Disentangling occupation- and sector-specific technological change Zs´ofia L. B´ar´any Sciences Po
and
Christian Siegel University of Kent
2017 February IHS
B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
1
Motivation Substantial changes in labor market outcomes in recent decades in most developed economies structural change: massive reallocation of labor between sectors polarization: I
I
employment shifting from middle to low- and high-income occupations average wage growth slower for middle-income occupations than at both extremes
Both patterns explained by differential productivity growth structural change focuses on differences across industries polarization focuses on differences across occupations.
B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
2
These two phenomena have been connected in Barany and Siegel (2015) we show that structural change can lead to polarization across occupations Goos, Manning and Salomons (2014) suggest that differential occupation intensity across sectors and differential occupational productivity growth can lead to employment reallocation across sectors Duernecker and Herrendorf (2015), and Lee and Shin (2015) show that the above mechanism provides dynamics consistent with structural change
B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Sector-occupation employment shares 1960-2007 L employment
0.25
G employment
0.4
0.2
0.3
0.15 0.2 0.1 0.1
0.05 0 1960
1970
1980
1990
2000
2010
0 1960
1970
1980
1990
2000
2010
H employment
0.6 0.5
total
0.4
manual
0.3
routine
0.2
abstract
0.1 0 1960
1970
1980
B´ ar´ any and Siegel (Sciences Po, Kent)
1990
2000
2010
Disentangling occupation- and sector-specific technological change 2017 February
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Given the large overlap between sectoral and occupational employment a model where productivity growth differences are restricted to be either sectoral or occupational can do a decent job in matching both occupational and sectoral employment shares, but this loads all technological change onto occupations or sectors ⇒ matters for policy implications. Goal of this paper: disentangle to what extent technological change is specific to firms in particular industries (the output they produce) or, to workers in certain occupations (the task content).
B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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This paper Builds a parsimonious model three types of labor: manual, routine, abstract (for now exogenous supply) three sectors: low-skilled services, goods and high-skilled services which use all three types of labor as input demand for sectoral output determined by consumer preferences Uses US Census data between 1960 and 2007 on relative occupational wages labor income share of occupations within sectors distribution of output across sectors, and relative sectoral prices growth of aggregate output To pin down sector and occupation specific technological change B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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This paper Decomposes the sector-occupation specific technological change using a factor model into a sector specific and an occupation specific component. Generates counterfactual series taking as given the path of occupational labor supplies to gauge the importance of sector specific and occupation specific technological change on relative wages sectoral value added shares sector-occupation employment shares. B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Model
B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Production three sectors in the economy: low-skilled services, L, goods, G , and high-skilled services, H each uses all three types of labor in producing output I I I
manual routine abstract
firms operate in perfect competition assume CES production function in all sectors, J ∈ {L, G , H} h
YJ = (αmJ mJ )
B´ ar´ any and Siegel (Sciences Po, Kent)
ρ−1 ρ
+ (αrJ rJ )
ρ−1 ρ
+ (αaJ aJ )
ρ−1 ρ
ρ i ρ−1
Disentangling occupation- and sector-specific technological change 2017 February
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Households - Labor supply
unit measure of consumers/workers each worker has a specific occupation I I I
fm fraction manual fr fraction routine fa fraction abstract
each worker supplies one unit of labor inelastically in the sector which provides the highest wage ⇒ occupational wage rates must equalize across sectors in equilibrium
B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Households - Consumption stand-in household maximizes utility subject to its budget constraint
max
cL ,cG ,cH
aL (cL + c L )
ε−1 ε
ε−1 ε
+ aG cG
+ aH (cH + c H )
ε−1 ε
ε ε−1
s. t. pL cL + pG cG + pH cH ≤ fm wm + fr wr + fa wa
where aL + aG + aH = 1
B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Equilibrium
there are six markets in this economy I I
3 labor markets: that of manual, routine and abstract labor 3 goods markets: that of LS serv, goods and HS services
six prices: wm , wr , wa and pL , pG , pH one can be normalized wlog; normalize wr = 1 equilibrium: all markets clear given prices
B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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The firm’s problem
i ρ h ρ−1 ρ−1 ρ−1 ρ−1 ρ ρ ρ + (αrJ rJ ) + (αaJ aJ ) max pJ (αmJ mJ )
mJ ,rJ ,aJ
− wm mJ − wr rJ − wa aJ where firms take pJ and wm , wr , wa as given
B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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The firm’s problem continued firm FOCs pin down optimal relative labor use ρ ρ−1 wr αmJ , wm αrJ ρ ρ−1 aJ wr αaJ = . rJ wa αrJ
mJ = rJ
and the price of sector J output in terms of wage rates pJ =
ρ−1 αmJ
B´ ar´ any and Siegel (Sciences Po, Kent)
1 wmρ−1
+
ρ−1 αrJ
1 wrρ−1
+
ρ−1 αaJ
1 waρ−1
1 1−ρ
.
Disentangling occupation- and sector-specific technological change 2017 February
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The household’s problem – sectoral demands implies the following demand schedule for the stand-in household (assuming an internal solution) ε fm wm + fr wr + fa wa + pL c L + pH c H aL CL = − c L, pL aLε pL1−ε + aGε pG1−ε + aHε pH1−ε ε aG fm wm + fr wr + fa wa + pL c L + pH c H CG = , pG aLε pL1−ε + aGε pG1−ε + aHε pH1−ε ε aH fm wm + fr wr + fa wa + pL c L + pH c H − cH. CH = pH aLε pL1−ε + aGε pG1−ε + aHε pH1−ε
B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Market clearing using goods market clearing, YL = CL , YG = CG and YH = CH optimal use of occupation o labor in sector J ρ CJ αJo pJ oJ = wo αoJ where pJ and CJ depend on occupational wage rates wm and wa the equilibrium boils down to finding wm and wa such that the labor markets clear: rL + rG + rH = fr m L + m G + m H = fm (aL + aG + aH = fa )
B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Quantitative exercise Two measures change exogenously over time: 1 supply of different types of labor: fm , fr , fa 2 sector and occupation specific technological change: αJo for J ∈ {L, G , H} and o ∈ {m, r , a} → we back these out from the data using the production side of the model We decompose the change in αJo into a sector- and an occupation-specific component. We are interested in the contribution of these two components to the evolution of occupational wage rate differentials sectoral and occupational employment shares sectoral value added shares B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Calibration
B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Calibration Strategy Values taken from the literature: elasticity of substitution in production between different types of labor, ρ = 1.42 elasticity of substitution in consumption between different goods, ε = 0.2 For the rest, two step procedure following Buera, Kaboski and Rogerson (2015) 1 calibrate αJo in each period to match the labor income shares of occupations within each sector, the relative sectoral prices, and the overall growth rate of the economy taking as given relative occupational wages and the sectoral distribution of output 2 calibrate the utility function parameters to match the sectoral distribution of output in the initial and final period B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Calibration Targets 1. Use US Census and ACS data between 1960 and 2007 to get wage and employment targets occupational categories: manual, routine, abstract details sectoral categories: low-skilled services, goods, high-skilled services details relative occupational wage rates wm , wa by comparing the hourly wages of a narrowly defined group: 25 year old men labor income shares of occupations within each sector wo foJ w oJ LoJ θJo = P =P i wi fiJ i w iJ LiJ raw vs efficiency units of labor, wage rates vs occ-ind wages use θJo , the occupational wage rates, and sectoral value added shares to get the implied efficiency units of labor in different occupations: fm , fr , fa B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Calibration Targets 2.
Use BEA data between 1960 and 2007 to get relative sectoral prices distribution of sectoral output output growth rate between periods
B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Step 1: Technological parameters – labor income shares → pin down the relative productivities within sectors and period for each year of data (1960, 1970, 1980, 1990, 2000, 2007) the labor income share of occupation o in sector J can be expressed as: iρ h ρ−1 αJo pJ YJ αJo w o wo αJo wo wo oJ θJo = = = ρ−1 ρ−1 ρ−1 pJ YJ pJ YJ αJm + αwJrr + αwJaa wm in each sector J we can use two of these, say for o = m, a to back out the ratios αJr /αJm and αJa /αJm B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Step 1: Technological parameters – relative sectoral prices → pin down the relative productivities across sectors, within period given the ratios αJr /αJm and αJa /αJm in each sector J the relative price of sector J to K can expressed as pJ αKm = pK αJm
1 ρ−1 wm
1 ρ−1 wm
+
+
αrJ αJm
αKr αKm
ρ−1
1 wrρ−1
ρ−1
1 wrρ−1
+
+
αJa αJm αKa αKm
ρ−1
1 waρ−1
ρ−1
1
1 1−ρ
.
waρ−1
we can back out the value of αLm /αHm and αGm /αHm which matches the relative sectoral price pL /pH and pG /pH
B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Step 1: Technological parameters – growth rate of the economy → pins down the relative productivities over time assuming that the distribution of value added across sectors is met in the economy in each period taking the occupational wages and the previously calculated αJo (relative to αHm ) as given we can calculate the output produced in each sector of the economy in each period calculate real growth of the economy using initial period prices and current period sectoral output we can back out the change in αHm over time to match the growth of output (VA quantity index) per capita initial value of αHm can be normalized wlog B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Step 2: Preference parameters given all the production side parameters, the supply of different types of labor, and ε = 0.2, calibrate c L , c H , aL , aG to match the distribution of the fraction of value added between sectors in the initial and final year, in 1960 and 2007 this also guarantees that the relative occupational wages in 1960 and 2007 are met in equilibrium Note: the occupational wages and the distribution of value added will only be matched in the initial and final period; in interim periods it won’t be (perfectly), thus also the labor income shares and relative prices will not be perfectly matched. B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Exogenous and calibrated parameters fm fr fa
ρ ε cL cH aL aH
1960 1970 1980 1990 2000 Occupational labor supplies 0.109 0.103 0.109 0.106 0.116 0.628 0.591 0.536 0.492 0.449 0.262 0.306 0.355 0.402 0.436
Description elasticity of substitution in production elasticity of substitution in consumption non-homotheticity term in L non-homotheticity term in H weight on L weight on H
2007 0.168 0.425 0.407
Value 1.42 0.2 80.07 122.07 0.059 0.922
all targets B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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The evolution of calibrated cell productivities ln 3
ln
Lm
2
ln
Gm
1.5
2
0
1
1
-2
0.5
0 1960
-4 1960
1980
ln 6
2000
1980
ln
Lr
6
2000
0 1960
1980
ln
Gr
4.5
5.5
Hm
2000
Hr
4 5.5
5 4.5 1960
3.5
1980
ln 5
2000
5 1960
1980
ln
La
5
4
4
3
3
2000
3 1960
1980
ln
Ga
6
2000
Ha
5
2 1960
1980
2000
B´ ar´ any and Siegel (Sciences Po, Kent)
2 1960
1980
2000
4 1960
1980
2000
Disentangling occupation- and sector-specific technological change 2017 February
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Decomposition
B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Factor model decomposition Relate cell productivity change to a neutral, a sector, an occupation effect, as well as a residual α d Jot ≡ ln αJo,t − ln αJo,t−1 X X X =β0 + βt dt + γJ dJ + δo do t>1970
+
XX t
J∈{L,H}
ζJt dt dJ +
t
J
o∈{r ,a}
XX
ηot dt do + εJot
o
where dt – time dummies dJ – sector dummies do – occupation dummies B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Counterfactual technological processes To shut down occupation-specific changes we generate the following series: sec = ln αJo,t−1 + β0 + βt + γJ + ζJt + ln αJot
X δo + ηot 3
o∈{r ,a}
To shut down sector-specific changes we generate the following series: occ ln αJot = ln αJo,t−1 + β0 + βt + δo + ηot +
X γJ + ζJt 3
J∈{L,H}
Importance of different components: factors time&sec&occ time time&sec time&occ R2 0.7412 0.0221 0.2001 0.5632 B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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The evolution of counterfactual cell productivities ln 4
ln
Lm
2
ln
Gm
3
0
2
-2
1
Hm
2
0 1960
1980
ln 7
2000
-4 1960
1980
ln
Lr
8
6
7
5
6
2000
0 1960
1980
ln
Gr
5
2000
Hr
4
4 1960
1980
ln 5
2000
5 1960
1980
ln
La
6
2000
3 1960
1980
ln
Ga
8
2000
Ha
4 6
4 3 2 1960
1980
2000
calculated
B´ ar´ any and Siegel (Sciences Po, Kent)
2 1960
1980
time-varying sec & occ
2000
4 1960
time-varying sector
1980
2000
time-varying occ
Disentangling occupation- and sector-specific technological change 2017 February
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Results
B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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The evolution of employment shares 0.3
L: data vs full model
0.3
L: exp sector
0.3
0.2
0.2
0.2
0.1
0.1
0.1
0 1960 1970 1980 1990 2000 2010
0 1960 1970 1980 1990 2000 2010
G: data vs full model
0 1960 1970 1980 1990 2000 2010
G: exp sector
G: exp occ
0.4
0.4
0.4
0.3
0.3
0.3
0.2
0.2
0.2
0.1
0.1
0.1
0 1960 1970 1980 1990 2000 2010
0 1960 1970 1980 1990 2000 2010
H: data vs full model
0 1960 1970 1980 1990 2000 2010
H: exp sector
H: exp occ
0.6
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0 1960 1970 1980 1990 2000 2010 total
B´ ar´ any and Siegel (Sciences Po, Kent)
0 1960 1970 1980 1990 2000 2010 manual
routine
L: exp occ
0 1960 1970 1980 1990 2000 2010 abstract
Disentangling occupation- and sector-specific technological change 2017 February
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The evolution of value added shares 0.3
Value added share L
0.4
0.28
Value added share G
0.35
0.26
0.3
0.24 0.25 0.22 0.2
0.2
0.15
0.18 0.16 1960
0.7
1970
1980
1990
2000
2010
0.1 1960
1970
1980
1990
2000
2010
Value added share H
0.65 data
0.6
labor+calc alphas
0.55
labor+sec+occ
0.5
labor only labor+sector
0.45
labor+occ
0.4 0.35 1960
1970
1980
B´ ar´ any and Siegel (Sciences Po, Kent)
1990
2000
2010
Disentangling occupation- and sector-specific technological change 2017 February
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The evolution of relative prices Low-skilled service relative price
2 1.8 1.6 1.4 1.2 1 0.8 0.6 1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
2005
2010
High-skilled service relative price
5 4 3 2 1 0 1960
1965
1970
data
B´ ar´ any and Siegel (Sciences Po, Kent)
1975
lab+calc
1980 lab+sec+occ
1985
1990 lab only
1995 lab+sec
2000
lab+occ
Disentangling occupation- and sector-specific technological change 2017 February
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The evolution of relative occupational wages Manual relative wage
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 1960
1965
1970
1975
1980
1985
1990
1995
2000
1995
2000
2005
2010
2005
2010
Abstract relative wage
1.4
1.2
1
0.8
0.6 1960
1965 data
1970
1975
lab+calc
1980 lab+sec+occ
1985
1990 lab only
lab+sec
lab+occ
more B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Conclusion conduct an accounting exercise to extract sector-occupation cell productivities from labour income shares over time decompose these into a sectoral, an occupational, and a residual component find a role for both the sector and the occupation component occupation component explains most of the overall change in cells’ productivity model counterfactuals show that I I
occupation component important for relative wages sectoral component is key for employment shares and sectoral value added
overall suggests that occupation specific technology is not the main driver of structural change B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Work in progress / to be done
add an occupational choice stage I I I
such that occupational labor supplies react to wages cost of switching/entering occupations (in utility terms) relevant only for the counterfactuals (in baseline fo s matched)
sensitivity analysis with respect to ρ and ε I I I
changing ρ implies different α baseline results are invariant to ρ ρ might matter in the counterfactuals
B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Thank you
B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
39
Occupation classification 1
2
3
Manual: low-skilled non-routine farmers, housekeeping, cleaning, protective service, food prep and service, building, grounds cleaning, maintenance, personal appearance, recreation and hospitality, child care workers, personal care, service, healthcare support Routine construction trades, extractive, machine operators, assemblers, inspectors, mechanics and repairers, precision production, transportation and material moving occupations, sales, administrative support, sales, administrative support sales, administrative support Abstract: skilled non-routine managers, management related, professional specialty, technicians and related support
back B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Industry classification 1
Low-skilled services: personal services, entertainment, low-skilled transport (bus service and urban transit, taxicab service, trucking service, warehousing and storage, services incidental to transportation), low-skilled business and repair services (automotive rental and leasing, automobile parking and carwashes, automotive repair and related services, electrical repair shops, miscellaneous repair services), retail trade, wholesale trade
2
Goods: agriculture, forestry and fishing, mining, construction, manufacturing
3
High-skilled services: professional and related services, finance, insurance and real estate, communications, high-skilled business services, communications, utilities, high-skilled transport , public administration
back B´ ar´ any and Siegel (Sciences Po, Kent)
Disentangling occupation- and sector-specific technological change 2017 February
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Calibration Targets pL /pG pH /pG ΨL ΨG ΨH growth ωm ωr ωa θmL θrL θaL θmG θrG θaG θmH θrH θaH
1960 1 1 0.220 0.352 0.428 0.744 1.000 1.158 0.131 0.647 0.221 0.030 0.772 0.198 0.095 0.481 0.424
B´ ar´ any and Siegel (Sciences Po, Kent)
1970 1.153 1.145 0.216 0.314 0.470 1.329 0.838 1.000 1.218 0.116 0.634 0.251 0.034 0.736 0.230 0.099 0.414 0.486
1980 0.914 1.014 0.210 0.304 0.486 1.637 0.797 1.000 1.028 0.129 0.635 0.236 0.036 0.727 0.237 0.103 0.387 0.510
1990 0.977 1.449 0.204 0.246 0.551 2.050 0.859 1.000 1.192 0.133 0.606 0.260 0.037 0.640 0.323 0.090 0.332 0.579
2000 1.019 1.880 0.213 0.217 0.570 2.538 0.866 1.000 1.317 0.153 0.543 0.303 0.034 0.606 0.360 0.086 0.267 0.646
2007 0.969 1.906 0.199 0.209 0.592 2.794 0.862 1.000 1.337 0.179 0.539 0.282 0.090 0.591 0.319 0.127 0.254 0.618
back
Disentangling occupation- and sector-specific technological change 2017 February
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The evolution of occ employment shares within sec 0.8
L: data vs full model
0.8
L: exp sector
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0 1960 1970 1980 1990 2000 2010
0.8
G: data vs full model
0 1960 1970 1980 1990 2000 2010
0.8
G: exp sector
0 1960 1970 1980 1990 2000 2010
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0 1960 1970 1980 1990 2000 2010
0.8
H: data vs full model
0 1960 1970 1980 1990 2000 2010
0.8
H: exp sector
0.8
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
B´ ar´ any and Siegel (Sciences Po, Kent)
0 1960 1970 1980 1990 2000 2010
G: exp occ
0 1960 1970 1980 1990 2000 2010
0.6
0 1960 1970 1980 1990 2000 2010
L: exp occ
H: exp occ
0 1960 1970 1980 1990 2000 2010
Disentangling occupation- and sector-specific technological change 2017 February
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The evolution of relative sectoral wages Low-skilled service relative wage
1 0.98 0.96 0.94 0.92 0.9 0.88 0.86 1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
2005
2010
High-skilled service relative wage
1.1 1.05 1 0.95 0.9 0.85 0.8 1960
1965 data
1970
1975
lab+calc
B´ ar´ any and Siegel (Sciences Po, Kent)
1980 lab+sec+occ
1985
1990 lab only
1995 lab+sec
2000
lab+occ
Disentangling occupation- and sector-specific technological change 2017 February
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