Popular in Social Sciences

Political Science

V-Dem’s Conceptions of Democracy and Their Consequences

University of Notre Dame Michael Coppedge

Central Monetary Authorities

Macroeconomic modelling of CBDC: a critical review

European Central Bank Ulrich Bindseil

Social Identities and Divisions

A new explanation of antisemitism: Jew hatred as a civilisational phenomenon

Boston University Liah Greenfeld

Central Monetary Authorities

Financing Private Credit

Federal Reserve Bank of New York Nina Boyarchenko

Economics

Failing Banks

Massachusetts Institute of Technology Emil Verner

Culture and Society

The City as a Growth Machine: Toward a Political Economy of Place

New York University Harvey Moltoch

American Politics

Offshoring and non-monotonic employment effects across industries in general equilibrium

View more insights in Social Sciences - Economics - Macroeconomics

View more figures from Universität Paderborn

View the page of Daniel Baumgarten

Read the Study

Next in Economics

Fiscal policy volatility and capital misallocation

Shengyu Li | UNSW Sydney

Effects of monetary policy on household expectations: The role of homeownership

Shihan Xie | University of Illinois at Urbana-Champaign

How does addressing the mismatch between theory and practice in offshoring analysis reveal non-monotonic employment effects across industries, as evidenced by post-Iron Curtain Germany?

Image courtesy of interviewee. November 14, 2024

Log-in or Sign-up to Faculti
Currently viewing this subject insight as a guest. You have insight(s) remaining for this month. Login to view 8000+ figures on the platform.

Generating Workspace...

Export as PDF

The study addresses two key contexts: a real-world context marked by a significant increase in offshoring—defined as the international fragmentation of production—and an academic context where existing research on the labor market effects of offshoring shows no clear consensus. Offshoring has been linked to cost savings for firms but raises concerns about potential negative impacts on domestic workers.

Despite a substantial body of theoretical and empirical research, findings have often been ambiguous, with many empirical results appearing small compared to public concerns. The authors argue that traditional models, which rely on one or two sector frameworks, may yield misleading conclusions due to the interconnected nature of industries in general equilibrium.

They introduce a theoretical model—the general oligopolistic equilibrium (GOLE) model—to analyze employment effects across many interconnected industries that vary in their offshoring potential. The study finds that industries with low and high offshoring intensity tend to lose employment, while those with an intermediate range gain. This video creates a hump-shaped employment pattern influenced by direct displacement effects and positive productivity effects associated with offshoring.

The research emphasizes the need for refined empirical models that account for non-monotonic relationships between offshoring and employment. It suggests that adjustment assistance should be broadly available to workers affected by offshoring, rather than narrowly targeted, as many impacted individuals may not directly experience job loss but still suffer from broader economic shifts. Overall, the study calls for a deeper understanding of how offshoring impacts labor markets beyond straightforward predictions.

Language:

Export as PDF

Export as PDF
Here are five key points from the video:

1. **Convideo of Offshoring**: The study addresses offshoring, which refers to the international fragmentation of production where various activities in the production chain are located across different countries. The process has been increasingly common since the 1980s, although it has slowed down recently, raising concerns about its impact on domestic workers.

2. **Empirical Analysis and Theoretical Misalignment**: The existing research on offshoring and its effects on domestic workers shows ambiguous and often minimal results, contradicting public concerns. The authors note a mismatch between the theoretical frameworks, which often assume linear relationships, and actual empirical practices that might overlook the complexities of labor market interconnections among industries.

3. **General Equilibrium Effects**: The study proposes a new theoretical framework that incorporates a general equilibrium model, acknowledging that industries are interconnected within the labor and capital markets. This interconnectedness can lead to non-monotonic employment patterns where both low and high offshoring industries may lose jobs, while those in the middle gain employment.

4. **Hump-Shaped Employment Patterns**: The authors observe that industries with intermediate levels of offshoring gain employment, while low and high offshoring-intensive industries see job losses. This result is attributed to the interplay between displacement effects (jobs moving abroad) and productivity effects (cost savings enabling firms to expand production).

5. **Implications for Policy and Empirical Research**: The findings suggest that empirical analyses should refine their methods to account for non-linear relationships in the effects of offshoring on employment. The research also implies that adjustment assistance for workers affected by offshoring needs to consider the broader impacts on industries that do not directly engage in offshoring. This highlights the importance of understanding the varying adjustment burdens across different sectors.

Export as PDF
Here are some key quotes and concepts extracted from the video:

1. **Convideo of the Study**:
- "The convideo for this study was twofold... a remarkable increase in offshoring... the international fragmentation of production."

2. **Concerns of Offshoring**:
- "On the one hand, has been associated with cost savings from the perspective of firms... potentially negative effects for domestic workers... offshoring has always been a high priority, both for academics, but also for policy makers."

3. **Existing Research**:
- "There was already a large body of research... that analyzed the effects of offshoring on domestic workers... empirical results were fairly ambiguous and also mostly fairly small, contrary to public concerns."

4. **Theoretical and Empirical Mismatch**:
- "We noted... a certain mismatch between the main theories that people use and relied on, and common empirical practice."

5. **Assumptions in Empirical Studies**:
- "You treat all of these different industries as separate and independent units of analysis... if you just include a linear term that there should be a linear monotonic relationship."

6. **Introduction of New Framework**:
- "We argue... that this approach might lead to misleading conclusions... there is a hump-shaped and non-monotonic employment pattern across industries."

7. **Effects of Offshoring**:
- "Offshoring always induces two main effects... the direct displacement effect... the positive productivity effect."

8. **Empirical Analysis Focus**:
- "We analyze employment effects across aggregated industries after the fall of the Iron Curtain."

9. **Shock and Offshoring Potential**:
- "We characterize this offshoring potential... using an indicator variable of offshore ability... we get this hump-shaped employment."

10. **Impact on Different Industries**:
- "Industries with an intermediate range of offshore tasks gain employment... most offshoring intensive industries lose employment."

11. **General Equilibrium Effects**:
- "These low offshoring intensive industries... suffer from an increase in domestic production costs... increase in domestic wages means production costs go up."

12. **Policy Implications**:
- "A big part of the adjustment burden is borne by those industries where you do not see any direct offshoring taking place."

These quotes reflect the main ideas and findings presented in the video regarding offshoring, its effects on employment across industries, and the implications for research and policy.

X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:90000

1
00:00:00.265 --> 00:00:02.485
The context for this study was twofold.

2
00:00:02.605 --> 00:00:04.805
I mean, there was a real world context, I would say,

3
00:00:04.805 --> 00:00:07.205
and there was an academic context, okay?

4
00:00:07.205 --> 00:00:10.405
So the real world context is that there was a remarkable

5
00:00:10.965 --> 00:00:12.365
increase in offshoring.

6
00:00:12.505 --> 00:00:15.045
And by offshoring we mean essentially the international

7
00:00:15.245 --> 00:00:16.405
fragmentation of production, right?

8
00:00:16.405 --> 00:00:19.165
That more and more activities of the production chain

9
00:00:19.785 --> 00:00:21.405
are located in different countries

10
00:00:21.505 --> 00:00:23.445
and in different parts of the world, right?

11
00:00:23.465 --> 00:00:26.125
And this process, I mean, has been increasing

12
00:00:26.225 --> 00:00:28.085
for a fairly long period of time.

13
00:00:28.225 --> 00:00:32.885
So let's say at least since the 1980s until more

14
00:00:32.885 --> 00:00:37.045
or less, the financial crisis in the year 2009, 2010,

15
00:00:37.045 --> 00:00:39.165
around this year, then it has been slow,

16
00:00:39.425 --> 00:00:40.525
has slowed down a bit,

17
00:00:40.785 --> 00:00:41.925
but for a fairly long time,

18
00:00:41.925 --> 00:00:44.645
there's really an incredible increase in this

19
00:00:44.885 --> 00:00:45.925
fragmentation of production.

20
00:00:46.425 --> 00:00:48.925
And then this process, on the one hand, has been associated

21
00:00:48.925 --> 00:00:51.325
with cost savings from the perspective of firms, right?

22
00:00:51.325 --> 00:00:52.485
Which is good, right?

23
00:00:52.485 --> 00:00:53.805
Otherwise probably they wouldn't do it.

24
00:00:54.145 --> 00:00:55.285
But on the other hand,

25
00:00:55.285 --> 00:00:58.445
they have always been this concerns about potentially

26
00:00:58.765 --> 00:01:00.965
negative effects for domestic workers, right?

27
00:01:01.105 --> 00:01:02.885
And, so

28
00:01:02.885 --> 00:01:05.965
therefore, analyzing these labor market effects

29
00:01:05.965 --> 00:01:08.245
of offering has always been a high priority, both

30
00:01:08.305 --> 00:01:10.925
for academics, but also for policy makers.

31
00:01:10.985 --> 00:01:13.365
So that's essentially one part of the context.

32
00:01:14.265 --> 00:01:17.725
Now, the more academic context is that when we,

33
00:01:17.725 --> 00:01:19.845
when our study started, started,

34
00:01:19.895 --> 00:01:22.445
there was already a large body of research, right?

35
00:01:22.445 --> 00:01:25.925
That analyzed the, these questions, the effects

36
00:01:25.945 --> 00:01:30.445
of offshoring on domestic workers, both theoretical studies

37
00:01:30.745 --> 00:01:33.925
and empirical studies, yet, I mean,

38
00:01:34.005 --> 00:01:36.245
there was no clear consensus, right?

39
00:01:36.385 --> 00:01:39.205
So the results were, I mean, at,

40
00:01:39.205 --> 00:01:41.725
at least the empirical results were fairly ambiguous

41
00:01:41.725 --> 00:01:45.005
and also mostly fairly small, contrary

42
00:01:45.185 --> 00:01:46.925
to public concerns.

43
00:01:47.905 --> 00:01:51.885
So, however, now we noted, right, when we really had

44
00:01:51.885 --> 00:01:53.445
to look at these empirical size

45
00:01:53.445 --> 00:01:56.285
and theoretical size, that there was a certain mismatch

46
00:01:56.285 --> 00:01:59.725
between the main theories that people use and relied on,

47
00:02:00.385 --> 00:02:03.205
and common empirical practice, right?

48
00:02:03.225 --> 00:02:06.245
And more specifically, so most of these theoretical modes,

49
00:02:06.245 --> 00:02:09.565
they were developed in one sector frameworks

50
00:02:09.705 --> 00:02:11.325
or two sector frameworks,

51
00:02:11.345 --> 00:02:13.085
and explain the labor market effects.

52
00:02:13.105 --> 00:02:14.965
And these settings, however,

53
00:02:15.305 --> 00:02:17.085
in the empirical studies is you need a lot

54
00:02:17.085 --> 00:02:18.125
of variation, right?

55
00:02:18.145 --> 00:02:19.485
You need differences.

56
00:02:19.745 --> 00:02:21.485
And then common setup was

57
00:02:21.485 --> 00:02:25.005
to use variation across a large number of industries, right?

58
00:02:25.065 --> 00:02:28.725
And then do what we call a reduced form regression.

59
00:02:29.105 --> 00:02:31.285
We have some labor market outcome,

60
00:02:31.285 --> 00:02:33.245
let's say the change in employment, for example,

61
00:02:33.265 --> 00:02:34.405
as your outcome variable.

62
00:02:35.555 --> 00:02:38.775
And then you regress this on measures

63
00:02:38.775 --> 00:02:42.615
of offshoring intensity as your main explanatory variables.

64
00:02:42.635 --> 00:02:44.895
And the common reduced form approach was

65
00:02:44.895 --> 00:02:47.575
to have a linear offshoring term on the right hand side.

66
00:02:48.195 --> 00:02:50.495
And this approach essentially has a

67
00:02:50.495 --> 00:02:51.975
few implicit assumptions.

68
00:02:51.975 --> 00:02:55.095
So first of all, you treat all of these different industries

69
00:02:55.155 --> 00:02:57.615
as separate and independent units of analysis.

70
00:02:57.635 --> 00:02:59.525
So that's essentially one implicit assumption.

71
00:03:00.145 --> 00:03:03.085
And on top, you implicitly assume if you just include a

72
00:03:03.085 --> 00:03:05.285
linear term that there should be a linear monotonic

73
00:03:05.285 --> 00:03:06.405
relationship, right?

74
00:03:06.405 --> 00:03:08.845
So the, depending on the coefficient that you obtain,

75
00:03:09.225 --> 00:03:10.645
you would reduce either

76
00:03:10.645 --> 00:03:12.885
that more off showing always is positive,

77
00:03:12.885 --> 00:03:14.285
always leads to more employment.

78
00:03:14.825 --> 00:03:16.465
Or if the coefficient is negative,

79
00:03:16.465 --> 00:03:19.465
you would reduce more options, is always bad, always leads

80
00:03:19.465 --> 00:03:22.185
to less employment, or you get zero confusion than you

81
00:03:22.185 --> 00:03:23.265
would deduce, okay?

82
00:03:23.495 --> 00:03:25.905
Doesn't matter at all. And that's essentially where we

83
00:03:26.415 --> 00:03:29.145
come in and say, okay, we argue in this paper, right?

84
00:03:29.145 --> 00:03:32.225
That we are currently talking about that this approach,

85
00:03:32.765 --> 00:03:35.105
we might lead to misleading conclusions, right?

86
00:03:35.285 --> 00:03:37.745
In particular, if these industries are connected

87
00:03:37.745 --> 00:03:39.785
to each other in, in general equilibrium,

88
00:03:39.935 --> 00:03:42.385
because then our key results will be,

89
00:03:42.385 --> 00:03:45.265
and we will talk about it surely,

90
00:03:45.415 --> 00:03:46.985
that there is an hump shaped

91
00:03:47.005 --> 00:03:49.785
and non monotonic employment pattern across industries

92
00:03:50.175 --> 00:03:52.545
that differ in their offering industries.

93
00:03:52.645 --> 00:03:54.105
So employment use,

94
00:03:54.105 --> 00:03:57.425
both in the low i intense offshoring intensive industries

95
00:03:57.445 --> 00:03:59.385
and the high offshoring intensive industries.

96
00:03:59.845 --> 00:04:01.785
And the industries with an intermediate range

97
00:04:02.365 --> 00:04:05.865
of these offshore tasks, they gain employment.

98
00:04:06.125 --> 00:04:07.385
The theoretical part.

99
00:04:07.645 --> 00:04:09.825
So the study consists of the theoretical part

100
00:04:09.845 --> 00:04:11.025
and empirical parts.

101
00:04:11.045 --> 00:04:13.785
And the, the part, our aim was

102
00:04:13.885 --> 00:04:16.905
to have a model theoretical model that speaks

103
00:04:16.965 --> 00:04:19.145
to this common empirical setup, right?

104
00:04:19.145 --> 00:04:21.745
That, that informs the common empirical setup.

105
00:04:22.285 --> 00:04:25.425
So where we have large number of industries, not just one

106
00:04:25.425 --> 00:04:27.505
or two, but really a large number of industries

107
00:04:28.135 --> 00:04:31.185
that are connected to each other in, in general equilibrium.

108
00:04:31.445 --> 00:04:32.865
And that differ.

109
00:04:33.205 --> 00:04:35.305
So these industries, they should differ in their

110
00:04:35.305 --> 00:04:36.425
scope for offshoring.

111
00:04:36.575 --> 00:04:37.945
Okay? So this was our aim.

112
00:04:38.565 --> 00:04:42.465
Now, more precisely what we do is we incorporate offshoring

113
00:04:43.005 --> 00:04:45.665
in the, so-called general oligo

114
00:04:46.265 --> 00:04:47.865
oligopolistic equilibrium model.

115
00:04:48.005 --> 00:04:52.105
So that's abbreviate as the goal model GOLE model

116
00:04:52.255 --> 00:04:55.945
that was introduced by the late Peter Neri, okay?

117
00:04:55.945 --> 00:04:57.025
And what we have in this,

118
00:04:57.085 --> 00:04:59.585
in this framework is we have essentially a

119
00:04:59.585 --> 00:05:00.945
continuum of industries.

120
00:05:01.795 --> 00:05:05.065
These industries, we index them by the letter Z, right?

121
00:05:05.065 --> 00:05:07.785
And these industries are defined over the unit intervals.

122
00:05:07.785 --> 00:05:10.545
So they range from zero to one, okay?

123
00:05:10.545 --> 00:05:11.745
And this letter z,

124
00:05:11.745 --> 00:05:14.585
this running variable at the same time also corresponds

125
00:05:14.585 --> 00:05:18.545
to the share of offshore world tasks in each industry.

126
00:05:18.815 --> 00:05:21.305
Okay? So we assume that these,

127
00:05:21.365 --> 00:05:24.825
all these different industries, they differ in the share

128
00:05:24.825 --> 00:05:27.905
of activities that can move abroad, right?

129
00:05:28.125 --> 00:05:31.745
Be it, because some industries they rely very heavily on,

130
00:05:31.805 --> 00:05:34.505
on face-to-face contact on geographic proximity.

131
00:05:35.075 --> 00:05:37.345
Let's say if you work in a bar

132
00:05:37.365 --> 00:05:39.225
or restaurant that these activities

133
00:05:39.225 --> 00:05:41.345
that you cannot really offshore,

134
00:05:41.565 --> 00:05:44.665
and in other industries, maybe most of the activities,

135
00:05:44.665 --> 00:05:46.905
they can move abroad, right?

136
00:05:46.965 --> 00:05:49.785
So now in the theory, this chair

137
00:05:49.785 --> 00:05:54.305
of task can range from zero 0% to 100%, right?

138
00:05:54.305 --> 00:05:56.905
Which of course is extreme, but that's the setup.

139
00:05:57.825 --> 00:06:01.005
And, and now these industries are also,

140
00:06:01.005 --> 00:06:02.885
they're connected to each other in generic Librium

141
00:06:02.885 --> 00:06:05.925
because they, all of them rely on the same labor market.

142
00:06:06.105 --> 00:06:08.405
All of them rely on the same capital market.

143
00:06:08.905 --> 00:06:12.885
So that's where these feedback effects come into play. Okay?

144
00:06:12.885 --> 00:06:16.245
So that's essentially the framework that we use.

145
00:06:16.755 --> 00:06:19.365
It's important maybe to note that, I mean,

146
00:06:19.995 --> 00:06:21.285
it's not really necessary

147
00:06:21.305 --> 00:06:23.565
to have this specific framework, right?

148
00:06:23.585 --> 00:06:26.085
We have, I mean, other frameworks could also have worked

149
00:06:26.225 --> 00:06:29.045
as long as the conditions that are just outlined,

150
00:06:29.045 --> 00:06:32.365
having many industries differences in offering potential.

151
00:06:32.895 --> 00:06:35.405
These in, genetically linkages, as long

152
00:06:35.405 --> 00:06:38.525
as this is fulfilled, other, setups would also work.

153
00:06:38.865 --> 00:06:40.525
We have chosen this gold setup

154
00:06:40.525 --> 00:06:42.085
because it's fairly tractable.

155
00:06:42.705 --> 00:06:44.885
Um, I mean it's fairly Parsons.

156
00:06:44.885 --> 00:06:46.845
It allows us to derive closed form

157
00:06:46.845 --> 00:06:48.285
solutions that are fairly elegant.

158
00:06:48.905 --> 00:06:51.525
So that's essentially why, why we use this model.

159
00:06:51.825 --> 00:06:56.285
We focus on the West German experience in the fall of,

160
00:06:56.425 --> 00:06:58.565
in the aftermath of the fall of the iron Kern.

161
00:06:58.565 --> 00:07:02.525
So essentially what we do is we analyze employment effects

162
00:07:02.625 --> 00:07:04.925
across this aggregated industries

163
00:07:05.575 --> 00:07:07.165
after the fall of the iron Kern.

164
00:07:07.165 --> 00:07:09.205
And we argue that that's an attractive

165
00:07:09.465 --> 00:07:12.925
and the fitting setup for, for our model, for our purposes.

166
00:07:13.235 --> 00:07:15.645
Because first of all, it's essentially this,

167
00:07:16.075 --> 00:07:17.125
it's a shock, right?

168
00:07:17.125 --> 00:07:20.285
That a shock that came, I mean, unexpected

169
00:07:20.385 --> 00:07:22.485
and also fairly sudden, okay?

170
00:07:22.505 --> 00:07:25.605
So it's barefoot centric for, qualifies

171
00:07:25.605 --> 00:07:27.765
as a quasi exogenous shock.

172
00:07:27.865 --> 00:07:31.845
Has that has also been used in, in for similar type

173
00:07:31.845 --> 00:07:33.005
of questions, right?

174
00:07:33.625 --> 00:07:36.685
And, and it also, I mean, it matters for offshoring

175
00:07:36.685 --> 00:07:40.325
because arguably it greatly improved the opportunities

176
00:07:40.345 --> 00:07:42.205
of German firms to engage in

177
00:07:42.215 --> 00:07:43.885
production fragmentation, right?

178
00:07:43.885 --> 00:07:47.125
Because most of these now formally socialist countries

179
00:07:47.125 --> 00:07:49.485
that are just the Germany's dose doorstep,

180
00:07:49.545 --> 00:07:50.805
so they're very close by.

181
00:07:51.665 --> 00:07:55.605
Um, they also had much lower wage levels than Germany.

182
00:07:55.905 --> 00:07:58.965
So therefore they were attractive horing destinations.

183
00:07:59.025 --> 00:08:00.925
And that's not only something that we think,

184
00:08:00.925 --> 00:08:03.725
but also that has been documented in, in other studies

185
00:08:03.755 --> 00:08:07.885
that really German firms offshore a lot of their activities

186
00:08:08.465 --> 00:08:09.525
to Eastern Europe.

187
00:08:10.225 --> 00:08:12.565
Um, so that's why we use it now, a key variable

188
00:08:12.565 --> 00:08:13.725
that we need, right?

189
00:08:13.725 --> 00:08:15.205
In the, in the empiric analysis

190
00:08:15.265 --> 00:08:19.165
to define this offshoring potential that varies over,

191
00:08:19.595 --> 00:08:22.725
over industries and essentially the x ante.

192
00:08:22.725 --> 00:08:26.245
So before the fall of the iron curtain, what's the potential

193
00:08:26.245 --> 00:08:28.805
of these different industries to engage in horing?

194
00:08:29.115 --> 00:08:31.005
Essentially what we do to,

195
00:08:31.025 --> 00:08:33.525
to characterize this horing potential is,

196
00:08:33.825 --> 00:08:35.805
so we adopted two step procedure.

197
00:08:36.385 --> 00:08:38.885
So first of all, we use an indicator variable

198
00:08:39.025 --> 00:08:40.085
of offshore ability.

199
00:08:40.225 --> 00:08:43.165
So that was originally proposed by Ellen Blind

200
00:08:43.165 --> 00:08:44.245
and Ellen Kruger.

201
00:08:44.825 --> 00:08:49.085
So they have defined at the occupation level, essentially,

202
00:08:49.085 --> 00:08:52.525
they have coded different occupations from zero

203
00:08:52.665 --> 00:08:56.765
as no offshore to five as completely offshore.

204
00:08:57.515 --> 00:09:01.445
Essentially, we have used this occupation level indicator

205
00:09:01.945 --> 00:09:04.605
and aggregated it to the industry level,

206
00:09:04.635 --> 00:09:08.765
essentially exploiting that the occupational compositions

207
00:09:09.225 --> 00:09:11.885
of these industries that they differ, okay?

208
00:09:11.885 --> 00:09:13.765
So in some industries they have a lot

209
00:09:13.765 --> 00:09:16.085
of offshore occupations, other industries,

210
00:09:16.155 --> 00:09:19.365
they have just a small amount of offshore occupations.

211
00:09:19.505 --> 00:09:21.005
The key result is indeed

212
00:09:21.005 --> 00:09:23.085
that we get this hump shaped employment

213
00:09:23.085 --> 00:09:24.325
pattern across industries.

214
00:09:24.505 --> 00:09:27.885
So both industries that have a low offering intensity

215
00:09:28.065 --> 00:09:30.245
and that have a high offering intensity,

216
00:09:30.435 --> 00:09:32.005
they lose employment.

217
00:09:32.345 --> 00:09:36.485
And the ones with an intermediate range they gain now in the

218
00:09:36.485 --> 00:09:38.085
theoretical model, right?

219
00:09:38.405 --> 00:09:41.525
I mean, maybe to understand this result from a point

220
00:09:41.525 --> 00:09:45.285
of view, it's worth first pointing out that, I mean,

221
00:09:45.295 --> 00:09:48.565
offshoring always induces two main effects, right?

222
00:09:48.565 --> 00:09:51.765
And that's not something that we, I mean, introduced,

223
00:09:51.765 --> 00:09:54.845
but that we also had in, in previous models.

224
00:09:55.425 --> 00:09:58.005
So the first effect is that,

225
00:09:58.185 --> 00:10:01.045
is the direct displacement effect, okay?

226
00:10:01.065 --> 00:10:02.605
So that some tasks now

227
00:10:02.605 --> 00:10:04.485
that were previously performed in the home country,

228
00:10:04.485 --> 00:10:06.045
they now move abroad.

229
00:10:06.545 --> 00:10:10.045
So this displacement displacement effect typically reduces

230
00:10:10.325 --> 00:10:11.605
domestic employment, right?

231
00:10:12.345 --> 00:10:13.885
Um, on the other hand,

232
00:10:14.195 --> 00:10:17.325
there's the positive productivity effect, okay?

233
00:10:17.325 --> 00:10:20.445
Because as firms engage in offshoring, they are able

234
00:10:20.445 --> 00:10:21.925
to lower their production costs.

235
00:10:22.425 --> 00:10:25.405
So this then allows them to produce a lower cost

236
00:10:25.405 --> 00:10:27.605
and to expand their production, okay?

237
00:10:27.605 --> 00:10:29.925
So that typically this effect typically works

238
00:10:29.945 --> 00:10:33.525
to the advantage of, of domestic workers.

239
00:10:34.145 --> 00:10:37.245
Now, the interplay between these two effects also explains

240
00:10:37.245 --> 00:10:39.725
what happens now across different industries.

241
00:10:40.265 --> 00:10:42.485
So the most offshoring intensive industries,

242
00:10:42.875 --> 00:10:44.045
they lose employment

243
00:10:44.045 --> 00:10:46.845
because of the dominating displacement effect.

244
00:10:47.075 --> 00:10:50.205
Okay? So they experience greatest cost savings, that's good,

245
00:10:50.205 --> 00:10:52.845
they're able to expand their production, that's also good.

246
00:10:53.185 --> 00:10:55.245
But anyway, if 90%

247
00:10:55.245 --> 00:10:57.725
or so of all the activities are performed overseas,

248
00:10:58.235 --> 00:11:00.605
then you still will lose domestic employment.

249
00:11:02.265 --> 00:11:05.445
So it's more tricky is why is it the case

250
00:11:05.475 --> 00:11:08.725
that even the industries where no offshoring takes place

251
00:11:08.785 --> 00:11:11.805
or only limited offering, why they do they lose employment?

252
00:11:11.995 --> 00:11:14.285
Okay? And that's where these general equilibrium

253
00:11:14.285 --> 00:11:15.525
effects come into play.

254
00:11:16.305 --> 00:11:20.085
And for two reasons in, in our framework, first of all.

255
00:11:20.705 --> 00:11:25.085
So these, these low offshoring intensive industries,

256
00:11:25.085 --> 00:11:28.085
they typically suffer from increase in domestic production

257
00:11:28.085 --> 00:11:29.325
cost-wise, this the case

258
00:11:29.325 --> 00:11:32.605
because typically the productivity effect will lead

259
00:11:32.605 --> 00:11:34.445
to an increase in domestic wages.

260
00:11:35.145 --> 00:11:37.045
Now, increase in domestic wages means

261
00:11:37.045 --> 00:11:38.365
production costs go up.

262
00:11:38.545 --> 00:11:40.045
In particular, those industries

263
00:11:40.075 --> 00:11:43.445
that rely very heavily on domestic workers, so

264
00:11:43.445 --> 00:11:45.725
that cannot offshore their production, okay?

265
00:11:45.725 --> 00:11:48.965
So therefore they essentially become more expensive,

266
00:11:49.105 --> 00:11:50.165
so this hurts them.

267
00:11:50.865 --> 00:11:53.445
Um, on the other hand, and it's also a similar channel.

268
00:11:53.945 --> 00:11:56.925
So we also think that offshoring will, lead

269
00:11:56.945 --> 00:12:00.965
to uneven profit opportunities across industries, right?

270
00:12:01.025 --> 00:12:03.885
So profit opportunities will increase the most in the

271
00:12:03.885 --> 00:12:05.325
offshoring intensive industries

272
00:12:05.475 --> 00:12:07.845
because they're, the cost savings are the greatest,

273
00:12:08.425 --> 00:12:10.005
and they will increase lease.

274
00:12:10.105 --> 00:12:12.725
So they will even decline in the industries

275
00:12:12.725 --> 00:12:14.245
with limited offshoring.

276
00:12:14.625 --> 00:12:18.565
So if there are uneven profit opportunities, then we expect,

277
00:12:18.565 --> 00:12:21.565
at least in the long run, that capital will be reallocated,

278
00:12:21.965 --> 00:12:24.325
I mean towards the, industries

279
00:12:24.325 --> 00:12:26.365
with the greatest profit opportunities

280
00:12:26.475 --> 00:12:28.165
with the greatest offshoring potential.

281
00:12:28.585 --> 00:12:31.965
So this will then lead to firm exit in the industries

282
00:12:31.965 --> 00:12:35.405
with low offshoring intensity towards industries

283
00:12:35.405 --> 00:12:36.925
with a higher offshoring intensity.

284
00:12:37.345 --> 00:12:40.725
And this channel also destroys than

285
00:12:41.475 --> 00:12:43.125
jobs in, yeah.

286
00:12:43.225 --> 00:12:44.525
In, in,

287
00:12:44.525 --> 00:12:47.525
for domestic workers in the low offshoring intensive

288
00:12:47.525 --> 00:12:51.085
industries, we need to think carefully in these settings.

289
00:12:51.655 --> 00:12:54.365
Where do we expect to find the effects? Right?

290
00:12:54.365 --> 00:12:56.245
So offshoring has certain effects.

291
00:12:56.505 --> 00:12:59.765
So we, we are quite sure that will also affect domestic

292
00:13:00.295 --> 00:13:01.645
labor markets, right?

293
00:13:01.925 --> 00:13:04.165
Domestic workers, but where do we find these effects?

294
00:13:04.985 --> 00:13:07.765
Now, our, our study provides a cautionary tale

295
00:13:07.825 --> 00:13:10.125
to some extent because it shows that part

296
00:13:10.125 --> 00:13:11.405
of the adjustment burden,

297
00:13:11.465 --> 00:13:15.085
or in, in fact a big part of the adjustment burden is born

298
00:13:15.085 --> 00:13:18.045
by those industries where you do not see any direct

299
00:13:18.045 --> 00:13:19.645
offshoring taking place, right?

300
00:13:19.705 --> 00:13:21.685
So where, where these effects are the least

301
00:13:22.085 --> 00:13:23.165
expected, maybe extent.

302
00:13:24.145 --> 00:13:26.725
So that's maybe one implication at all sorts

303
00:13:26.725 --> 00:13:29.885
of implications, for empirical work.

304
00:13:30.355 --> 00:13:33.045
Okay? Because, now, I mean we need

305
00:13:33.045 --> 00:13:34.405
to think more carefully about the

306
00:13:34.405 --> 00:13:35.685
correct empirical approach.

307
00:13:36.315 --> 00:13:39.605
Just using this linear shoring term probably doesn't do it.

308
00:13:39.605 --> 00:13:42.485
We need to account for this potential non modernity,

309
00:13:42.585 --> 00:13:44.445
at least over longer horizons.

310
00:13:44.865 --> 00:13:47.845
So now as setup, we essentially have include a squared

311
00:13:48.085 --> 00:13:50.445
shoring term or higher polynomials to account

312
00:13:50.445 --> 00:13:52.405
for this mono non monotonicity.

313
00:13:52.825 --> 00:13:54.485
And it's indeed the case that only

314
00:13:54.485 --> 00:13:57.125
with this refined empirical exercises,

315
00:13:57.145 --> 00:13:58.485
we do find an important role.

316
00:13:58.825 --> 00:14:01.485
If we erroneously just plug in the linear term,

317
00:14:01.505 --> 00:14:05.005
we would get essentially zero and insignificant coefficient.

318
00:14:05.505 --> 00:14:08.925
And finally, coming back to your policy question, I mean,

319
00:14:09.385 --> 00:14:13.965
to some extent it also speaks to the design

320
00:14:14.345 --> 00:14:16.725
of adjustment assistance.

321
00:14:16.945 --> 00:14:18.205
So in some countries, they have

322
00:14:18.805 --> 00:14:22.005
specific trade adjustment assistance in order

323
00:14:22.065 --> 00:14:25.605
to help workers that have lost their jobs in response

324
00:14:25.625 --> 00:14:26.925
to a trade shock, for example,

325
00:14:26.985 --> 00:14:28.805
if there is an increase in imports

326
00:14:28.805 --> 00:14:32.405
or if some, operations move abroad, okay?

327
00:14:32.875 --> 00:14:35.605
However, in our case, essentially, again, I am,

328
00:14:35.825 --> 00:14:38.445
I'm repeating myself, big part of the adjustment burn is

329
00:14:39.115 --> 00:14:41.085
born by those workers in industries.

330
00:14:41.305 --> 00:14:44.005
We don't see this trade shock directly, right?

331
00:14:44.025 --> 00:14:46.525
And then, so they probably wouldn't qualify for any

332
00:14:46.525 --> 00:14:48.645
of this targeted adjustment assistance,

333
00:14:48.995 --> 00:14:50.605
even though they probably

334
00:14:50.955 --> 00:14:53.005
Will need the biggest help, right?

335
00:14:53.665 --> 00:14:55.445
So this would speak in favor

336
00:14:55.445 --> 00:14:58.805
of having more general adjustment assistance for workers

337
00:14:59.305 --> 00:15:00.765
as opposed to this targeted,

338
00:15:00.795 --> 00:15:02.925
because it may be very difficult

339
00:15:03.065 --> 00:15:05.325
to really target the specific group

340
00:15:05.325 --> 00:15:06.445
that is affected the most.

Understanding Offshoring: Context and Implications

00:00:00 - The context for this study was twofold. I mean, there was a real world context, I would say, and there was an academic context, okay? So the real world context is that there was a remarkable increase in offshoring. And by offshoring we mean essentially the international fragmentation of production, right? That more and more activities of the production chain are located in different countries and in different parts of the world, right? And this process, I mean, has been increasing for a fairly long period of time. So let's say at least since the 1980s until more

00:00:37 - around this year, then it has been slow, has slowed down a bit, but for a fairly long time, there's really an incredible increase in this fragmentation of production. And then this process, on the one hand, has been associated with cost savings from the perspective of firms, right? Which is good, right? Otherwise probably they wouldn't do it. But on the other hand, they have always been this concerns about potentially negative effects for domestic workers, right? And, so

00:01:05 - of offering has always been a high priority, both for academics, but also for policy makers. So that's essentially one part of the context. Now, the more academic context is that when we, when our study started, started, there was already a large body of research, right? That analyzed the, these questions, the effects of offshoring on domestic workers, both theoretical studies and empirical studies, yet, I mean,

Mismatches in Theoretical Models and Empirical Practices in Offshoring Studies

00:01:36 - So the results were, I mean, at, at least the empirical results were fairly ambiguous and also mostly fairly small, contrary to public concerns. So, however, now we noted, right, when we really had to look at these empirical size and theoretical size, that there was a certain mismatch between the main theories that people use and relied on, and common empirical practice, right? And more specifically, so most of these theoretical modes,

00:02:09 - or two sector frameworks, and explain the labor market effects. And these settings, however, in the empirical studies is you need a lot of variation, right? You need differences. And then common setup was to use variation across a large number of industries, right? And then do what we call a reduced form regression. We have some labor market outcome, let's say the change in employment, for example, as your outcome variable. And then you regress this on measures

00:02:42 - And the common reduced form approach was to have a linear offshoring term on the right hand side. And this approach essentially has a few implicit assumptions. So first of all, you treat all of these different industries as separate and independent units of analysis. So that's essentially one implicit assumption. And on top, you implicitly assume if you just include a linear term that there should be a linear monotonic relationship, right? So the, depending on the coefficient that you obtain,

Challenging Linear Assumptions in Offshoring Employment Effects

00:03:10 - that more off showing always is positive, always leads to more employment. Or if the coefficient is negative, you would reduce more options, is always bad, always leads to less employment, or you get zero confusion than you would deduce, okay? Doesn't matter at all. And that's essentially where we come in and say, okay, we argue in this paper, right? That we are currently talking about that this approach, we might lead to misleading conclusions, right? In particular, if these industries are connected to each other in, in general equilibrium,

00:03:42 - and we will talk about it surely, that there is an hump shaped and non monotonic employment pattern across industries that differ in their offering industries. So employment use, both in the low i intense offshoring intensive industries and the high offshoring intensive industries. And the industries with an intermediate range of these offshore tasks, they gain employment. The theoretical part. So the study consists of the theoretical part and empirical parts.

00:04:13 - to have a model theoretical model that speaks to this common empirical setup, right? That, that informs the common empirical setup. So where we have large number of industries, not just one or two, but really a large number of industries that are connected to each other in, in general equilibrium. And that differ. So these industries, they should differ in their scope for offshoring. Okay? So this was our aim. Now, more precisely what we do is we incorporate offshoring

Framework of the GOLE Model: Analyzing Offshoring in Connected Industries

00:04:46 - oligopolistic equilibrium model. So that's abbreviate as the goal model GOLE model that was introduced by the late Peter Neri, okay? And what we have in this, in this framework is we have essentially a continuum of industries. These industries, we index them by the letter Z, right? And these industries are defined over the unit intervals. So they range from zero to one, okay? And this letter z, this running variable at the same time also corresponds

00:05:18 - Okay? So we assume that these, all these different industries, they differ in the share of activities that can move abroad, right? Be it, because some industries they rely very heavily on, on face-to-face contact on geographic proximity. Let's say if you work in a bar or restaurant that these activities that you cannot really offshore, and in other industries, maybe most of the activities,

00:05:46 - So now in the theory, this chair of task can range from zero 0% to 100%, right? Which of course is extreme, but that's the setup. And, and now these industries are also, they're connected to each other in generic Librium because they, all of them rely on the same labor market. All of them rely on the same capital market. So that's where these feedback effects come into play. Okay? So that's essentially the framework that we use.

Framework Selection for Analyzing Employment Effects Post-Iron Curtain

00:06:19 - it's not really necessary to have this specific framework, right? We have, I mean, other frameworks could also have worked as long as the conditions that are just outlined, having many industries differences in offering potential. These in, genetically linkages, as long as this is fulfilled, other, setups would also work. We have chosen this gold setup because it's fairly tractable. I mean it's fairly Parsons. It allows us to derive closed form

00:06:48 - So that's essentially why, why we use this model. We focus on the West German experience in the fall of, in the aftermath of the fall of the iron Kern. So essentially what we do is we analyze employment effects across this aggregated industries after the fall of the iron Kern. And we argue that that's an attractive and the fitting setup for, for our model, for our purposes. Because first of all, it's essentially this, it's a shock, right?

00:07:20 - and also fairly sudden, okay? So it's barefoot centric for, qualifies as a quasi exogenous shock. Has that has also been used in, in for similar type of questions, right? And, and it also, I mean, it matters for offshoring because arguably it greatly improved the opportunities of German firms to engage in production fragmentation, right? Because most of these now formally socialist countries

Offshoring Potential and Industry Variability Post-Iron Curtain

00:07:49 - so they're very close by. they also had much lower wage levels than Germany. So therefore they were attractive horing destinations. And that's not only something that we think, but also that has been documented in, in other studies that really German firms offshore a lot of their activities to Eastern Europe. so that's why we use it now, a key variable that we need, right? In the, in the empiric analysis to define this offshoring potential that varies over,

00:08:22 - So before the fall of the iron curtain, what's the potential of these different industries to engage in horing? Essentially what we do to, to characterize this horing potential is, so we adopted two step procedure. So first of all, we use an indicator variable of offshore ability. So that was originally proposed by Ellen Blind and Ellen Kruger. So they have defined at the occupation level, essentially, they have coded different occupations from zero

00:08:57 - Essentially, we have used this occupation level indicator and aggregated it to the industry level, essentially exploiting that the occupational compositions of these industries that they differ, okay? So in some industries they have a lot of offshore occupations, other industries, they have just a small amount of offshore occupations. The key result is indeed that we get this hump shaped employment

Offshoring Dynamics: Employment Effects Across Industries

00:09:24 - So both industries that have a low offering intensity and that have a high offering intensity, they lose employment. And the ones with an intermediate range they gain now in the theoretical model, right? I mean, maybe to understand this result from a point of view, it's worth first pointing out that, I mean, offshoring always induces two main effects, right? And that's not something that we, I mean, introduced, but that we also had in, in previous models.

00:09:58 - is the direct displacement effect, okay? So that some tasks now that were previously performed in the home country, they now move abroad. So this displacement displacement effect typically reduces domestic employment, right? on the other hand, there's the positive productivity effect, okay? Because as firms engage in offshoring, they are able to lower their production costs. So this then allows them to produce a lower cost and to expand their production, okay?

00:10:29 - to the advantage of, of domestic workers. Now, the interplay between these two effects also explains what happens now across different industries. So the most offshoring intensive industries, they lose employment because of the dominating displacement effect. Okay? So they experience greatest cost savings, that's good, they're able to expand their production, that's also good. But anyway, if 90% or so of all the activities are performed overseas,

The Impact of Offshoring on Domestic Employment: General Equilibrium Effects

00:11:02 - So it's more tricky is why is it the case that even the industries where no offshoring takes place or only limited offering, why they do they lose employment? Okay? And that's where these general equilibrium effects come into play. And for two reasons in, in our framework, first of all. So these, these low offshoring intensive industries, they typically suffer from increase in domestic production cost-wise, this the case

00:11:32 - to an increase in domestic wages. Now, increase in domestic wages means production costs go up. In particular, those industries that rely very heavily on domestic workers, so that cannot offshore their production, okay? So therefore they essentially become more expensive, so this hurts them. on the other hand, and it's also a similar channel. So we also think that offshoring will, lead to uneven profit opportunities across industries, right?

00:12:03 - offshoring intensive industries because they're, the cost savings are the greatest, and they will increase lease. So they will even decline in the industries with limited offshoring. So if there are uneven profit opportunities, then we expect, at least in the long run, that capital will be reallocated, I mean towards the, industries with the greatest profit opportunities with the greatest offshoring potential. So this will then lead to firm exit in the industries

Impacts of Offshoring on Domestic Labor Markets

00:12:35 - with a higher offshoring intensity. And this channel also destroys than jobs in, yeah. In, in, for domestic workers in the low offshoring intensive industries, we need to think carefully in these settings. Where do we expect to find the effects? Right? So offshoring has certain effects. So we, we are quite sure that will also affect domestic labor markets, right? Domestic workers, but where do we find these effects?

00:13:07 - to some extent because it shows that part of the adjustment burden, or in, in fact a big part of the adjustment burden is born by those industries where you do not see any direct offshoring taking place, right? So where, where these effects are the least expected, maybe extent. So that's maybe one implication at all sorts of implications, for empirical work. Okay? Because, now, I mean we need to think more carefully about the correct empirical approach.

00:13:39 - We need to account for this potential non modernity, at least over longer horizons. So now as setup, we essentially have include a squared shoring term or higher polynomials to account for this mono non monotonicity. And it's indeed the case that only with this refined empirical exercises, we do find an important role. If we erroneously just plug in the linear term, we would get essentially zero and insignificant coefficient. And finally, coming back to your policy question, I mean,

Rethinking Trade Adjustment Assistance for Affected Workers

00:14:14 - of adjustment assistance. So in some countries, they have specific trade adjustment assistance in order to help workers that have lost their jobs in response to a trade shock, for example, if there is an increase in imports or if some, operations move abroad, okay? However, in our case, essentially, again, I am, I'm repeating myself, big part of the adjustment burn is born by those workers in industries.

00:15:05 - And then, so they probably wouldn't qualify for any of this targeted adjustment assistance, even though they probably Will need the biggest help, right? So this would speak in favor of having more general adjustment assistance for workers as opposed to this targeted, because it may be very difficult to really target the specific group that is affected the most.

Faculti AI can make mistakes

Research

Policy

Analysis