The labor market consequences of electricity adoption ...The labor market consequences of...

The labor market consequences of electricity adoption:concrete evidence from the Great Depression

Miguel Morin

23 February 2015

Miguel Morin Technology adoption and the labor market 1/34

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Motivation: the technological unemployment debate

Technological unemployment:

I direct replacement of workers with technologyI short- to medium-term unemployment (up to 10 years)

Revival of debate with new wave of technology:

I water power: “employment of machinery is frequently detrimental to[the] interests [of the labouring class]” (Ricardo, 1817)

I steam engine: “demand for commodities is not demand for labour”(Mill, 1871)

I electricity: “technological unemployment ... due to our discovery ofmeans of economising the use of labour” (Keynes, 1933)

I computers: “the second machine age ... and the workless economy”(Brynjolfsson et al., 2014)

Luddite fallacy (Easterly, 2001)

I labor-saving = productivity-enhancingI adjustment on output margin, not employment

This paper: testing the theory of technologicalunemployment with electricity adoption

Choice of period and technology:

I variation in electricity prices (not computers)I labor market data: Census of Manufactures 1929-1935

Causal e↵ect of cheaper electricity on labor market outcomes andoutput

Identification strategy:

I geography as an instrument for electricity pricesI concrete industry ideal to study electricity adoptionI newly digitized plant-level dataset, 1929-1935

Literature review of economic history and electrification

first paper to use Instrumental Variables to test the margin ofadjustment in technological unemployment:

I plant-level data with employment, wages, quantity, electric capital,I instrument for change in price of electricityI disentangles technology from o↵shoring and unionization

existing studies with evidence of directed technical change andtechnological upgrading:

I Hanlon (forthcoming): decrease in supply of American cotton in 1860sI Hornbeck and Naidu (2013): decrease in supply of cheap labor in 1930s

existing historical studies with Ordinary Least Squares and withoutplant-level data:

I productivity growth (Field, 2000)I decline in labor share (Woolf, 1994)I routinization of production (Gray, 2013)

Outline

1 Data

2 Model

3 Empirics

4 Conclusion

Outline

1 Data

2 Model

3 Empirics

4 Conclusion

Why the concrete industry?

clean identification:

I downstream of cement, concrete plants locate near customersI location orthogonal to geography of electricity prices

manual labor / electric power from the grid:

I no electric generation or steam powerI test of technological unemployment

competitive industry:

I <5% of plants have less than 5 competitorsI rules out strategic adoption

Examples of labor-saving machinery:

“the power-driven concrete mixer has practically displaced handmixing” (Jerome, 1934)

conveyor systems, power shovels, machinery for crushing, grinding

Data sources

Labor market outcomes from Census of Manufactures, 1929-1935

I wages, employment, value added, physical output (tons of concrete),and electric horsepower for 630 continuing plants

I total employment, productivity, labor share

Geographic variables from state- or county-level publications:

I electricity prices from Census of Electric Light and Power Stations(Stigler and Friedland, 1963)

I coal share of power in electric capacity by state from Census of ElectricLight and Power Stations in 1927

I housing boom, bank deposit suspensions, and unionization fromFishback et al. (2012) and Kimborough and Snowden (2007)

Data sources

Outline

1 Data

2 Model

3 Empirics

4 Conclusion

Model: technology to produce output

Production function:

Yi ,t = Ai ,t K↵NE ,i ,t L

�NR,i ,t M

1�↵��i ,t

plant i , Total Factor Productivity Ai ,t , non-electric capital KNE ,i ,t ,labor in nonroutine occupations LNR,i ,t

Third factor:

Mi ,t =

��1�

E ,i ,t + L

��1�

R,i ,t

◆ ��1

electric capital KE ,i ,t , labor in routine occupations LR,i ,t

Crucial assumptions:

I elasticity of substitution � > 1I trend in rental rate rE ,t &

�NR,i ,t M

1�↵��i ,t

Third factor:

Mi ,t =

��1�

E ,i ,t + L

��1�

R,i ,t

◆ ��1

�NR,i ,t M

1�↵��i ,t

Third factor:

Mi ,t =

��1�

E ,i ,t + L

��1�

R,i ,t

◆ ��1

Assumption: price of electricity decreases exponentially

ity (1

1900 1910 1920 1930 1940 1950Year

Residential consumers Industrial consumersAll consumers

Source: Historical Statistics of the United States, adjusted for inflation

Model’s predictions

Labor share, labor productivity, capital intensity:

wi,tLi,t

pi,tYi,t= � + �

✓rE ,i,t

wR,i,t

◆1��!�1

% in rE ,i,t

� + �

✓rE ,i,t

◆1��!�1!�1

& in rE ,i,t

KE ,i,t

✓rE ,i,t

wR,i,t

◆�1 ��

wR,i,t

wNR,i,t+

✓1 +

��

wR,i,t

wNR,i,t

◆✓rE ,i,t

wR,i,t

◆��1!�1

& in rE ,i,t ,

wages by occupation wR,i ,t and wNR,i ,t , average wage wi ,t , rental rateof electric capital rE ,i ,t .

Model’s predictions as regression equations:

Labor share, labor productivity, capital intensity:

� logwi ,tLi ,t

pi ,tYi ,t= constant + a� log (pE ,k,t) + error ,

� logYi ,t

Li ,t= constant + b� log (pE ,k,t) + error ,

� logKE ,i ,t

Li ,t= constant + c� log (pE ,k,t) + error ,

state-level price of electricity pE ,k,t

Predictions: a > 0, b < 0, c < 0

firm-level variation within state ) attenuation bias

plant-level outcome on state-level variation ) state-level clustering ofstandard errors

Additional regressions:

Prices, output, revenue, and employment

� log pi ,t = constant + a

0� log (pE ,k,t) + error

� logYi ,t = constant + b

� log pi ,tYi ,t = constant + c

� log Li ,t = constant + d

Technological unemployment:

0 =?, b

0 = 0, c

0 = 0, d

Luddite fallacy:

0 > 0, b

0 > 0, c

0 > 0, d

Outline

1 Data

2 Model

3 Empirics

4 Conclusion

Identification strategy

Causal e↵ect of cheaper electricity on labor market outcomes

Ideal experiment: random allocation of electricity prices

Natural variation in electricity prices, with two main technologies:

I hydroelectric power: 90% e�ciency, no technical progressI coal power: 25% e�ciency, exogenous technical progress

Concrete plants locate near customers, not near cheap electricity:

I ready-mix concrete has a few hours to reach destinationI concrete is 6th most dispersed industry

outcome = cst + b ⇥ treatment

� log L = cst + b ⇥ �log pE

“First-stage regression”

CT DCDE

WV-.04

ricity

.2 .4 .6 .8 1Coal share of power in 1927

Slope: -0.03 t-statistic: -4.59 F-statistic: 21.1R2: 0.35 Observations: 42

Validity of the instrument

Price variation exogenous to location of concrete plants (“as good asrandom”)

Small industry with no market power in electricity market

Price change over 1927-1937 instrumented with initial level (capacity)

I State-level and plant-level fixed e↵ects di↵erenced out

Plant-level shocks (e.g., Total Factor Productivity) absent from ratios

More robustness checks later

Cheaper electricity ) labor share of revenue &

(1) (2) (3) (4) (5)Dependent variable � labor share� price of electricity 1.969** 2.949** 1.883** 2.277** 1.714**

(0.947) (1.348) (0.850) (0.982) (0.856)� price of cement -0.0205

(0.0316)Farm share in 1920 -0.143**

(0.0581)Log-personal income in 1929 -0.102***

(0.0389)� housing in 1920s -0.00671

(0.0153)bank suspensions 0.00114

(0.0462)initial size (plant) 0.00562*

(0.00325)initial productivity (plant) 0.0654***

(0.00550)unionization -0.00164*

(0.000937)Herfindahl index -0.00862

(0.0465)Observations 621 576 621 621 618

First-stage F -statistic 11.14 9.771 12.51 11.19 11.90Number of states/clusters 42 32 42 42 41

Clustered standard errors in parentheses. Significance levels: *** p<0.01, ** p<0.05, * p<0.1

Cheaper electricity ) labor productivity %

(1) (2) (3) (4) (5)Dependent variable � labor productivity� price of electricity -5.648*** -7.343*** -5.224*** -5.827*** -5.531***

(2.047) (2.086) (1.882) (1.996) (1.934)� price of cement 0.0775

(0.0559)Farm share in 1920 0.283**

(0.135)Log-personal income in 1929 0.159*

(0.0836)� housing in 1920s 0.0368

(0.0299)bank suspensions -0.00460

(0.0797)initial size (plant) 0.00682

(0.00595)initial productivity (plant) -0.0845***

(0.0108)unionization 0.00134

(0.00119)Herfindahl index 0.0760

(0.0750)Observations 483 445 483 483 481

Cheaper electricity ) electric capital intensity %

(1) (2) (3) (4) (5)Dependent variable � electric capital intensity� price of electricity -6.959*** -6.623*** -5.585*** -6.692*** -5.838***

(2.114) (1.587) (1.424) (2.185) (1.544)� price of cement 0.0627*

(0.0374)Farm share in 1920 0.0465

(0.139)Log-personal income in 1929 0.164**

(0.0681)� housing in 1920s 0.113***

(0.0893)initial size (plant) 0.0535***

(0.00568)initial productivity (plant) -0.0365**

(0.0154)unionization 0.00378**

(0.00149)Herfindahl index -0.314***

(0.107)Observations 475 444 475 475 472

Summary 1: electricity was a labor-saving technology

Technological convergence caused:

I labor share & 19%I productivity % 35%I electrical intensity % 41%

Magnitudes:

I similar to actual changesI about 0.5 s.d. of distribution

Summary 1: electricity was a labor-saving technology

I labor share & 19%I productivity % 35%I electrical intensity % 41%

Magnitudes:

I similar to actual changesI about 0.5 s.d. of distribution

Cheaper electricity ) output prices &

(1) (2) (3) (4) (5)Dependent variable � output price� price of electricity 3.540** 3.704** 3.275** 3.500** 3.448**

(1.502) (1.550) (1.509) (1.499) (1.456)� price of cement -0.0774***

(0.0274)Farm share in 1920 -0.0789

(0.0893)Log-personal income in 1929 -0.0660

(0.0580)� housing in 1920s -0.0414**

(0.0184)bank suspensions 0.0879*

(0.0516)initial size (plant) -0.00756*

(0.00406)initial productivity (plant) -0.0116*

(0.00686)unionization -0.000385

(0.0554)Observations 454 421 454 453 452

Cheaper electricity ) revenue unchanged

(1) (2) (3) (4) (5)Dependent variable � revenue� price of electricity 2.160 2.779 0.904 1.553 2.349

(1.467) (2.119) (1.347) (1.081) (1.448)� price of cement -0.0360

(0.0502)Farm share in 1920 -0.0647

(0.0727)� housing in 1920s -0.0867***

(0.0579)initial size (plant) -0.0682***

(0.0140)unionization 0.00189

(0.0695)Observations 630 585 630 628 627

Cheaper electricity ) output unchanged

(1) (2) (3) (4) (5)Dependent variable � output� price of electricity -1.936 -3.035 -2.417 -1.997 -1.823

(2.806) (2.743) (2.487) (2.435) (2.841)� price of cement 0.0427

(0.0554)Farm share in 1920 0.104

(0.103)Log-personal income in 1929 0.0329

(0.0723)� housing in 1920s -0.0393

(0.0134)unionization 0.00289*

(0.0994)Observations 454 421 454 453 452

Cheaper electricity ) employment &

(1) (2) (3) (4) (5)Dependent variable � employment� price of electricity 3.405*** 4.307*** 2.688*** 3.005** 3.276***

(1.226) (1.601) (0.958) (1.187) (1.174)� price of cement -0.0567*

(0.0321)Farm share in 1920 -0.162

(0.109)Log-personal income in 1929 -0.131**

(0.0582)� housing in 1920s -0.0574**

(0.00339)initial productivity (plant) 0.0135

(0.0110)unionization 9.21e-05

(0.00173)Herfindahl index 0.113**

(0.0476)Observations 621 576 621 621 618

Summary 2: electricity caused job losses

I concrete prices & 20%I no e↵ect on outputI employment & 21%

Magnitudes:

I electricity could explain up to 80% of job losses in concrete industry

Results also robust to:

I using the relative price of electricity pE ,k,t/wk,tI dropping counties near dam construction

Magnitudes:

Falsification tests: cheaper electricity and materials share

(1) (2) (3) (4) (5)Dependent variable �materials share� price of electricity 1.085 0.640 1.178 1.156 0.854

(0.773) (1.000) (0.822) (0.795) (0.670)� price of cement -0.0132

(0.0123)Farm share in 1920 0.0338

(0.0298)� housing in 1920s -0.00163

(0.0465)initial size (plant) 0.00933***

(0.00285)initial productivity (plant) 0.00422

(0.00535)unionization -0.00102**

(0.0247)Observations 599 557 599 598 596

Validity: the sample seems balanced on observables

(1) (2) (3) (4) (5)Dependent variable electric cap. labor

in 1929 productivity employment revenue intensity sharecoal share in 1927 0.197 0.0612 0.116 -0.314* 0.00261

(0.172) (0.183) (0.208) (0.156) (0.0182)Constant 5.746*** 1.859*** 10.33*** 1.307*** 0.295***

(0.133) (0.130) (0.160) (0.134) (0.0146)Observations 569 628 630 563 630R-squared 0.003 0.000 0.001 0.007 0.000

Validity: change in coal share uncorrelated with change indemand

IDILIN

MNMOMS

-.4-.2

ity, 1

-.5 0 .5Growth in housing construction, 20-24 to 25-29

Slope: -0.10 t-statistic: -1.26R2: 0.03 Observations: 48 Correlation: -18%

Outline

1 Data

2 Model

3 Empirics

4 Conclusion

Conclusion:The labor market consequences of technological adoption

Testing the theory of technological unemployment

I geography as instrumentI new dataset from the concrete industry

Causal e↵ect of cheaper electricity:

I capital intensity %I labor share &I labor productivity %I no e↵ect on outputI employment &

Electricity adoption could explain up to 80% of job losses in theconcrete industry

Future research: what do these workers become?

I geography as instrument

I new dataset from the concrete industry

Any comments?

mabm3@cam.ac.uk

Additional slides

Extension: results are more precise for continuing plants

With entry and exit, similar point estimates, lower precision

Plant exit due to reasons uncorrelated with electricity:

Dependent variable exit = 1, continuing = 0

plant-levellog⇣

pi,tYi,t

⌘|1929 -0.176***

log (Li,1929) -0.152***log (KE ,i,t/Li,t) |1929 -0.000139

50 miles around county with GIShousing boom 0.188***

bank suspensions 0.181Constant 2.386***

Observations 1,963R-squared 0.116

Model: firm’s objective

Representative firm, perfect competition, maximizes intertemporalprofits:

profitst = Yt � wt (LNR,t + LR,t)� rNE ,tKNE ,t � rE ,tKE ,t

Simplification for closed-form solution:

KNC ,t = INC ,t

KC ,t = IC ,t

) rC ,t = exp (�bt), rNC ,t = 1

KNC ,t = INC ,t

KC ,t = IC ,t

) rC ,t = exp (�bt), rNC ,t = 1

KNC ,t = INC ,t

KC ,t = IC ,t

) rC ,t = exp (�bt), rNC ,t = 1

Map of the coal share of power

Blue = high coal share.

Manufacturing was still adopting electricity in 1930s

illion

1890 1900 1910 1920 1930 1940Year

The Great Depression accelerated the decline in the laborshare of income of concrete

1909 1929 1933 1939Year

Endogeneity: OLS < IV

Supply-and-demand:

� log

✓KE ,i ,t

◆= c � d � log

✓pE ,i ,t

◆+ demandi ,t

� log

✓KE ,i ,t

◆= c

0� log

✓pE ,i ,t

◆+ supplyi ,t

� log

✓pE ,i ,t

0�c � c

0 + demandi ,t � supplyi ,t�

Positive correlation between regressor and error term biases �d

toward 0

Similar for labor share of income (in the paper)

The labor market consequences of electricity adoption ...The labor market consequences of...

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