFP ,,,,,,5,

Note: va=log(value added), l=log(labor force), k=log(tangible capital), m=log(materials, e=log(energy), c=log(IT). If IT included need to remove from tangible capital.


Defining industry (or aggregate) productivity

Define a simple industry productivity index: Pt

Where:

ωi,t is the productivity of establishment i in period t (i.e. log(labor productivity) or log (TFP))

si,t is the share of establishment i in the industry in period t (i.e. the share of employment or sales in industry employment or sales)

titit sP ,,


Industry productivity can increase through two channels

• Within Firms (Traditional view)– The same firms become more productive (e.g. new

technology spreads quickly to all firms, like Internet)

• Between Firms (“Schumpeterian”view)– Low TFP firms exit and resources are reallocated to

high TFP firms• High TFP firms expand (e.g. more jobs) & low TFP

firms contract (e.g. less jobs)• Exit/entry

27


These two effects are well known to cricket fans

Within batsman (each batsman improves)

Between batsman (more time for your best batsman)

28


Decomposing productivity (1)

Productivity growth for a balanced panel of establishments can be broken down into three terms:

termCross ))((

rmBetween te )(

rm Within te)(

1,,1,,

1,1,,

1,,1,

1,1,,,1

titititi

tititi

tititi

tititititt

ss

ss

s

ssPP

Within term is included in representative agent models, while the between and cross terms would not be

Reallocation


Decomposing productivity (2)

Allowing for entry and exit requires two more terms:

Exit term )(

Entry term )(

termCross ))((

termBetween )(

termWithin )(

,,

,,

,

,

1,,1,,

1,1,,

1,,1,

1,1,,,1

AverageExitExitti

AverageEntryEntryti

titititi

tititi

tititi

tititititt

titi

titi

s

s

ss

ss

s

ssPP

This is the Bailey, Hulten and Campbell (1992) decomposition


*


Total reallocation (between, entry and exit) accounts for about ½ of manufacturing TFP growth

*Combines -0.08 “between” and 0.34 “cross”


(A) Treats all reallocation within establishments as “within” growth (large establishments in balanced panel have 500+ employees)

(B) Reallocation terms most likely to be downward biased by miss measured prices (Foster, Haltiwanger and Syversson, 2008)

So in manufacturing re-allocation of factors probably accounts for the majority of productivity growth

This is probably even an underestimate


Source: Foster, Haltiwanger & Krizan (2000 and 2006)

Reallocation (including entry) accounts for almost all Retail TFP growth

0

0.2

0.4

0.6

0.8

1

Retail

ContinuingEstablishments

Net entry


Source: Hsieh and Klenow (2008); mean=1

Differences in reallocation also a factor in explaining cross country TFP gaps


BACK-UP

Reallocation also appears to vary over the cycle: Usually higher in recessions except for the Great Recession (maybe because finance dictated growth rather than TFP during this?)

Normal is Zero Change in Unemployment, Mild is 0.01 Change, Sharp is 0.03 Change.High Productivity is 1 std dev above mean, Low Productivity is 1 std dev below mean.


The recession (falling output) is now over but the recovery (return to levels) is very slowUnemployment rate, seasonally adjusted (Source BLS)

Unemployment is still 4% above “normal” levels

Things look even worse in CaliforniaUnemployment rate, seasonally adjusted (Source BLS)

Unemployment is particularly a low-skill issueUnemployment rate, seasonally adjusted

Although the recession could have been worseIndustrial production, normalized to 100 at the start of the recessions (Source FRB)

5060

7080

9010

0

0 10 20 30 40Months since the start of the recession

Great Recession (2007-2009)

Great Depression (1929-1933)

December 2007

May 2009

Similar persistence of TFP & management

Source: Bloom, Sadun and Van Reenen (2012), “Management as a technology: new empirics and old theories”, Stanford mimeo

JOLTS monthly worker turnover data


Still massive churn – including quits – in depths of the recession (I quit a job in December 2001)

Jolts – updated to 2012

Nick Bloom, Econ 247, 2015 Nick Bloom Productivity and Reallocation.

Documents

Transcript of Nick Bloom, Econ 247, 2015 Nick Bloom Productivity and Reallocation.