Personnel Economics Job Design - JKU · The Polarization of Job Opportunities in the U.S. labor...

Personnel Economics

Job Design(Chapters 7 & 8)

Nicole Schneeweis (JKU Linz) Personnel Economics 1 / 26

Open Questions

How are jobs designed? What tasks are combined into jobs?

In what cases is multitasking better than specialization?

What are the advantages and disadvantages of teams?

What is free-riding?

Do social norms change production in teams?

How have jobs changed in recent decades?


Job Design: How can jobs be characterized?

SkillsAbility and human capital required to do the job

DiscretionNumber of decisions that an employee has to make (decentralizeddecision making)

MultitaskingNumber of tasks that are bundled in a job

InterdependenceHow close is the job related to other jobs in the company? (connect-edness)


Job Design: Job characteristics


Job Design: Job characteristics

Relationship between these characteristics?

Coherent (complements) or incoherent (substitutes)

Correlation of characteristics: actual/predicted probability

actual probability: fraction of jobs in the sampleeg. the fraction 0.0541 of all jobs are LLLL (low in all four dimen-sions)predicted probability: if there were no relationshipeg. Prob(LLLL) = 0.251*0.190*0.194*0.195 ⇒ 0.0017

LLLL can be observed around 32-times as frequently as predicted=⇒ Job characteristics are positively correlated =⇒ Complements


Job Design: Skills, tasks & decisions

Scope of skills (tasks that can be performed)

Increases �exibility: helping out (more important in small �rms)Facilitates communication: knowledge and understanding other jobsFurthers innovation:

knowledge about many aspects of production enables workers to beinnovativeless resistance to changes in tasks and if certain tasks get obsolete

Scope of tasks (tasks that are performed) =⇒ specialization vs mul-titasking

Specialization (division of labor)

Adam Smith's Wealth of Nations (1776): pin factoryimportant characteristic of modern societiesperfection, time saving, specialized human capital and trainingmore important in large �rms


Job Design: Skills, tasks & decisions

Multitasking

related tasks that need similar skillslower transaction costs (transportation, communication, bureaucracy)tasks are not independent but complementary (task B would be per-formed more e�cient, if task A was performed)furthers innovation and on-the-job learningintrinsic motivation (through the job)more di�cult to evaluate performance (monitoring)

Decisions

Multitasking requires and allows decentralized decision making

=⇒ If tasks are complementary to each other, multitasking is reasonable.Multitasking requires high skills and allows learning on-the-job, which inturn enables employees to make decisions


Intrinsic motivation


Core job characteristics and intrinsic motivation

Skill variety: providing workers with theability to perform more tasks or learn moreskills

Task identity: extent to which a worker'sjob entails completing an observable pieceof work

Task signi�cance: extent to which theworker �nds the product or service valuable

Autonomy: giving the worker greater dis-cretion over how the work is performed andgreater ability to make decisions

Feedback: providing the worker with infor-mation on the e�ects of actions and deci-sions


Job Design: Teams

When to use teams

Many tasks are complementary, too many for one workerCoordination between organizational units, members of each unitSpecialization, without loosing advantages of multitaskingKnowledge Transfer: some individuals have speci�c information, thatis needed by others

Disadvantages of teams

Decision making is di�cult: long discussions, politics and power, re-sult not optimal =⇒ team leader with certain authority to decideFree-riding: individual e�ort is di�cult to observe from outside, possi-ble to hide behind the team, reduces motivation and e�ort


Free-riding in Teams

Kandel & Lazear, Journal of Political Economy 1992

The �rm

Production: factors of production are e�ort-levels of workersY = f (e) e = e1 . . . eN

Payment: single e�ort not observable, depends on total outputPay = Y

N

The worker

Utility: individuals like payment (consumption) but dislike e�ortUi = Y

N − C(ei )

Costs: e�ort is costly, marginal costs of e�ort increaseC(ei ),C ′(ei ) > 0,C ′′(ei ) > 0


Individual utility maximization versus social planer

Individuals maximize their utility

maxei Ui = YN − C(ei )

Optimality:f ′i (e)N − C ′(ei ) = 0 =⇒ f ′

i (e)N = C ′(ei ) ∀i

Result: e = e1 . . . eN

Social planer maximizes welfare function (e�cient solution)

maxe1..eN V = Y −∑N

i=1 C(ei )

Optimality: f ′i (e)− C ′(ei ) = 0 =⇒ f ′i (e) = C ′(ei ) ∀i

Result: e* = e∗1 . . . e∗N

Result: e* > e


Exercise I: Model of free-riding

Y = 4∏N

i=1 e12i

Pay = 4∏N

i=1e

12

iN

C = e2i

N = 2


Solution to Exercise I: Model of free-riding

Individual utility maximization

Reaction functions: e1 = e13

2 /223 and e2 = e

13

1 /223

Results:

e = {e1 = 0.5, e2 = 0.5}Y (e) = 2U = {U1 = 0.75, U2 = 0.75}

Social planer (e�cient solution)

Reaction functions: e1 = e13

2 and e2 = e13

1Results:

e* = {e∗1 = 1, e∗2 = 1}Y (e*) = 4U* = {U∗1 = 1, U∗2 = 1}


Exercise II: Model of free-riding

Y = 4∏N

i=1 e12i

Pay = 4∏N

i=1e

12

iN

C = 12 e2

i

N = 2


Knowledge transfer

Knowledge transfer can be a major bene�t of team production

Two conditions have to hold:

Team members have individual information that can �ow among themembers (i.e. information-sets are not fully overlapping).

The individual information of one member is valuable to some otherteam member (i.e. to carry out his or her tasks, one member canbene�t from the information-set of the other member).


Knowledge transfer

Figure: Knowledge transfer: Kate and Tor perform tasks for which the other'sinformation-set is needed


No knowledge transfer

Figure: No knowledge transfer: Other information-set not needed (Figure 8.2),overlapping information-set and tasks (Figure 8.3)


Job Design Teams

Team size

too small: lower level of knowledge transfer and lower level of free-ridingtoo large: communication problems and more free-riding

Peer monitoring

team members observe each other, reduces free-ridingmore e�ective in small teams: observability, motivation because ofgreater dependency among team members

Norms

informal policies, practices or a set of beliefs that is held by the ma-jority of the grouppower of the norm depends on sanctions against deviations


Free-riding and norms

Utility function with norms

Ui = f (e)N − C(ei ) + γ(ei − e)

e captures the norm (desired e�ort level)if ei > e utility risesif ei < e utility declines

Ui = f (e)N − C(ei ) + γei − γe

U ′i = f ′i (e)N − C ′(ei ) + γ if e is determined externally

U ′i = f ′i (e)N − C ′(ei ) + γ − γe′ if e is determined within the group


Polarization in jobs David Autor (MIT)

The Polarization of Job Opportunities in the U.S. labor market(Center for American Progress and The Hamilton Project, 2010)

Why Are There Still So Many Jobs? The History and Future of Work-place Automation (Journal of Economic Perspectives, 2015)

More employment in jobs with

high wages and high educationlow wages and low education

Less employment in jobs with

medium wages and medium education


Polarization USA

10 center for american progress | www.americanprogress.org

the polarization of Job opportunities in the U.s. labor market

Sex differences in job polarization

The polarization of employment into low- and high-skill occupations has unfolded with increasing velocity over the past two decades. But this polarization did not occur evenly among the sexes, as is shown in Figure 4.

The first set of columns in Figure 4 plot the change between 1979 and 2007 in the share of employment in high-, middle-, and low-skill occupations among each sex. The share of male employment in middle-skill occupations dropped by 7.0 per-cent. For females, the fall was even larger at 15.8 percent. Yet this “hollowing out” of the occupational distribution had dif-ferent consequences for the sexes. Females moved dramati-cally upward in the occupational distribution as they departed the center. Male employment instead moved in roughly equal measures to the tails of the distribution—that is, to high-wage, high-skill and low-wage, low-skill jobs.

The second set of bars in Figure 4 breaks these patterns by edu-cation group, showing that the share of males with no more than a high school education employed in middle-skill occupa-tions dropped by 3.9 percent between 1979 and 2007. More

than the entirety of this decline is accounted for by a corre-sponding rise in employment in low-skill service occupations.

Simultaneously, the share of employment among males with some college education declined in both middle- and high-skill occupations. Even among males with a four-year col-lege degree, employment in high-wage occupations declined noticeably, with the slack taken up approximately evenly by middle- and low-skill occupations.

Some portion of this occupational shift is arguably mechani-cal. As the share of workers with higher educations rises, it is inevitable that some subset will take traditionally noncol-lege jobs. Put simply, when a third of the workforce is college educated, not all college-educated workers will be managers or professionals. Nevertheless, the decline of middle-skill jobs has clearly displaced males toward the tails of the occu-pational distribution. And the net effect is an increase in the share of males in low-skill occupations compared to the share of males in high-skill occupations.

Figure 4 paints a more encouraging picture for females. Women with less than a four-year college degree experienced

Source: May/ORg CPS data for earnings years 1979-2007. See note to figure 12. The 10 broad occupations are classified as belonging to one of three broad skill groups.

fIgURE 4

Changes in occupational employment shares by education and sex, 1979–2007

Males

Females

Percentage change in occupational employment shares

-20%

-16%

-12%

-8%

-4%

0%

4%

8%

12%

16%

20%

All

Low

Occupation skill group Occupation skill group Occupation skill group Occupation skill group

Medium High Low Medium High Low Medium High Low Medium High

High school or less Some college College +

Definitions of skill groups

High skill: Managerial, professional, and technical occupations

Medium skill: Sales, office/admin, production, and operators

Low skill: Protective service, food prep, janitorial/cleaning, personal care/services


Polarization One explanation

Skill-biased technological change: information technology/automation

What kinds of jobs can be replaced by new technologies?

Routine tasks: well-de�ned, standard procedureeg. o�ce work, accounting, repetitive production work

=⇒ medium wages and medium education

What kinds of jobs can't be replaced by new technologies?

Abstract tasks: problem solving, intuitioneg. managers, lawyers, medical doctors

=⇒ high wages and high education

Manual tasks: situational, language, personal contacteg. jobs in the service sector, health jobs

=⇒ low wages and low education


Polarization USA Occupations

David H. Autor 13

Figure 2 illustrates this pattern for the United States by plotting percentage point changes in employment by decade for the years 1979–2012 for ten major occupational groups encompassing all of US nonagricultural employment. (More

that the growth of low-wage service employment does not commence in the United States until the 2000s, a finding that is at odds with all other work using contemporary occupation codes of which I am aware (including the Bureau of Labor Statistic’s own tabulations of Occupational Employment Statistics data for this time period provided in Alpert and Auyer 2003, table 1). At a methodological level, work in this area always requires adjustments and judgment calls in comparing occupational data across Census years, but the adjustments that Mishel et al. apply to the data generate occupational patterns that appear anomalous. Substantively, I believe the main issue is not whether employment polarization has occurred—on this, the evidence appears unambiguous—but the extent to which these occupational employment shifts are helpful for understanding wage polarization or wage inequality more broadly.

Figure 2 Change in Employment by Major Occupational Category, 1979–2012 (the y-axis plots 100 times log changes in employment, which is nearly equivalent to percentage points for small changes)

Sources: Author using data from the 1980, 1990, and 2000 Census IPUMS files, American Community Survey combined file 2006–2008, and American Community Survey 2012. The sample includes the working-age (16–64) civilian noninstitutionalized population. Employment is measured as full-time equivalent workers.Notes: Figure 2 plots percentage point changes in employment (more precisely, the figure plots 100 times log changes in employment, which is close to equivalent to percentage points for small changes) by decade for the years 1979–2012 for ten major occupational groups encompassing all of US nonagricultural employment. Agricultural occupations comprise no more than 2.2 percent of employment in this time interval, so this omission has a negligible effect.

−20

−10

0

10

20

30

40

100 ×

log

Ch

ange

in E

mpl

oym

ent

Personal care

Food/cleaning service

Protective service

Operators/laborers

Production

Office/admin

SalesTechnicians

Professionals

Managers

1979–1989 1989–1999 1999–2007 2007–2012


Polarization EU Countries

Why Are There Still So Many Jobs? 15

reasoning does not take into account the role played by the three mitigating forces discussed above: complementarity, demand elasticity, and labor supply.

Let’s first consider the effect of computerization on wages in abstract task-intensive occupations such as managerial, professional, and technical occupations. These occu-pations all draw upon large bodies of constantly evolving expertise: for example, medical knowledge, legal precedents, sales data, financial analysis, programming languages, and economic statistics. Information technology and computerization should strongly complement workers performing abstract task-intensive jobs. By dramatically lowering the cost and increasing the scope of information and analysis available to them, computerization enables workers performing abstract tasks to further specialize in their area of comparative advantage, with less time spent on acquiring and crunching information, and more time spent on interpreting and applying it. By the same token, information technology substitutes for many of the

Figure 3 Change in Occupational Employment Shares in Low, Middle, and High-Wage Occupations in 16 EU Countries, 1993–2010

Source: Goos, Manning, and Salomons (2014, table 2).Notes: High-paying occupations are corporate managers; physical, mathematical, and engineering professionals; life science and health professionals; other professionals; managers of small enterprises; physical, mathematical, and engineering associate professionals; other associate professionals; life science and health associate professionals. Middle-paying occupations are stationary plant and related operators; metal, machinery, and related trade work; drivers and mobile plant operators; office clerks; precision, handicraft, craft printing, and related trade workers; extraction and building trades workers; customer service clerks; machine operators and assemblers; and other craft and related trade workers. Low-paying occupations are laborers in mining, construction, manufacturing, and transport; personal and protective service workers; models, salespersons, and demonstrators; and sales and service elementary occupations.

−14.9%

−12.1% −12.0% −10.9% −10.8% −10.7% −10.6% −10.6% −10.4% −10.3%

−9.6% −8.6% −8.5%

−7.6% −6.7%

−4.9%

−18%

−15%

−12%

−9%

−6%

−3%

0%

3%

6%

9%

12%

15%

Ireland

Belgium

SpainUnited Kingdom

Luxembourg

Greece

Finland

ItalyAustria

Denmark

Sweden

France

Norway

Netherlands

Germany

Portugal

Low payingMiddle payingHigh paying


Questions?


Personnel Economics Job Design - JKU · The Polarization of Job Opportunities in the U.S. labor...

Documents

Transcript of Personnel Economics Job Design - JKU · The Polarization of Job Opportunities in the U.S. labor...