kobe 1995 earthquake case study

• 0 Shopping Cart

1995 Kobe Earthquake

At 05.46 on 17th January 1995 an earthquake measuring 7.2 on the Richter scale struck the heavily populated city of Kobe, Japan. The earthquake occurred along the destructive plate boundary where the Pacific and the Philippine Plate (oceanic) meet the Eurasian (continental) plate.

Many freeways and buildings were destroyed, despite the strict building regulations, and 5000 were killed. Fires spread as a result of broken gas mains. 250,000 people were left homeless.

Latest Blog Entries

Related Topics

Use the images below to explore related GeoTopics.

Previous Topic Page

Topic home, next topic page, share this:.

• Click to share on Twitter (Opens in new window)
• Click to share on Facebook (Opens in new window)
• Click to share on Pinterest (Opens in new window)
• Click to email a link to a friend (Opens in new window)
• Click to share on WhatsApp (Opens in new window)
• Click to print (Opens in new window)

Please Support Internet Geography

If you've found the resources on this site useful please consider making a secure donation via PayPal to support the development of the site. The site is self-funded and your support is really appreciated.

Search Internet Geography

Pin It on Pinterest

• Click to share
• Print Friendly

• History & Society
• Science & Tech
• Biographies
• Animals & Nature
• Geography & Travel
• Arts & Culture
• Games & Quizzes
• On This Day
• One Good Fact
• New Articles
• Lifestyles & Social Issues
• Philosophy & Religion
• Politics, Law & Government
• World History
• Health & Medicine
• Browse Biographies
• Birds, Reptiles & Other Vertebrates
• Bugs, Mollusks & Other Invertebrates
• Environment
• Fossils & Geologic Time
• Entertainment & Pop Culture
• Sports & Recreation
• Visual Arts
• Demystified
• Image Galleries
• Infographics
• Top Questions
• Britannica Kids
• Saving Earth
• Space Next 50
• Student Center

• Why is an earthquake dangerous?
• What are earthquake waves?
• How is earthquake magnitude measured?
• Where do earthquakes occur?

Kōbe earthquake of 1995

Our editors will review what you’ve submitted and determine whether to revise the article.

• h2g2 - The Kobe Earthquake

Kōbe earthquake of 1995 , (Jan. 17, 1995) large-scale earthquake in the Ōsaka-Kōbe (Hanshin) metropolitan area of western Japan that was among the strongest, deadliest, and costliest to ever strike that country.

The earthquake hit at 5:46 am on Tuesday, Jan. 17, 1995, in the southern part of Hyōgo prefecture, west-central Honshu . It lasted about 20 seconds and registered as a magnitude 6.9 (7.3 on the Richter scale). Its epicentre was the northern part of Awaji Island in the Inland Sea, 12.5 miles (20 km) off the coast of the port city of Kōbe; the quake’s focus was about 10 miles (16 km) below the earth’s surface. The Hanshin region (the name is derived from the characters used to write Ōsaka and Kōbe) is Japan’s second largest urban area, with more than 11 million inhabitants; with the earthquake’s epicentre located as close as it was to such a densely populated area, the effects were overwhelming. Its estimated death toll of 6,400 made it the worst earthquake to hit Japan since the Tokyo-Yokohama (Great Kantō) earthquake of 1923 , which had killed more than 140,000. The Kōbe quake’s devastation included 40,000 injured, more than 300,000 homeless residents, and in excess of 240,000 damaged homes, with millions of homes in the region losing electric or water service. Kōbe was the hardest hit city with 4,571 fatalities, more than 14,000 injured, and more than 120,000 damaged structures, more than half of which were fully collapsed. Portions of the Hanshin Expressway linking Kōbe and Ōsaka also collapsed or were heavily damaged during the earthquake.

The earthquake was notable for exposing the vulnerability of the infrastructure . Authorities who had proclaimed the superior earthquake-resistance capabilities of Japanese construction were quickly proved wrong by the collapse of numerous supposedly earthquake-resistant buildings, rail lines, elevated highways, and port facilities in the Kōbe area. Although most of the buildings that had been constructed according to new building codes withstood the earthquake, many others, particularly older wood-frame houses, did not. The transportation network was completely paralyzed, and the inadequacy of national disaster preparedness was also exposed. The government was heavily criticized for its slow and ineffectual response, as well as its initial refusal to accept help from foreign countries.

In the aftermath of the Kōbe disaster, roads, bridges, and buildings were reinforced against another earthquake, and the national government revised its disaster response policies (its response to the 2004 quake in Niigata prefecture was much faster and more effective). An emergency transportation network was also devised, and evacuation centres and shelters were set up in Kōbe by the Hyōgo prefectural government.

HISTORIC ARTICLE

Jan 17, 1995 ce: kobe earthquake.

On January 17, 1995, a major earthquake struck near Kobe, Japan, killing more than 6,000 and making more than 45,000 people homeless.

Earth Science, Geology

Loading ...

On January 17, 1995, a major earthquake struck near the city of Kobe, Japan, killing more than 6,000 and making more than 45,000 people homeless. Japan is where four major tectonic plates —the Eurasian, Philippine, Pacific, and North American—meet and interact, making it one of the most geologically active regions on Earth. The Kobe quake was a result of an east-west strike-slip fault where the Eurasian and Philippine plates interact. The quake had a moment magnitude of 6.9 and cost more than $100 billion in damage. The Kobe government spent years constructing new facilities to attract back the 50,000 people who left after the quake.

Media Credits

The audio, illustrations, photos, and videos are credited beneath the media asset, except for promotional images, which generally link to another page that contains the media credit. The Rights Holder for media is the person or group credited.

Last Updated

October 19, 2023

User Permissions

For information on user permissions, please read our Terms of Service. If you have questions about how to cite anything on our website in your project or classroom presentation, please contact your teacher. They will best know the preferred format. When you reach out to them, you will need the page title, URL, and the date you accessed the resource.

If a media asset is downloadable, a download button appears in the corner of the media viewer. If no button appears, you cannot download or save the media.

Text on this page is printable and can be used according to our Terms of Service .

Interactives

Any interactives on this page can only be played while you are visiting our website. You cannot download interactives.

Related Resources

We value your ideas and suggestions. Please contact the maintainer of this site. This site can be found at: http://www.geography-site.co.uk Last Modified on: March 17, 2011 © Geography Site

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

The Long-Run Socio-Economic Consequences of a Large Disaster: The 1995 Earthquake in Kobe

Affiliation Economics Department, College of St Benedict|St John’s University, St. Joseph, MN, United States of America

* E-mail: [email protected]

Affiliation School of Economics and Finance, Victoria University of Wellington, Wellington, New Zealand

Affiliation Department of Economics, Yale University, New Haven, CT, United States of America

Affiliations Graduate School of Economics, The University of Tokyo, Tokyo, Japan, Research Institute of Economy, Trade and Industry, Tokyo, Japan

• William duPont IV, 
• Ilan Noy, 
• Yoko Okuyama, 
• Yasuyuki Sawada

• Published: October 1, 2015
• https://doi.org/10.1371/journal.pone.0138714
• Reader Comments

We quantify the ‘permanent’ socio-economic impacts of the Great Hanshin-Awaji (Kobe) earthquake in 1995 by employing a large-scale panel dataset of 1,719 cities, towns, and wards from Japan over three decades. In order to estimate the counterfactual—i.e., the Kobe economy without the earthquake—we use the synthetic control method. Three important empirical patterns emerge: First, the population size and especially the average income level in Kobe have been lower than the counterfactual level without the earthquake for over fifteen years, indicating a permanent negative effect of the earthquake. Such a negative impact can be found especially in the central areas which are closer to the epicenter. Second, the surrounding areas experienced some positive permanent impacts in spite of short-run negative effects of the earthquake. Much of this is associated with movement of people to East Kobe, and consequent movement of jobs to the metropolitan center of Osaka, that is located immediately to the East of Kobe. Third, the furthest areas in the vicinity of Kobe seem to have been insulated from the large direct and indirect impacts of the earthquake.

Citation: duPont IV W, Noy I, Okuyama Y, Sawada Y (2015) The Long-Run Socio-Economic Consequences of a Large Disaster: The 1995 Earthquake in Kobe. PLoS ONE 10(10): e0138714. https://doi.org/10.1371/journal.pone.0138714

Editor: Toshiyuki Ojima, Hamamatsu University School of Medicine, JAPAN

Received: March 22, 2015; Accepted: September 2, 2015; Published: October 1, 2015

Copyright: © 2015 duPont IV et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: Data is publicly available through the Japanese Statistical Agency website: http://www.stat.go.jp/english/data/ssds/outline.htm .

Funding: YS received funding from the Research Institute of Economy, Trade and Industry (Japan). IN received funding from NOAA-USA (Sea Grant NA09OAR4170060). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The Great Hanshin-Awaji earthquake (hereafter, the Kobe earthquake) struck at 5:46 a.m. on January 17, 1995, on Awaji Island, in Japan’s Hyogo prefecture ( Fig 1 ) offshore from Hyogo’s main metropolitan center, the city of Kobe ( Fig 2 ). The earthquake affected an area that was, at the time, home to 4 million people and contained one of Japan’s main industrial clusters. The earthquake, which had registered 7.3 on the Richter scale, cost 6,432 lives, resulted in 43,792 injured, and damaged 639,686 buildings, of which 104,906 were completely destroyed [ 1 ]. The Kobe earthquake was responsible for one of the largest direct economic losses due to a natural hazard in recorded human history. While we understand well the direct impact of the Kobe earthquake, we know much less about its impacts in the long-term. Surveys suggest that the people of Kobe experienced a prolonged and significant adverse impact on their well-being [ 2 – 3 ]. We know a lot less about Kobe’s economic recovery. Did the Kobe earthquake in 1995 indeed cause permanent losses to the economies of Kobe and other surrounding areas? Or can the recorded sense of deteriorating well-being be explained through mechanisms other than a real decline in the economic circumstances of the region?

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0138714.g001

https://doi.org/10.1371/journal.pone.0138714.g002

Some early papers concluded that the devastation wrought by the 1995 Kobe earthquake did not have any long-term impact on the Japanese economy, nor much impact on Kobe itself [ 4 ], though others were less sanguine about the disasters impact [ 5 ]. We discuss this literature more thoroughly in the next question. Ultimately the answer to this question should be based on a comparison between the actual Kobe economy, and a counter-factual Kobe without the earthquake. The conventional approach has been to compare the development of post-quake Kobe with the trends observed in Japan excluding Kobe [ 6 ]. However, such an approach raises questions about the arbitrariness of selection and the degree to which the comparison unit (Japan excluding Kobe) is indeed a credible proxy for Kobe’s counterfactual (the city without an earthquake). This difficulty is compounded by the fact that the earthquake occurred a few years after Japan had entered the “lost decade”—a prolonged recession following the collapse of the housing market circa 1990.

The synthetic control method we adopt here, introduced by [ 7 – 8 ], overcomes these shortcomings by adopting a data-driven control-group selection procedure. At its most basic, the counter-factual observations are constructed as a weighted average of available control units that were not affected. Thus, this synthetic control approximates the most relevant characteristics of the treated unit prior to the treatment. In our case, the counterfactual Kobe without an earthquake was constructed from a weighted average of cities and towns that were not directly affected by the earthquake and were far away from its epicenter.

The 1995 Kobe earthquake was maybe the first large scale disaster to affect a high income industrialized economy; and the decades since have seen several other large disasters in high income countries so that disasters are seen today not only as a risk for lower income developing countries. Hurricane Katrina in 2005, and the earthquakes in Chile in 2010 and Japan and New Zealand in 2011 are all examples of this risk. As such, the last few years have seen renewed interest in understanding what happened to Kobe in the aftermath of its earthquake, and it is to this literature that we are contributing. In the next section, we discuss previous research on Kobe, and its conclusions; in section 3 we provide more detail about our data and methodology we use; and in section 4 we describe our results.

Research on the 1995 Kobe Earthquake

Recently, two papers, using different methodologies and Japanese prefecture (province/state) level datasets, reached opposing conclusions about the prefecture-level economic long-term impact of the quake [ 9 – 10 ]. The literature, thus, is far from reaching a consensus conclusion about the aggregate impact of the earthquake on economic activity, even at the prefecture level. Neither, of course, can these two papers explain their findings, as the analysis of prefecture level data masks dramatic heterogeneities in impacts within Hyogo prefecture in the amount of direct losses with much of Kobe City and surrounding areas dramatically damaged, but the rest of the prefecture largely unaffected. Equally, we would expect significant heterogeneities in the long-term indirect impacts for these separate geographical areas within the prefecture.

Davis and Weinstein, in a set of influential papers, investigated the long-term impact of large-scale war-related destruction in Japan using detailed bombing data from World War II [ 11 – 12 ]. In these two papers, they found no long-term impact of the bombing campaigns on the level and distribution of economic activity many years later. Yet, research examining the impact of disasters on Japanese localities is generally less sanguine.

Today, there is a large literature looking at the short-term impact of catastrophic events such as natural disasters and terrorism events on various economic (growth, poverty, inflation, employment; e.g. [ 13 ]), social (well-being, social capital; e.g [ 14 ]), and demographic (population composition, fertility; e.g. [ 15 – 16 ]) variables. The natural disaster literature already includes several regression meta-analyses; e.g. [ 17 – 18 ]. Still, the literature on the long-term impact of economic shocks is relatively sparse, but the weight of the evidence suggests no lasting impact of even catastrophic shocks at the national level [ 19 ] but significant impacts at more local/regional levels in terms of even very long-term recovery–[ 20 ] analyses the impact of an event 50 years after the fact.

On Kobe, [ 21 ] reports re-distribution of population and economic activity across city districts, and thus of aggregate economic activity. [ 22 ] also analyzes population dynamics across the geography of the city, and includes a qualitative and richly detailed exploration of the policy decisions and their consequences across the city. Both papers find that the geography of recovery is quite complex with some areas experiencing post-disaster booms (in terms of population) and others struggling.

Additional papers focus on other aspects of recovery in Kobe. [ 23 ], for example, finds that investment in social capital has also increased in the quake’s aftermath. [ 5 – 6 ] examine the time-series data for Kobe following the disaster, and measure the impact of the event by comparing the Kobe dynamics to what happened to the national economy (i.e., implicitly assuming the Kobe would have followed nation-wide trends without the disaster). Both papers note a long-term adverse effect on gross regional product of the Kobe region. In [ 6 ], however, this effect dissipates by 2005. [ 5 ] largely compares Kobe to its pre-quake state, and her methodology does not account for the changes in the Japanese economic potential that occurred in the 1990s and 2000s and that may have been unique to Kobe and other regions with similar economic structure.

The micro-econometric literature includes several papers that examine labor and income data at the individual/household level. They conclude that the Kobe Earthquake had long-lasting adverse effects on individuals, groups, and households in this region [ 24 – 28 ]. [ 29 ] examines the impact of the earthquake on industrial creative destruction, business continuity and firm survival. [ 30 – 31 ] both focus on more short term impacts, but describe some of the mechanisms that may have led to the long-term impacts that we describe later. [ 30 ] focuses on the spatial distribution of housing reconstruction post 1995, while [ 31 ] examines the recovery, or lack-thereof, of the Kobe port.

Besides data-driven regression analysis, several papers have opted to use other methodologies to examine the impact of the Kobe earthquake. Of particular interest in the input-output analysis conducted by [ 32 ]. Input-output analysis is static by construction, however, unless the designer of the model allows for some structural mechanism for change, as is done in [ 32 ]. The main advantage of input-output analysis is that it permits a lot of detail about the sectoral decomposition of impacts; yet, it lacks a mechanism to critically examine the obtained results and interpret them. As such, it should be seen as a method that complements our own findings, rather than aims to replace them.

Here, we aim to establish a counterfactual with enough geo-spatial detail to provide insights on the heterogeneous ways in which the economy of the region was impacted, and thus assist in attempts to describe the mechanisms that led to these long-term effects. We employ a large panel data of Japanese cities, towns, and wards (districts/counties) observed annually for over three decades (1980–2010).

Data & Method

We collected data for all Japanese cities, towns, and wards. Between 1980 and 2010 there were 719 mergers between cities and towns. This, together with missing observations, reduced our sample to 1719 cities, towns, and wards. For all these, we obtain information on 67 variables, so that our dataset is constructed from 1,763,153 observations. Due to missing data, however, we were only able to use 53 of the 67 variables (46 of 67 when we were examining wards).

Since we wanted our synthetic counterfactual to be representative of Kobe and other cities from an economic standpoint we selected variables which theory would suggest have influence on the makeup of the local economy. These variables fell into 6 categories; demographic (such as population), environmental (such as habitable land area), economic (such as total taxable income), government (such as government expenditure), labor (such as number of unemployed), and spatial-economic (such as number of retail shops). 34 of the variables that we used were collected by the census bureau in Japan, and thus have a frequency of every 5 years. The remainder of the data was collected by various Japanese organizations and ministries and had varying frequencies of 1, 3 and 5 years. All of this data is available through: http://www.stat.go.jp/english/data/ssds/outline.htm .

One of the advantages of the synthetic control methodology is that additional variables act as descriptors or observations that provide additional information about how the constructed synthetic matches the treated unit. Since these variables act only as observations and do not otherwise interact with one another, it is not important if their timing matches. Therefore, we do not a priori rule out the use of the any of the variables available in the dataset. Our choice is mostly dictated thus by data availability. Thus all 53 variables (46 when we examined wards) are used in vector Z .

The synthetic control methodology defines a treated unit, in this case every city and town that was directly affected by the earthquake, and the identification of non-treated units, in this case all Japanese cities and towns that were not directly affected and are not in Hyogo or Osaka prefectures (as these may have been affected indirectly).

From Eq ( 2 ), one of the key requirements of the synthetic control methodology is that not only should the counterfactual match the variable of interest during the pretreatment period, but it should also be able to match each of the predictor variables as well. The predictor balance is a table that shows the value of each of the predictor variables for both the variable of interest and the synthetic control. This information is available from the authors upon request.

A plausible way to examine the statistical robustness of synthetic control estimates is to examine placebo impacts (impact assessment for geographical units that were not, in reality, affected by the disaster—similarly to the placebo effect in medical studies). This approach is, however, difficult in our case, given the very large dataset we are using (much bigger than what was used in the previously cited papers [ 7 – 8 ]). We nevertheless include placebo results for our main variables of interest in ( S1 Fig ) and discuss them in the text below.

https://doi.org/10.1371/journal.pone.0138714.g003

https://doi.org/10.1371/journal.pone.0138714.g004

In this particular example, only 4 cities received positive weights to serve as the counterfactual for the city of Kobe. They were Kitakyushu (45%), Sapporo (40%), Nagoya (14%) and Yokohama (1%). An advantage of the synthetic control methodology is its built-in transparency—the ability to check whether or not the locations that make up the counterfactual have similar qualities to the affected location. Three of these cities are important coastal cities in Japan. The one exception is Sapporo, which gains access to the coast through the cities of Otaru and Ishikari. Two of these cities, Nagoya and Yokohama, are major Japanese port cities, and three are the designated capital of their respective prefectures (the exception is Kita-Kyushu). As Kobe serves as a satellite of Osaka, this is mirrored by Yokohama, which is a satellite of Tokyo, and to a far lesser degree Kita-Kyushu and nearby Fukuoka. In short, these cities are similar to Kobe in many ways, so that this matching exercise appears to be reasonable on both quantitative and qualitative grounds.

For Kobe City, we find permanent negative but small impact on total population: around 2% decline in population 15 years after the earthquake, after an initial larger decline in the immediate disaster’s aftermath ( Fig 3 ). Similar figures available in ( S1 Fig ) show that the permanent loss of population in Kobe City can be identified for both males and females. Nishinomiya, shown in ( Fig 4 ), provides an illuminating contrast. After a sharp decline in the immediate aftermath of the earthquake, a decline that was bigger than that experienced in Kobe City, Nishinomiya ended up with population gain; the population 15 years after the earthquake has increased by 10% relative to what it would have been had the earthquake not occurred.

For Kobe’s population estimate, we make two observations from the placebo results: First, the goodness-of-fit for Kobe’s population estimates pre-event is better than for most other cities in our dataset. Second, the post-event trajectory of Kobe is not significantly outside the range of estimates for other regions. This second observation suggests that the small identified impact on Kobe’s population indeed does not indicate a statistically robust and large deviation from its expected trend.

(Figs 3 and 4 ) show the impact of the earthquake for only two geographical units. In order to summarize the information included in the results for every impacted city/town/ward in the region, we plot these on a map. We color every geographical unit with the estimated impact on the variable of interest, calculated as the difference between the synthetic and the actual observation for that region (as the distance between the two lines in (Figs 3 and 4 ) expressed in percent); blue colors denote decreases and the reds denote increase. Only those results for which the pre-event fit is sufficient ( RMSPE ≤ 10%; see footnote 7) are presented. These maps allow us to observe more clearly the spatial patterns we found. In all figures, the top panel presents our estimates using the city-level data. Thus, the impact plotted for Kobe City is estimated for the city as a whole, using a control group composed of other Japanese cities. The bottom panel provides more detail by focusing on differential impacts across the nine wards of Kobe City; these impacts are estimated using the ward-level dataset.

During the first year after the earthquake, there was a short-term dip in population across the whole area nearest to the epicenter, and including the urban Eastern corridor toward Osaka. In the longer-run, however, we observe heterogeneities in permanent population trends. Figures available in ( S1 Fig ) present the population impact maps for the aggregate figures, and disaggregated by gender and age and using several population measures from different sources. In (Figs 5 and 6 ), we observe a pattern of movement away from the most severely affected areas. However, regions to the east, that were also seriously impacted initially, seem to gain in long run, suggesting that proximity to Osaka may be a driver of population recovery. These patterns are not uniform; Sumoto city, for example, which is located near the epicenter, has been largely unaffected in the long-run, implying that the community and industry employment characteristics matter as well. One possibility, elaborated on by [ 14 ], is that the community is a major determinant of these differing recovery trajectories, and that cohesive communities recover faster and more completely. [ 29 ] on the other hand, emphasize industry/sector characteristics as determinants of recovery trajectories. Our data does not allow us to distinguish between these differing explanations, and it is likely that they all interact in complex ways to determine outcomes.

https://doi.org/10.1371/journal.pone.0138714.g005

https://doi.org/10.1371/journal.pone.0138714.g006

(Figs 7 and 8 ) include an examination of the day-time population of the area we examine. These estimates suggest that there is a uniform and persistent decline of population even in the longer-term. This decline in daytime population is even observed for towns to the East, for which we observed population increases in (Figs 5 and 6 ). This suggests that the increase in population observed to the East of Kobe City is driven by people who have moved to these areas from the devastated center, but have also switched their location of employment eastward to Osaka.

https://doi.org/10.1371/journal.pone.0138714.g007

https://doi.org/10.1371/journal.pone.0138714.g008

Another intriguing trend, presented in ( Fig 9 ), is the increase in the number of people over the age of 65. When compared with other geographical units in Japan (the synthetic control), Kobe City seemed to have gained more. While we do not know the exact reasons for this shift, we can speculate that it may be associated with either people returning to their cultural roots (as the impact of the earthquake leads to shifts in preferences), or that over-65, living mostly on fixed incomes, are moving to a place where living costs are more manageable (both because of the relative economic decline of the region and the generous government support).

https://doi.org/10.1371/journal.pone.0138714.g009

For income, as we can see from ( Fig 10 ), Kobe City partially bounced back after the earthquake, but there still appears to be a permanent loss in income. Again, we find intra-regional heterogeneous variations in income recovery. While the areas East of Kobe seem to gain in long run, other parts closer to central Kobe lost substantial amount of income, suggesting once more that the proximity to Osaka as a new provider of employment and income may be a driver of the (partial) economic recovery in Kobe’s Eastern region.

https://doi.org/10.1371/journal.pone.0138714.g010

We next study aggregate unemployment ( Fig 11 ), and then employment in the secondary (manufacturing) and tertiary (services) sectors in (Figs 12 and 13 ), respectively. Equivalent analysis of the number of businesses in the secondary and tertiary sectors is available in S1 Fig . The evidence on aggregate unemployment is quite clear. Unemployment increased, both in the short- and in the long-term, and both in Kobe City itself, and in the peripheral towns. Remarkably, the evidence seems to suggest a stronger adverse impact in the long-term (15 years after the earthquake).

https://doi.org/10.1371/journal.pone.0138714.g011

https://doi.org/10.1371/journal.pone.0138714.g012

https://doi.org/10.1371/journal.pone.0138714.g013

The secondary (manufacturing) sector in Kobe City declined both in the short- and long-terms; this decline is observable in both the number of secondary-sector businesses operating in the city, and the level of employment in this sector. The spatial distribution is quite different for the tertiary sector (services). As before, we observe a short-term decline for Kobe City, its wards, and the surrounding towns in both number of operating businesses and employment. However, once we examine the longer-horizon, 15 years after the earthquake, we observe an increase in the number of tertiary (services) businesses operating, accompanied by a smaller increase in employment when evaluated against employment trends elsewhere in Japan. Essentially, it appears that Kobe City experienced a shift from secondary to tertiary employment. This shift may explain the declines in aggregate total taxable income, and as the wages in service sector employment are typically lower than in the industrial/manufacturing sector.

Conclusions, Caveats, and Future Considerations

The three central empirical regularities that emerged from our synthetic control analysis are: First, incomes and, to a lesser extent, the population of Kobe City have both decreased. This effect of the earthquake lasted for over fifteen years, indicating a significant permanent negative impact. Such a negative impact can be found especially in the central area (e.g., Chuo, Hyogo, and Nagata wards), which is closest to the epicenter of the earthquake. These population shifts to outside of the central area were also identified by [ 5 ]. In addition to further confirming these earlier results, we also document the difference between the dynamics for daytime and nighttime populations. This suggests a residential move to the East of Kobe, a shift in employment from Kobe to Osaka, and an increase in the population of the elderly. All these observed patterns are potentially very important if we are to understand the policy implications of these transitions. We further document rigorously that this decline in the central areas also involved a shift from manufacturing to services, a shift that goes beyond the general de-industrialization and shift to services that has been enfolding in Japan and in cities similar to Kobe in the last two decades (as our counterfactuals account for those general trends).

Second, the surrounding areas, in particular East of Kobe (e.g., Nishinomiya city), experienced positive permanent impacts in terms of total taxable income after facing short-run negative effects in the immediate aftermath of the earthquake. Previous research has not examined incomes, but we note that this positive impact did not result in increased employment in this region. Rather, this region’s increased population is mostly employed in nearby Osaka (further to the East).

Third, the peripheral areas in Hyogo seem to have been insulated from the large direct and indirect impacts of the earthquake. This further confirms previous findings from other case studies that disasters are localized events, and do not entail larger and more permanent impacts further afield, as noted by [ 14 ] and [ 19 ], for example. It also further highlights the need to focus on the immediate locale, and not mis-interpret the economic state of the larger region as an indicator of what is occurring in the immediate location of the disaster (as we have seen frequently done).

Some important caveats for this work are worth mentioning. ( S1 Fig ) includes a variety of placebo tests we conducted to establish the statistical significance of our results (as distinct from their real economic significance). We have not been able to always do that, so in some cases our results are more tentative. The general malaise afflicting the Japanese economy in the 1990s (and later into the 2000s) is accounted for by the synthetic control methodology, and that is probably the most important advantage of this method. However, if that malaise was at least partially made worse by the events in Kobe in 1995, then our estimates are understated. Given previous research on other case studies, we find that unlikely.

Another potential drawback of our description is that our examination of labour markets focuses only on employees, even though this is probably the most important mechanism in the changes we describe. A potentially fruitful research agenda is to identify more specifically the changes from the employers’ perspectives. [ 29 ], for example, attempts to identify the incentives and circumstances that guided employers’ decisions to exit the region. This research agenda, however, is still not sufficiently conclusive in our view.

Once the spatial and dynamic responses of each region, city and ward has been described, the next research task is to identify the policy determinants of these differing trajectories, and to further investigate whether possible policy shifts could have led to more favorable outcomes. This, unfortunately, is outside the scope of this paper. Instead of relying on the ward-level dataset we used, other alternative sources of information and methodology may yield additional insights about the process of recovery (or lack thereof) in Kobe post-1995, and especially on its policy determinants (e.g. [ 22 ]).

Finally, it is worth repeating that we believe that the long-term or permanent costs of disasters may be significant as they impose large permanent impacts on human wellbeing in affected regions [ 33 , 34 ]. Our results here suggest that one such large catastrophic shock, the 1995 Kobe earthquake, did indeed impose long-term quantifiable costs on the affected region. These costs are typically not clearly identified and are thus not considered when assessing the benefits from disaster risk reduction and mitigation policies.

This failure leads to under-investment in reducing risks from disasters (the direct costs), and in trying to mitigate their impacts (the indirect long-term ones). Maybe more importantly and less obviously, we also believe that this failure to recognize the long-term permanent impacts leads to complacency during the post-disaster recovery process itself. Policymakers and the public believe that recovery will inevitably be achieved, and are thus mostly making policy and electoral decisions based on short-term considerations rather than in an attempt to guide this long-term process on a more successful and improved trajectory.

A different concern and motivation for our research agenda is the well-documented increasing economic costs of natural disasters [ 35 ], even if there is uncertainty regarding the reasons for this trend. The socio-economic dynamics we investigated here are bound to become more important in the future, even if some of the more dire predictions regarding the impact of climate change on extreme climatic events do not materialize [ 36 ]. Our publics, our governments, our international organizations, and the international agreements and covenants we agree on (most relevant is the recently agreed UN Sendai Framework for Disaster Risk Reduction) must take into account these long-term permanent impacts in guiding future actions. Awareness of these potentially long-term adverse impacts should lead, ceteris paribus, to more concentrated and effective investment in disaster risk prevention and reduction, and to better policy-making in the aftermath of catastrophes.

Supporting Information

S1 fig. additional variables and placebo results..

https://doi.org/10.1371/journal.pone.0138714.s001

Acknowledgments

This research is a part of the project “Post-disaster Recovery Policies and Insurance Mechanisms against Disasters: Case Studies on Earthquakes in Japan and Floods in Thailand” undertaken at the Research Institute of Economy, Trade & Industry (RIETI). The authors would like to thank RIETI for generous support for the project. The authors are also grateful for their helpful suggestions and comments from the seminar participants at RIETI—in particular, Atsushi Nakajima, Masahisa Fujita, Masayuki Morikawa and Hiroyuki Nakata. We would also like to thank numerous seminar audiences in universities and conferences. Noy’s work was partly funded by a grant from the National Oceanic and Atmospheric Administration, Project R/IR-22, which is sponsored by the University of Hawaii Sea Grant College Program under Institutional Grant No. NA09OAR4170060. The opinions expressed and arguments employed in this paper are the sole responsibility of the authors and do not necessarily reflect those of RIETI or the Ministry of Economy, Trade and Industry of Japan or NOAA and any of its sub-agencies.

Author Contributions

Conceived and designed the experiments: IN YS. Performed the experiments: WD YO. Analyzed the data: WD YO. Wrote the paper: IN YS WD YO.

• 1. City of Kobe. The Great Hanshin-Awaji Earthquake Statistics and Restoration Progress. 2011.
• View Article
• Google Scholar
• PubMed/NCBI
• 6. Beniya S. The Evaluation of the Status of Disaster Areas by using Recovery Indicators—in the case of the Great Hanshin-Awaji Earthquake. 2nd International Conference on Urban Disaster Reduction. 2007.
• 14. Aldrich D. Building Resilience. Chicago University Press. 2012.
• 22. Edgington D. Reconstructing Kobe: The Geography of Crisis and Opportunity. University of British Columbia Press. 2010.
• 23. Yamamura E. Natural disasters and social capital formation: The impact of the Great Hanshin-Awaji earthquake. MPRA Paper 44493, University Library of Munich, Germany. 2013.
• 35. UNISDR. Global Assessment Report on Disaster Risk Reduction. Geneva, Switzerland: United Nations Office for Disaster Risk Reduction. 2013.
• 36. IPCC. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. 2012.

Login to your account

Change password, your password must have 8 characters or more and contain 3 of the following:.

• a lower case character, 
• an upper case character, 
• a special character 

Password Changed Successfully

Your password has been changed

Create a new account

Can't sign in? Forgot your password?

Enter your email address below and we will send you the reset instructions

If the address matches an existing account you will receive an email with instructions to reset your password

Request Username

Can't sign in? Forgot your username?

Enter your email address below and we will send you your username

If the address matches an existing account you will receive an email with instructions to retrieve your username

• This Journal
•   
• Institutional Access

Cookies Notification

Our site uses javascript to enchance its usability. you can disable your ad blocker or whitelist our website www.worldscientific.com to view the full content., select your blocker:, adblock plus instructions.

• Click the AdBlock Plus icon in the extension bar
• Click the blue power button
• Click refresh

Adblock Instructions

• Click the AdBlock icon
• Click "Don't run on pages on this site"

uBlock Origin Instructions

• Click on the uBlock Origin icon in the extension bar
• Click on the big, blue power button
• Refresh the web page

uBlock Instructions

• Click on the uBlock icon in the extension bar

Adguard Instructions

• Click on the Adguard icon in the extension bar
• Click on the toggle next to the "Protection on this website" text

Brave Instructions

• Click on the orange lion icon to the right of the address bar
• Click the toggle on the top right, shifting from "Up" to "Down

Adremover Instructions

• Click on the AdRemover icon in the extension bar
• Click the "Don’t run on pages on this domain" button
• Click "Exclude"

Adblock Genesis Instructions

• Click on the Adblock Genesis icon in the extension bar
• Click on the button that says "Whitelist Website"

Super Adblocker Instructions

• Click on the Super Adblocker icon in the extension bar
• Click on the "Don’t run on pages on this domain" button
• Click the "Exclude" button on the pop-up

Ultrablock Instructions

• Click on the UltraBlock icon in the extension bar
• Click on the "Disable UltraBlock for ‘domain name here’" button

Ad Aware Instructions

• Click on the AdAware icon in the extension bar
• Click on the large orange power button

Ghostery Instructions

• Click on the Ghostery icon in the extension bar
• Click on the "Trust Site" button

Firefox Tracking Protection Instructions

• Click on the shield icon on the left side of the address bar
• Click on the toggle that says "Enhanced Tracking protection is ON for this site"

Duck Duck Go Instructions

• Click on the DuckDuckGo icon in the extension bar
• Click on the toggle next to the words "Site Privacy Protection"

Privacy Badger Instructions

• Click on the Privacy Badger icon in the extension bar
• Click on the button that says "Disable Privacy Badger for this site"

Disconnect Instructions

• Click on the Disconnect icon in the extension bar
• Click the button that says "Whitelist Site"

Opera Instructions

• Click on the blue shield icon on the right side of the address bar
• Click the toggle next to "Ads are blocked on this site"

System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

Long-run effect of a disaster: case study on the kobe earthquake.

• YASUHIDE OKUYAMA

Graduate School of Social System Studies, University of Kitakyushu, 4-2-1 Kitagata, Kokuraminami-ku, Kitakyushu 802-8577, Japan

E-mail Address: [email protected]

Search for more papers by this author

In 1995, the Kobe earthquake occurred in the second largest economic region of Japan, and its economic damages were accounted around 10 trillion yen. This paper presents an empirical investigation of long-run economic effects of the event based on a time-series data. The results indicate that the event had created statistically significant deviations from the pre-earthquake growth path of Kobe. In addition, the comparison with the projected pre-event growth path revealed that the long-run effects have resulted in a steady decline of per capita GRP, while the short-run impacts led to some positive impacts from recovery and reconstruction during the first several years.

• long-run effects
• time-series analysis
• regional economy
• Albala-Bertrand, JM [ 1993 ] The Political Economy of Large Natural Disasters: With Special Reference to Developing Countries . Oxford, UK: Clarendon Press. Crossref ,  Google Scholar
• Albala-Bertrand, JM [ 2007 ] Globalization and localization: An economic approach , Handbook of Disaster Research , eds. Rodriguez, H, EL Quarantelli and RR Dynes New York: Springer, pp. 147–167. Crossref ,  Google Scholar
• Baade, RA, R Baumann and V Matheson [ 2007 ] Estimating the economic impact of natural and social disasters, with an application to hurricane. Urban Studies 44 (11), 2061–2076. Crossref , Web of Science ,  Google Scholar
• Cavallo, E, S Galiani, I Noy and J Pantano (2010). Catastrophic natural disasters and economic growth. Inter-American Development Bank Working Paper Series: No. IDB-WP-183. Google Scholar
• Cuaresma, JC, J Hlouskova and M Obersteiner [ 2008 ] Natural disasters as creative destruction? Evidence from developing countries. Economic Inquiry 46 (2), 214–226. Crossref , Web of Science ,  Google Scholar
• Coffman, M and I Noy [ 2011 ] Hurricane Iniki: Measuring the long-term economic impact of a natural disaster using synthetic control. Environment and Development Economics 17 , 187–205. Crossref , Web of Science ,  Google Scholar
• Dacy, DC and H Kunreuther [ 1969 ] The Economics of Natural Disasters . New York: The Free Press. Google Scholar
• DuPont, W and I Noy (2012). What happened to Kobe? A reassessment of the impact of the 1995 earthquake in Japan. University of Hawaii, Department of Economics Working Paper Series: No. 12-4. Google Scholar
• Greenberg, M-R, M Lahr and N Mantell [ 2007 ] Understanding the economic costs and benefits of catastrophes and their aftermath: A review and suggestions for the U.S. federal government. Risk Analysis 27 (1), 83–96. Crossref , Web of Science ,  Google Scholar
• Hendry, DF [ 1995 ] Dynamic Econometrics . New York, NY: Oxford University Press. Crossref ,  Google Scholar
• Hornbeck, R (2009). The enduring impact of the American dust bowl: Short and long run adjustments to environmental catastrophe. NBER Working Paper Series: W15605. Google Scholar
• Hyogo Prefecture Government (2010). Status of Recovery and Reconstruction After the Hanshin-Awaji Great Earthquake (in Japanese). Google Scholar
• Kunreuther, H and A Rose (eds.) [ 2004 ] The Economics of Natural Hazards, Volume I & II . Northampton, MA: Edward Elgar. Google Scholar
• Odell, KA and MD Weidenmier (2002). Real shock, monetary aftershock: The San Francisco earthquake and the panic of 1907. NBER Working Paper Series: W9176. Google Scholar
• Okuyama, Y (2003). Economics of natural disasters: A critical review. Research Paper 2003–12, Regional Research Institute, West Virginia University. Google Scholar
• Okuyama, Y [ 2007 ] Economic modeling for disaster impact analysis: Past, present, and future. Economic Systems Research 19 (2), 115–124. Crossref ,  Google Scholar
• Okuyama, Y (2009). Critical review of methodologies on disaster impact estimation. A Background Paper for the Global Facility for Disaster Reduction and Recovery, The World Bank. Google Scholar
• Okuyama, Y [ 2010 ] Globalization and localization of disaster impacts: An empirical examination. CEFifo Forum 11 (2), 56–66. Google Scholar
• Okuyama, Y [ 2014 ] Disaster and economic structural change: Case study on the 1995 Kobe earthquake. Economic Systems Research 26 (1), 98–117. Crossref , Web of Science ,  Google Scholar
• Okuyama, Y and SE Chang , (eds.) [ 2004 ] Modeling Spatial and Economic Impacts of Disasters . New York: Springer. Crossref ,  Google Scholar
• Okuyama, Y and S Sahin (2009). Impact estimation of disasters: A global aggregate for 1960 to 2007. Policy Research Working Paper 4963, The World Bank. Google Scholar
• Okuyama, Y and JR Santos [ 2014 ] Disaster impact and input-output analysis. Economic Systems Research 26 (1), 1–12. Crossref , Web of Science ,  Google Scholar
• Okuyama, Y, GJD Hewings and M Sonis [ 1999 ] Economic impacts of an unscheduled, disruptive event: A Miyazawa multiplier analysis , Understanding and Interpreting Economic Structure , eds. Hewings, GJD, M Sonis, M Madden and Y Kimura , New York: Springer-Verlag, pp. 113–144. Crossref ,  Google Scholar
• Rasmussen, TN (2004). Macroeconomic implications of natural disasters in the Caribbean. IMF Working Paper WP/04/224, International Monetary Fund. Google Scholar
• Rose, A [ 2004 ] Economic principles, issues, and research priorities in hazard loss estimation , Modeling Spatial and Economic Impacts of Disasters , eds. Okuyama, Y and SE Chang , New York: Springer, pp. 13–36. Crossref ,  Google Scholar
• Skidmore, M and H Toya [ 2002 ] Do natural disasters promote long-run growth? Economic Inquiry 40 (4), 664–687. Crossref , Web of Science ,  Google Scholar
• Analysis of the post-earthquake economic recovery of the most severely affected areas in the 2008 Wenchuan earthquake Pengyu Chen 21 July 2022 | Natural Hazards, Vol. 114, No. 3
• Health-education-disaster green low-carbon endogenous economic growth model and its new accompanying effects Bingyue Wan and Lixin Tian 1 Jul 2022 | Journal of Cleaner Production, Vol. 359
• MILAN DENG , 
• LIN WANG , 
• LONG YIN , and 
• LIFANG HUANG
• Faheem Aslam , 
• Paulo Ferreira , 
• Fahd Amjad , and 
• Toward a Long-Term Economic Damage Reduction from an Urban Disaster: Lessons from the 1995 Kobe Earthquake Toshihisa Toyoda 17 November 2021
• A CGE Framework for Modeling the Economics of Flooding and Recovery in a Major Urban Area Aaron B. Gertz, James B. Davies and Samantha L. Black 14 February 2019 | Risk Analysis, Vol. 39, No. 6
• Quantifying the spatial ripple effect of the Bohai Sea ice disaster in the winter of 2009/2010 in 31 provinces of China Cailin Wang, Jidong Wu, Xin He, Mengqi Ye and Yu Liu 9 October 2018 | Geomatics, Natural Hazards and Risk, Vol. 9, No. 1
• Seeking a Better Recovery Process from Major Natural Disasters: A Lesson from Several Growth Models with Multiple Equilibria Kei Hosoya 1 Jan 2017 | Theoretical Economics Letters, Vol. 07, No. 05
• How shaky was the regional economy after the 1995 Kobe earthquake? A multiplicative decomposition analysis of disaster impact Yasuhide Okuyama 28 August 2015 | The Annals of Regional Science, Vol. 55, No. 2-3
• The Rise and Fall of the Kobe Economy from the 1995 Earthquake Yasuhide Okuyama 1 August 2015 | Journal of Disaster Research, Vol. 10, No. 4

Recommended

Published: 17 November 2015

• Disaster Medicine

Disaster and economic structural change: Case study on the 1995 Kobe earthquake

• January 2014
• Economic Systems Research 26(1)

• The University of Kitakyushu

Kobe Earthquake

The Kobe Earthquake – an earthquake in an HIC (High Income Country)

Kobe is located in the south east of Japan, near a destructive plate margin. It is a megacity and has one of the largest container ports in the World.  Although further from a plate margin than most of the cities in Japan, Kobe is still found on a fault line. 

The earthquake that hit Kobe during the winter of 1995 measured 6.9 on the Richter scale. At this plate margin, the Pacific plate is being pushed under the Eurasian plate, stresses build up and when they are released the Earth shakes. This is known as an earthquake happening along a subduction zone. The focus was only 16km below the crust and this happened on the 17th Jan 1995 at 5.46am. 10 million people live in this area.

Effects The effects of this earthquake were catastrophic for a HIC.  Despite some buildings having been made earthquake proof during recent years many of the older buildings simply toppled over or collapsed.  A lot of the traditional wooden buildings survived the earthquake but burnt down in fires caused by broken gas and electricity lines. Other effects included; •  More than 5000 died in the quake • 300,000 were made homeless • More than 102,000 buildings were destroyed in Kobe, especially the older wooden buildings. • Estimated cost to rebuild the basics = £100 billion. • The worst affected area was in the central part of Kobe including the main docks and port area. This area is built on soft and easily moved rocks, especially the port itself which is built on reclaimed ground. Here the ground actually liquefied and acted like thick soup, allowing buildings to topple sideways. • Emergency aid for the city needed to use damaged roads but many of them were destroyed during the earthquake. • Raised motorways collapsed during the shaking.  Other roads were affected, limiting rescue attempts. • Many small roads were closed by fallen debris from buildings, or cracks and bumps caused by the ground moving. • The earthquake occurred in the morning when people were cooking breakfast, causing over 300 fires, which took over 2 days to put out.

Responses to the quake Water, electricity, gas, telephone services were fully working by July 1995 and the railways were back in service by August 1995 A year after the earthquake, 80% of the port was working but the Hanshin Expressway was still closed. By January 1999, 134,000 housing units had been constructed but some people still had to live in temporary accommodation. New laws were passed to make buildings and transport structures even more earthquake proof. More instruments were installed in the area to monitor earthquake movements. Most new buildings and roads have, in the last 20 years, been designed to be earthquake proof, schools and factories have regular earthquake drills, etc. Despite this, many older buildings still collapsed or caught fire. This led to many blocked roads and massive problems of homelessness. Electricity and water supplies were badly damaged over large areas. This meant no power for heating, lights, cooking, etc. Clean, fresh water was in short supply until April 1995. The government and city authorities were criticised for being slow to rescue people and for refusing offers of help from other countries.

By 松岡明芳 ( GFDL )

Solutions ; Preparation – A lot of the buildings in Kobe and Japan made after the 1960s are earthquake proof (necessary by law) with counterweights on the roofs and cross steel frames.  Many of the damaged buildings in Kobe were built before this period and were made of wood, which caught fire. People are educated on earthquake preparation in Japan. Prediction – Japan has the world’s most comprehensive prediction programme with thousands of seismometers and monitoring stations in Japan designed to give warning.  Kobe hadn’t had an earthquake in 400years and had less prediction equipment than other areas of Japan. Aid – The Japanese rejected international offers of aid and dealt with the earthquake itself.  All of the homeless people were dealt with reasonably quickly and the city recovered thanks to government money.

NEXT TOPIC - Haiti Case study

©2015 Cool Geography

• Copyright Policy
• Privacy & Cookies
• Testimonials
• Feedback & support

• TOP CATEGORIES
• AS and A Level
• University Degree
• International Baccalaureate
• Uncategorised
• 5 Star Essays
• Study Tools
• Study Guides
• Meet the Team
• Hazardous Environments

Kobe Earthquake - A Case Study.

It was 5.46am on January 17th 1995; many who lived in the port town of Kobe were still in bed, when the largest earthquake since 1923 when Tokyo was devastated and 142,000 where killed.

It measured 7.2 on the Richter scale but it was not only the sheer force of the quake with the epicentre only 20km away it resulted in the destruction of many buildings and the loss of numerous lives.

         Kobe is positioned on the margin of the Eurasian Plate where the Philippine Sea Plate is subducted below. Immediately south of Osaka Bay is the fault Median Tectonic Line, and it was sudden movement along this fault that triggered the earthquake that hit Kobe, and devastated so many lives.

 The area around Kobe is built on soft and easily moved rocks, in particular the port itself, which is built on reclaimed ground. Here the ground liquefied acting like thick soup, forcing buildings to collapse sideways, causing many homes to sink and resulting in the huge cranes in the harbour toppling over into the sea. Making the effects of the quake even worse.

This is a preview of the whole essay

        The effects of the earthquake where enormous, primary, secondary, long and short term. I think the most devastating primary effect is the amount of people that lost their lives;  over 5,400 people were killed, more than 27,000 injured, and due to the amount of buildings that were ruined (More than 102,000 buildings) it left 300,000 homeless. It was not only building s that where damaged during the quake, matters were made worse when rescue operations were hindered by roads, railways and other modes of transport all being damaged. Kobe is actually situated on a strip of flat land between high mountains and the sea. This narrow strip of land carries all the communications routes between northeastern Japan and western Japan. Emergency aid for the city needed to use these routes, but many of them were destroyed during the earthquake. Gas and electricity supplies were also damaged, like most cities, services like water, gas, electricity and sewerage were provided through a system of underground pipes and cables, and when the ground began to shake, the more rigid pipes weren't able to move as well so they split. Almost three quarters of the water supply across the entire city was cut off, gas pipes leaked gas into the air, and sewers discharged their contents into the streets.

Against the fire fighters best efforts there were at least a dozen major fires that burned for up to two whole days before they were brought under control. And research has suggested that 500 deaths were due to fires, and that almost 7000 buildings were destroyed by fire alone.

If the earthquake hadn’t destroyed enough physical things it also destroyed the city of Kobe’s confidence in its central governments ability to cope with the crises, the earthquake showed how venerable such a complex modern city can be, the authorities admitted that they were overwhelmed with the magnitude of the disaster for example it took almost 2 years for the port of Kobe to be fully operational again, professor Katsuki Takiguchi from the Tokyo institute of technology said “ The most valuable lesson……is that…..Japan is not earthquake proof. We always believed that Japan was ahead of everybody else. It turned out we are not.”

        Preparation for an earthquake is always hard as it is hard to know when it will happen and how disastrous the consequences will be. And the Kobe earthquake showed that there were certainly some problems with the already standing preparation, in the earthquake people were seen running outside buildings with the risk of flying debris and also ignoring small fires. However since the 1995 quake in Kobe Japan has introduced a few measures to try and save as many lives as possible if such a disaster should strike again. Four times a year the school children of Japan are put through earthquake and fire drills and kits for use during an earthquake can be bought from department store (these kits include a bucket-for fires, bottled water, food, radio, torch, first aid kit and protective head gear.)

        The government has also made a disaster prevention day every year on the 1 st  of September were companies and families alike can learn new and better way to act during an earthquake which could save lives this day also marks the day of the Kanto earthquake in Tokyo.

        As I have mentioned earlier trying to predict an earthquake is very difficult, but with careful observation of the key areas, even a few minutes of notice can save lives, such method as measuring the amounts of radon gas emitted, also changes in ground levels, and monitoring the accumulation of strain along the fault can all suggest an earthquake.

Document Details

• Word Count 806
• Page Count 3
• Level AS and A Level
• Subject Geography

Related Essays

Case Study of The Kobe earthquake.

Kobe, Japan (Case Study)

kobe earthquake

The Kobe Earthquake.

• Open access
• Published: 06 December 2013

Analysis of the rupture process of the 1995 Kobe earthquake using a 3D velocity structure

• Yujia Guo 1 ,
• Kazuki Koketsu 1 &
• Taichi Ohno 2  

Earth, Planets and Space volume  65 ,  pages 1581–1586 ( 2013 ) Cite this article

1931 Accesses

7 Citations

Metrics details

A notable feature of the 1995 Kobe (Hyogo-ken Nanbu) earthquake is that violent ground motions occurred in a narrow zone. Previous studies have shown that the origin of such motions can be explained by the 3D velocity structure in this zone. This indicates not only that the 3D velocity structure significantly affects strong ground motions, but also that we should consider its effects in order to determine accurately the rupture process of the earthquake. Therefore, we have performed a joint source inversion of strong-motion, geodetic, and teleseismic data, where 3D Green’s functions were calculated for strong-motion and geodetic data in the Osaka basin. Our source model estimates the total seismic moment to be about 2.1 × 10 19 N m and the maximum slip reaches 2.9 m near the hypocenter. Although the locations of large slips are similar to those reported by Yoshida et al. (1996), there are quantitative differences between our results and their results due to the differences between the 3D and 1D Green’s functions. We have also confirmed that our source model realized a better fit to the strong motion observations, and a similar fit as Yoshida et al. (1996) to the observed static displacements.

1. Introduction

The Kobe (Hyogo-ken Nanbu) earthquake, with a JMA magnitude of 7.3, occurred at 5:46 on January 17, 1995 (JST), and claimed more than 6,000 lives. The damaging ground motions with a JMA intensity of 7 were generated in a narrow zone called the “damage belt” of the Kobe area. Kawase (1996) concluded that the “damage belt” resulted from the constructive interference of direct S -waves and basin-edge-induced diffracted/Rayleigh waves. Furumura and Koketsu (1998) showed that this zone arises from strong amplification and ray bending in the sedimentary basin below Kobe city in conjunction with the multipathing effects at a basin/bedrock boundary. These previous studies imply that the ground-motion amplification in the Kobe area was significantly affected by a 3D velocity structure such as a basin edge. Thus, we should inevitably consider its effects in precisely determining the rupture process of this earthquake.

In some previous studies (e.g., Hashimoto et al., 1996 ; Ide et al., 1996 ; Kikuchi and Kanamori, 1996 ; Sekiguchi et al., 2000 ) separate inversions of static displacement, teleseismic body waves, or strong motions were performed to analyze the rupture process of this earthquake. In these studies, half-space velocity structure models were used for calculating Green’s functions of static displacements, and 1D stratified velocity structure models were used for those of teleseismic body waves or strong motions. In addition to these separate inversions, joint inversions have been conducted using strong motions and static displacements by Horikawa et al. (1996) , and using strong motions, teleseis-mic body waves, and static displacements by Wald (1996) and Yoshida et al. (1996) . 1D stratified and half-space velocity structure models were also used in these studies.

The use of a 3D velocity structure model and the calculation of 3D Green’s functions are very useful methods for separating the effects of the source and site when the time duration of strong motions is longer than the duration of the rupture process. In addition, the use of 3D strong-motion Green’s functions can result in an improvement of the fitting between the observed and synthetic data, especially in later phases. The combined use of geodetic and strong-motion data can also provide a stabilizing constraint on the slip distribution ( Wald and Graves, 2001 ). For these reasons, in this study, we refine a 3D velocity structure model and calculate 3D Green’s functions for the strong-motion and geodetic data in the Osaka basin in order to incorporate the effects of a 3D velocity structure. As in previous studies, we also use 1D stratified velocity structure models for teleseismic and strong-motion data outside the Osaka basin. A half-space velocity structure model is used for geodetic data outside the Osaka basin. We then performed a joint inversion of strong motions, static displacements, and teleseismic body waves.

According to Graves and Wald (2001) , the use of well-calibrated 3D Green’s functions provides very good resolution of the slip distribution of a source model, and adequately separates source and 3D propagation effects. In contrast, using a set of inexact 3D Green’s functions allows only partial recovery of the slip distribution. This indicates that a 3D velocity structure model should be carefully validated before performing a source inversion. Therefore, we also perform this validity test and refine the 3D velocity structure through waveform modeling of ground motion data from aftershocks.

2. Refinement of the 3D Velocity Structure Model

The velocity structure of the Osaka basin has been extensively investigated in recent years. Our initial model for the refinement was as follows. We adopted the 3D sediment-bedrock interface estimated by Afnimar et al. (2002) , who carried out a joint inversion of refraction and gravity data. For the velocity structure of the bedrock, we used the same 1D stratified velocity structure model as in Yoshida et al. (1996) . Table 1 lists this bedrock part and three sedimentary layers of the basin part of our initial model. The top surface of each sedimentary layer is set to be proportional to the depth of the sediment-bedrock interface. The common proportionality constants were estimated from microtremor array measurements and seismic reflection survey data by Kagawa et al. (2004) .

The sediment-bedrock interface z in Table 1 varies from station to station, and the depths of the top surfaces for sedimentary layers 2 and 3 are r 1 z and r 2 z , respectively, where r 1 and r 2 are common proportionality constants. In our refinement procedure, r 1 , r 2 , and z j ( j = 1, 2,…), where j represents the number of stations, are treated as unknowns. We calibrated these unknowns using ground motion data from aftershocks.

We chose the three aftershocks listed in Table 2 as target events. Data from the Kobe area were used in the refinement because the velocity structure model in this area strongly affects 3D Green’s functions. They were recorded by the Committee of Earthquake Observation and Research in the Kansai Area (CEORKA), Iwata et al. (1996) , and the Earthquake Research Institute, University of Tokyo (see Nagano et al., 1999 ).

In modeling ground motions, we calculated synthetic velocity waveforms using the finite element method (FEM) with voxel meshes ( Koketsu et al., 2004 ; Ikegami et al., 2008 ) with intervals of 40 m. The source time function was a smoothed ramp function with a rise time of 0.5 s. Next, we checked whether the synthetic waveforms agreed with the observed ground motions in the frequency range 0.3–1.0 Hz. If not, we calibrated the depth z beneath a station and the common proportionality constants r 1 and r 2 by trial and error, then three-dimensionally combined them with the depths surrounding the station using the interpolating method of Smith and Wessel (1990) . Thereafter, we again performed waveform modeling. We repeated this successive procedure until the synthetic waveforms fit the observed waveforms well with regard to their specific phases and travel times and the sum of squares of the residual of their waveforms shows the smallest value. In our refined model, the common proportionality constants r 1 and r 2 , which were 0.19 and 0.47 ( Kagawa et al., 2004 ) in the initial model, were revised to 0.08 and 0.39, respectively.

Figure 1 shows the resultant S -wave velocity structures beneath the stations and waveform comparisons for the aftershock at 16:19 on February 2, 1995. The observed waveforms were compared with the synthetic waveforms computed with the initial and refined models.

The S -wave velocity structure beneath each station in the initial model (blue lines) and refined model (red lines), and comparison of transverse components for the aftershock at 16:19 on February 2, 1995, with the observed velocity waveforms (black traces), the synthetic waveforms calculated from the initial model (blue traces), and the refined model (red traces). The stations (red triangles) and three aftershocks (yellow stars) used in the refinement are plotted in the uppermost and leftmost map. The number above the traces is the maximum amplitude of the observed velocity waveform in cm s −1 . The S -wave velocity structure and waveforms at station KBU are not shown because the initial model of KBU is good enough and therefore was not refined.

3. Source Inversion

We performed a joint inversion of strong motions, static displacements and teleseismic body waves. For this inversion, we used the method of Yoshida et al. (1996) which is based on the formulation of multiple time windows. The inversion was stabilized by imposing a smoothness constraint with a discrete Laplacian in space and time, whose weight is determined by minimizing Akaike’s Bayesian Information Criterion ( Akaike, 1980 ). We assumed two fault planes as shown in Fig. 2 . One fault plane is in Awaji Island and the other is in the Kobe area. The strike and dip of the former were set as 43° and 75°, and those of the latter as 52° and 95°. The two fault planes were divided into 4 × 4 km 2 subfaults. The slip vector on each subfault was represented by a linear combination of two components in the direction of 180° ± 45°, and the time history of each component was represented by three ramp functions with a rise time of 1 s.

Stations with (A) strong motions, (B) strong motions where 3D Green’s functions were calculated, (C) teleseismic body waves, and static displacements where (D) half-space and (E) 3D Green’s functions were calculated. The epicenter is marked by a yellow star. Red triangles in (A), (B) and (C) represent the stations. In (D) and (E), the stations that measure horizontal displacements (red), vertical displacements along a leveling route (blue) and three component displacements (black) are plotted. (B) also shows the two fault planes (blue rectangles).

We introduced a positivity constraint to confine the slip angles within 180° ± 45°.

The fault model described above is the same as that of Yoshida et al. (1996) . However, they used only 1D stratified layer velocity structure models to calculate Green’s functions for strong motions and teleseismic body waves, and only a half-space velocity structure model for static displacements. In our source inversion, we used a 3D velocity structure model refined as mentioned in the previous section for the strong-motion and geodetic data for the Osaka basin. We used the same Green’s functions as Yoshida et al. (1996) for the data outside the Osaka basin. The datasets shown in Fig. 2 and their time duration, weights, filtering and sampling were also the same as those in Yoshida et al. (1996) . We note that the sensor orientation of the borehole seismometer installed at the strong-motion station KPI has been corrected.

For calculating 3D Green’s functions, we again used the FEM with voxel meshes. We then derived geodetic Green’s functions by averaging calculated ground motions from about 50 s to 70 s.

4. Conclusions and Discussion

In this section, we summarize and discuss our results mainly by comparing them with those of Yoshida et al. (1996) .

In our source model, the rupture velocity for the first time window was set as 3.1 km/s, which is roughly equivalent to 90% of the S -wave velocity at the depth of the hypocenter and provides the best fit to the observed data. This rupture velocity is higher than that of Yoshida et al. (1996) , which was 2.5 km/s. Unlike in the case of their model, the sedimentary layers are included in our 3D velocity structure model. Since these additional sedimentary layers delay the travel times of 3D strong-motion Green’s functions at stations inside the 3D basin structure, such as KOB and OSA (see Fig. 3(A) ), the rupture velocity was set to be higher than that of Yoshida et al. (1996) to compensate for these delays in the travel times. The rupture velocity in our model is also comparable with that of Horikawa et al. (1996) , or Sekiguchi et al. (2000) , both of whose velocity structure models include sedimentary layers.

Comparisons of the 1D (blue lines) and 3D (red lines) strong-motion Green’s functions with rake angles of 225° for (A) the NS components from a subfault in the deeper part of the Kobe fault, and (B) the EW components from a subfault in the shallow part along the Nojima fault. The filtering and sampling for these Green’s functions are the same as those in Yoshida et al. (1996) . The maximum amplitude is given in cm s −1 below each trace. We can see in (A) that the 3D Green’s function at station KOB has a notably larger amplitude than the 1D Green’s function.

Figure 4 illustrates the slip distribution and the snapshots of rupture during each of the 1-s time windows. The total seismic moment was estimated to be about 2.1 × 10 19 N m ( M w = 6.8), which is slightly smaller than those of the joint inversions performed by Wald (1996) and Yoshida et al. (1996) . In the slip distribution, a very large slip, with a maximum value of 2.9 m near the hypocenter, is found beneath the Akashi Strait. We can also see two other zones of large slip: one along the Nojima fault in the northern, shallow part of Awaji Island, and the other in the deeper part of the Kobe fault. Although this slip pattern consisting of these large-slip zones is similar to that mentioned by Yoshida et al. (1996) , the amount of slip in each zone is different. For example, the maximum slip along the Nojima fault in our model is less than that reported by Yoshida et al. (1996) by about 0.3 m. This may be attributed to the sedimentary layers we introduced. These layers caused a larger amplification of the main phases in the 3D strong-motion Green’s functions, as shown in Fig. 3(B) , and resulted in smaller slips than those in Yoshida et al. (1996) being recovered to match the observed data at station AWA. On the other hand, the slip zone beneath the Akashi Strait looks more compact but has greater slips than that in Yoshida et al. (1996) . These slips, whose vertical components of geodetic Green’s functions indicate uplift (see Fig. 5(B) ), occurred to explain the considerable uplifts observed at the stations near the Akashi Strait. In addition, the larger-slip zone under the city center of Kobe spreads through a wider area than Wald (1996) and Yoshida et al. (1996) indicated, and this zone probably contributed to the violent ground motions and extreme disaster in the city.

(A) Final slip distribution obtained by the inversion, and (B) the growth of rupture depicted by the snapshots of slip distribution at every 1 s. Arrows and a white star in (A) denote subfault slips on the hanging walls and the hypocenter, respectively.

Comparisons of the 3D (red arrows) and half-space (blue arrows) geodetic Green’s functions. (A) The horizontal displacements of the rake angle of 135° for a subfault in the shallow part along the Nojima fault, and (B) the vertical displacements of the rake angle of 225° for a subfault beneath the Akashi Strait, are shown. The yellow star represents the epicenter.

Figure 4(B) shows two rupture propagations in our source model. One propagated toward the shallow part along the Nojima fault, and the other propagated from the deeper part beneath the Akashi Strait to the zone beneath the city center of Kobe. This figure also shows a large slip around the hypocenter in the early stage, and this consequently contributes to the large final slip in this area.

The use of 3D strong-motion Green’s functions in our inversion resulted in a better fit to the observed strong motions. For the strong motions, the variance reduction, which measures the degree of agreement between the observed and synthetic data, was nearly 10% higher than that in Yoshida et al. (1996) . Figure 6 shows that the synthetic waveforms are generally consistent with the observations at the stations KOB, KPI and OSA in the Osaka basin. For the static displacements, we have confirmed that the degree of fit to the observed data was almost equivalent to that of Yoshida et al. (1996) . This indicates that static displacements are less sensitive to a 3D velocity structure than strong motions. Figure 5 shows the comparison of 3D and half-space geodetic Green’s functions, where only small differences can be seen.

Comparisons of the observed (black) and synthetic waveforms from inversion using 1D (blue) or 3D (red) Green’s functions at stations KOB, KPI and OSA. The maximum amplitude is given in cm s −1 above each trace. Because the sensor orientation of the borehole seismometer at station KPI has been corrected, the maximum amplitudes for the EW and NS components are different between the blue and red traces.

Afnimar, K. Koketsu, and K. Nakagawa, Joint inversion of refraction and gravity data for the three-dimensional topography of a sediment-basement interface, Geophys. J. Int. , 151 , 243–254, 2002.

Article   Google Scholar  

Akaike, H., Likelihood and the Bayes Procedure, 143–166, Univ. Press, Valencia, Spain, 1980.

Google Scholar  

Furumura, T. and K. Koketsu, Specific distribution of ground motion during the 1995 Kobe earthquake and its generation mechanism, Geophys. Res. Lett. , 25 , 785–788, 1998.

Graves, R. W. and D. J. Wald, Resolution analysis of finite fault source inversion using one- and three-dimensional Green’s functions, 1. Strong motions, J. Geophys. Res. , 106 , 8745–8766, 2001.

Hashimoto, M., T. Sagiya, H. Tsuji, Y. Hatanaka, and T. Tada, Co-seismic displacement of the 1995 Hyogo-ken Nanbu Earthquake, J. Phys. Earth , 44 , 255–279, 1996.

Horikawa, H., K. Hirahara, Y. Umeda, M. Hashimoto, and F. Kusano, Simultaneous inversion of geodetic and strong-motion data for the source process of the Hyogo-ken Nanbu, Japan, Earthquake, J. Phys. Earth , 44 , 455–471, 1996.

Ide, S., M. Takeo, and Y. Yoshida, Source model of the 1995 Hyogo-ken Nanbu Earthquake determined by near-field strong-motion records, J. Phys. Earth , 44 , 649–653, 1996.

Ikegami, Y., K. Koketsu, T. Kimura, and H. Miyake, Finite-element simulations of long-period ground motions: Japanese subduction-zone earthquakes and the 1906 San Francisco Earthquake, J. Seismol. , 12 , 161–172, 2008.

Iwata, T., K. Hatayama, H. Kawase, and K. Irikura, Site amplification of ground motions during aftershocks of the 1995 Hyogo-ken Nanbu Earthquake in severely damaged zone—array observation of ground motions at Higashinada ward, Kobe city, Japan, J. Phys. Earth , 44 , 553–561, 1996.

Kagawa, T., B. Zhao, K. Miyakoshi, and K. Irikura, Modeling of 3D basin structures for seismic wave simulations based on available information on the target area: Case study of the Osaka Basin, Japan, Bull. Seismol. Soc. Am. , 94 , 1353–1368, 2004.

Katao, H., N. Maeda, Y. Hiramatsu, Y. Iio, and S. Nakao, Detailed mapping of focal mechanisms in/around the 1995 Hyogo-ken Nanbu Earthquake rupture zone, J. Phys. Earth , 45 , 105–119, 1997.

Kawase, H., The cause of the damage belt in Kobe: “The basin-edge effect,” Constructive interference of the direct S-wave with the basin-induced diffracted/Rayleigh waves, Seismol. Res. Lett. , 67 , 25–34, 1996.

Kikuchi, M. and H. Kanamori, Rupture process of the Kobe, Japan, Earthquake of Jan. 17, 1995, determined from teleseismic body waves, J. Phys. Earth , 44 , 429–436, 1996.

Koketsu, K., H. Fujiwara, and Y. Ikegami, Finite-element simulation of seismic ground motion with a voxel mesh, Pure Appl. Geophys. , 161 , 2183–2198, 2004.

Nagano, M., K. Kudo, and M. Takemura, Simulation analyses of the array strong motion records from the aftershocks of the 1995 Hyogo-ken Nanbu Earthquake ( M j = 7.2) regarding the irregularity of the underground structure in Nagata ward, Kobe, J. Seismol. Soc. Jpn. , 52 , 25–41, 1999.

Sekiguchi, H., K. Irikura, and T. Iwata, Fault geometry at the rupture termination of the 1995 Hyogo-ken Nanbu Earthquake, Bull. Seismol. Soc. Am. , 90 , 117–133, 2000.

Smith, W. H. F. and P. Wessel, Gridding with continuous curvature splines in tension, Geophysics , 55 , 293–305, 1990.

Wald, D. J., Slip history of the 1995 Kobe, Japan, Earthquake determined from strong motion, teleseismic, and geodetic data, J. Phys. Earth , 44 , 489–503, 1996.

Wald, D. J. and R. W. Graves, Resolution analysis of finite fault source inversion using one-and three-dimensional Green’s functions, 2. Combining seismic and geodetic data, J. Geophys. Res. , 106 , 8767–8788, 2001.

Yoshida, S., K. Koketsu, B. Shibazaki, T. Sagiya, T. Kato, and Y. Yoshida, Joint inversion of near- and far-field waveforms and geodetic data for the rupture process of the 1995 Kobe Earthquake, J. Phys. Earth , 44 , 437–454, 1996.

Download references

Acknowledgments

We thank CEORKA, GSI, JMA, JUNCO, the Kobe city government, Prof. Iwata (Kyoto Univ.), and Prof. Nagano (Tokyo Science Univ.) for providing data. We used GMT for drawing the figures. We also thank two reviewers Atsushi Nozu and Haruko Sekiguchi, and the Editor Tatsuhiko Hara for helpful comments.

Author information

Authors and affiliations.

Earthquake Research Institute, University of Tokyo, Bunkyo-ku, Tokyo, 1-1-1 Yayoi, 113-0032, Japan

Yujia Guo & Kazuki Koketsu

OYO RMS Corporation, Minato-ku, Tokyo, 4-9-9, Akasaka, 107-0052, Japan

Taichi Ohno

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Yujia Guo .

Rights and permissions

Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Guo, Y., Koketsu, K. & Ohno, T. Analysis of the rupture process of the 1995 Kobe earthquake using a 3D velocity structure. Earth Planet Sp 65 , 1581–1586 (2013). https://doi.org/10.5047/eps.2013.07.006

Download citation

Received : 11 March 2013

Revised : 16 July 2013

Accepted : 19 July 2013

Published : 06 December 2013

Issue Date : December 2013

DOI : https://doi.org/10.5047/eps.2013.07.006

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Kobe earthquake
• rupture process
• 3D Green’s functions

The power of people: social capital’s role in recovery from the 1995 Kobe earthquake

• Original Paper
• Published: 18 August 2010
• Volume 56 , pages 595–611, ( 2011 )

Cite this article

• Daniel P. Aldrich 1  

5711 Accesses

239 Citations

16 Altmetric

Explore all metrics

Despite the regularity of disasters, social science has only begun to generate replicable knowledge about the factors which facilitate post-crisis recovery. Building on the broad variation in recovery rates within disaster-affected cities, I investigate the ability of Kobe’s nine wards to repopulate after the 1995 Kobe earthquake in Japan. This article uses case studies of neighborhoods in Kobe alongside new time-series, cross-sectional data set to test five variables thought to influence recovery along with the relatively untested factor of social capital. Controlling for damage, population density, economic conditions, inequality and other variables thought important in past research, social capital proves to be the strongest and most robust predictor of population recovery after catastrophe. This has important implications both for public policies focused on reconstruction and for social science more generally.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Natural Hazards, Disasters, and Demographic Change: The Case of Severe Tornadoes in the United States, 1980–2010

Neighborhood Social Capital and Crime

Sustainable Urban Living and Social Capital: Some Evidence from Crisis-Hit Greece

The data used in this study are available for replication on the author’s DataVerse Network homepage.

The term NPO includes groups classified by the Japanese government as nonprofit public-interest entities ( kōeki hōjin ) along with school, religious, medical, and social welfare organizations. Pekkanen points out that in Japan, the borrowed term “NGO” typically refers to organizations involved in international work, while “NPO” involves domestically active groups (2000: 116 fn 12).

Tierney and Goltz ( 1997 : 6) suggested that the lack of an official request from the governor, devastation of communications networks, logistical problems, the size of the event, and ambivalent attitudes toward the role of military in society were responsible for the SDF’s sluggish reaction (see also Yasui 2007 : 97).

According to Tsuji ( 2001 : 218, Figures 9–1 and 9–2) the Nagata district had 35% of its population in emergency housing after the earthquake, and it took a full year for them to return to permanent housing. In a similar-sized district of Fukushima, 70% of its population moved into emergency shelters but all had moved out within 100 days.

As autoregressive distributed lag models often create high levels of multicollinearity, I tested the data for multicollinearity using the Variance Inflation Factor (VIF) approach, and found relatively low levels of interaction among the variables (with a VIF of less than 7 for all models) (Rabe-Hesketh and Everitt 2007 : 69). A second concern for ADLs is that variables within them remain strictly stationary. A multivariate augmented Dickey-Fuller test indicated that we can reject the null hypothesis that the processes for 1, 2, and 3 period lags are nonstationary (that is, lack equilibrium). Further, the Hausman specification test determines whether a fixed or random effects model is most appropriate when using time-series data, and it indicated in this case that a fixed effects model fit more closely (Chi square value of 0.0001).

Ahern J, Galea S (2006) Social context and depression after a disaster: the role of income inequality. J Epidemiol Community Health 60(9):766–770

Article   Google Scholar  

Albala-Bertrand JM (1993) Political economy of large natural disasters: with special reference to developing countries. Oxford, Clarendon Press

Google Scholar  

Aldrich DP (2008) Site fights: divisive facilities and civil society in Japan and the west. Cornell University Press, Ithaca and London

Aldrich DP (2010) Fixing recovery: social capital in post-crisis resilience. J Homel Secur 7 (July)

Aldrich DP, Crook K (2008) Strong civil society as a double-edged sword: siting trailers in post-katrina New Orleans. Polit Res Q 61(3):378–389

Beck N, Katz J (2004) Time-series cross-section issues: dynamics 2004. Working paper

Beck N, Katz J (2009) Modeling dynamics in time-series cross-section political economy data. Working paper

Beggs J, Haines V, Hurlbert J (1996) Situational contingencies surrounding the receipt of informal support. Soc Forces 75(1):201–222

Birkland T (2006) Lessons of disaster: policy change after catastrophic events. Georgetown University Press, Washington DC

Birner R, Wittmer H (2003) Using social capital to create political capital: how do local communities gain political influence? A theoretical approach and empirical evidence from Thailand. In: Dolsak N, Ostrom E (eds) The Commons in the New Millenium: challenges and adaptations. MIT Press, Cambridge, pp 291–334

Buckland J, Rahman M (1999) Community based disaster management during the 1997 Red River Flood in Canada. Disasters 23(2):174–191

Kobe City NPO Database http://www.kobenpomap.com/photo/hojin/120080129130902.xls

Cutter S, Emrich C (2006) Moral Hazard, Social catastrophe: the changing face of vulnerability along the hurricane coasts. Annal Am Acad Polit Soc Sci 604:102–112

Cutter S, Finch C (2008) Temporal and spatial changes in social vulnerability to natural hazards. Proc Nat Acad Sci 105(7):2301–2306

Dacy D, Kunreuther H (1969) The economics of natural disasters: implications for federal policy. The Free Press, New York

Davis DR, Weinstein DE (2002) Bones, bombs, and break points: the geography of economic activity. Am Econ Rev 92(5):1269–1289

Dynes R (2005) Community social capital as the primary basis of resilience. University of Delaware Disaster Research Center Preliminary Paper #344

Evans N (2001) Community planning in Japan: the case of Mano, and its experience in the Hanshin earthquake. Unpublished Ph.D. dissertation for School of East Asian Studies, University of Sheffield

Haas P (2004) When does power listen to truth? A constructivist approach to the policy process. J Eur Public Policy 11(4):569–592

Haas J, Eugene K, Robert W, Bowden MJ (eds) (1977) Reconstruction following disaster. MIT University Press, Cambridge

Hagiwara T, Jinushi T (2005) Hanshin Awaji Daishinsai no Higai to Fukkō–Chiikiteki Bunseki–[Damage and reconstruction of the Hanshin–Awaji earthquake: a spatial analysis]. Paper prepared for the 10th anniversary of Hanshin-Awaji earthquake memorial academic symposium, Kobe University

Haque CE (2003) Perspectives of natural disasters in East and South Asia, and the Pacific States. Nat Hazards 49:465–483

Hattori K (2003) Reconstruction of disaster and recreation of local heritage. From international symposium on Asian heritage, 22 August to 10 September, Malaysia

Hirschman A (1970) Exit, voice, and loyalty: responses to decline in firms, organizations, and states. Harvard University Press, Cambridge

Horne J (2005) Carving a better city. New Orleans Times-Picayune, 13 Nov

Horwich G (2000) Economic lessons of the Kobe earthquake. Econ Dev Cult Change 48(3):521–542

Hoyois P, Below R, Scheuren J-M, Guha-Sapir D (2007) Annual disaster review: numbers and trends. Centre for Research on the Epidemiology of Disasters

Ikeda H (2004) Comparative study of housing recovery measures taken after the 1999 Kocaeli earthquake and the 1995 Hanshin-Awaji earthquakes. In: Nakabayshi I (ed) A comparative study of urban reconstruction processes after earthquakes in Turkey, Taiwan, and Japan. Graduate School of Urban Science, Tokyo Metropolitan University, Tokyo, pp 33–38

Inui K (1998) Sanka ni okeru Goui/kettei to wa nanika [What is an agreement/decision in participation?], in Hanshin-Awaji Daishinsai Fukkō ni kansuru Tokubetu Chōsa Kenkyū Purojekuto (The Great Hanshin-Awaji Earthquake Reconstruction Reseach Project) (ed) Hanshin - Awaji Daishinsai Sōgo Kenkyū Ronbunshu [the Great Hanshin-Awaji Earthquake Comprehensive Study Report]. Kyoto, Japan, Ritsumeikan University, pp 243–249

Kamel N, Loukaitou-Sideris A (2004) Residential assistance and recovery following the Northridge earthquake. Urban Stud 41(3):533–562

Keele L, Kelly N (2005) Dynamic models for dynamic theories: the ins and outs of lagged dependent variables. Polit Anal 14(2):186–205

Kōbe shi Sōmukyoku Tōkeika [Kobe City General Affairs Bureau, Statistics Department] (various years). Kōbe shi Tōkeisho 1990–2005 [Kobe City Statistics]. Kobe, Kobe shi

Kōbe shi Senkyo Kanri Iinkai [Kobe City Election Committee]. Kakushu Senkyo no Tōhyō Ritsu: Kekka Nado [The Rates and Results of Various Elections]. Available online at http://www.city.kobe.jp/cityoffice/60/06.html

Krishna A (2007) How does social capital grow? A seven-year study of villages in India. J Polit 69(4):941–956

Lin N (2008) A network theory of social capital. In: Castiglione D, Van Deth JW, Wolleb G (eds) The handbook of social capital. Oxford University Press, New York, pp 50–69

Liu A, Fellowes M, Mabanta M (2006) A one-year review of key indicators of recovery in post-storm New Orleans. Brookings Institution

Murosaki Y (2007) The great Hanshin earthquake and fire. J Disaster Res 2(4):298–302

Nakabayashi I, Ichiko T (2004) A comparative study of built up reconstruction measures and strategy among earthquake disasters of Hanshin Awaji, East Marmar, and 921 Chi-chi. In: Itsuki N (ed) A comparative study of urban reconstruction processes after earthquakes in Turkey, Taiwan, and Japan. Graduate School of Urban Science, Tokyo Metropolitan University, Tokyo, pp 5–14

Nakagawa Y, Shaw R (2004) Social capital: a missing link to disaster recovery. Int J Mass Emerg Disasters 22(1):5–34

Nossiter A, Eaton L (2007) Violent protest over housing in New Orleans, New York Times 21 Dec

Olshansky R (Forthcoming) Reconstruction after the Kobe earthquake. In Olshansky, Johnson, Topping (eds) Reconstruction in comparative perspective. University of Illinois

Olson M (1965) The logic of collective action; public goods and the theory of groups. Harvard University Press, Cambridge

Pais J, Elliott JR (2008) Places as recovery machines: vulnerability and neighborhood change after major hurricanes. Soc Forces 86(4):1415–1453

Pekkanen R (2000) Japan’s new politics: The case of the NPO law. J Japanese Stud 26(1):111–146

Putnam R (2000) Bowling alone: the collapse and revival of American community. Simon & Schuster, New York

Rabe-Hesketh S, Everitt B (2007) A handbook of statistical analyses using stata, 4th edn. Chapman and Hall, New York

Rovai E (1994) The social geography of disaster recovery: differential community responses to the north coast earthquakes. Assoc Pac Coast Geogr Yearbook 56:49–74

Sawada Y, Shimizutani S (2008) How do people cope with natural disasters? Evidence from the Great Hanshin-Awaji (Kobe) earthquake in 1995. J Money Credit Bank 40(2–3):463–488

Sekikawa C, Sakurai M, Song M (2006) Kōreisha Shūgō Jutaku ni kansuru Kenkyū–Korekuteibu Haujingu no jirei wo Tōshite [Collective Housing for Seniors: An analysis of examples of such housing]. Kyōto Kyōiku Daigaku Kiyō [Bulletin of Kyoto University of Education], no. 109, pp 85–98

Shaw R, Goda K (2004) From disaster to sustainable civil society: the Kobe experience. Disasters 28(1):16–40

Sinclair B, Hall T, Alvarez RM (2008) Flooding the vote: Hurricane Katrina and voter participation in New Orleans. Voting technology project of caltech working paper #70

Sorensen A (2007) Changing governance of shared spaces: Machizukuri as institutional innovation. In: Sorensen A, Funck C (eds) Living cities in Japan: citizens’ movements, Machizukuri and local environments. Routledge, London, pp 56–90

Stallings R, Quarantelli EL (1985) Emergent citizen groups and emergency management. Public Adm Rev 45:93–100

Suga M (2007) Atarashii komyuniti no keisei to tenkai [Forming and developing a new community] in Masaki Urano, Jun Oyane, and Tadahiro Yoshikawa eds., Fukkō komyuniti ron Nyūmon [Introduction to Theories on the Reconstruction of Communities]. Kōbundō Publishers, Tokyo, pp 98–100

Tajika E (2000) Public support for livelihood rebuilding. In: Committee for global assessment of earthquake countermeasures, Hyogo Prefectural Government (ed) Assessment reports of the global assessment of earthquake countermeasures, vol 4, Support of disaster victims. Hyogo Prefecture, Hyogo Prefectural Government, pp 119–168

Tandon R, Mohanty R (2000) Civil society and governance: a research study in India. Society for Participatory Research in Asia, New Delhi

Tatsuki S (2008) The role of civil society for long-term life recovery from a Megadisaster. Paper prepared for the 2008 annual meeting of the American political science association, 28–31 Aug

Tatsuki S, Hayashi H (2002) Seven critical element model of life recovery: general linear model analyses of the 2001 Kobe Panel Survey Data. Paper prepared for the proceedings of 2nd workshop for comparative study on urban earthquake disaster management, 14–15 Feb

Tierney K, Goltz J (1997) Emergency response: lessons learned from the Kobe earthquake. University of Delaware Disaster Research Center Preliminary Paper #260

Tobin G, Montz B (1997) Natural hazards: explanation and integration. The Guilford Press, New York

Tsuji K (2001) Saigai Katei to Saisei Katei: Hanshin-Awaji Daishinsai no Shōjojishi [the course of disaster and recovery: a short epic about the Hanshin Awaji earthquake]. Kōyō Shobō Publishers, Kyoto

United States Small Business Administration [US SBA] (2006) Entrepreneurship: the foundation for economic renewal in the Gulf coast region. In: Proceedings of the 11 April conference, New Orleans, LA

Van Deth JW (2008) Measuring social capital. In: Castiglione D, Jan van Deth W, Guglielmo W (eds) The handbook of social capital. Oxford University Press, New York, pp 150–176

Warner C (2006) Census tallies Katrina changes, nut the changing New Orleans area is a moving target. Times-Picayune Wednesday, 7 June

West M (2005) Law in everyday Japan: sex, sumo, suicide, and statutes. University of Chicago Press, Chicago

Yasui E (2007) Community vulnerability and capacity in post disaster recover: the cases of Mano and Mikura neighborhoods in the Wake of the 1995 Kobe earthquake. Unpublished Ph.D. dissertation for the University of British Columbia

Download references

Acknowledgments

The author wishes to acknowledge Takahiro Yamamoto for his excellent research assistance. The archival and theoretic work for this paper was carried out while the author was on an Abe Fellowship sponsored by the Center for Global Partnership and administered by the Social Science Research Council. Pat Boling, Erik Cleven, Paul Danyi, Jay McCann, Leigh Raymond, Mark Tilton, and Laurel Weldon provided helpful feedback.

Author information

Authors and affiliations.

Department of Political Science, Purdue University, 100 N. University Street, West Lafayette, IN, 47907, USA

Daniel P. Aldrich

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Daniel P. Aldrich .

Rights and permissions

Reprints and permissions

About this article

Aldrich, D.P. The power of people: social capital’s role in recovery from the 1995 Kobe earthquake. Nat Hazards 56 , 595–611 (2011). https://doi.org/10.1007/s11069-010-9577-7

Download citation

Received : 20 April 2010

Accepted : 24 June 2010

Published : 18 August 2010

Issue Date : March 2011

DOI : https://doi.org/10.1007/s11069-010-9577-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Kobe earthquake
• Population recovery
• Social capital
• Find a journal
• Publish with us
• Track your research