How Do Hours Worked Vary with Income? Cross-Country Evidence and Implications Alexander Bick, Arizona State University Nicola Fuchs-Sch¨ undeln, Goethe University Frankfurt, CFS and CEPR David Lagakos, UC San Diego and NBER

EEA Annual Congress August 24, 2017

Motivation • How do average hours worked vary with aggregate income?

⇒ We do not know: lack of data for low-income countries

1

Motivation • How do average hours worked vary with aggregate income?

⇒ We do not know: lack of data for low-income countries

• Why should we care?

⇒ Important for cross-country productivity differences

⇒ Important for cross-country welfare differences

1

Motivation • How do average hours worked vary with aggregate income?

⇒ We do not know: lack of data for low-income countries

• Why should we care?

⇒ Important for cross-country productivity differences

⇒ Important for cross-country welfare differences

• How do individual hours worked vary with individual income?

1

Measuring Hours Worked

2

A New Data Set of Internationally Comparable Hours Worked • Household surveys for 80 countries from 2005 or closest avail. year

,→ Nationally representative and have 5,000+ individuals aged 15+

* European Labor Force Surveys (26)

* IPUMS (7)

* Individual surveys (47)

⇒ Large efforts to harmonize surveys

3

Why all that Effort? Low Quality of Available Data! • Maddison Data (25 countries) - 1870, 1913, 1950, 1973, 1990 and younger - Yet, lot of arbitrary assumptions: * up to 1913: hours per worker taken from UK for all countries * 1950: mostly inter- or extrapolated (e.g. hours for Australia taken from US, Peru is average of 6 other Latin American countries, etc.)

4

Why all that Effort? Low Quality of Available Data! • Maddison Data (25 countries) - 1870, 1913, 1950, 1973, 1990 and younger - Yet, lot of arbitrary assumptions: * up to 1913: hours per worker taken from UK for all countries * 1950: mostly inter- or extrapolated (e.g. hours for Australia taken from US, Peru is average of 6 other Latin American countries, etc.)

• Total Economy Database/PWT (67 countries) - Annual time-series back to 1950 - Most data points before 1990 are inter- or extrapolated - Asia: Data source for hours per worker mostly missing

4

Why all that Effort? Low Quality of Available Data! • Maddison Data (25 countries) - 1870, 1913, 1950, 1973, 1990 and younger - Yet, lot of arbitrary assumptions: * up to 1913: hours per worker taken from UK for all countries * 1950: mostly inter- or extrapolated (e.g. hours for Australia taken from US, Peru is average of 6 other Latin American countries, etc.)

• Total Economy Database/PWT (67 countries) - Annual time-series back to 1950 - Most data points before 1990 are inter- or extrapolated - Asia: Data source for hours per worker mostly missing

⇒ None of these datasets has any individual level information 4

49 “Core Countries” with Most Comparable Data

1

Hours Information (a) Producing output counted in NIPA

⇒ includes self-employment and unpaid family work

(b) Actual (not usual) hours worked at all jobs (not just primary job)

(c) In the last/recent reference week

5

49 “Core Countries” with Most Comparable Data

1

Hours Information (a) Producing output counted in NIPA

⇒ includes self-employment and unpaid family work

(b) Actual (not usual) hours worked at all jobs (not just primary job)

(c) In the last/recent reference week

2

Survey covers a full year

5

Sample Countries

Country Income Group Core Non-Core N/A

Sample countries cover close to 40% of world population.

6

How Do Hours Worked Vary with Aggregate Income?

7

Key Fact: Avg. Weekly Hours per Adult Decrease with GDPpc 50 45 40

KHM

Hours per Week

35

TZA VNM KEN LAO

30 25

MWI

TLS RWAUGA

ECU

GHA

MNG PAK

20 15

IRQ

PER COL

USA BWA CZE CYP LVA CHE EST PRT TUR MUS ALB SVN ROM SVK GRC DNK NAM ZAF LTU AUT GBR POL HUN SWE DEU IRL ESPFIN FRANLD BGR ITA BEL

10 5 0 7

7.5

8

8.5 9 9.5 ln(GDP per Capita)

8

10

10.5

11

Key Fact is Broad-Based

• Hours per adult are higher in low-income countries - Full sample of countries - By demographic group (gender, age and education) - In manufacturing and services, but not for agriculture

9

Key Fact is Broad-Based

• Hours per adult are higher in low-income countries - Full sample of countries - By demographic group (gender, age and education) - In manufacturing and services, but not for agriculture

• Hours in home prod. of services also higher in low-inc. countries - Lower data quality for non-market hours - Bridgman et al (’17): 136 time-use surveys since 1960 (43 countries)

9

Cross-Country Evidence in Line with U.S. Time-Series 50 45

Hours per Week

40

KHM

35

TZA KEN

VNM

PER

LAO

30

1900 GHA

25

ECU

MNG

1944 COL

TLS MWI

RWA

PAK

UGA

20

USA

BWA

1933 NAM

ALB ROM ZAF

LVA EST MUS TUR LTU SVK HUN POL

BGR

15

CYP CZE PRT SVN GRC

2005 CHE

DNK AUT GBR FIN SWE IRL ESP DEU NLD FRA ITA BEL

IRQ

10 5 0 7

7.5

8

8.5 9 9.5 ln(GDP per Capita)

Core Countries

10

10.5

U.S. Time-Series (1900-2005)

Source U.S. time-series: Ramey and Francis (2009)

10

11

Extensive and Intensive Margin Country Income Group Hours Per Adult Employment Rate Hours Per Worker

Low 28.5 75.3 38.4

High 19.0 54.5 35.0

• Hours per adult are decreasing in GDP per Capita Low- vs. high-inc. countries: 29 hrs/week vs. 19 hrs/week Decrease of empl. rate accounts for 3/4 of decrease of hours per adult

11

Extensive and Intensive Margin

Hours Per Adult Employment Rate Hours Per Worker

Low 28.5 75.3 38.4

Country Income Group Middle High 21.7 19.0 52.7 54.5 41.1 35.0

• Hours per adult are decreasing in GDP per Capita Low- vs. high-inc. countries: 29 hrs/week vs. 19 hrs/week Decrease of empl. rate accounts for 3/4 of decrease of hours per adult • Differences between both margins Employment rate: decreases only b/w low- & middle-inc. countries Hours per worker: hump-shaped pattern (driven by agriculture) 11

Aggregate Implications of Higher Hours in Poor Countries 1

Development Accounting - Accounting for differences in GDP per worker across countries * Caselli (2005), Klenow and Rodriguez Clare (1997), Hall and Jones (1999), Hsieh and Klenow (2010)

⇒ Accounting for hours per worker: 15% larger productivity differences

12

Aggregate Implications of Higher Hours in Poor Countries 1

Development Accounting - Accounting for differences in GDP per worker across countries * Caselli (2005), Klenow and Rodriguez Clare (1997), Hall and Jones (1999), Hsieh and Klenow (2010)

⇒ Accounting for hours per worker: 15% larger productivity differences

2

Welfare Differences - CEV of flow utility over cons. & leisure

(Jones and Klenow, 2016)

- Neo-classical growth model w/ subsistence cons.

(Ohanian et al., 2008)

⇒ Accounting for hours per worker: welfare diffs. 60% larger 12

How Do Hours Worked Vary with Individual Income?

13

Measuring Individual Income: A Challenge for Poor Countries

• Two monthly earning variables: 1

Earnings from paid employment * Highly selected sample in low-income countries: ⇒ Avg. share of wage workers: 23.4% ⇒ Avg. hours of wage workers: 48.2 (vs. 38.4)

14

Measuring Individual Income: A Challenge for Poor Countries

• Two monthly earning variables: 1

Earnings from paid employment * Highly selected sample in low-income countries: ⇒ Avg. share of wage workers: 23.4% ⇒ Avg. hours of wage workers: 48.2 (vs. 38.4)

2

Total earnings (incl. income from self-employment) → Paper

14

Measuring Individual Income: A Challenge for Poor Countries

• Two monthly earning variables: 1

Earnings from paid employment * Highly selected sample in low-income countries: ⇒ Avg. share of wage workers: 23.4% ⇒ Avg. hours of wage workers: 48.2 (vs. 38.4)

2

Total earnings (incl. income from self-employment) → Paper

• Hourly wage =

monthly earnings actual hours in last week∗4.3

14

90

Hours per Worker by Hourly Wage Decile for Wage Earners

70

80

TZA

Hours per Week 30 40 50 60

KEN UGA GHA TLS RWA

KHM PAK VNM

0

10

20

MWI

-6

-5

-4

-3

-2 -1 0 ln(Hourly Wage) Low-Income

15

1

2

3

4

90

Hours per Worker by Hourly Wage Decile for Wage Earners

70

80

TZA

Hours per Week 30 40 50 60

KEN UGA GHA

BWA ECU MNG COL KHM PAK IRQ VNM ROM ALB PERMUS POL LTU LVA BGR

TLS RWA

0

10

20

MWI

-6

-5

-4

-3

-2 -1 0 ln(Hourly Wage)

Low-Income

15

1

2

Middle-Income

3

4

90

Hours per Worker by Hourly Wage Decile for Wage Earners

70

80

TZA

Hours per Week 30 40 50 60

KEN UGA GHA

BWA ECU MNG COL KHM PAK IRQ VNM ROM ALB PER

TLS RWA

LVA

EST CZE HUN GBR LTU MUS TUR SVN DEU POLPRT AUT CYP FIN FRA ESP DNKIRL BEL GRC SWE ITA NLD

BGR

0

10

20

MWI

-6

-5

-4

-3

Low-Income

-2 -1 0 ln(Hourly Wage) Middle-Income

15

1

2

3

4

High-Income

Is Aggregate or Individual Income More Important?

• Aggregate Income: also captures institutional features

• Individual wage: income vs. substitution effect

16

Is Aggregate or Individual Income More Important?

• Aggregate Income: also captures institutional features

• Individual wage: income vs. substitution effect

⇒ Run following regression on pooled data from all countries:

log (hic ) = α + βlog (wic ) + γlog (GDPphc ) + δ1 ageic + δ2 ageic 2 + ic

16

Is Aggregate or Individual Income More Important?

• Aggregate Income: also captures institutional features

• Individual wage: income vs. substitution effect

⇒ Run following regression on pooled data from all countries:

log (hic ) = α + βlog (wic ) + γlog (GDPphc ) + δ1 ageic + δ2 ageic 2 + ic

• Here focus on men – results for women qualitatively similar

16

Elasticities of Hours to Agg. and Ind. Income for Men ln Hours

ln (Hourly Wage)

−0.096 (0.035) –

Country Fixed Effects R2 Obs.

No 0.075 405,431

ln (GDP per Hour)

ln Hours –

ln Hours

ln Hours

−0.094 (0.032)

0.018 (0.060) −0.105 (0.052)

– −0.116 (0.039)

No 0.118 405,431

No 0.119 405,431

Yes 0.245 405,431

• Both country and individual income negatively related to hours • Coefficient on country income insignificant in joint regression

⇒ Individual income more important than country income

17

.1

.2

Country-Specific Hours-Wage Elasticities Increase w/ GDPpc

BGR ALB MWI

RWA

ROM

KHM

-.1

βw

0

USA

TZA

PAK

FIN ITA ESP

CYP GRC

MNG ECU IRQ

-.3

-.2

KEN GHA UGA

SVK PRT

POL PER BWA COL MUS

VNM TLS

GBR BEL FRA LVA IRL CHE AUT EST CZE DEU DNK SWE LTU SVN HUN NLD

TUR

7

7.5

8

8.5 9 9.5 ln(GDP per Capita)

10

10.5

• Elasticity negative for most countries, positive for richest 18

11

.1

.2

Cross-Country Evidence in Line with U.S. Time-Series

USA

GBR BEL FRA IRLCHE AUT DEU DNK SWE NLD

LVA BGR EST CZE LTU HUN SVN ALB MWI

RWA

ROM

KHM

-.1

βw

0

USA-1991

USA-1895

VNM TLS

TZA

PAK

FIN SVK PRTUSA-1973 ITA ESP

POL

CYP GRC

MUS

MNG ECU IRQ

-.3

-.2

KEN GHA UGA

PER BWA COL

TUR

7

7.5

8

8.5 9 9.5 ln(GDP per Capita)

10

10.5

11

• U.S. estimates from Costa (2000) for 1890s, 1973, and 1991 19

Variation of Employment Rates by Education

• For non-working, education as proxy for permanent income

• How do employment rates vary by education?

⇒ Show evidence for prime-aged men, same patterns for older individuals and for women

20

0

10

20

Employment Rate (in %) 30 40 50 60 70 80

90 100

Education Gradient of Employment Rate

Low-Inc. Countries

Middle-Inc. Countries

< Secondary

High-Inc. Countries

Sec. Completed

> Sec.

Sample: Men Aged 25-54

• Low-income countries:

empl. rates flat

21

by education

0

10

20

Employment Rate (in %) 30 40 50 60 70 80

90 100

Education Gradient of Employment Rate

Low-Inc. Countries

Middle-Inc. Countries

< Secondary

High-Inc. Countries

Sec. Completed

> Sec.

Sample: Men Aged 25-54

• Low-income countries:

empl. rates flat

by education

• Middle- & high-inc. countries: empl. rates increase in education

21

0

10

20

Employment Rate (in %) 30 40 50 60 70 80

90 100

Education Gradient of Employment Rate

Low-Inc. Countries

Middle-Inc. Countries

< Secondary

High-Inc. Countries

Sec. Completed

> Sec.

Sample: Men Aged 25-54

• Low-income countries:

empl. rates flat

by education

• Middle- & high-inc. countries: empl. rates increase in education

⇒ As for hours-wage elasticity, slope turns more positive 21

(Aguiar and Hurst, 2007)

Conclusion

22

How Do Hours Worked Vary with Income? 1

New data set of internationally comparable hours worked measures

2

Aggregate level evidence: - 29 hrs/week in low-income vs. 19 hrs/week in high-income countries * 15% larger productivity differences (development accounting) * 60% larger welfare differences (growth model with subsistence cons.)

- Key fact is broad based 3

Individual level evidence: - Individual wage more important than country income for decrease - Within country patterns differ b/w low- and high-income countries 23

Looking Ahead: A Basic Labor-Leisure Model

max {ln(c − c¯) + αln(1 − h) − qIh>0 } h

s.t. c =wh(1 − τ ) + T

• Without c¯ and T , inc. and substitution effect cancel out

24

Looking Ahead: A Basic Labor-Leisure Model

max {ln(c − c¯) + αln(1 − h) − qIh>0 } h

s.t. c =wh(1 − τ ) + T

• Without c¯ and T , inc. and substitution effect cancel out

• Low-inc. countries: w are low, T = 0 → income effect dominates

24

Looking Ahead: A Basic Labor-Leisure Model

max {ln(c − c¯) + αln(1 − h) − qIh>0 } h

s.t. c =wh(1 − τ ) + T

• Without c¯ and T , inc. and substitution effect cancel out

• Low-inc. countries: w are low, T = 0 → income effect dominates

• High-inc. countries: w are high, T > 0 → substit. effect dominates

24

Social Benefits Increase with GDPpc 30

DEU

Social Benefits as % of GDP

25

FRA BEL AUT

20 SRB

15

POLSVK HUN TUR

MNG

10

BGR BRA ROM

TUN ARM COL EGY PRYZAF PER SLV ALB IDN

5

BOL

0 7

7.5

8

8.5 9 9.5 ln(GDP per Capita)

ITA NLD DNK SWE SVN FIN IRL PRT GRC CZE GBR ESP

RUS LTU EST LVA

AUS CAN

USA CHE

MUS CHL

10

Source: Government Finance Statistics, IMF

25

CYP

10.5

11

A Quantitative Exploration • Quantitative importance of subsistence cons. and the welfare state - across countries on the aggregate level - within countries on the individual level

26

A Quantitative Exploration • Quantitative importance of subsistence cons. and the welfare state - across countries on the aggregate level - within countries on the individual level

• Qualitative challenge - Two margins - Agricultural sector

26

A Quantitative Exploration • Quantitative importance of subsistence cons. and the welfare state - across countries on the aggregate level - within countries on the individual level

• Qualitative challenge - Two margins - Agricultural sector

• Complementary mechanism: leisure goods

26

(e.g., Vandenbroucke ’09, Aguiar et al. ’17)

How Do Hours Worked Vary with Income? Cross ...

Aug 24, 2017 - Hours in home prod. of services also higher in low-inc. countries ..... Sec. Sample: Men Aged 25-54. • Low-income countries: empl. rates flat.

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