Popular in Social Sciences

Books

City Time: On Being Sentenced to Rikers Island

Independent Scholar David A. Campbell

Culture and Society

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

New York University Harvey Moltoch

Economics

Gender and Economics

University of California Santa Barbara Shelly Lundberg

American Politics

Keeping the Republic

Boston College Marc Landy

Banks and Policy Organisations

Greening the financial sector

European Investment Bank Matteo Ferrazzi

British Politics

Entrepreneurial thinking: the politics and practice of policy impact

University of Sheffield Matthew Flinders

Culture and Society

Carceral Entrapments: Views from the Prison/Street Interface in India

Indian Institute of Technology Delhi Mahuya Bandyopadhya

The regional consequence of a disaster

View more insights in Social Sciences - Economics - Labor Economics

View more figures from Lincoln University

View the page of David Dyason

Read the Study

Large-scale events such as disasters, wars and pandemics disrupt the economy by diverging resource allocation, which could alter employment growth within the economy during recovery. David Dyason discusses the employment transition following a disaster by removing this stimulus injection and evaluating the economy’s performance during recovery.

Image courtesy of interviewee. May 22, 2024

Generating Workspace

Export as PDF

This video video discusses the impact of large-scale disruptive events, such as disasters, pandemics, and wars, on economic activity and societal structures. These events generally have lasting negative effects, forcing economic adjustments that can affect labor, production, and consumption. The focus is on the economic response to disasters, particularly through government stimulus interventions designed to mitigate negative effects.

The case study looks at the Christchurch earthquakes in New Zealand from 2010-2011, highlighting the significant regional damage and the extensive investment required for recovery. The research aims to understand how resource allocation in the labor market changes in response to such disasters and evaluates the effects of stimulus on industry performance.

Key findings indicate that while disasters lead to short-term negative economic impacts, they can also foster positive changes by replacing outdated technology and promoting development opportunities. The construction industry is identified as the primary beneficiary of recovery efforts, seeing an increase in its economic contribution during the rebuilding phase.

The study also emphasizes that the effects of disasters vary by type, location, and industry, noting distinct employment patterns over time. However, the text mentions that understanding how employment transitions occur in recovery periods remains complex and requires further research. The researchers suggest that post-recovery, employment tends to stabilize and the underlying economic performance can return to pre-disaster levels. Future research could explore the effects of other disaster types and their severity on recovery dynamics.

Language:

Export as PDF

X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

00:00.285 --> 00:03.275
Large scale disruptive events, such

00:03.275 --> 00:05.475
as pandemics, disasters, and wars.

00:06.145 --> 00:08.955
They tend to have a lasting effect on society,

00:09.615 --> 00:12.315
and also they force economic activity to adjust.

00:13.605 --> 00:17.505
Now, disasters not only adjust economic activity,

00:17.645 --> 00:20.265
but it also affects the way in which economic

00:20.265 --> 00:21.345
systems function.

00:22.285 --> 00:25.225
So this is, this is typically of a negative impact,

00:25.285 --> 00:28.785
and these negative impacts have negative consequences on

00:28.805 --> 00:32.425
assets, on labor, on production, and as well as consumption.

00:33.455 --> 00:37.115
Now, as the economy moves away from the equilibrium,

00:37.115 --> 00:39.955
as a result of these disruptive events disrupt,

00:40.175 --> 00:42.835
you often get, uncertainty.

00:42.975 --> 00:45.995
People don't know what to respond, what to expect.

00:46.415 --> 00:48.315
Um, governments dunno how to respond.

00:49.295 --> 00:52.275
So therefore, when we, when we look at these kind of events,

00:52.585 --> 00:56.995
there's value in assessing how the economy respond,

00:57.695 --> 00:59.915
and especially there's value in understanding

01:00.135 --> 01:03.195
how the economy through employment transitions

01:03.195 --> 01:04.675
during this recovery period

01:05.535 --> 01:07.475
and this period can last quite a long time.

01:08.455 --> 01:10.275
So in particular, when we look at economic,

01:10.415 --> 01:11.835
the economic response and,

01:11.835 --> 01:14.515
and that economic response to disaster, this is often

01:14.515 --> 01:17.035
through stimulus and a stimulus intervention,

01:17.775 --> 01:19.915
and that influences the recovery rate.

01:20.335 --> 01:22.635
So you would have the, the investment

01:22.855 --> 01:26.115
or the stimulus intervention from government in order

01:26.215 --> 01:29.595
to mitigate those negative effects of, of the disaster.

01:31.145 --> 01:35.005
Now, we would think that, you know, when you do invest,

01:35.195 --> 01:38.325
when you do have these stimulation, interventions,

01:38.325 --> 01:40.165
that there would be a quick response

01:40.905 --> 01:42.405
to those negative impacts.

01:43.185 --> 01:46.045
Um, but the, those impacts are likely also

01:46.045 --> 01:48.445
to change depending on the type of industries

01:48.445 --> 01:49.845
or the sectors that are influenced.

01:50.425 --> 01:53.725
Now, in the case of an earthquake, which is the topic of,

01:53.825 --> 01:57.965
of this study, it often leads to instant loss

01:57.965 --> 02:00.645
of infrastructure, not just infrastructure,

02:00.705 --> 02:01.805
but also human capital.

02:02.265 --> 02:04.045
And that could take years to replace.

02:04.545 --> 02:06.445
So within 20 10, 20 11,

02:06.535 --> 02:10.165
Canterbury in New Zealand had a significant number

02:10.165 --> 02:14.045
of earthquakes, and it's in a regional basis.

02:14.145 --> 02:17.205
So the impact of this, of this earthquake was regional.

02:17.225 --> 02:18.245
It wasn't national.

02:19.105 --> 02:22.005
Um, but that led to significant number

02:22.185 --> 02:25.525
or an amount of investment from government,

02:25.905 --> 02:28.965
not just government, but also insurance to actually rebuild

02:29.545 --> 02:31.165
the infrastructure that was lost.

02:31.865 --> 02:33.925
So the city had significant damage.

02:33.925 --> 02:37.165
Christchurch itself had significant damage within the CBD

02:37.595 --> 02:39.845
essentially closing it off for two years.

02:40.705 --> 02:45.525
Beyond the CBD, we had approximately 170,000 properties

02:46.035 --> 02:49.005
that required repair,

02:49.065 --> 02:51.245
or improvement as a result of the damage.

02:51.905 --> 02:54.925
Uh, and, and this amounted to approximately 40 billion.

02:56.465 --> 02:59.485
The study essentially tries to contribute to

02:59.955 --> 03:02.765
what we call the growing regional disaster

03:02.875 --> 03:03.925
economies research.

03:04.305 --> 03:06.725
We are looking at Canterbury, New Zealand as a case study,

03:07.345 --> 03:10.805
and we are now trying to determine,

03:10.985 --> 03:13.765
and assess how resource

03:14.285 --> 03:16.285
allocation within the labor market,

03:16.285 --> 03:19.365
particularly the labor market, how that is affected

03:19.465 --> 03:23.005
as a result of, of these disaster activities and events.

03:23.625 --> 03:28.365
So several studies have, have been done on, on this nexus

03:28.365 --> 03:30.365
between the economy and disasters.

03:30.785 --> 03:35.045
And, and mostly of these studies have found two things.

03:35.105 --> 03:38.725
The first thing is I found mostly negative economic impacts

03:38.725 --> 03:42.765
associated with disasters, especially as it reduces the,

03:42.865 --> 03:44.965
the economic activity or economic value.

03:45.065 --> 03:47.605
And this is measured through GDP,

03:48.225 --> 03:50.245
and they're not just looking at GDP value,

03:50.265 --> 03:51.525
but they're also looking at growth.

03:52.065 --> 03:53.165
So these growth

03:53.345 --> 03:54.765
or negative growth effects

03:55.385 --> 03:57.965
are predominantly evident in the short term,

03:58.905 --> 04:03.645
and it is more pronounced in developing economies where

04:04.365 --> 04:06.085
stimulus injection takes time

04:06.225 --> 04:09.005
or is either slow in response to the event.

04:09.825 --> 04:11.725
On the other hand, you have

04:13.125 --> 04:15.085
positive benefits in the short term.

04:15.625 --> 04:18.645
Uh, you've got the stimulus injection, and,

04:18.665 --> 04:21.565
and often this foster positive outcomes.

04:22.165 --> 04:25.045
'cause what it tends to do is it replaces

04:25.105 --> 04:27.085
or it forces governments and,

04:27.085 --> 04:30.805
and local institutions to actually replace old technology

04:31.355 --> 04:32.445
with new technology.

04:33.385 --> 04:34.445
Um, and,

04:34.665 --> 04:36.925
and through that, that through this whole process

04:37.065 --> 04:40.485
of recovery, you get this improved,

04:41.525 --> 04:42.605
adoption of technology.

04:43.385 --> 04:46.245
It often also leads to a clean slate for development.

04:46.245 --> 04:49.005
So while this is essentially not ideal,

04:49.785 --> 04:52.325
it does provide planners and, and,

04:52.325 --> 04:57.125
and city place making an opportunity to, to have new,

04:57.125 --> 05:00.125
and open space available to improve on

05:00.195 --> 05:01.885
what was there originally.

05:02.705 --> 05:05.165
So in addition to that,

05:05.385 --> 05:07.805
you also have different types of disasters.

05:08.905 --> 05:10.405
Uh, and, and these different types

05:10.405 --> 05:13.205
of disasters have a different impact on the

05:13.205 --> 05:14.845
growth of the economy.

05:15.265 --> 05:17.925
So where you have a meteorological disaster,

05:17.975 --> 05:20.125
which is essentially something to do with weather,

05:20.305 --> 05:24.325
mostly floods, what happens when you assess those kind

05:24.325 --> 05:28.685
of events is you often have a, a, a pro

05:29.485 --> 05:31.365
positive short-term impact.

05:32.265 --> 05:35.485
But when you look at geological disasters such

05:35.485 --> 05:39.845
as earthquakes, bigger adv, bigger disasters like

05:39.845 --> 05:41.845
that, you often have a negative impact.

05:42.475 --> 05:44.525
Also, it depends on where it takes place.

05:45.225 --> 05:47.725
So sometimes, if it,

05:47.725 --> 05:49.565
if it takes place within the rural environment,

05:49.625 --> 05:51.925
the impact is likely to be different compared to

05:51.925 --> 05:54.205
how large it will be if it is within an urban area.

05:55.625 --> 05:59.485
So at at this point, it mainly reveals

05:59.995 --> 06:02.965
that there is a, a mixture of results,

06:04.025 --> 06:07.245
and at most of the, most

06:07.245 --> 06:09.085
of the results reveal also

06:09.085 --> 06:11.485
that there is this gap within the, the

06:12.005 --> 06:14.845
regional understanding of how it interacts

06:14.845 --> 06:19.805
and how it influences the, the value of different kinds

06:19.825 --> 06:21.325
of, of, of industries.

06:22.935 --> 06:26.675
So with all these complexities, with the,

06:27.295 --> 06:31.035
in assessing disasters, specifically with relating, relating

06:31.035 --> 06:32.235
to the type of disaster

06:32.335 --> 06:35.475
and the duration of the scale scale,

06:36.115 --> 06:37.395
researchers are trying

06:37.455 --> 06:40.555
to look at ways in which they can actually assess, you know,

06:40.585 --> 06:41.795
this, this relationship.

06:42.375 --> 06:44.835
Now, the positive news for research is, is the fact

06:44.835 --> 06:47.515
that there is an increasing number of,

06:47.575 --> 06:49.915
of data points available, data available

06:49.975 --> 06:51.355
to assess disasters.

06:52.135 --> 06:55.715
So this, this essentially helps researchers to understand

06:56.335 --> 06:59.915
how the disaster economic next nexus operate.

07:00.775 --> 07:04.195
Uh, in addition, disaster studies tend to also mention,

07:04.935 --> 07:07.435
and the, the, the, the limited

07:08.335 --> 07:12.925
or the, the, the importance of, of accurate information

07:13.625 --> 07:16.485
and an accurate information during these disaster times

07:16.545 --> 07:19.685
or just after these disaster times are often difficult

07:20.025 --> 07:22.765
to source because there's breakdown of communication.

07:23.345 --> 07:25.845
Um, so that also makes it difficult for governments

07:25.845 --> 07:27.605
to essentially determine what they need to do.

07:28.785 --> 07:31.685
Now, the question then is how does

07:31.685 --> 07:36.245
that Im impact the assessment of, of research Now

07:36.875 --> 07:38.205
various ways

07:38.385 --> 07:41.645
and economic indicators are used to assess the impact

07:41.705 --> 07:44.125
for disasters, within literature.

07:44.585 --> 07:47.005
As you can, you can assess the impact either

07:47.065 --> 07:49.285
by looking at something like GDP per capita,

07:49.905 --> 07:52.125
you can look at production functions

07:52.575 --> 07:54.965
where if you take away some production,

07:55.235 --> 07:57.285
what is gonna be the, the impact,

07:57.305 --> 07:58.525
on, on the bigger economy.

07:59.355 --> 08:02.885
Some researchers look at nighttime lights and,

08:02.885 --> 08:05.485
and others like this study look at impulse

08:06.085 --> 08:09.445
response models similar to your input output kind of models.

08:11.095 --> 08:14.235
Now, luckily over time there's been an improvement in the

08:14.235 --> 08:15.315
data availability,

08:15.975 --> 08:18.155
and most studies, most reach

08:18.795 --> 08:21.995
researchers use the emergency events database.

08:22.735 --> 08:26.435
Um, so what this database does is it collects all the

08:26.435 --> 08:28.555
information of disasters,

08:28.855 --> 08:33.275
and then a researcher would add with these events,

08:33.305 --> 08:36.275
this database, they would add in GDP data.

08:36.415 --> 08:40.275
So they would look at, a national or regional GDP data

08:41.055 --> 08:43.755
and then consider essentially the aggregate

08:43.755 --> 08:45.155
performance of the economy.

08:46.655 --> 08:49.395
Now, unfortunately, the problem with, with, well,

08:49.395 --> 08:52.035
not the problem, but the limitation of this is the fact

08:52.035 --> 08:56.805
that when you include GDP data at, at an aggregate level,

08:57.105 --> 08:59.605
you actually include the stimulus injection as well.

09:00.025 --> 09:02.765
And that is something that this study is trying to eliminate

09:02.765 --> 09:04.045
and remove to assess

09:04.185 --> 09:06.445
how the underlying economy is performing.

09:07.345 --> 09:11.805
So it remains largely unknown how the, the in the industries

09:11.865 --> 09:12.885
or the sectors

09:12.985 --> 09:16.445
and their employment transition through a recovery period

09:16.775 --> 09:21.165
after a, a disaster such as an earthquake and the,

09:21.505 --> 09:24.845
and obviously the, the performance of the economy is likely

09:24.945 --> 09:28.805
to look different depending on that scale of the stimulus

09:29.265 --> 09:31.365
and also the type of disaster.

09:32.465 --> 09:33.765
So what this research does,

09:33.985 --> 09:38.525
and it try to, it's trying to assess the performance

09:38.905 --> 09:42.285
and the transition of employment for a regional economy

09:42.905 --> 09:45.405
by then removing this investment stimulus.

09:46.065 --> 09:48.205
And that will then give us insight into

09:48.425 --> 09:51.965
how employment transition at a regional

09:52.105 --> 09:54.045
and also at an industry level,

09:54.175 --> 09:57.045
makes the literature reveal a strong, a strong link

09:57.515 --> 10:02.285
between coral, construction and, and, and recovery.

10:03.025 --> 10:06.685
Now, within economic theory, this relationship between

10:07.205 --> 10:10.525
economic growth and construction is, is well recognized.

10:11.025 --> 10:13.325
So while there is this positive association,

10:13.355 --> 10:16.765
this relationship is not always, linear.

10:17.775 --> 10:18.875
So, so what does that mean?

10:19.615 --> 10:22.275
It essentially means that if there is an earthquake,

10:22.695 --> 10:26.475
it is highly likely that the industry that will benefit

10:27.225 --> 10:29.435
from a stimulus will be construction.

10:30.455 --> 10:31.715
And in addition to that

10:32.635 --> 10:35.835
construction activity has strong backward linkages

10:35.835 --> 10:38.795
with the rest of the economy such as manufacturing.

10:39.375 --> 10:42.955
So for the study, looking at Christchurch, you know,

10:43.055 --> 10:46.755
the region within, in the region within New Zealand,

10:46.915 --> 10:51.075
which is affected by, by these earthquakes,

10:51.775 --> 10:56.395
we are interested to understand how this contribution

10:56.655 --> 10:59.275
of stimulus is likely to impact the economy.

10:59.455 --> 11:00.875
And the expectation is

11:00.875 --> 11:03.755
that this contribution would predominantly be within

11:03.935 --> 11:05.155
the construction industry.

11:06.865 --> 11:11.005
Now, when assessing the construction industry, we find that

11:11.825 --> 11:15.765
before the earthquakes, the economic contribution

11:15.785 --> 11:19.565
of the construction industry was around 5% of,

11:19.825 --> 11:20.925
of economic activity,

11:21.785 --> 11:25.925
but this increased to around 7.5, 7.8%

11:26.545 --> 11:29.365
of GDP during the peak of the rebuild.

11:30.385 --> 11:33.525
So now when this is compared to the national average,

11:33.705 --> 11:37.565
the New Zealand average, we see that this cons, this

11:38.205 --> 11:42.365
contribution or this share has remained relatively stable.

11:42.905 --> 11:45.925
So while it has gone up in Canterbury, the,

11:46.025 --> 11:48.565
the national average of, of, of con

11:48.705 --> 11:50.965
of construction has remains relatively stable.

11:52.025 --> 11:56.285
Now, various studies have have assessed what is the,

11:56.395 --> 11:58.725
what is a good contribution

11:58.745 --> 12:01.365
to have from construction within the economy.

12:01.945 --> 12:05.765
Uh, and, and so these, these studies have found that,

12:06.185 --> 12:10.285
all these general consensus, consensus that this would range

12:10.285 --> 12:13.885
between three to 5% for most economies.

12:14.795 --> 12:15.895
So with this in mind,

12:16.725 --> 12:19.135
this study then looks at this relationship

12:19.135 --> 12:21.815
between construction or building activity

12:22.635 --> 12:25.375
and economic activity for the regional economy

12:25.435 --> 12:26.535
before the disaster.

12:27.875 --> 12:30.095
And we are looking at whether

12:30.255 --> 12:32.655
or not that relationship remains stable.

12:33.035 --> 12:35.855
Now, unfortunately, we can't assess that for Canterbury,

12:35.875 --> 12:38.855
but we can assess that for New Zealand as a whole.

12:40.375 --> 12:45.115
So when, when you assess, this level of,

12:45.495 --> 12:49.555
of, of relationship between these two variables, essentially

12:49.585 --> 12:51.955
what we are trying to determine is we're trying to determine

12:52.145 --> 12:53.395
what would the economy

12:53.495 --> 12:56.715
of Canterbury look like if there was no earthquake?

12:57.545 --> 13:01.995
What is the underlying economy? Can we quantify that?

13:02.375 --> 13:06.755
So by using the principles of, you know, a a three to 5%

13:07.275 --> 13:08.435
contribution to the economy,

13:08.465 --> 13:11.235
they're looking at canterbury's, economy

13:11.335 --> 13:14.875
and saying, well, it has been consistently around 5%.

13:15.815 --> 13:19.675
We, that, that, that gives us strong evidence to say

13:19.675 --> 13:23.195
that we can use a, a construction value

13:23.575 --> 13:26.635
as a counterfactual scenario for this research.

13:27.055 --> 13:29.795
So essentially saying, what would be the value

13:29.895 --> 13:32.435
of construction if there was no earthquake?

13:34.035 --> 13:38.695
Um, so that provides us with a scenario, what is,

13:39.365 --> 13:40.615
what the economy would look like

13:40.615 --> 13:42.255
without, the earthquakes.

13:42.915 --> 13:46.535
And then we could use that in order to,

13:47.125 --> 13:50.615
compare the actual numbers, the actual employment numbers,

13:50.875 --> 13:54.455
the actual, growth numbers for Canterbury

13:54.725 --> 13:57.495
with this counterfactual economy.

13:59.295 --> 14:00.995
So we've, we've established that first.

14:00.995 --> 14:02.795
Secondly, we then also use

14:02.905 --> 14:05.035
what we call an input output model.

14:05.615 --> 14:09.235
Now, the input output model is a, a regional economy model,

14:09.775 --> 14:13.115
and it's used to measure the interdependencies

14:13.115 --> 14:14.755
between the various industries

14:14.775 --> 14:16.075
or sectors within the economy.

14:17.375 --> 14:18.435
Now, in this instance,

14:18.945 --> 14:21.835
what we are doing is we are removing the economy,

14:22.145 --> 14:25.835
economy wide impacts of the, the stimulation.

14:26.295 --> 14:29.155
So we're looking at what is the counterfactual,

14:29.155 --> 14:30.755
what is the underlying economy,

14:31.335 --> 14:36.195
and removing the stimulus that that is above that level of,

14:36.655 --> 14:39.275
of, of construction activity, removing that out

14:39.275 --> 14:42.675
of the economy, essentially, then leaving us

14:42.705 --> 14:46.755
with the underlying economy or the demand driven economy.

14:48.045 --> 14:50.825
And in the timeline that we're considering this assessment

14:51.145 --> 14:55.845
On is between 2011 to 2021.

14:56.185 --> 14:57.525
So then we, what we will do,

14:57.795 --> 15:01.005
what we did in the study is we actually compared,

15:01.145 --> 15:04.965
the actual level of employment versus the underlying

15:04.985 --> 15:09.405
or demand driven level of employment, removing all these

15:10.165 --> 15:13.605
stimulus that is above the normal level

15:13.625 --> 15:15.725
of economic activity for the region.

15:16.225 --> 15:20.165
So stimulus investments, has, has driven the economy

15:20.225 --> 15:21.525
and has helped with the recovery

15:21.625 --> 15:24.285
of the Canterbury region after the disaster.

15:24.865 --> 15:27.405
But another question is, you know, what it,

15:27.515 --> 15:30.325
what changed in terms of the employment for the region?

15:30.425 --> 15:32.005
So when we look at the results of,

15:32.025 --> 15:36.365
of the model in this work, at the peak of the rebuild,

15:36.985 --> 15:39.125
more or less an additional 18,

15:39.385 --> 15:43.125
or excuse me, an additional 13,800 people were

15:43.405 --> 15:45.085
employed, in Canterbury.

15:45.865 --> 15:48.445
Now, this peak was reached around four years

15:48.615 --> 15:50.165
after the earthquake.

15:51.225 --> 15:54.285
Now comparing that with the underlying economy,

15:54.315 --> 15:58.205
essentially the counterfactual level of employment

15:58.465 --> 16:00.405
or level of economy,

16:00.545 --> 16:04.405
so this reveal a significant investment stimulus

16:05.435 --> 16:10.365
that is, that was driven significant investment,

16:10.705 --> 16:14.005
is account stimulus is accounted by,

16:15.105 --> 16:16.725
for the employment opportunities

16:16.725 --> 16:18.925
that were created within the region.

16:20.405 --> 16:22.065
And by looking at the data, we,

16:22.605 --> 16:24.825
we can observe two distinct periods.

16:25.325 --> 16:27.265
So the first period that we can,

16:27.285 --> 16:31.065
we can identify is within the initial five years.

16:31.805 --> 16:33.345
So after the initial five years,

16:33.365 --> 16:37.545
you had the earthquake in 2011, around 20 15, 20 16,

16:38.045 --> 16:40.065
you had the peak in employment opportunities.

16:40.965 --> 16:44.265
Uh, and when you look more deeply into the,

16:44.535 --> 16:47.065
into the assessment and into the growth patterns,

16:47.485 --> 16:51.185
you would see that this period is, is, is characterized

16:51.445 --> 16:54.025
by growth primarily from the stimulus.

16:54.325 --> 16:57.785
So this is essentially a first five year period that is

16:58.465 --> 17:02.575
stimulus driven from 2016 onwards.

17:02.835 --> 17:06.695
The second, the second distinct period is, is visible.

17:06.915 --> 17:08.455
And that is the period where

17:09.155 --> 17:12.455
we move away from a stimulus driven employment

17:12.995 --> 17:15.215
to more demand driven employment.

17:15.595 --> 17:17.695
So essentially what we're starting to see is we're starting

17:17.695 --> 17:22.615
to see a change or overlap from the stimulus investment.

17:23.275 --> 17:25.695
Uh, as the stimulus investment is going down,

17:25.875 --> 17:28.375
you start seeing the, the demand,

17:28.875 --> 17:31.855
the local demand from the underlying economy starting

17:31.875 --> 17:33.175
to drive the economy.

17:34.125 --> 17:37.065
But this is best seen when you look at the diagrams within,

17:37.445 --> 17:38.785
within the research paper.

17:40.315 --> 17:43.575
But on a sector level, at a local level,

17:43.635 --> 17:47.335
at a industry level, you also see changes and,

17:47.355 --> 17:50.285
and small nuances with regards to employment

17:50.345 --> 17:51.605
and how that has changed.

17:52.625 --> 17:56.205
So while construction is the industry that has,

17:56.235 --> 17:57.645
that is benefiting the most,

17:57.825 --> 18:01.845
we also see benefits in manufacturing, in wholesale trade,

18:02.625 --> 18:06.165
professional services, real estate services,

18:06.165 --> 18:07.725
with additional benefits.

18:08.025 --> 18:10.845
To be more specific, what we actually observe is

18:10.845 --> 18:15.645
that without the stimulus injection, within the economy,

18:16.825 --> 18:19.605
we, we, we have seen a negative

18:20.135 --> 18:22.445
employment level in construction.

18:22.985 --> 18:25.645
And this is mostly to do with the GFC

18:25.865 --> 18:30.365
and the lingering effects of what the GFC, still,

18:30.775 --> 18:32.845
still left on, within the economy.

18:34.245 --> 18:38.625
So the result also revealed that growth within some

18:38.625 --> 18:40.225
of these industries were not the same.

18:40.925 --> 18:42.105
Uh, so for example,

18:42.285 --> 18:45.025
you had manufacturing employment growth

18:45.095 --> 18:46.625
peaking a little later.

18:47.085 --> 18:48.425
So six years, six years

18:48.425 --> 18:50.145
after the, the stimulus

18:51.665 --> 18:53.625
construction peaked a lot earlier.

18:53.765 --> 18:55.625
So you had more or less two years

18:55.715 --> 18:58.505
after the, the earthquake,

18:58.525 --> 19:00.545
you had the peak in the construction growth.

19:01.155 --> 19:03.825
While an industry like wholesale

19:03.825 --> 19:08.145
and retail trade, the peak was around seven years later.

19:08.245 --> 19:10.785
So you don't have the consistent performance

19:11.285 --> 19:12.545
of all the industries

19:12.565 --> 19:15.385
and employment, all peaking at the same time.

19:15.485 --> 19:19.105
You see these different, different peaks,

19:19.215 --> 19:20.945
depending on the type of activity.

19:21.995 --> 19:25.095
So one would expect that employment levels will decline

19:25.865 --> 19:28.855
below historical growth, in the period

19:29.105 --> 19:30.735
after the rebuild.

19:30.835 --> 19:32.575
So once rebuild activities

19:32.715 --> 19:37.695
or the, the, the stimulus activity starts

19:37.695 --> 19:40.695
to wind down, you would expect that

19:41.585 --> 19:43.935
historical growth levels will go down.

19:43.935 --> 19:46.375
Employment levels will go down similar to

19:46.375 --> 19:48.695
what we see in in business cycles.

19:48.725 --> 19:50.735
However, this didn't happen.

19:51.695 --> 19:54.195
Um, we actually started to see convergence

19:54.195 --> 19:56.115
between the underlying economy

19:56.735 --> 19:59.115
and the stimulus driven economy.

19:59.165 --> 20:02.555
These two growth rates were growing towards one another.

20:03.295 --> 20:05.915
Um, so after a period of, of more

20:05.915 --> 20:09.555
or less 10 years where you had elevated employment

20:09.555 --> 20:11.995
opportunities driven predominantly

20:12.015 --> 20:14.235
by the stimulus injection, we,

20:14.375 --> 20:16.355
we could now see this convergent.

20:16.495 --> 20:19.315
So there was, instead of divergence, we saw

20:20.235 --> 20:23.235
convergence getting together and the economy essentially

20:23.235 --> 20:24.395
started to operate.

20:24.855 --> 20:28.675
Um, normally. So normal demand driven activity was now the

20:28.675 --> 20:30.035
driver of the economy.

20:31.105 --> 20:33.125
So, so we suggest,

20:33.345 --> 20:35.565
or the results essentially suggest that

20:36.265 --> 20:39.805
any additional employment benefit from the stimulus,

20:39.805 --> 20:43.085
within the built environment has subsided

20:43.215 --> 20:47.925
after a decade as stimulus activity starts to peak

20:47.945 --> 20:50.805
and overall employment levels start to decline

20:51.455 --> 20:52.765
below the national growth.

20:53.105 --> 20:56.805
We left with a question, how is employment transition

20:56.905 --> 20:59.845
and how does that, how does it perform,

20:59.865 --> 21:02.245
for the underlying economy, for the demand driven economy?

21:02.945 --> 21:05.565
So the findings of of this study suggests

21:05.565 --> 21:09.005
that in the short term, the response to stimulus injection

21:09.215 --> 21:13.565
after an earthquake is timely and it was timely

21:14.225 --> 21:17.045
and it limited the negative consequences of the disaster.

21:18.325 --> 21:20.785
Or to state it a little differently without the initial

21:21.105 --> 21:23.105
stimulus, the initial year

21:23.115 --> 21:26.345
after the earthquake would've experienced slow

21:26.445 --> 21:27.905
to moderate growth at best,

21:28.125 --> 21:31.545
but it didn't, it experienced significant employment growth.

21:32.645 --> 21:33.665
So with this in mind,

21:34.445 --> 21:36.905
the employment transition in construction

21:37.165 --> 21:39.665
and its industries benefited most,

21:40.605 --> 21:44.985
and this was supported at an industry level,

21:45.315 --> 21:48.865
especially after, a period of

21:49.845 --> 21:54.225
low growth between 2009 and 2016.

21:54.325 --> 21:57.265
So while the national economy experienced low growth,

21:57.725 --> 22:00.425
the local economy actually experienced really good growth

22:00.425 --> 22:02.025
within the construction industry.

22:02.995 --> 22:05.945
Employment transition reached its long-term

22:06.135 --> 22:07.585
equilibrium after 10 years.

22:08.685 --> 22:11.265
The first five years again was stimulus driven.

22:12.525 --> 22:16.025
The second five years was basically mostly demand driven.

22:16.645 --> 22:20.705
And by 2021, the level of employment already converged

22:20.965 --> 22:22.625
to its long-term growth position.

22:23.525 --> 22:27.185
Uh, with the construction industry, again, contributing to

22:27.185 --> 22:31.705
around 5% of cross value added like we've be spoken earlier,

22:32.125 --> 22:33.665
that's more or less the 5%

22:34.015 --> 22:36.025
that Canterbury is in equilibrium with.

22:37.005 --> 22:40.465
So further research with regards to what we have, what,

22:40.465 --> 22:44.105
what was done in this paper is to now look at the effect

22:44.125 --> 22:47.105
of other type of disasters, that comes to mind,

22:47.775 --> 22:50.385
also assessing the severity of the disasters

22:50.385 --> 22:52.785
and the influence on the recovery period.

22:53.725 --> 22:54.985
Uh, it's possible

22:55.205 --> 22:57.865
and likely that the recovery period could be linked

22:58.325 --> 23:00.105
to the magnitude of the disaster,

23:00.245 --> 23:03.425
but this needs further research in order to assess,

23:03.425 --> 23:04.625
whether or not that is true.

00:00 - Large scale disruptive events, such as pandemics, disasters, and wars. They tend to have a lasting effect on society, and also they force economic activity to adjust. Now, disasters not only adjust economic activity, but it also affects the way in which economic

00:20 - systems function. So this is, this is typically of a negative impact, and these negative impacts have negative consequences on assets, on labor, on production, and as well as consumption. Now, as the economy moves away from the equilibrium, as a result of these disruptive events disrupt, you often get, uncertainty. People don't know what to respond, what to expect. governments dunno how to respond. So therefore, when we, when we look at these kind of events,

00:52 - there's value in assessing how the economy respond, and especially there's value in understanding how the economy through employment transitions during this recovery period and this period can last quite a long time. So in particular, when we look at economic, the economic response and, and that economic response to disaster, this is often through stimulus and a stimulus intervention, and that influences the recovery rate.

01:20 - So you would have the, the investment or the stimulus intervention from government in order to mitigate those negative effects of, of the disaster. Now, we would think that, you know, when you do invest, when you do have these stimulation, interventions, that there would be a quick response to those negative impacts. but the, those impacts are likely also to change depending on the type of industries or the sectors that are influenced.

01:50 - Now, in the case of an earthquake, which is the topic of, of this study, it often leads to instant loss of infrastructure, not just infrastructure, but also human capital. And that could take years to replace. So within 20 10, 20 11, Canterbury in New Zealand had a significant number of earthquakes, and it's in a regional basis. So the impact of this, of this earthquake was regional. It wasn't national.

02:19 - but that led to significant number or an amount of investment from government, not just government, but also insurance to actually rebuild the infrastructure that was lost. So the city had significant damage. Christchurch itself had significant damage within the CBD essentially closing it off for two years. Beyond the CBD, we had approximately 170,000 properties that required repair, or improvement as a result of the damage.

02:51 - and, and this amounted to approximately 40 billion. The study essentially tries to contribute to what we call the growing regional disaster economies research. We are looking at Canterbury, New Zealand as a case study, and we are now trying to determine, and assess how resource allocation within the labor market, particularly the labor market, how that is affected as a result of, of these disaster activities and events.

03:23 - So several studies have, have been done on, on this nexus between the economy and disasters. And, and mostly of these studies have found two things. The first thing is I found mostly negative economic impacts associated with disasters, especially as it reduces the, the economic activity or economic value. And this is measured through GDP, and they're not just looking at GDP value, but they're also looking at growth. So these growth

03:53 - or negative growth effects are predominantly evident in the short term, and it is more pronounced in developing economies where stimulus injection takes time or is either slow in response to the event. On the other hand, you have positive benefits in the short term. you've got the stimulus injection, and, and often this foster positive outcomes. 'cause what it tends to do is it replaces

04:25 - or it forces governments and, and local institutions to actually replace old technology with new technology. and, and through that, that through this whole process of recovery, you get this improved, adoption of technology. It often also leads to a clean slate for development. So while this is essentially not ideal, it does provide planners and, and,

04:52 - and city place making an opportunity to, to have new, and open space available to improve on what was there originally. So in addition to that, you also have different types of disasters. and, and these different types of disasters have a different impact on the growth of the economy. So where you have a meteorological disaster, which is essentially something to do with weather,

05:20 - mostly floods, what happens when you assess those kind of events is you often have a, a, a pro positive short-term impact. But when you look at geological disasters such as earthquakes, bigger adv, bigger disasters like that, you often have a negative impact. Also, it depends on where it takes place. So sometimes, if it, if it takes place within the rural environment, the impact is likely to be different compared to

05:51 - how large it will be if it is within an urban area. So at at this point, it mainly reveals that there is a, a mixture of results, and at most of the, most of the results reveal also that there is this gap within the, the regional understanding of how it interacts and how it influences the, the value of different kinds of, of, of industries. So with all these complexities, with the,

06:27 - in assessing disasters, specifically with relating, relating to the type of disaster and the duration of the scale scale, researchers are trying to look at ways in which they can actually assess, you know, this, this relationship. Now, the positive news for research is, is the fact that there is an increasing number of, of data points available, data available to assess disasters.

06:52 - So this, this essentially helps researchers to understand how the disaster economic next nexus operate. in addition, disaster studies tend to also mention, and the, the, the, the limited or the, the, the importance of, of accurate information and an accurate information during these disaster times or just after these disaster times are often difficult to source because there's breakdown of communication. so that also makes it difficult for governments to essentially determine what they need to do.

07:28 - Now, the question then is how does that Im impact the assessment of, of research Now various ways and economic indicators are used to assess the impact for disasters, within literature. As you can, you can assess the impact either by looking at something like GDP per capita, you can look at production functions where if you take away some production, what is gonna be the, the impact,

07:57 - on, on the bigger economy. Some researchers look at nighttime lights and, and others like this study look at impulse response models similar to your input output kind of models. Now, luckily over time there's been an improvement in the data availability, and most studies, most reach researchers use the emergency events database. so what this database does is it collects all the information of disasters,

08:28 - and then a researcher would add with these events, this database, they would add in GDP data. So they would look at, a national or regional GDP data and then consider essentially the aggregate performance of the economy. Now, unfortunately, the problem with, with, well, not the problem, but the limitation of this is the fact that when you include GDP data at, at an aggregate level, you actually include the stimulus injection as well. And that is something that this study is trying to eliminate

09:02 - and remove to assess how the underlying economy is performing. So it remains largely unknown how the, the in the industries or the sectors and their employment transition through a recovery period after a, a disaster such as an earthquake and the, and obviously the, the performance of the economy is likely to look different depending on that scale of the stimulus and also the type of disaster. So what this research does,

09:33 - and it try to, it's trying to assess the performance and the transition of employment for a regional economy by then removing this investment stimulus. And that will then give us insight into how employment transition at a regional and also at an industry level, makes the literature reveal a strong, a strong link between coral, construction and, and, and recovery. Now, within economic theory, this relationship between economic growth and construction is, is well recognized.

10:11 - So while there is this positive association, this relationship is not always, linear. So, so what does that mean? It essentially means that if there is an earthquake, it is highly likely that the industry that will benefit from a stimulus will be construction. And in addition to that construction activity has strong backward linkages with the rest of the economy such as manufacturing. So for the study, looking at Christchurch, you know,

10:43 - the region within, in the region within New Zealand, which is affected by, by these earthquakes, we are interested to understand how this contribution of stimulus is likely to impact the economy. And the expectation is that this contribution would predominantly be within the construction industry. Now, when assessing the construction industry, we find that before the earthquakes, the economic contribution of the construction industry was around 5% of,

11:19 - of economic activity, but this increased to around 7.5, 7.8% of GDP during the peak of the rebuild. So now when this is compared to the national average, the New Zealand average, we see that this cons, this contribution or this share has remained relatively stable. So while it has gone up in Canterbury, the, the national average of, of, of con of construction has remains relatively stable. Now, various studies have have assessed what is the,

11:56 - what is a good contribution to have from construction within the economy. and, and so these, these studies have found that, all these general consensus, consensus that this would range between three to 5% for most economies. So with this in mind, this study then looks at this relationship between construction or building activity and economic activity for the regional economy before the disaster.

12:27 - And we are looking at whether or not that relationship remains stable. Now, unfortunately, we can't assess that for Canterbury, but we can assess that for New Zealand as a whole. So when, when you assess, this level of, of, of relationship between these two variables, essentially what we are trying to determine is we're trying to determine what would the economy of Canterbury look like if there was no earthquake? What is the underlying economy? Can we quantify that?

13:02 - So by using the principles of, you know, a a three to 5% contribution to the economy, they're looking at canterbury's, economy and saying, well, it has been consistently around 5%. We, that, that, that gives us strong evidence to say that we can use a, a construction value as a counterfactual scenario for this research. So essentially saying, what would be the value of construction if there was no earthquake? so that provides us with a scenario, what is,

13:39 - what the economy would look like without, the earthquakes. And then we could use that in order to, compare the actual numbers, the actual employment numbers, the actual, growth numbers for Canterbury with this counterfactual economy. So we've, we've established that first. Secondly, we then also use what we call an input output model. Now, the input output model is a, a regional economy model,

14:09 - and it's used to measure the interdependencies between the various industries or sectors within the economy. Now, in this instance, what we are doing is we are removing the economy, economy wide impacts of the, the stimulation. So we're looking at what is the counterfactual, what is the underlying economy, and removing the stimulus that that is above that level of, of, of construction activity, removing that out

14:39 - of the economy, essentially, then leaving us with the underlying economy or the demand driven economy. And in the timeline that we're considering this assessment On is between 2011 to 2021. So then we, what we will do, what we did in the study is we actually compared, the actual level of employment versus the underlying or demand driven level of employment, removing all these stimulus that is above the normal level of economic activity for the region.

15:16 - So stimulus investments, has, has driven the economy and has helped with the recovery of the Canterbury region after the disaster. But another question is, you know, what it, what changed in terms of the employment for the region? So when we look at the results of, of the model in this work, at the peak of the rebuild, more or less an additional 18, or excuse me, an additional 13,800 people were employed, in Canterbury.

15:45 - Now, this peak was reached around four years after the earthquake. Now comparing that with the underlying economy, essentially the counterfactual level of employment or level of economy, so this reveal a significant investment stimulus that is, that was driven significant investment, is account stimulus is accounted by, for the employment opportunities that were created within the region.

16:20 - And by looking at the data, we, we can observe two distinct periods. So the first period that we can, we can identify is within the initial five years. So after the initial five years, you had the earthquake in 2011, around 20 15, 20 16, you had the peak in employment opportunities. and when you look more deeply into the, into the assessment and into the growth patterns, you would see that this period is, is, is characterized

16:51 - by growth primarily from the stimulus. So this is essentially a first five year period that is stimulus driven from 2016 onwards. The second, the second distinct period is, is visible. And that is the period where we move away from a stimulus driven employment to more demand driven employment. So essentially what we're starting to see is we're starting to see a change or overlap from the stimulus investment. as the stimulus investment is going down,

17:25 - you start seeing the, the demand, the local demand from the underlying economy starting to drive the economy. But this is best seen when you look at the diagrams within, within the research paper. But on a sector level, at a local level, at a industry level, you also see changes and, and small nuances with regards to employment and how that has changed. So while construction is the industry that has,

17:56 - that is benefiting the most, we also see benefits in manufacturing, in wholesale trade, professional services, real estate services, with additional benefits. To be more specific, what we actually observe is that without the stimulus injection, within the economy, we, we, we have seen a negative employment level in construction. And this is mostly to do with the GFC and the lingering effects of what the GFC, still,

18:30 - still left on, within the economy. So the result also revealed that growth within some of these industries were not the same. so for example, you had manufacturing employment growth peaking a little later. So six years, six years after the, the stimulus construction peaked a lot earlier. So you had more or less two years

18:55 - after the, the earthquake, you had the peak in the construction growth. While an industry like wholesale and retail trade, the peak was around seven years later. So you don't have the consistent performance of all the industries and employment, all peaking at the same time. You see these different, different peaks, depending on the type of activity. So one would expect that employment levels will decline

19:25 - below historical growth, in the period after the rebuild. So once rebuild activities or the, the, the stimulus activity starts to wind down, you would expect that historical growth levels will go down. Employment levels will go down similar to what we see in in business cycles. However, this didn't happen. we actually started to see convergence

19:54 - between the underlying economy and the stimulus driven economy. These two growth rates were growing towards one another. so after a period of, of more or less 10 years where you had elevated employment opportunities driven predominantly by the stimulus injection, we, we could now see this convergent. So there was, instead of divergence, we saw convergence getting together and the economy essentially

20:23 - started to operate. normally. So normal demand driven activity was now the driver of the economy. So, so we suggest, or the results essentially suggest that any additional employment benefit from the stimulus, within the built environment has subsided after a decade as stimulus activity starts to peak and overall employment levels start to decline below the national growth.

20:53 - We left with a question, how is employment transition and how does that, how does it perform, for the underlying economy, for the demand driven economy? So the findings of of this study suggests that in the short term, the response to stimulus injection after an earthquake is timely and it was timely and it limited the negative consequences of the disaster. Or to state it a little differently without the initial stimulus, the initial year after the earthquake would've experienced slow

21:26 - to moderate growth at best, but it didn't, it experienced significant employment growth. So with this in mind, the employment transition in construction and its industries benefited most, and this was supported at an industry level, especially after, a period of low growth between 2009 and 2016. So while the national economy experienced low growth, the local economy actually experienced really good growth

22:00 - within the construction industry. Employment transition reached its long-term equilibrium after 10 years. The first five years again was stimulus driven. The second five years was basically mostly demand driven. And by 2021, the level of employment already converged to its long-term growth position. with the construction industry, again, contributing to around 5% of cross value added like we've be spoken earlier, that's more or less the 5%

22:34 - that Canterbury is in equilibrium with. So further research with regards to what we have, what, what was done in this paper is to now look at the effect of other type of disasters, that comes to mind, also assessing the severity of the disasters and the influence on the recovery period. it's possible and likely that the recovery period could be linked to the magnitude of the disaster, but this needs further research in order to assess,

23:03 - whether or not that is true.

Faculti AI can make mistakes