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Supplementary Online Appendix

##  Appendix A Search strategy and selection procedure of studies

We closely followed the PRISMA 2020 guidelines to ensure the quality of the
systematic search process [131]. A scoping review was used to identify
relevant search terms in widely cited studies. Subsequently, a computerized
search was performed using the search term that appeared either in the title,
abstract, or list of keywords of studies, namely: ‘technolog*’ combined with
‘labo$r’ or ‘employment’. Specifically, the search strings used in Web of
Science included:

  * •

Query: (TS=((technolog*) AND (labo$r OR *employment)) AND WC=(ECONOMICS OR
MANAGEMENT OR BUSINESS OR BUSINESS, FINANCE OR SOCIOLOGY OR INDUSTRIAL
RELATIONS LABOR OR DEVELOPMENT STUDIES OR SOCIAL SCIENCES INTERDISCIPLINARY OR
HISTORY OR SOCIAL ISSUES OR URBAN STUDIES OR GEOGRAPHY) AND LA=(English) AND
DT=(Article OR Book Chapter OR Early Access OR Proceedings Paper OR Review))

  * •

Timespan: 1988-01-01 to 2021-04-21 (Publication Date)

  * •

Web of Science Index: Social Sciences Citation Index (SSCI)

The search was conducted in the Web of Science (WoS) Core Collection database.
We opted for WoS because of its proven suitability as a principal search
engine for systematic reviews [94]. As we are interested in the economic
impact of technological change, we only included records from the Social
Sciences Citation Index (SSCI) of WoS and limited our search to the
categories: Economics; Management; Business; Business, Finance; Sociology;
Industrial Relations and Labor; Development Studies; Social Sciences,
Interdisciplinary; History; Social Issues; Urban Studies; and Geography. The
search was restricted to the most relevant document types and included
articles, book chapters, early access documents, proceeding studies, and
reviews, and documents in English language. The search covered articles
published during the time period 1988-April 2021.

The initial search in WoS resulted in 8,748 studies published between 1
January 1988 and 21 April 2021.666See Appendix Figure
LABEL:fig:selectionprocedure for an overview of the selection process of
relevant studies. Given the large search outcome, six independent researchers
were involved in screening the relevant records to ensure the timely and
unbiased completion of the process. The procedure was structured in such a way
that each researcher evaluated 1/6 of the retrieved records in a first round,
i.e. approximately 1,450 per researcher. In a second round, the six sets of
records were rotated such that a second researcher re-assessed the previously
screened records. To avoid bias, the procedure ensured that in the second
round, two separate researchers were assigned to the set of records screened
by the original reviewer in the first round. To illustrate, if researcher A
screened articles 1 to 1,450 in the first round, then in the second round,
researcher B screened articles 1 to 725 and researcher C screened articles 726
to 1,450. The first and second rounds of screening were conducted
independently such that the assessment of the first reviewer was unknown to
the second set of reviewers.

Based on the title and abstract, every researcher screened each record and
assessed whether it is considered for this review as: relevant (“yes”);
potentially relevant (“maybe”); or irrelevant (“no”). In this first step,
studies were considered relevant if the independent variable is related to
technology and the dependent variable is related to (un)employment (inclusion
criterion 1). In a second step, records that were considered relevant by both
researchers (“yes”/ “yes”) or relevant by one and potentially relevant by the
other (“yes”/“maybe”) were kept. Vice versa, records that coded with
(“no”/“no”) and (“no”/“maybe”) were excluded. Mutual uncertainties (“maybe”/
“maybe”) and strong disagreements (“yes”/“no”) were subjected to further
reviewing by a randomly assigned third reviewer, who made the final decision
whether to in- or exclude the article.

In step 3, the remaining 252 studies were assigned to three researchers, i.e.
the authors of this paper, who each independently assessed 1/3 of the
remaining studies against the following inclusion criteria: (1) examines the
effect of technological change on employment; (2) makes a significant
empirical contribution, i.e. excluding purely theoretical studies; and (3)
covers at least one developed country and the period after the 1980s.777Here,
countries are defined as developed if classified as high-income by the [165].
Regarding (1), we included studies relying on direct and indirect measures of
technological change such direct measures of technology adoption and use, but
also indirect indicators reflecting structural changes in the production
technology. For employment, we considered direct measures of labor demand such
as employment, hours worked, and employment rates, but also measures that
provide insights on the dynamics at the labor market such as wages, wage bill,
employment shares of different skill or occupational groups, and substitution
elasticities. Finally, we did not impose any specific restrictions on the
level of analyses. Therefore, our final selection of studies investigates the
impact of technological progress at various levels of aggregation, i.e. at the
macro (e.g. country), meso (e.g. sectors, industries), micro (e.g. firms,
individuals), regional (e.g. regions, states, cities), and other (e.g. skills,
occupations) levels of data aggregation.

Step 4 of the systematic search led to the inclusion of 127 studies. These
studies were coded along a scheme that was iteratively refined throughout the
process. We recorded the countries studied, the period covered, the outcome
variable(s), and extracted descriptive information about the empirical
operationalization of technological change, the measurement of the employment
effects, the insights that can be gained about the three effects (replacement,
reinstatement, and real income), and, if applicable, information about the net
employment effect of technologies. We also extracted information about the
level of analysis (e.g. country, region, firm, employee, occupation), data
sources, sample size, methodology, potential heterogeneity, if applicable, and
bibliometric information about the author, publication year and outlet.

##  Appendix B Additional Tables and Figures

  * •

Notes: Columns (1)-(3) present the share and number (#) of studies by the
primary type of empirical methodology used in each study to identify the
effect(s) of interest. ‘Descriptive’ refers to studies using descriptive
statistics and conceptual analyses that link macro-level stylized facts to
empirically reported technology-trends at the micro-level. ‘Regression’ refers
to any regression-based analysis or other quantitative inferential methods
with empirical foundation. ‘Simulation’ captures simulation methods, e.g.
DSGE. These statistics are reported in each row panel within any, replacement,
reinstatement, real income, and net employment effect reported, respectively.
Note that there is no overlap in methods reported, i.e. more than one primary
method used in each study, and thus the shares across columns add up to one,
up to rounding.

Reinstatement support depends weak no support na/nr share 0.50 0.06 0.05 0.02
0.38 # 64 7 6 2 48 Real income support depends weak no support na/nr share
0.18 0.03 0.02 0.03 0.74 # 23 4 2 4 94 Total employment positive depends weak
negative na/nr share 0.20 0.24 0.13 0.13 0.30 # 26 31 16 16 38

  * •

Notes: Columns (1)-(4) present the share and number (#) of studies with
empirical results that support, depend (on various characteristics, e.g. type
of technology or level of analysis), are weak, and do not support the presence
of the effect considered in each row panel. Column (5) presents the share and
number of studies out of the total sample of studies which do not examine the
effect of interest. The row row-panels present results for the replacement,
reinstatement, real income and total employment effect. The total # of studies
is 127.

  * •

Notes: Columns (1)-(4) present the share and number (#) of studies employing
macro, meso, micro, and regional level of analysis, respectively when
reporting any type of finding on the reinstatement effect.

Studies Categories Findings for technology effects on # Authors & year
Technology Level of Empirical Net Replacement Reinstatement Real income group
analysis methods employment finding type 1 [2] robots meso regression weak
support support support productivity 2 [3] robots regional regression negative
support no support support productivity 3 [4] ICT micro regression negative
support na/nr na/nr na/nr 4 [6] TFP-style meso descriptive na/nr support
support na/nr na/nr 5 [7] other/indirect micro regression negative support
na/nr na/nr na/nr 6 [10] ICT micro regression positive no support na/nr na/nr
na/nr 7 [9] ICT regional regression positive na/nr support na/nr na/nr 8 [11]
ICT micro regression positive no support support na/nr na/nr 9 [15] ICT
regional regression depends support support na/nr na/nr 10 [14] ICT regional
regression na/nr support support na/nr na/nr 11 [16] ICT macro regression
na/nr weak weak na/nr na/nr 12 [17] ICT meso descriptive na/nr support na/nr
support productivity 13 [18] ICT meso regression na/nr support support na/nr
na/nr 14 [12] TFP-style macro; regression negative support support support
productivity; meso income 15 [13] ICT macro descriptive weak support support
support productivity; income 16 [19] ICT macro regression na/nr support
support support productivity 17 [20] ICT micro regression na/nr na/nr na/nr no
support productivity 18 [23] other/indirect macro other negative na/nr na/nr
na/nr na/nr (simulation) 19 [25] ICT micro regression positive support support
na/nr na/nr 20 [26] TFP-style macro regression na/nr support support na/nr
na/nr 21 [27] ICT regional regression weak no support support na/nr na/nr 22
[28] ICT meso regression weak support support support income 23 [29] TFP-style
meso regression depends support support support productivity; prices 24 [30]
ICT meso regression na/nr no support na/nr na/nr na/nr 25 [32] ICT; meso
regression depends support support support income robots 26 [33]
other/indirect micro regression na/nr support na/nr depends income 27 [34]
TFP-style meso regression weak support na/nr na/nr na/nr 28 [35] innovation
meso regression na/nr depends na/nr na/nr na/nr 29 [36] robots regional;
regression na/nr support na/nr na/nr na/nr meso 30 [37] ICT macro descriptive
weak no support depends na/nr na/nr 31 [38] TFP-style macro regression weak
depends support support productivity; income 32 [39] ICT; meso regression weak
depends weak na/nr na/nr other/indirect 33 [43] other/indirect macro;
descriptive na/nr support support na/nr na/nr meso 34 [44] innovation micro
descriptive positive no support support na/nr na/nr 35 [45] robots micro
regression positive support na/nr depends productivity 36 [46] innovation
regional regression depends na/nr support na/nr na/nr 37 [47] ICT macro other
weak na/nr weak na/nr na/nr (simulation) 38 [48] TFP-style macro regression
negative support na/nr support productivity 39 [49] ICT micro regression na/nr
na/nr na/nr support productivity 40 [50] innovation meso regression depends
support support support output 41 [51] ICT micro regression na/nr na/nr na/nr
no support productivity 42 [52] robots meso regression negative support na/nr
weak prices; income 43 [53] innovation micro regression na/nr depends na/nr
depends productivity; income 44 [54] innovation micro regression positive
support support na/nr na/nr 45 [55] robots meso regression weak support
support na/nr na/nr 46 [56] robots meso regression positive no support support
support productivity 47 [58] ICT micro regression na/nr support na/nr na/nr
na/nr 48 [59] ICT meso descriptive positive support na/nr na/nr na/nr 49 [60]
TFP-style meso regression negative weak na/nr no support productivity; income
50 [61] robots micro regression na/nr support na/nr na/nr na/nr 51 [62] TFP-
style macro regression depends na/nr depends na/nr na/nr 52 [64] ICT macro
regression na/nr support na/nr na/nr na/nr 53 [65] robots meso other weak
support support na/nr na/nr (simulation) 54 [66] TFP-style macro other na/nr
support na/nr na/nr na/nr (simulation) • Note: Continued on next page

Table B.4: List of studies

Studies Categories Findings for technology effects on # Authors & year
Technology Level of Empirical Net Replacement Reinstatement Real income group
analysis methods employment finding type 55 [70] innovation micro regression
positive depends depends na/nr na/nr 56 [71] robots regional regression na/nr
support na/nr na/nr na/nr 57 [72] other/indirect regional regression positive
na/nr support support output 58 [73] innovation micro regression na/nr depends
na/nr na/nr na/nr 59 [74] other/indirect macro regression weak support support
na/nr na/nr 60 [75] other/indirect macro regression depends depends na/nr
na/nr na/nr 61 [76] ICT micro regression na/nr support weak na/nr na/nr 62
[77] TFP-style meso; descriptive depends depends support na/nr na/nr micro 63
[78] ICT micro regression na/nr support support na/nr na/nr 64 [80] robots
macro; regression positive no support na/nr depends productivity regional 65
[81] ICT; micro regression positive no support support na/nr na/nr innovation
66 [82] ICT micro regression depends no support support na/nr na/nr 67 [83]
ICT macro descriptive depends depends support na/nr na/nr 68 [84]
other/indirect macro; regression negative support support na/nr na/nr micro 69
[85] robots meso descriptive positive na/nr support na/nr na/nr 70 [86]
other/indirect meso regression na/nr support na/nr na/nr na/nr 71 [87] ICT
macro regression negative support no support support output 72 [89] robots
meso regression weak support support support prices; productivity; income 73
[90] TFP-style regional; regression depends support support support output
meso 74 [91] other/indirect macro descriptive depends weak support na/nr na/nr
75 [92] TFP-style meso descriptive na/nr support na/nr na/nr na/nr 76 [93]
other/indirect macro; regression na/nr support na/nr na/nr na/nr micro 77 [95]
TFP-style; meso regression na/nr support support na/nr na/nr other/indirect 78
[96] other/indirect macro regression depends support support na/nr na/nr 79
[97] other/indirect macro regression negative support na/nr na/nr na/nr 80
[98] TFP-style macro regression negative support na/nr na/nr na/nr 81 [99]
other/indirect macro regression weak weak na/nr na/nr na/nr 82 [101] ICT
regional regression depends no support support na/nr na/nr 83 [102] ICT meso
regression depends support support na/nr na/nr 84 [103] other/indirect macro
regression positive na/nr support na/nr na/nr 85 [104] robots meso regression
negative support na/nr support productivity 86 [105] ICT micro regression
depends support support na/nr na/nr 87 [106] other/indirect macro regression
negative na/nr na/nr na/nr na/nr 88 [107] TFP-style; regional regression
depends support support na/nr na/nr other/indirect 89 [108] ICT meso
regression depends support support na/nr na/nr 90 [110] innovation micro
regression depends depends depends na/nr na/nr 91 [111] robots macro
regression depends support support na/nr na/nr 92 [112] robots regional;
regression positive na/nr support na/nr na/nr meso 93 [114] ICT meso
descriptive positive support support na/nr na/nr 94 [115] other/indirect macro
regression depends support support na/nr na/nr 95 [116] other/indirect
regional regression positive support na/nr na/nr other 96 [118] ICT micro
regression depends weak support na/nr na/nr 97 [122] ICT; meso regression
na/nr support support na/nr na/nr other/indirect 98 [121] ICT; meso regression
depends support support na/nr na/nr other/indirect 99 [127] other/indirect
macro descriptive depends support support weak other 100 [129] ICT; macro;
regression depends depends depends na/nr na/nr other/indirect meso 101 [128]
ICT macro descriptive na/nr na/nr na/nr support productivity 102 [130]
other/indirect macro regression positive support support support output 103
[132] ICT micro regression na/nr no support na/nr na/nr na/nr 104 [133]
innovation micro regression depends depends depends na/nr na/nr 105 [134]
other/indirect micro regression depends support na/nr na/nr na/nr 106 [136]
innovation micro regression weak na/nr weak na/nr na/nr 107 [137] innovation
micro regression weak na/nr weak na/nr na/nr 108 [138] other/indirect micro
regression positive no support support na/nr na/nr 109 [139] other/indirect
macro regression depends support support na/nr na/nr 110 [140] other/indirect
macro other na/nr support support na/nr na/nr (simulation) 111 [143] TFP-style
macro regression positive no support support na/nr na/nr 112 [144]
other/indirect macro other na/nr na/nr na/nr no support productivity
(simulation) 113 [145] other/indirect macro other positive na/nr na/nr na/nr
na/nr (simulation) • Note: Continued on next page

Table B.5: List of studies – continued from previous page

Studies Categories Findings for technology effects on # Authors & year
Technology Level of Empirical Net Replacement Reinstatement Real income group
analysis methods employment finding type 114 [146] other/indirect macro
descriptive depends no support na/nr na/nr na/nr 115 [148] other/indirect
micro regression na/nr no support na/nr na/nr na/nr 116 [150] ICT macro;
regression na/nr na/nr na/nr support productivity meso 117 [152] innovation
micro descriptive positive na/nr support na/nr na/nr 118 [156] other/indirect
meso descriptive depends support support na/nr na/nr 119 [157] innovation
micro regression depends depends depends na/nr na/nr 120 [158] other/indirect
micro regression positive na/nr support na/nr na/nr 121 [160] innovation micro
regression na/nr support support na/nr na/nr 122 [161] ICT meso regression
na/nr na/nr na/nr support productivity; output 123 [162] other/indirect macro
regression negative support na/nr na/nr na/nr 124 [163] TFP-style macro
regression negative support na/nr na/nr na/nr 125 [164] ICT macro; regression
na/nr support na/nr na/nr na/nr meso 126 [166] innovation meso regression
positive na/nr support na/nr na/nr 127 [167] other/indirect macro regression
positive na/nr support na/nr na/nr • Notes: This table presents descriptive
information about the 127 studies used in this review after a systematic
search of the literature on technology and jobs. This includes information
about: the author and publication year; various classifications of interest
based on the technology group, level of analysis (e.g. country, region, firm,
employee, occupation), and empirical methods used in each study; and the
findings about net employment and focal effects, i.e. replacement,
reinstatement, and real income, of technologies. Finally, for the real income
effect we also present the relevant variable of interest. See Section 3 for
more details on the coding scheme.

Table B.6: List of studies – continued from previous page