man-made disasters: a cross-national analysis
TRANSCRIPT
International Business Review 20 (2011) 466–476
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International Business Review
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Man-made disasters: A cross-national analysis
Hoon Park *
Department of Finance, College of Business Administration, University of Central Florida, P.O. Box 161400, Orlando, FL 32816-1400, United States
A R T I C L E I N F O
Article history:
Received 6 December 2009
Received in revised form 26 August 2010
Accepted 31 August 2010
Keywords:
Inverted u-curve
Man-made disaster
National culture
Safety performance
Safety Kuznet curve
A B S T R A C T
This research investigates the impact of national culture and several institutional factors
on the safety performance of society and establishes statistically significant relationships
between those variables. As expected, the research results reveal that some cultural
variables such as uncertainty avoidance, gender orientation and institutional variables
such as the degree of law avoidance can directly influence the safety performance of the
society. The findings also support the inverted u-curve (Safety Kuznet curve) hypothesis
indicating even if we expect a negative trend at the beginning stage of industrialization, we
can expect a positive trend in safety performance as their income level continues to
improve beyond a certain point.
� 2010 Elsevier Ltd. All rights reserved.
1. Introduction
When we have a catastrophic accident, two major sources can be attributed as a cause for the accident. The first sourcecontains totally exogenous factors such as bad weather, hurricanes, tsunamis or other types of forces which are beyond ourcontrol. The second source includes human factors such as poor judgment, poor working condition, poor maintenance ofequipment and/or the negligence of the operators which will lead to a so-called ‘‘man-made disaster’’. Recent sobering man-made disasters include the coal mining accident in Xinxing, China (November 23, 2009), which resulted in the death of 104miners due to gas explosion and overcrowded working condition underground, and another the coal mine blast in Shanxi,China (February 21, 2009) that resulted in 74 fatalities. The recent guilty verdict for one of the worst industrial disasters,Bhopal accident in India (December 3, 1984), is a solemn warning for a catastrophic man-made disaster. The U.S. NationalTransportation Safety Board’s (NTSB) data also corroborates the fact that human error is the root cause of the many majoraccidents in the aviation industry. In the summary of its annual Sigma report about disasters that occurred in 2002, the Swissmultinational reinsurance giant Swiss Re revealed that while the financial and human losses caused by exogenous factors,such as weather, exceeded those of man-made disasters attributed to human factors, the latter were still significant;furthermore, the number of man-made occurrences was greater than that of natural disasters (Zanetti, Enz, Menzinger,Mehlhorn, & Suter, 2003). Unfortunately, however, most of the existing predictive models failed to identify the root causes ofthese human errors.
Several researchers (Helmreich, Merritt, & Sherman, 1996; Koval and Flyod, 1998; Meshkati, 1996) imply that one of themajor aspects of society that can possibly influence the amount of human errors in man-made disaster is national culturethrough its impact on the organizational culture and individual behaviors. However, most of the discussions on impact ofnational culture on safety records have been tangential or anecdotal at best in most of the previous studies. Considering thepotentially significant impact of national culture on the overall safety performance of the nations, it is very surprising to find
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doi:10.1016/j.ibusrev.2010.08.004
H. Park / International Business Review 20 (2011) 466–476 467
a paucity of the empirical research that directly focuses on the relationship between these two variables. As a consequence,the profession offers no credible insights to understand the root cause of human errors particularly in the records of man-made disasters. Consequently, multinational enterprises (MNEs) and insurance industry continuously suffer a great deal ofhuman and financial losses in their global operation. Thus, the primary focuses of this research is to have a betterunderstanding by establishing a statistical relationship between a set of societal variables including national culture andsafety performance of the society.
2. Background research
National culture
As Smith, Dugan and Trompenaars (1996) argue, identification of reliable dimensions of cultural variation should help createa framework that is capable both of integrating diverse attitudinal and behavioral empirical phenomena and of providing a basisfor hypothesis generation. To this end, researchers have worked diligently to identify major cultural dimensions. There havebeen five pioneering research projects undertaken to identify cultural dimensions of value; see Hofstede (1980, 1983, 1991),Schwartz and Bardi (2001), Smith et al. (1996), Inglehart, Basanez, Diez_Medrano, Halman, and Luijkx (2004), Inglehart et al.(1998), Leung and Bond (2004) and GLOBE project initiated by House, Hanges, Javidan, Dofman, and Gupta (2004).
Despite numerous controversies surrounding the validity of the construction, reliability of the measure, generalization ofthe findings, and robustness of the dimensions identified (Erez & Earley, 1993; Javidan et al., 2006; Schwartz, 1994; Smith &Schwartz, 1997), perhaps one of the most important classic studies that provided a profound impact on the cross-culturalresearch is Hofestede’s work (1980) primarily because of its large sample size and the robustness of the dimensionsidentified.
Smith et al. (1996) and Schwartz and Bardi’s (2001) work also offers a richer reflection of a national core culture at atheoretical level. However, due to a limited sample size of 43 countries, when cultural data is combined with other data, suchas socio-economic ones, actual numbers of usable samples in the population are quickly reduced to the extent that itpresents a statistical problem for lack of enough degree of freedom and thereby limits its utility significantly. It also presentsa serious generalization problem for research findings in cross-cultural settings.
Inglehart et al.’s (2004) World Value Survey project conducted extensive studies in 81 countries and provided valuablecross-cultural data about several key elements of their cultures such as life in general, environment, work, family, religionand national identify. However, the project focused more on people’s perception and their ‘‘value’’ rather than focusing onhow they are actually behaving and did not provide any relevant information about the cultural dimensions that we need forthis research.
Leung and Bond (2004) identified a five factor structure through their social axiom survey on items extracted from thepsychological literature and factor analysis of the data collected from five cultures. They have confirmed the robustness ofthis structure in over 40 cultural groups. However, a culture-level factor analysis based on 41 cultural groups has yieldedonly two factors—dynamic externality and societal cynicism, which also limited the utility of the dimensions in extensivecross-cultural research.
Recently, in search of a better understanding of national culture in conjunction with the leadership behavior, House andhis associates (House et al., 2004) conducted a massive research study across 62 countries under the name of GLOBE project.They have identified nine cultural dimensions which were actually prior dimensions formulated primarily based uponHofstede’s dimensions, different value system developed by Kluckhohn and Strodtbeck (1961) and the interpersonalcommunication literature of Sarros and Woodman (1993). Even if Hofstede presented some concerns particularly about theoperationalization of ‘‘practice’’ versus ‘‘value’’ in his critique of the GLOBE project (House et al., 2004), one of the greatestadvantages of their research design is to distinguish cultural practices (‘‘as is’’) in a society from cultural value (‘‘should be’’),which enhances the construct validity of each cultural dimension. Another advantage of using their dimensions is the factthat they unravel multidimensional aspects of the previously identified cultural dimensions by refining them into additionaldimensions. For example, masculine versus feminine dimension, previously identified, is further refined into genderegalitarianism and assertiveness dimension. Individualism versus collectivism was further refined into societal andorganizational level one. This further refinement of the previous dimensions significantly reduces the amount of confusion intheir constructions, and helps us make a better interpretation by enhancing the construct validity of each dimension whenthey are used in cross-cultural research.
For current research, we have special interest in four dimensions identified by the GLOBE study in conjunction with themajor topic that we want to investigate. Those are uncertainty avoidance, performance orientation, individualism-collectivism and gender egalitarianism. Thus, we propose a series of following hypotheses for those four cultural dimensionsto investigate the impact of national culture on the safety performance of the countries.
3. Cultural variables
3.1. Uncertainty avoidance
This dimension is considered to represent the amount of anxiety/stress and the extent to which a society tends toconsider itself threatened by risk, uncertainty and ambiguous situations. A High Uncertainty avoidance ranking indicates the
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country has a low tolerance for uncertainty and ambiguity. GLOBE project (House et al., 2004) defines this cultural dimensionas the extent to which members of an organization or society strive to avoid uncertainty by relying on established socialnorms, rituals, and bureaucratic practices. In general, Uncertainty avoidance reflects the extent to which ambiguoussituations are threatening to the people in the society, to which rules and orders are preferred and to which ambiguity istolerated. People in high-risk avoidance cultures will take any action to reduce uncertainty. This ultimate search for a meansto reduce anxiety and tension puts pressure on people to develop better safety training systems and equipment. The peoplein this type of culture will have a greater level of uncertainty and anxiety over their security and safety in their lives. Theyhave a stronger inner urge to search for the ultimate instruments such as clear rules and instructions, which will reduce theirperceived anxiety and uncertainty over their security and safety. They tend to have more fear of safety failures than thepeople in low uncertainty avoidance culture, and thereby constantly work hard trying to develop better and clear rules andto abide by those rules in order to maximize their sense of security.
High uncertainty avoidance, as expressed by the penchant for advanced planning to void the uncertainty in the future,appears to facilitate safety climate and to implement the safety policy. High uncertainty avoidance culture emphasizes oncontrol. This helps ensure that many intertwined details of safety regulations require coordination among the many differentunits and supervising the violation of the safety rules in the units.
Based on the above reasoning, a following hypothesis is established.
Ho #1. The countries with low uncertainty avoidance Culture are likely to have more man-made accidents than those withHigh uncertainty avoidance culture.
3.2. Individualism-collectivism
There has been extensive research on individualism and collectivism at all levels (societal, organization and individual)for the last several decades in many different academic disciplines such as sociology, anthropology and psychology. Despitesome disagreements regarding the constructs; multidimensional versus uni-dimensional (Hofstede, 1980; Schwartz, 1994)or societal versus organizational, a general consensus is the fact that even if organizational individualism and collectivism isdistinct from societal individualism and collectivism, the two levels are expected to be interrelated (Gelfand et al., 2002). Thegeneral consensus of the previous research in this area points to the fact that members of a collectivist culture frequently optto cooperate, while members of individualistic culture show a marked tendency to avoid cooperation (Choi, 1996; Eby &Dobbins, 1997; Triandis, 1994). This lack of cooperation among the members in individualistic culture can easily jeopardizethe safety of the whole unit. Along this line of thought, a following hypothesis is established:
Ho #2. The countries with individualistic cultures are likely to have more man-made accidents than those with collectivecultures.
3.3. Gender egalitarianism
Gender egalitarianism dimension is a subset of ; Hofstede’s (1980, 1988) masculinity–femininity dimension, which wasfurther refined by as society’s beliefs about whether members’ biological sex should determine the roles that they play intheir society. Societies with greater gender egalitarianism rely less on biological sex to determine the allocation of rolesbetween the sexes and they seek to minimize the differences between the roles of males and females in the societies. Ingeneral, ‘‘Masculine culture’’ or lesser gender egalitarian culture promotes and rewards ‘‘masculine’’ values such asassertiveness, aggressiveness and risk taking behavior. For this reason, Schneider and Gunnarson (1990) suggest that being‘‘macho’’ and taking risk can be one of the values and assumption that could breed an unsafe culture in an organization. Thus,along this line of thought, a following hypothesis is proposed in relation to this cultural dimension:
Ho #3. The countries with masculine culture are likely to have more man-made accidents than those with feminine culture.
3.4. Performance orientation
People in high performance oriented societies tend to view the external world as what they have to challenge or controlwhereas, people in low performance oriented societies tend to view their relationship with the world as subjugation orharmony (Kluckhohn & Strodtbeck, 1961). Thus, it is believed that the adequate amount of high performance orientation inthe society will produce a positive impact on economic development through its impact on goal achievement.
However, too much emphasis on goal achievement in high performance oriented culture can be very detrimental to safetyperformance of the society. The people with high performance orientation have a penchant to demonstrate thirst for betterachievement, and more ambitious goals and expectations. They also tend to value only those individuals and groups thatproduce results and achieve their goals (Trompennars & Hampden-Turner, 1998). Outcome is a lot more important than theprocess and ambition provides drives in this type of culture. More often than not, this type of culture cultivates anenvironment where everybody wants to be successful in fast and big ways. However, in most cases, ‘‘big’’ and ‘‘fast’’
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achievement can only be possible through illegal and abnormal processes. An ambitious man would do anything possible toachieve his big goal in a fast way. This kind of culture put lots of pressure on the people to achieve their goal in fast wayregardless of legitimacy. In order to achieve something faster than others, people often need to cut the corners. People in thistype of country try to cut corners or expedite the wheel of progress by offering ‘‘grease money’’ to bypass the safetyregulations and achieve their goals quickly (Brademas & Heimann, 1998). Along this line of reasoning, a following hypothesisis established.
Ho #4. The countries with higher performance orientation are likely to have more man-made accidents that those withlower performance orientation.
4. Societal variables
4.1. Level of corruption
One of the important societal variables that can also influence the safety records of a country can be the level ofcorruption. As several researchers (Murray-Rust, Hammond, & Vander Velde, 1994; Wade, 1982) pointed out, negotiation forthe safety of the workplace or the quality of workmanship/product as the result of non-compliance in return of publicofficial’s personal gain are also frequently attributed to widespread corruption.
Murphy, Shleifer, and Vishny (1991) argue that when corruption is prevalent, highly educated and talented people will bemore likely to engage in rent seeking than in productive work. More often than not, in many corrupted countries, publicservants often try to find alternative sources of income that may not be legal. Along the line of efficiency-wage mechanisms(Haque & Sahay, 1996), when civil servants are not paid enough to make ends meet, they are forced to use their position andpower to seek rent in exchange for the safety of the public, especially when the expected cost of being caught and fired is low.Unfortunately, the lack of government resources to buy this additional layer-decent salaries for public servants in manycountries, will make the average public official so vulnerable and put a great deal of pressure on them to take offered bribes inreturn of lenient application of the safety rules and regulations. Thus, the level of corruption can significantly jeopardize thesafety performance of the society.
Ho #5. The countries with the higher level of corruption are likely to have more man-made accidents than those with thelower level of corruption.
4.2. Quality of bureaucrats
Another very important societal variable that can influence the level of safety performance is ‘‘the quality andintegrity of bureaucrats’’ that are in charge of developing and implementing safety rules and regulations. If thegovernment officials who are in charge of developing and implementing safety rules and policies do not have thisquality and lose their integrity and cave in to the pressure from the relevant industries, the whole society will suffernegative consequences of these incompetent public servants. As Tanzi (1998) argues, the quality of bureaucracy variesgreatly among countries. In some, public sector jobs lend a great deal of prestige and status. In others, this is much lessthe case. Klimo (1997) indicates that tradition and the effect that it has on the pride that individuals have in working forthe government may explain why some bureaucracies are much more efficient than others. Thus, the followinghypothesis is established.
Ho #6. The countries with the lower bureaucratic quality are likely to have more man-made accidents than those with thehigher bureaucratic quality.
4.3. Rule of law
Another important societal factor that can potentially influence the safety performance of the society is the law abidingtradition and strength of the legal system in a country. The International Atomic Energy Authority (1998; see also Peckitt,Glendon, & Booth, 2004, p. 25) highlighted safety culture as being affected by both respect for law and a personalresponsibility characteristic of national culture, among other factors. Not only can disasters be precipitated by a lack of lawabidance on the part of internal company personnel, but they can also be caused by lawbreakers external to the company. Inthe context of addressing pipeline insurance issues, Radevsky and Scott (2006, pp. 2–3) noted that fires and explosionsresulting from pipeline ruptures can cause ‘‘serious damage and casualties’’, and that such disasters can be caused byillegal, larcenous tapping of pipelines. One can argue that one of the major factors that influence the level of illegal activitysuch as violation of the safety rules is the probability that those who commit these illegal activities would be apprehendedand penalized. The low chance of a safety rule violators being punished or penalized by a weak legal system indirectlyencourages safety violation by creating an environment more propitious for non-abidance. The penalty structure and theseverity of penalty when they are caught can be also important factors in determining the extent of safety violation in thatcountry.
[(Fig._1)TD$FIG]
Fig. 1.
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Thus, the Rule of Law that represents ‘‘the strength of legal system, penalty structure and the citizen’s willingness toaccept the established legal system as a means to implement laws and adjudicate disputes’’ can be directly related to theoverall safety performance in the society. Based on this line of thought, a following hypothesis is established:
Ho #7. The countries with the lesser degree of rule of law are likely to have more man-made accidents than those with thehigher degree of rule of law.
4.4. National income
The level of national income can be another factor that may influence the overall safety performance of the societythrough its affordability of safety equipment and training, awareness and concern for safety problems in the society. Toyaand Skidmore (2007) studied 151 countries to determine the correlation between the impacts of natural, rather than man-made, disasters and a number of causal factors, including national income. They noted that ‘‘for many low-income persons(and by extension nations) the costs of employing precautionary measures may be prohibitive’’ (Toya & Skidmore, 2007, p.6). If precautionary measures concerning natural disasters can be cost-prohibitive, it stands to reason that precautions forman-made disasters might also be cost-prohibitive.
Even if an investigation of the impact of national income on the safety performance is not one of our primary concerns inthis research, we were very curious about the subject. However, the relations between the level of income and theperformance records of the society may not be linear rather inverted u-curve as in the case of the relationship suggested byEnvironmental Kuznet Curve (Grossman & Krueger, 1995; Stern, Common, & Barbier, 1996).1 Fig. 1 contains illustrations of au-curve and inverted u-curve. Kellenberg and Mobarak (2008, p. 789) found a Kuznets inverted u-curve relationship betweennational income and natural disasters such as floods, landslides, and windstorms. As with the Toya and Skidmore (2007)study, Kellenberg and Mobarak (2008) explored natural disasters instead of man-made ones, doing so by studying 133countries. It seems worthwhile to perform a similar investigation that focuses on man-made disasters, for there are a numberof safety-related factors that might be tied to income, which could lead to an inverted u-curve relationship.
At lower levels of economic development, the ability of a nation to afford safety equipment and training may be verymuch limited. However, it is equally true that the machine and equipment they use are also limited to simple rudimentaryagricultural equipment/machineries and accidents/injuries are limited to small numbers and scales. This situation mayreduce the size and scale of accidents. This will level off the negative aspects of low-income on safety performance of thenation through lack of resources. Thus, the overall performance of the developing countries may not be as bad as one maypredict.
However, as a nation’s economic development accelerates with intensified industrialization processes, they pay moreattention to economic growth rather than the quality of life and safety of the workers. In a growth driven economy, they willtake any action to reduce cost, including engaging in illegal activities, such as cutting the corners, when they experiencedifficulty acquiring the resources necessary for growth. The growth driven policy puts pressure on individuals andorganizations to find alternative ways to ensure rapid growth that may not be legal. In many developing countries where
1 Environmental Kuznet Curve is a phenomenon which suggests an inverted u-shaped relationship between income growth and environmental
conditions. That is, environmental conditions, such as air pollution and contamination, seem to worsen initially with increases in income in low-income
countries, but appear to improve as they benefit from economic growth once some critical level of income has been reached later.
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they have pressure for rapid growth, they do not have any luxury to think about what is safe or what is not. This would putundeniable pressure on them to do anything possible to grow fast and lower the cost. The whole society will have enormouspressure to squeeze material gain regardless of safety or quality of life. This will significantly influence their prioritizationprocess and force them to choose growth or saving cost first over safety.
However, at higher levels of national income, affordability for safer equipments and training coupled with increasedsafety awareness and enforcement of safety regulations will definitely help them improve their safety records. Restorationof equitable income distribution will reduce the pressure to get material gain over the safety of human being. This willresult in gradual decline of safety violation and improve the safety performance of the nation. Summing up, at low level ofeconomy, safety performance is relatively high because of the use of rudimentary use of the equipment. However, asnational income rises, safety performance deteriorates basically because of their growth mentality which can easilynegotiate safety over growth or cost saving. However, at the higher level of income, safety performance will improve againas their affordability of safety equipment and awareness increase. Based on above reasoning, a following proposition isestablished:
Ho #8. An inverted u-curve is expected in the relationship between the level of safety performance and the level of nationalincome.
5. Control variables
Huntington (1915) strongly supported the so-called ‘‘climate hypothesis’’ which proposes that most of the variances insocial and economic behaviors can be explained by physical climates and suboptimal climate. Thus, we want to control theimpact of these two climatic variables (ambient temperature and altitude) on the safety performance of a society in thisstudy for following reasons.
5.1. Ambient temperature
The ambient temperature of a society may be one factor to influence the safety performance of a society through itsimpact on the environment where people work. Parker (2000) indicates that an equivalent amount of physical effort is moreproductive in temperate and polar regions compared to equatorial regions. In contrast, Hofstede (1980) argues that theadverse climate created survival challenges for traditional societies. Van de Vilert, Huang and Levine (2002) even suggest a‘‘thermal demands-resources’’ theory which proposes that basic human need for thermal comfort makes life in extremeclimates more demanding than in moderate climates. Gupta and Hages (2004) argue, people may adapt to the effects ofclimate and may enact cultural behaviors that lack any relationship with the physical climate.
5.2. Altitude
Many researchers suggested that human performance can be significantly affected by the altitude through its impact onhumidity, temperature, low pressure and the amount of oxygen that they take in (Ashcroft, 2000). Many researches alsoreported a wide range of altitude-related serious sickness such as hypoxia and pulmonary edema and a variety of physicalproblems from severe exertion to sleep disturbance (Armstrong, 2000; Hornbein & Schoene, 2001). This extensive researchin medical science indicates the high altitude can negatively influence the productivity of ‘‘highlanders’’ in general andthereby affect the overall safety performance of the society in the region.
6. Data
The reinsurance firm Swiss Re produces a series of reports called Sigma, which contains data about disasters. For thedependent variable of this research (MANIND13), total number of man-made disasters was employed, which appeared onSigma for last 13 years from 1990 to 2002. However, the number of space, aviation and shipping accidents was excluded fromthe original data set simply because not all the countries in the sample population have these types of industries. Thedependent variable, man-made disasters for this research was recalculated by dividing the total number of man-madedisasters by total number of the population in a nation to control the size effect of the population.
As discussed in the earlier section of this paper, we decided to use the ‘‘practice (as is)’’ scores of the cultural dimensionsdeveloped by GLOBE project primarily because of its higher construct validity and additional clarity for some culturaldimensions which are crucial for this study such as performance orientation and gender egalitarianism.
For the degree of corruption, the CPI (corruption perception index) developed by Transparency Internationalwas employed. The validity and reliability of the CPI index has been endorsed by Lancaster and Montinola (1997)and the index has been widely used in many studies (Al-Marhubi & Fahim, 2000; Husted, 1999; Swamy, Knack, Lee, & Azfar,1999).
The IRIS data set compiled by the PRS (Political Risk Services) group was used for ‘‘Bureaucratic quality’’. The data set isnamed IRIS after the IRIS Center at the University of Maryland; the acronym came from ‘‘Institutional Reform and theInformal Sector’’, although the center now uses the acronym only, due to an expansion of the center’s scope. The IRIS data set
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was initially constructed for the IRIS Center, based on data from the International Country Risk Guide (ICRG) of the PRSGroup. Since the IRIS data fairly represents the overall quality of public servants and the degree of rigor to recruit and trainthe public officials in a country, it can be used as a proxy for the overall bureaucratic quality that can be used as a shockabsorber for the pressure from corruption. A higher value indicates better bureaucratic quality.
For the variable ‘‘Rule of LAW’’, IRIS data from PRS group was also used because it represents the strength of the legalsystem and the degree of society’s dependence on law and order tradition to settle claims against physical forces/illegalmeans. A higher value is representative of a stronger legal system and greater societal dependence on law and order.
For national incomes, the GNP per capita data was adopted to represent the national income eliminating the size effect.The actual data for this variable was drawn from World Development Indicators published by World Bank (2007).
As a proxy for temperature, geographical latitude of capital cities was used for temperature as Hofstede (1980) did for hisstudy and we take the altitude of the capital cities as a proxy for that of the country. The data were collected from WorldAlmanac (2007, The World Almanac and Book of Facts).
7. Data analysis and discussion
The binding constraint on the number of countries is sheer data availability. The total number of countries for which allthe data are available and included in this research is limited to 39. Table 1 shows the descriptive statistics for theindependent variable used in the model.
First, the Pearson product-moment coefficients of correlation were calculated for all 10 variables in the model. Fourcultural variables—uncertainty avoidance (UAI), performance orientation (PERFSP), institutional individualism-collectivism(INDINS), gender egalitarianism (GENDER), three societal variables—revised socio-political instability (REVSPI), quality ofbureaucracy (BUREAU), rule of law (LAW), two climatic variables—ambient temperature (TEMP) and altitude (ALTFT), andone economic variable—GNP per capita (GNP). The results are reported in Table 2.
Two independent variables in the model display high level of correlations with other variables. ‘‘Revised Corruption Index(REVCPI)’’ display a relatively high level of correlation with three other variables (GNP; r = 0.801, LAW; r = 0.846, BUREAU;�0.836). ‘‘Quality of Bureaucracy’’ (BUREAU) also shows high level of correlations with two other variables (GNP; r = 0.797and LAW; r = 0.855). Since this relatively high level of correlation can cause a serious multi-collinearity problem for themodel, a multiple regression (Model 1) with the collinearity diagnostics was conducted with these ten variables. As expectedthe collinearity statistics of the variables reveal relatively high VIF for two variables (REVCPI and BUREAU), which displayhigher than the suggested cut-off point 5 (6.582 and 5.751 respectively). The results of regression analysis of model 1 arereported in Table 3.
Accordingly, we decided to eliminate the above two variables (REVCPI and BUREAU) from the original model 1 andconducted the second round of regression (model 2) without those two variables. The results of regression analysis of model2 are reported again in Table 3.
To our surprise, despite the fact that the significant portion of disastrous accidents can still be attributed to manyunknown factors such as unexpectedly bad weather, split second misjudgment of the situation and/or unfathomablebehaviors of human beings at emergency, a large portion of the man-made accidents are still very much explained by thevariables in the model (R2 = 0.525).
Another noticeable finding of this research is that most of the variables in the model turned out to be significant exceptthree variables; INDINS, PERFSP and TEMP. Even if our sample size was limited only to 39, the statistical results support mostof the hypotheses established before. The result of regression analysis of model 2 reveals that five variables (UAISP, GENDER,
Table 1
Summary statistics.
N Minimum Maximum Mean Std. deviation Variance
UAISP 39 3.30 5.32 4.2488 .60677 .368
INDINS 39 3.25 5.22 4.2535 .47668 .227
PERFSP 39 3.20 4.94 4.1474 .40250 .162
GENDER 39 2.50 3.93 3.3524 .30056 .090
LAW 39 1.38 6.00 4.4654 1.37028 1.878
BUREAU 39 1.22 6.00 4.4001 1.41066 1.990
REVCPI 39 .00 8.00 4.0641 2.59113 6.714
TEMP 39 8.25 32.50 20.7518 7.03500 49.491
ALTFT 39 7.00 9446.00 1521.3077 2420.85364 5860532.324
GNPCAP 39 .37 43.60 14.9696 12.43256 154.569
Valid N (listwise) 39
MANIND13: Man-made Disaster for the period between 1990 and 2002 published by Sigma. UAISP: Uncertainty Avoidance scores (practice) developed by
GLOBE project. INDINS: Institutional Individualism-Collectivism scores (practice) developed by GLOBE project. PERFSP: Performance Orientation scores
(practice) developed by GLOBE project. GENDER: Gender Egalitarianism scores (practice) developed by GLOBE project. LAW: ‘‘Rule of Law’’ drawn from IRIS
data set compiled by the PRS group. BUREAU: ‘‘Quality of Bureaucracy’’ drawn from the IRIS data set compiled by the PRS group. REVCPI: Revised Corruption
Perception Index developed by Transparency International. TEMP: Ambient Temperature collected from World Almanac (2007). ALTFT: Altitude collected
from GNPCAP: GNP/Capita drawn from World Development Indicators published by World Bank (2007).
Table 2
Correlation amongst independent variables.
1 2 3 4 5 6 7 8 9 10 11
1 MANIND13 1.000
2 UAISP 0.269 1.000
(0.049)
3 INDINS �0.047 0.524 1.000
(0.389) (0.000)
4 PERFSP 0.122 0.525 0.535 1.000
(0.230) (0.000) (0.000)
5 GENDER �0.139 0.263 0.003 �0.137 1.000
(0.199) (0.053) (0.492) (0.203)
6 LAW 0.044 0.713 0.299 0.287 0.218 1.000
(0.395) (0.000) (0.032) (0.038) (0.091)
7 BUREAU 0.055 0.682 0.405 0.347 0.155 0.855 1.000
(0.370) (0.000) (0.005) (0.015) (0.174) (0.000)
8 REVCPI �0.186 �0.778 �0.410 �0.401 �0.298 �0.846 �0.836 1.000
(0.128) (0.000) (0.005) (0.006) (0.033) (0.000) (0.000)
9 TEMP �0.095 �0.441 �0.194 �0.033 �0.100 0.620 �0.696 0.645 1.000
(0.282) (0.002) (0.118) (0.421) (0.273) (0.000) (0.000) (0.000)
10 ALTFT 0.112 �0.442 �0.389 �0.182 �0.050 �.499 �0.423 0.488 0.148 1.000
(0.248) (0.002) (0.007) (0.134) (0.381) (0.001) (0.004) (0.001) (0.185)
11 GNPCAP 0.347 0.663 0.319 0.362 0.207 0.779 0.797 �0.801 �0.621 �0.439 1.000
(0.015) (0.000) (0.024) (0.012) (0.103) (0.000) (0.000) (0.000) (0.000) (0.003)
This table shows the Pearson product-moment correlation coefficients in the models of Table 3. MANIND13: Man-made Disaster for the period between
1990 and 2002 published by Sigma. UAISP: Uncertainty Avoidance scores (practice) developed by GLOBE project. INDINS: Institutional Individualism-
Collectivism scores (practice) developed by GLOBE project. PERFSP: Performance Orientation scores (practice) developed by GLOBE project. GENDER:
Gender Egalitarianism scores (practice) developed by GLOBE project. LAW: ‘‘Rule of Law’’ drawn from IRIS data set compiled by the PRS group. BUREAU:
‘‘Quality of Bureaucracy’’ drawn from the IRIS data set compiled by the PRS group. REVCPI: Revised Corruption Perception Index developed by Transparency
International. TEMP: Ambient Temperature collected from World Almanac (2007). ALTFT: Altitude collected from World Almanac (2007). GNPCAP: GNP per
Capita drawn from World Development Indicators published by World Bank (2007).
Table 3
Cross-national determinants of man-made disaster; results of panel data regressions 1990–2002.
Dependent variable: MANIND13 Model
1 2 3
(Constant) 3.830 2.330 1.901
(0.007) (0.030) (0.074)
UAISP 0.467 0.530 0.606
(0.008)*** (0.003)*** (0.001)***
INDINS �0.133 �0.180 �0.151
(0.389) (0.255) (0.326)
PERFSP �0.370 �0.314 0.335
(0.076)* (0.128) (0.097)*
GENDER �0.661 �0.549 0.604
(0.004)*** (0.013)** (0.006)***
LAW �0.183 �0.202 �0.246
(0.056)** (0.015)** (0.004)
BUREAU �0.138
(0.136)
REVCPI �0.085
(0.117)
TEMP 0.018 0.017 0.026
(0.177) (0.168) (0.050)**
ALTFT 7.10E�005 6.02E�005 8.00E�005
(0.022)** (0.052)** (0.015)**
GNPCAP 0.033 0.032 0.068
(0.000)*** (0.000)*** (0.005)***
GNPGNP* �0.001
(0.099)*
Durbin-Watson 2.462 2.370 2.497
F statistics 3.982 4.144 4.239
R2 0.587 0.525 0.568
Adjusted R2 0.440 0.398 0.434
(1) VIF for two variables (REVCPI and BUREAU) which display higher than the suggested cut-off point 5 in model 1are eliminated from model 2 and 3. (2)
GNPGNP* (square term of GNP) is added to model 3 to test the inverted u-curve relationship between safety performance and national income.* Significant at 0.10 level.** Significant at 0.05 level.*** Significant at 0.01 level.
H. Park / International Business Review 20 (2011) 466–476 473
.
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LAW, GNPCAP and ALTFT) are significant at p< 0.05. Consequently, these statistical results strongly support our hypotheses# 1 (uncertainty avoidance). The case like Denmark who has one of the highest level of UAI (uncertainty avoidance index)and displays the lowest number of MANIDS13 (man-made disaster index) clearly corroborates the statistical findings of thisresearch and support the hypothesis. Regarding GENDER, the fact that gender egalitarian country such as Sweden (feminineculture) demonstrates lower level of man-made disaster per capital also verify the findings of this research and intuitivelysupport the Hypothesis #3. Two countries (Canada and Austria) with the higher numbers in LAW demonstrate the lowestlevel of MANINDIS13 and validate the relationship proposed by hypothesis #7 (LAW).
Finally, square term of GNP was added to test the Safety Kuznet Curve and conducted another regression (model 3). Theresult of regression based on model 3 is reported in Table 3.
As seen in Table 3, the sign of GNPCAP in model 3 is positive while that of GNP2 (represented by GNPGNP*) isnegative and the reported value associated with GNP2 in model 3 is 0.099, which would be significant at a threshold ofp< 0.10. This situation, in which the coefficient of a variable is positive and the coefficient of the square of that variableis negative, is indicative of an inverted u-curve, as illustrated in Fig. 1. In that figure, the inverted u-curve is the onedisplayed on the right, with a positive b1 coefficient and a negative b2 coefficient. Because this corresponds to thepositive sign and negative sign of the coefficients of GNPCAP and GNPGNP* respectively shown in model 3 of Table 3, theresult is clearly suggestive of an inverted u-curve. At a p< 0.10 threshold, this result corroborates our expectation thatthe overall safety records in developing countries is relatively acceptable initially mainly for lack of any major industrialactivities but as their economy grows; their safety records will be deteriorated quickly as their priority change togrowth over safety. However, eventually safety performance will improve as they have more resources and improvedsafety consciousness as their economy advance to more industrialized stage. Dramatically increased frequency inmining accidents in China in recent years since their economy start to grow leaps and bounds clearly corroborate ourfindings. If a p< 0.05 threshold is preferable, which would indicate that the statistical analysis with current data cannotstrongly support hypothesis #8, one cannot wholly dismiss hypothesis #8 either; therefore, it would be worthwhile touse a new data set in the future to explore the Safety Kuznet Curve relationship between national income and safetyperformance.
Through the various models, the beta coefficients for most of all the variables in the models are relatively stable andcontinuously significant indicating those variables are crucial and robust to predict the frequency of the man-made disasterin a country.
8. Conclusion
This research successfully identified a set of variables which has statistically significant relationships with the safetyperformance of the society. The findings indicate that the safety performance is truly a function of every aspects ofsociety.
First of all, the research results reveal that some cultural and institutional factors such as Uncertainty Avoidance, GenderOrientation and the legitimacy of the legal system can directly influence the safety performance of the society.
The findings also support the inverted u-curve (Safety Kuznet curve) hypothesis for the relationship between the level ofnational income and the safety performance in a society. This indicates that we may be able to expect a positive trend insafety performance as their income level continues to improve beyond a certain point, even if we expect a negative trend atthe beginning sage of industrialization.
Since the research results reveal that the countries with low uncertainty avoidance, gender inegalitarian and weak legalsystem display penchants to have a higher level of man-made disaster, one of the major implications of the resultsis that the multinational insurance companies should pay extra caution to those countries that fit to this category whenthey sell their products and they can even justify the additional risk premium for the products that they sell to thosecountries.
Another major implication of the research findings is for the policy makers in the countries where they display a higherlevel of man-made disasters. The research results strongly suggest that they need to focus on some of those cultural variablesthat influence the safety performance through extensive training at work place and retrofit their legal system to ensuresafety performance of the countries.
Since the findings also support at least partially the inverted u-curve (Safety Kuznet curve) hypothesis, the policy makersof the newly industrialized countries or other rapidly growing economies such as China and India should be aware of thisspecial phenomenon and pay extra efforts to minimize the catastrophic man-made disaster. We believe the repeated miningaccidents in China in recent years are not just a series of unfortunate incidents. It is the result of system failure and aninability to understand the root cause of the problem.
Thus, the major contribution of this study is to shed more light on understanding the root causes of man-made accidents.The research findings will undoubtedly help and multinational Insurance firms and the policy makers of the countriesdevelop a better strategy and formula to improve their business practices and safety performance. They need to be moreculturally sensitive and attentive rather than applying a universal approach to avoid possible human and financial losses andto ensure the efficient allocation of scarce resources.
The results will also help practitioners and policy makers of the host countries to improve the efficacy of safety training byfocusing more on the problematic areas and allocating limited resources more efficiently in those areas.
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Appendix A
Country UAISP INDINS PERFSP GENDER LAW BUREAU REVCPI TEMP ALTFT GNPCAP
Argentina
3.6500 3.6600 3.6500 3.4900 3.463 3 7 21.5 82 8.87Australia
4.3900 4.2900 4.3600 3.4000 6 6 1.3 23.17 1837 20.14Austria
5.1600 4.3000 4.4400 3.0900 6 5.688 2.5 13.5 695 28.18Brazil
3.6000 3.8300 4.0400 3.3100 3.681 4 6 27.33 3799 4.78Canada
4.5800 4.3800 4.4900 3.7000 6 6 0.8 10.58 284 19.83Colombia
3.5700 3.8100 3.9400 3.6700 1.375 3.938 7.8 18.83 8354 2.18Costa Rica
3.8200 3.9300 4.1200 3.5600 4 2.925 4.4 25.33 3845 3.10Denmark
5.2200 4.8000 4.2200 3.9300 6 6 0 11.5 30 36.86Ecuador
3.6800 3.9000 4.2000 3.0700 4 3 7.7 22 9446 1.57El Salvador
3.6200 3.7100 3.7200 3.1600 2.725 2.688 6.4 32.17 2293 1.78Finland
5.0200 4.6300 3.8100 3.3500 6 5.906 0.4 8.25 39 25.48France
4.4300 3.9300 4.1100 3.6400 5.394 5.9 3.3 13.67 246 26.13Germany
5.1900 3.6750 4.1700 3.0800 5.426 5.07 2.1 13.08 112 28.30Greece
3.3900 3.2500 3.2000 3.4800 3.994 3.45 5.1 22.58 351 11.13Guatemala
3.3000 3.7000 3.8100 3.0200 1.644 1.219 6.9 25.5 4855 1.51Hong Kong
4.3200 4.1300 4.8000 3.4700 5.056 4.294 2.2 24.67 108 23.40India
4.1500 4.3800 4.2500 2.9000 2.731 3.838 7.1 31.83 708 0.37Indonesia
4.1700 4.5400 4.4100 3.2600 2.7 1.706 8 30.33 26 1.11Ireland
4.3000 4.6300 4.3600 3.2100 4.831 5.519 1.8 13 265 16.28Israel
4.0100 4.4600 4.0800 3.1900 3.125 4.413 2.9 23.08 2485 15.73Italy
3.7900 3.6800 3.5800 3.2400 5.156 4.519 5.4 20.5 66 20.01Japan
4.0700 5.1900 4.2200 3.1900 5.324 5.812 4.2 18.67 19 38.19Malaysia
4.7800 4.6100 4.3400 3.5100 4.219 3.518 4.7 30 127 4.46Mexico
4.1800 4.0600 4.1000 3.6400 3.194 2.944 6.7 22.33 7534 3.71Netherlands
4.7000 4.4600 4.3200 3.5000 6 6 1 12.25 10 25.19New Zealand
4.7500 4.8100 4.7200 3.2200 5.981 6 0.6 16.25 419 14.88Philippines
3.8900 4.6500 4.4700 3.6400 1.956 1.656 6.7 31.67 52 1.19Portugal
3.9100 3.9200 3.6000 3.6600 5.256 3.75 3.5 20.58 253 10.95Singapore
5.3100 4.9000 4.9000 3.7000 5.281 5.094 0.9 31 33 33.93South Korea
3.5500 5.2000 4.5500 2.5000 3.35 4.288 5.8 16.33 285 10.55Spain
3.9700 3.8500 4.0100 3.0100 4.844 4.231 3.9 19 2155 14.61Sweden
5.3200 5.2200 3.7200 3.8400 6 6 0.5 9.58 144 25.77Switzerland
5.1750 4.1400 4.9400 3.1950 6 6 1.1 14.42 1877 43.60China
4.9400 4.7700 4.4500 3.0500 5.031 4.575 4.7 26 29 0.86Thailand
3.9300 4.0300 3.9300 3.3500 3.95 4.381 7 32.5 7 2.72Turkey
3.6300 4.0300 3.8300 2.8900 3.313 3.375 6.6 17.92 2825 3.11UK
4.6500 4.2700 4.0800 3.6700 5.3 6 1.3 14.17 193 20.87USA
4.1500 4.2000 4.4900 3.3400 6 6 2.5 18.33 25 29.04Venezuela
3.4400 3.9600 3.3200 3.6200 3.85 2.906 7.7 25.92 3418 3.45References
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