Trends in the Labour Share

Trends in the Labour Share

Tony OConnor

09383581

[email protected]

November 28, 2014

Abstract

The objective of this paper is to investigate the change in the labour share of

income, or total value added, in common production functions. This paper finds

that the labour share, measured through the marginal productivity of production

labour, began to decline in the 1970s. This is contrary to the literature which date

the beginning of the decline to the 80s. A possible reconciliation here is that it took

time for the wage to adjust downwards to the marginal productivity of manual labour.

In contast, the marginal productivity of non-production labour has remained stable.

These findings seem to go against the hypothesis that technology and automation

was responsible for the decline, though not much new support can be found for the

idea that trade was responsible.

1 Introduction

Te objective of this paper is to analyse the shifts in the labour share of income,

measured as value added, using standard production functions.

1

Traditionally, the stability of the labour share has been regarded as a stylized

fact [Kaldor, 1961]. However, recent literature has indicated that the share of labour

in value added has been declining in the large majority of countries [Karabarbounis

and Neiman, 2013]. This paper will examine this question a panel of 473 industries,

over the years 1958-2009, to determine whether this occured at an industry level in

the U.S.

This paper will proceed as follows. First, we will examine the literature to date

detailing the trend in the labour share, and the factors that may be detrminging

its path. Second, the economic model and empirical approach of this paper will be

outlined. Third, a description of the data will be provided. Fourth, the estimations

results will be described and evaluated. Lastly, we will conclude.

2 Literature Review

Feenstra and Hanson [1999] note that not only did the wages of low-skilled workers

fell relative the the wages of high-skilled workers during the 1980 and the 1990s, but

the wages of the low-skilled fell in real terms. Using a modification of the conventioanl

price regression, they find that 35 percent of the incrase in the relative wage of

nonproduction workers is due to improvements in information technology, while a

further 15 percent is due to the international trade, principally outsourcing.

There are two dominant narratives explaining the decline in the labour share,

and accounting for why the wages of low-skilled workers experienced a steeper decline

than those of high-skilled workers. The first story is that improvements in technology

and automation is depressing the demand for low-skilled workers, leading to a fall

in their wages. However, under this story, one would expect the wages of those who

can understand and operate such machines to increase.

Alternatively, taking non-production workers as a grouping, then increases in

capital should increase their marginal product. Support for this is found is provied

2

in Autor et al. [2003], who find that computer capital sustitutes for workers per-

forming routine, manual non-cognitive task and complements peforming non-routine

problem-solving and complex communications tasks. Therefore, we would expect to

see the wages of production workers falling, and the wages of non-production workers

increasing. However, this effect may be attenuated by the observation made by Autor

et al. [2013] whereby the target of automation moved from production tasks towards

non-manufactirung information-processing tasks.

The second dominant story is that the decline in wages is due to the increase

in international trade. Concretely, a large global workforce of unskilled workers

has entered into the global labour market, and tis may depress the demand for

unskilled workers in developed economies, due to phenomena such as outsourcing

and import competition in product where unskilled workers are an abundant factor.

For example, Acemoglu et al. [2014], taking account of input-output linkages and

general equilibrium effects, find that the accelerating U.S. imports from China from

1999 to 2011 was possible responsible for job losses in the range of 2.0 to 2.4 million,

which if true would have exerted significant downward pressure on wages.

Alternative explanatios for the declining capital share have been proposed, such

as that of Azmat et al. [2012], who find that privatisation is responsible for up to

one-fifth of the decline. However, the methods used in this paper are not capable of

assessing this finding.

This paper will also naturally link itself to the expanding range of literature that

examines whether the capital share of income has been rising over the last fifty years.

A last related strand in the literature that this paper will later examine is that of the

elasticity of substitution. In order for the capital share of income to rise continuously,

along with a rising capital-output ratio, an elasticity of substitution exceeding 1 is

necessary; less formally, diminishing returns will need to set in slowly.

Chirinko [2008] provides an overview of the literature, and finds that while the

estimates have a wide range, a value of 0.4 to 0.6 is most likely given the evidence.

3

Given that a Cobb-Douglas function assumes a of 1, he infers that this functional

form has little support. This paper will estimate this elasticity for the data under

review, finding that it is not greatly different from 1.

3 Economic Model and Empirical Approach

This paper will seek to estimate the factor shares through the use of standard produc-

tion functions. With the assumption of perfect competition and constant returns to

scale, then the respective share in income of a factor equals the marginal product of

that factor [Bertola et al., 2006]. For example, consider the standard Cobb-Douglas

production function:

Y = AKL1

. Under under our two assumptions, then the share of income rewarded to capital is

, while that going to labour is 1 .

To estimate the factor shares going to labour and capital, we will estimate the

logged Cobb-douglas production function which is:

log(V ADD) = 01log(CAP ) + 2log(EMP ) (1)

Thus, we see that we can indirectly estimate the labour share of income, 1 ,

by running the above regression. Traditionally, these values have been assumed to

be in the range of 0.3 and 0.7, respectively.

3.1 Is = 1?

Given that the Cobb-Douglas function assumes an elasticity of substitution of one,

is only valid if this is the case, one ought to check this assumption. The literature

implies that many industries take on a Cobb-Douglas specification. Balistreri et al.

4

[2003] test 28 U.S. industries, and fail to reject the Cobb-Douglas specification if 20 of

the 28 industries. Therefore, in order to establish whether these regressions are valid

we will need to estimate the elasticity of substitution. We can do this by estimating

a Constant Elasticity of substitution (CES) production function, which allows to

vary, and which we can impute from the estimates. The CES production function

takes the form:

lnQi = 0 1

ln{Li + (1 )K

i }+ i (2)

3.2 Dealing with Simultaneity Bias

Marschak and Andrews [1944] noted that the input levels and unobserved produc-

tivity shocks may be correlated. More concretely, if a firm experiences a positive

productivity shock, they will raise output, and will achieve this through purchasing

more of the variable inputs such as labour, materials or energy. This will result

in the coefficient estimates on the variable inputs being biased upwards [Levinsohn

and Petrin, 2003]. Whether capital is upwardly or downwardly biased depends on

whether is is correlated or not with the productivity shock. Because the productivity

effect changes over time, it is not fixed and therefore using a fixed effect estimator

will lead to biased and inconsistent results. An possible instrument in this case would

be input prices, as we could see if changing inputs is due to changing input prices.

However, input prices are a weak instrument in this context, changes in the wage

being only weakly correlated with change is labour demanded. Lags are used instead.

Olley and Pakes [1996] attempts to correct this by using investment as a measure

for the unobserved productivity change; if a firm experienes a positive productivity

shock, then they ought to increase investment. However, Doms and Dunne [1998]

note that the time-series of plant-level investment exhibits lumpy behaviour, implying

adjustment costs may be convex. This finding leads Levinsohn and Petrin [2003] to

5

conclude that changes in investment may not be strictly proportional to changes in

productivity.

It should be noted that while this class of estimators were generally designed for

use with firm panels, they are also applicable to industry panels, as it is reasonable

to assume that the same dynamics may be at play, in the sense that an industry may

be affected by industry-wide productivity shocks that vary over time. If anything,

the problems may even be more acute when it comes to industry level shocks. For

example, Abraham and White [2006] find that the annual persistence of productivity

shocks at an industry level would produce autocorrelation estimates ranging from

0.80 to 0.91, against only 0.37 to 0.41 at a plant level.

4 Data

All data is taken from the NBER-CES Manufacturing Industry dataset [Becker et al.,

2013]. This is an panel of 473 NAICS industries. The data covers 1958-2009 with

variables such as output, employment, payroll and other input costs, investment,

capital stocks, TFP, and various industry-specific price indexes. For our dependent

variable, we use total value added in all specifications. A number of variables are

generated from the data. For example, in order to distinguish between the factor

shares being allocated to production and non-production workers, we calculate non-

production workers as being equal to Total Employment minus Production Workers.

5 Econometric Analysis

Firstly, we run the standard Cobb-Douglas regression as specified in Equation 1. In

Table 3, the only independent variables used are capital and labour, with labour

split into the number of production and non-production workers. The results are

presented in 3.

6

Table 1: Summary statistics

Name Variable Mean Std. Dev. N

NAICS NAICS 6-digit Codes 327009.662 8889.717 24596YEAR Year ranges from 58 to 09 1983.5 15.009 24596EMP Total employment in 1000s 34.814 45.053 24167PAY Total payroll in $1m 735.896 1252.867 24167PRODE Production workers in 1000s 25.423 33.651 24167PRODH Production worker hours in 1m 50.64 66.823 24167PRODW Production worker wages in $1m 443.796 736.828 24167VSHIP Total value of shipments in $1m 4799.495 13196.309 24167MATCOST Total cost of materials in $1m 2620.97 9721.219 24167VADD Total value added in $1m 2190.409 4710.187 24167INVEST Total capital expenditure in $1m 156.655 462.108 24167INVENT End-of-year inventories in $1m 585.429 1433.618 24162CAP Total real capital stock in $1m 2757.95 6388.03 24167EQUIP Real capital: equipment in $1m 1664.517 4145.339 24167PLANT Real capital: structures in $1m 1093.433 2418.355 24167TFP5 5-factor TFP index 1997=1.000 0.937 0.257 24167

Table 2: Cross-correlation table

Variables PRODE PRODH MATCOST VADD INVEST ENERGY CAPPRODE 1.000PRODH 0.997 1.000MATCOST 0.625 0.637 1.000VADD 0.818 0.830 0.765 1.000INVEST 0.657 0.663 0.701 0.830 1.000ENERGY 0.574 0.573 0.642 0.699 0.902 1.000CAP 0.655 0.655 0.751 0.791 0.931 0.939 1.000

7

Labour is split into production and non-production workers, as this will allow us

to examine how their respective factor shares change over time. All results are called

with robust standard errors to correct for heteroskedasticity.

Table 3: Cobb-Douglas Averages Production and Non-Production Workers

(1) (2) (3) (4) (5)1969 1979 1989 1999 2009

LOG(PRODE) 0.287 0.203 0.162 0.152 0.111

(8.89) (6.49) (5.27) (5.29) (3.16)

LOG(NPRODE) 0.402 0.421 0.458 0.413 0.411

(13.44) (14.98) (17.26) (15.05) (12.78)

LOG(CAP) 0.297 0.365 0.391 0.462 0.542

(17.26) (20.47) (21.45) (23.48) (21.01)

Constant 2.253 2.539 3.001 2.998 2.695

(24.80) (25.55) (28.98) (25.30) (17.90)

Observations 462 462 462 462 473Adjusted R2 0.939 0.934 0.926 0.929 0.921

t statistics in parentheses

Robust Standard Errors p < 0.05, p < 0.01, p < 0.001

In this table, five regressions are run, with each variable taking the logged value of

the previous 10-year average to eliminate any autocorrelative errors. Thus, for 1969,

in column 1, we have the natural log of the 10-year averages of capital, production

and non-production workers, regressed on the natural log of the 10-year average of

value added, where each industry forms one observation. In this manner, we estimate

an aggregate prodution function that is representative of the decade from 1959-1969.

A similar regression is run for each of the following four decades.

Firstly, in Table 3 the regression has strong explanatory power; more than 92% of

the variation in value added is explained by variation in capital and labour. Secondly,

all variables are significant at the 1% level, most at the 0.1% level. The coefficients

in this regression illustrate marginal productivites, for example in the 60s, a 1%

8

increase in the volume of production workers would have resulted in approximately

a 29% increase in value added, in aggregate.

A Ramsey Reset test was performed on each estimated model in 3. For all, we

fail to reject the null hypothesis that there are no omitted variables at the 1% level,

and all but one at the 5% level; this implies that the model is well specified.

The White test for heteroskedasticity was also performed on each estimated re-

gression. In general, we reject the null that the error terms exhibit homoskedasticity

at the 1% level in four of the five regressions; we reject five out of five at the 5% level.

For this reason, all regressions are called with robust standard errors, when possible.

We also look at variance infaltion factors, to screen for possible multicollinerity

which would inflate the standard errors. The average variance infaltion factor is less

than four, indicating that the multicollinearity is not high. Thus, it seems the effect

of the averaging is to eliminate the problem identified by Marschak and Andrews

[1944], evident in the high degree of correlation in Table 2.

5.1 Trend in the Cobb-Douglas Regressions

In this table, we see that the share of value added going to production labour (also

known as blue collar labour) has been in continuous decline, declining every decade.

In the 00s, it is just over one-third its value in the 60s. Interestingly, our finding here

may shed new light on the view that the income share of labour began to decline in

the 80s. In our results, nearly half of the 61% drop in marginal productivity occured

during the 70s. Interestingly, there may have been a lag before declines in marginal

productivity crossed over into declines in the labour share.

Interestingly, over the entire period the share of income going to non-production

workers has remained stable, even though it does exhibit a concave trend, peaking

in the 80s.

The income share going to capital has also greatly increased over the periods

9

analysed, nearly doubling from 0.3 in the 60s to 0.54 in the 00s.

Table 4: Cobb-Douglas Averages - Full Model

(1) (2) (3) (4) (5)1969 1979 1989 1999 2009

LOG(PRODE) 0.225 0.153 0.123 0.114 0.0121(7.79) (5.26) (4.27) (4.23) (0.40)

LOG(NPRODE) 0.388 0.413 0.435 0.367 0.429

(13.42) (14.79) (15.59) (12.28) (12.54)

LOG(CAP) 0.205 0.208 0.250 0.376 0.259

(6.69) (5.41) (5.71) (7.95) (5.92)

LOG(ENERGY) 0.0147 0.0208 -0.00602 -0.0562 0.0722

(0.61) (0.78) (-0.19) (-1.68) (2.75)

LOG(MATCOST) 0.166 0.209 0.217 0.222 0.278

(6.53) (8.91) (8.52) (9.08) (10.99)

Constant 2.082 2.383 2.639 2.365 2.615

(13.89) (13.84) (14.22) (11.90) (11.91)




The regression in Table 3 is expanded to include more control variables for each

of the five decades in Table 4. In order to check the robustness of these trends, an

extended Cobb-Douglas regression is run, including the energy and materials factor

input. Tis increases the Adjusted R2 of the model slighly, and has a far-reaching effect

on the coefficients, but does little to change the trends. The variable representing

materials and fuels is significant, though that of energy only become significant for

the 00s.

We see that the share of income going to production labour declines over the

entire period, even becoming insignificantly different from zero in the 00s.

The share of income going to non-production labour over the period increased,

10

though the trend is non-monotonic. Somewhat similarly, the share of income going

to capital increases over the period, but it finishes well beow the peak attained in

the 90s.

5.2 CES Regression

Addopting a cobb-Douglas functional form carries the assumption that the elasticity

of substitution is equal to 1. This is a strict assumpition that we will relax using the

a CES functional form. In this functional form, the elasticity of substitution must

be constant, though it may take on a value different from 1.

Two sets of CES regression are estimated, in line with Equation 2; therefore, delta

is the coefficient on total labour hours in production. In Table 5, we see that delta

declines from 0.723 in the 60s to 0.612 in the 90s.

When we look at the snapshot regressions in Table 6, we see that the share of

income going to labour experienced a steeper decline, going from 0.731 in 1969 to

0.554 in 1999. These estimates are significant at the 0.1% level.

In order the examine the applicability of the Cobb-Douglas regressions, we use

the CES parameter estimates to impute the elasticity of substitution, as = 11+

. As

we see from Table 5, the elasticity of substitution ranges from 1.18 to 0.97 (the 00s

estimation is invalid). In Table 6, it ranges from to 1.07 to 1.2 (th 2009 regression

is invalid). Thus, we see that the estimated elasticity is not too far from what is

required to assume the functional form is a Cobb-Douglas function.

5.3 Levinsohn-Petrin Regression Estimates

In order to deal with the simultaneity bias outlined in the literature review, we use

the Levinsohn-Petrin estimator, outlined by Levinsohn and Petrin [2003]. the essence

of this is that we use an intermediate input energy as a variable that can control for

any productivity shock. The estimates are presented in Table 7 and Table 8. The

11

Table 5: CES Estimates - 10yr Averages

(1) (2) (3) (4) (5)1969 1979 1989 1999 2009

b0Constant 1.111 1.438 1.775 2.243 0.221

(11.83) (9.90) (8.98) (9.93) (9.25)

rhoConstant -0.155 -0.0997 0.0293 -0.162 -18.26

(-1.75) (-1.01) (0.28) (-1.41) (.)

deltaConstant 0.723 0.634 0.496 0.612 0.775

(12.91) (7.86) (4.81) (5.56) (.)

Observations 462 462 462 462 473Adjusted R2 0.898 0.887 0.874 0.889 0.817 1.18 1.11 0.97 1.19 -0.05


sigma p < 0.05, p < 0.01, p < 0.001

Table 6: CES Estimates - Yearly Snapshots

(1) (2) (3) (4) (5)1969 1979 1989 1999 2009

b0Constant 1.286 1.702 2.147 2.293 4.662

(11.82) (10.33) (10.40) (8.09) (149.17)

rhoConstant -0.170 -0.104 -0.221 -0.0680 13.94

(-1.78) (-1.02) (-1.93) (-0.53) (.)

deltaConstant 0.731 0.623 0.661 0.554 0.908

(11.84) (7.04) (6.58) (4.09) (.)

Observations 462 462 462 473 473Adjusted R2 0.893 0.880 0.877 0.883 0.756 1.2 1.11 1.28 1.07 0.06



12

regression is run across all observations in the decade, as is done in in Levinsohn and

Petrin [2003].

In Table 7, we see that the contribution of production workers to value-added de-

clines across all decades, with the exception of the 90s when it temporarily increases.

The contribution of non production labour increases slightly over the period.

However, an important anomaly with this regression is the capital coefficient.

With capital exceeding 1 in every decade, we end up rejecting the null hypothesis of

constant returns to scale in every decade. The fact that the capital coefficient some-

times takes a value exceeding 2 leads one to doubt the consistency of this estimator

when applied to an industry panel. It may be the case that the grater persistence of

productivity shock in an industry renders this estimator biased and inconsistent.

Nevertheless, it is useful to note that even using this estimator the coefficient

declines in every decade, in both specifications of the model.

Table 7: Levinsohn-Petrin Estimates - Full Model

(1) (2) (3) (4) (5)60s 70s 80s 90s 00s

LOG(PRODE) 0.293 0.195 0.141 0.188 0.0930

(8.74) (9.56) (5.08) (7.61) (2.59)

LOG(NPRODE) 0.385 0.461 0.452 0.356 0.441

(12.15) (16.79) (19.10) (13.18) (11.54)

LOG(CAP) 1.332 1.201 2.212 2.251 1.980

(8.20) (3.86) (5.67) (8.49) (4.06)

Observations 4620 4620 4620 4653 4729Adjusted R2



13

Table 8: Levinsohn-Petrin Estimates - Production Hours

(1) (2) (3) (4) (5)60s 70s 80s 90s 00s

LOG(PRODH) 0.571 0.514 0.477 0.460 0.457

(23.22) (19.20) (16.76) (17.50) (19.12)

LOG(CAP) 1.842 1.988 4.590 3.222 3.635

(8.66) (3.99) (15.67) (7.67) (4.42)




5.4 IV Regressions

In order to further examine the robustness of the results, an instrumental variable

regression is run where the lagged level of the variable input, labour, is used as the

instrument for labour.

Two batteries of regressions are run in order to get estimates. First, in Table 9,

the IV regression is run over all the observations within a decade, with the caveat that

this estimator does not control for fixed effects. Driscoll-Kraay standard errors are

reported in order the attenuate any t-stat inflation caused by possible autocorrelation.

In Table 9, we see that the coefficient on labour declines across the entire sample

period. Conversely, the coefficient on capital rises by more than labour has fallen.

We complement this regression by running a series of IV snapshots, presented

in Table 10; that is, regression run on the observations within a single year only.

This eliminates any problems hat would come from not controlling for fixed effect or

autocorrelation, but it prevent from taking full advantage of the panel features.

We see in Table 10 that the results very closely mirror those just discussed. The

coeffiecnt on labour does fall, while the coefficient on capital rises.

14

Table 9: IV Estimates - Production Hours

(1) (2) (3) (4) (5)60s 70s 80s 90s 00s

LOG(PRODH) 0.590 0.496 0.511 0.462 0.456

(96.00) (46.52) (28.93) (65.71) (65.56)

LOG(CAP) 0.377 0.465 0.482 0.551 0.612

(61.75) (43.04) (31.08) (104.06) (53.01)

Constant 1.070 1.341 1.813 1.864 1.608

(46.55) (28.76) (49.25) (68.12) (31.59)



Driscoll-Kraay Standard Errors p < 0.05, p < 0.01, p < 0.001

Table 10: IV Snapshot Estimates - Production Hours

(1) (2) (3) (4) (5)1969 1979 1989 1999 2009

LOG(PRODH) 0.580 0.509 0.452 0.511 0.458

(23.60) (19.29) (17.82) (17.47) (11.87)

LOG(CAP) 0.378 0.477 0.544 0.544 0.650

(19.45) (21.83) (25.18) (21.33) (18.00)

Constant 1.286 1.570 1.784 1.859 1.298

(14.56) (14.50) (15.35) (14.50) (6.96)




15

5.5 Fixed Effect Estimates

For completeness, a fixed effect estimator is also run on the data. We find that

the coefficients on this to be somewhat erratic. Consider the coefficient on capital,

which rises from around0.6 in the 60s to 1.75 in the next decade, only to fall to 1

in the decade after. In contrast to most of the other estimators, there is no clear

trend exhibited. This is inline with with what Levinsohn and Petrin [2003] found;

they found that the fixed effect estimator is the most incompatible with all other

estimators for firm data. This is because there is no fixed effect; in our case the

productivity shock varies within industry over time.

Table 11: Fixed Effects Estimates

(1) (2) (3) (4) (5)60s 70s 80s 90s 00s

LOG(CAP) 0.642 1.754 0.993 0.821 0.616

(16.87) (22.94) (10.10) (12.61) (6.31)

LOG(PRODH) 1.029 0.542 0.503 0.665 0.634

(34.31) (6.27) (6.00) (17.70) (11.60)

Constant -2.199 -7.794 -1.848 -0.812 1.019(-12.36) (-20.60) (-4.03) (-1.93) (1.62)




6 Interpretation of Results

From all of the above regressions, a few things are clear.

First, the coefficient on labour has declined over time, in most econometric spec-

ifications of the production function. This indicates that the marginal productivity

of labour has declined since the sixties. This has resulted in the share of value

16

added going to labour declining in turn, a fact that has been mostly verified by the

literature.

Interestingly, when we decompose labour down into production and non-production

workers, we see that the share of income (adn the marginal productivity) of nonprduc-

tion workers has remained approximately stable, while that of production wrokers

has declined relatively steeply.

Recalling the literature review, it was stated that if technology was the dominant

story behind this structural change, then it would have been likely that the marginal

productivity of non production workers would have increased. As this does not seem

to have occurred, our results would seem to favour the trade story behing the decline

of labour.

However, perhaps the most interesting result is that the decline in marginal pro-

ductivity seems to have begun before the 80s, when the labour share started declin-

ing. This also favours the trade story, given that there was a well-known productivity

slowdown in the 70s.

Some important caveats need to be atttached. Firstly, nonproduction labour is a

broad category in and of itself; it is simply everyone who does not work directly with

production, and so would include secretaries to executives. Therefore, the stability o

returns to this broad category could mask a great deal of heterogeneity in marginal

productivity underneath.

Secondly, the proxy was simply the number of production and non production

workers. It is possible that the number of workers would not change, but one group

could work much more intensively withing a given year. This is unlikely for non-

production workers, and in various specification the number of production hours

was included explicity as a variable. It is also worth noting that in the correlogram

production hours and number of production workers were extremenly correlated -

0.996 - such that one could almost say they are the same variable.

17

7 Conclusion

The analysis allow us to make a number of conclusions.

First, the estimation strategy of taking 10 year averages and using each industry

as an observation seems to have been a successful specification strategy. The model is

well specified according to the Ramsey RESET Test, and multicollinearity is not an

issue. In addition, the trends in the coefficient ar robust to a wide range of different

estimation strategies.

Second, the new empirical contribution of this paper is that the decline in the

marginal productivity of production labour began in the 1970s. Most studies support

the view that this onyl started in to 80s. A possible reconciliation between these

two views is that it took time for the market to reduce the wage down to its level

of marginal productivity. In that sense, the regressions are telling us the forward

labour share.

Can these regressions identify the cause of the labour share shrickage; the trade

vs. technology question? Two points stand against the idea that technology ws

responsible. First, the idea that technology was responsible for the decline in the

labour share is incompatible with the fact that this decline commenced in the 70s, in

the midst of a productivity slowdown. However, as Nordhaus [2004] found, the pro-

ductivity slowdown was concentrated in energy-intensive sectors, so the technology

story cannot be completely dismissed.

Second, if technology was the driver, then one ought to have seen an increase in the

share of capital going to non-production workers. Admittedly, the non-production

workers variable is broad and may hide some heterogeneity within that category.

While no definitive conclusion can be reached here, the balance of the findings seem

to go against the idea that technology is responsible.

Could the decline have been due to the growth of international trade? Given

the countries have in general become more open to trade in the post-WW2 era, this

18

cannot be immediately ruled out. However, the wherewithal to answer this question

are not in this dataset, given that there is no information on international trade.

19

References

Arpad Abraham and Kirk White. The Dynamics of Plant-Level Productivity in U.S.

Manufacturing. Working Papers 06-20, Center for Economic Studies, U.S. Census

Bureau, July 2006. URL http://ideas.repec.org/p/cen/wpaper/06-20.html.

Daron Acemoglu, David Autor, David Dorn, Gordon H. Hanson, and Brendan Price.

Import competition and the great u.s. employment sag of the 2000s. Work-

ing Paper 20395, National Bureau of Economic Research, August 2014. URL

http://www.nber.org/papers/w20395.

David H. Autor, Frank Levy, and Richard J. Murnane. The skill content of re-

cent technological change: An empirical exploration. The Quarterly Journal of

Economics, 118(4):12791333, 2003. doi: 10.1162/003355303322552801. URL

http://qje.oxfordjournals.org/content/118/4/1279.abstract.

David H. Autor, David Dorn, and Gordon H. Hanson. Untangling trade and technol-

ogy: Evidence from local labor markets. Working Paper 18938, National Bureau

of Economic Research, April 2013. URL http://www.nber.org/papers/w18938.

Ghazala Azmat, Alan Manning, and John Van Reenen. Privatization and the decline

of labours share: International evidence from network industries. Economica,

79(315):470492, 2012. ISSN 1468-0335. doi: 10.1111/j.1468-0335.2011.00906.x.

URL http://dx.doi.org/10.1111/j.1468-0335.2011.00906.x.

Edward J. Balistreri, Christine A. McDaniel, and Eina Vivian Wong.

An Estimation of U.S. Industry-Level Capital-Labor Substitution.

Computational Economics 0303001, EconWPA, March 2003. URL

http://ideas.repec.org/p/wpa/wuwpco/0303001.html.

Randy Becker, Wayne Gray, and Jordan Marvakov. NBER-CES Manufacturing In-

dustry Database: Technical Notes, 2013.

20

Giuseppe Bertola, Reto Foellmi, and Josef Zweimller. Income Distribution in Macroe-

conomic Models. Princeton University Press, Princeton, 2006.

Robert S. Chirinko. : The Long And Short Of It. CESifo

Working Paper Series 2234, CESifo Group Munich, 2008. URL

http://ideas.repec.org/p/ces/ceswps/ 2234.html.

Mark E. Doms and Timothy Dunne. Capital Adjustment Patterns in Manufac-

turing Plants. Review of Economic Dynamics, 1(2):409429, April 1998. URL

http://ideas.repec.org/a/red/issued/v1y1998i2p409-429.html.

Robert C. Feenstra and Gordon H. Hanson. The impact of outsourcing and high-

technology capital on wages: Estimates for the united states, 19791990. The Quar-

terly Journal of Economics, 114(3):907940, 1999. doi: 10.1162/003355399556179.

URL http://qje.oxfordjournals.org/content/114/3/907.abstract.

N. Kaldor. Capital accumulation and economic growth. In F. A. Lutz and D. C.

Hague, editors, The Theory of Capital, page 177. St Martins Press, 1961.

Loukas Karabarbounis and Brent Neiman. The global decline of the labor share.

Working Paper 19136, National Bureau of Economic Research, June 2013. URL


James Levinsohn and Amil Petrin. Estimating production functions using inputs to

control for unobservables. The Review of Economic Studies, 70(2):pp. 317341,

2003. ISSN 00346527. URL http://www.jstor.org/stable/3648636.

Jacob Marschak and Jr. Andrews, William H. Random simultaneous equations

and the theory of production. Econometrica, 12(3/4):pp. 143205, 1944. ISSN

00129682. URL http://www.jstor.org/stable/1905432.

21

William Nordhaus. Retrospective on the 1970s productivity slowdown. Working

Paper 10950, National Bureau of Economic Research, December 2004. URL


G Steven Olley and Ariel Pakes. The Dynamics of Productivity in the Telecommuni-

cations Equipment Industry. Econometrica, 64(6):126397, November 1996. URL

http://ideas.repec.org/a/ecm/emetrp/v64y1996i6p1263-97.html.

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Documents

Trends in the Labour Share