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Risk level assessment and occupational health insurance expenditure: A gender imbalance.

Jahangir Khan (*)

Abstract

It is tested whether occupational risk explains differences in reimbursements from occupational-injury insurance schemes in relation to socioeconomic differences in all municipalities in Stockholm county, Sweden. An occupational risk level is formed, which considered the proportions of workers in various industrial sectors and the probability of a worker being injured in each. A regression analysis is performed, treating socioeconomic condition and risk level as predictors of reimbursement. After controlling for variation in socioeconomic factors, occupational-risk level explains the pattern of payments to men but not to women. From a gender perspective, it can be concluded that women, as a group, are not compensated for their occupational risks to the same extent as men. [C] 2002 Elsevier Science Inc. All rights reserved.

Keywords: Equity; Gender; Occupational injuries and diseases; Risk; Social insurance

1. Introduction

Postinjury compensation is paid according to specific social-insurance agreements (between employers' associations and trade unions) that cover most workers in Sweden (Statistics Sweden, 1995). Reimbursements from national occupational-health insurance represent cost of illness, and the burden this imposes on society. At an individual level, reimbursement is linked to each recipient's income and occupational-health conditions. A person with higher income gets more payments every time (s)he becomes injured. But at the societal level during a one-year period, the scenario is different. The deprived areas, where the number of low-income persons is higher (Table 1), are supposed to receive more payments in total than affluent areas with higher number of persons with high-income. The reason is that the persons with low income get injured more often. Thus, both at a point of time and a one-year period, incidences of injury is higher in a deprived area than in an affluent area.

The aim of this study is, therefore, to analyze the effect of socioeconomic conditions and occupational risk involved on reimbursements from occupational-injury insurance to workers in different industrial sectors in the municipalities of Stockholm in Sweden. The hypotheses intend to test for both male and female workers if there is a socioeconomic difference in reimbursement payments and if occupational risk explains differences in reimbursements from the occupational-injury insurance scheme. Finally, we will test if occupational injury risk explains the reimbursements to male and female workers for given socioeconomic condition.

2. Materials and method

The study is designed as an ecological analysis of socioeconomic differences in reimbursements among the 25 municipalities in Stockholm county. The analysis is divided into two parts. The first part comprises a descriptive statistics of reimbursements from occupational injury insurance on the basis of the socio-economic condition of the municipalities. The second part consists of regression analysis in order to observe the reflection of socioeconomic condition and occupational risk level on reimbursements.

All 777,456 registered residents in gainful work (383,800 men and 393,656 women) in 1993 are encompassed by the study with regard to reimbursements. Reimbursements (sickness compensation and disability annuity) of occupational injury insurance from Sweden's social-insurance registry for 1993 are employed.

Sickness compensation is paid to persons whose work capacity is reduced by at least 1/4 or more due to injury or disease. Compensation for the first 14 days is paid by the employer, and is thereafter covered by the occupational injury insurance. Sickness compensation encompasses persons with minimum income of 896 US$ during a year. No reimbursement is provided for persons on income exceeding 7.5 times "base amount," which is set annually by the Swedish Government (base amount for 1993 = 38,565 US$).

Persons who, as a consequence of work injury, had their personal income permanently reduced by at least one-fifth has a right to compensation in the form of annuity. Generally, the level of the annuity is calculated in relation to the insured person's sickness-compensation-based income.

2.1. Socio-economic variables

Six variables are selected in order to classify the 25 municipalities in Stockholm county into three groups (deprived, intermediate and affluent). The variables, used elsewhere, are average income from gainful work, dwelling space, education, immigrant density, proportion of welfare recipients and proportion of unemployed (Bobak et al., 1998; Friis et al., 1998; Janlert, 1991; Kawachi et al., 1999; Smith et al., 1998).

2.2. Specific socioeconomic municipality-groups

The 25 municipalities of the Stockholm county are allocated to three groups, in accordance with the socio-economic variables through K-means cluster analysis (SPSS, 1999, version 9.0). Five, seventeen and three municipalities have been allocated to deprived, intermediate and affluent municipality-groups respectively. Table 1 shows that affluent municipalities are on average possessing higher proportion of all variables that enrich socioeconomic condition of an area and vice versa.

The second part of the paper comprises three regression models having reimbursements from occupational injury insurance as the dependent variable, while socioeconomic condition and occupational injury risk in the municipalities as independent variables both separately and together respectively. The small number of observations (25 municipalities) and correlation among six socio-economic variables create a problem to determine the contribution of variables on reimbursements. We, therefore, have created two socioeconomic deprivation factors (SEF1 and SEF2) through a factor analysis (SPSS, version 9.0).

2.3. Socioeconomic deprivation factors

Two socioeconomic deprivation factors are created from six variables through factor analysis (SPSS, version 9.0). The first factor, SEF1, represents a combination of three variables -- average income, dwelling space and educational level. We suggest that SEF1 has a negative relationship with reimbursement. The second factor, SEF2, encompasses high values on immigration density, proportion unemployed and proportion of welfare recipients. This factor is hypothesized to be positively related to reimbursements from the occupational-health insurance system.

2.4. Occupational risk-level (ORL) assessment

All occupational injuries registered in Sweden in 1993 (19,464 accidents and 57,118 diseases) that resulted in a sickness of more than seven days for insurance purposes are used to assess the risk level of nine industrial sectors (Statistics Sweden, 1995). Frequency of occupational injuries, that is incidence per worker during 1993, is used to measure the probability of being injured in a specific industrial sector. Distribution of workers between industries is used to assess the risk level of each municipality. The following formula summarizes the definition of occupational-risk level (ORL) of a municipality:

[Risk.sub.mi] = [euro] [P.sub.wj] * [P.sub.j] (j = 1,2, .., 10; i = 1,2, .., 25)

[P.sub.wj] = proportion of workers in industry j, and [P.sub.j] = probability of a worker to be injured if (s)he works in industry j.

Occupational risk levels of each municipality -for male, female and all workers are assessed. Nine industries and one nonspecified worker group (j = 10), following the Nordic Occupational Classification System (SNI, 1992), are included in the study.

2.5. The regression models

The regression models will have the following structure,

Log (Re) = [alpha] + [[beta].sub.1] * SEF1 + [[beta].sub.2] * SEF1 + [[beta].sub.3] * ORL + [epsilon]

where, Re implies the reimbursements per 10 000 persons, a is a constant, [[beta].sub.1] [[beta].sub.2] and [[beta].sub.3] are coefficients of socioeconomic deprivation factors, that is, SEF1, SEF2 and occupational risk level respectively. [epsilon] is the error term. The short regression models are also analyzed by excluding independent variables from the model above.

3. Results

3.1. Part I

Socioeconomic difference in reimbursement payments -- descriptive statistics

During 1993 a total of USD 41,907,823 is paid out as sickness compensation, with male workers receiving 67%. USD 55,037,829 is paid in disability annuity, and 65% of this went to male workers. Table 2 shows reimbursements by payment category -- as total payments and per 10,000 workers in paid employment -- divided into the three municipality groups. The variation between municipalities in the intermediate and deprived groups is considerable, with ranges from 0.55 to 1.55 and from 0.96 to 1.80, respectively, compared with Stockholm County as a whole. All municipalities in the affluent group receive lower reimbursements than the county's average. All but one of the deprived municipalities shows higher reimbursement per 10,000 workers than the average for Stockholm County.

Payments to male workers, per 10,000 are much higher than those to female workers. Men received more than double the amount of reimbursement than women from the occupational sickness fund. The scenario is not much different with regard to the disability-annuity fund; in all affluent municipalities, reimbursement is much lower than the average for the County as a whole. By contrast, in the deprived-municipality group, all municipalities except Nynashanm showed higher reimbursement than the average for Stockholm County. Nynashamn received 4% less than the average.

3.2. Part II

Hypotheses test

In the regression analyses, the socioeconomic variables associated with low and high shares in reimbursements are compared (model 1, Table 3). Significant differences are observed for all workers. There is a significant difference in reimbursement related to both socioeconomic factors (SEF1 and SEF2)--for both men and women. As expected, the first factor (SEF1) is found to be negatively related to reimbursement, and the second (SEF2) positively related. Further, a simple regression analysis demonstrates that reimbursement from occupational-injury insurance is associated with risk level in the municipalities. Both of these outcomes are expected. The model (model 2, Table 3) provides a much stronger prediction for male than for female workers ([R.sup.2] = 62.4 for men and [R.sup.2] = 27.4 for women).

Male workers are at greater risk than female workers according to the risk-level assessment. The nationwide occupational risk levels (ORLs) for male and female workers are 0.208 and 0.181 respectively. The most hazardous municipalities in Stockholm are Norrtalje for men and Sodertalje for women. The safest municipality for male workers is Danderyd; its counterpart for female workers is Taby. The assessed ORL for every municipality is included in the regression models.

Socio-economic difference in ORL is observed. When both risk level and socioeconomic variations are taken into account at the same time, by means of a multiple regression model (model 3, Table 3), a difference between men and women is found with regard to reimbursements. After controlling for variation in socioeconomic condition, occupational-risk level explains the pattern of reimbursement payments to men. However, the occupational-risk level does not significantly explain the distribution of reimbursements to women. Nevertheless, differential reimbursement is explained by variation in socioeconomic conditions (model 1, Table 3).

Another multiple regression analysis, taking reimbursements as the dependent variable and income from gainful work, occupational risk level (ORL) and gender as the predictors has been performed. We have found the predictors, that is, income from gainful work (t-value 2.993; p-value = 0.004), ORL (t-value = 2.140; p-value = 0.038) and gender (t-value = 2.620; p-value = 0.012) to be significant.

4. Discussion

In this study, socioeconomic condition and occupational risk levels of the municipalities are estimated independently. The socioeconomic condition is assessed on the basis of all inhabitants, while occupational risk levels solely involve the working population. A socioeconomic difference in occupational risk is found in the analysis. Consequently, reimbursements from occupational-injury insurance also explain the socioeconomic difference. Occupational-injury risk also explains differences in reimbursements, and also gender differences. The adjusted correlation coefficient for the male model is 62.4, while that for the female model is only 27.4. One of the most interesting questions posed is whether occupational-risk level is still powerful enough to explain the distribution of reimbursements to male workers after controlling for socioeconomic variation. Reimbursements to male workers are explained well by risk in the workplace, but not those to female workers.

The two main findings are that there is socioeconomic variation in reimbursements and that there is an inconsistency between payments made to men and women. Concentration of workers in hazardous jobs contributes to the accumulation of occupational risk as well as to social deprivation in some geographic areas. Restructuring of occupational composition in municipalities, so that there is greater evenness across occupations would lead to greater equality in reimbursements by municipality. However, it is not only occupational structure that determines the pattern of reimbursements. Socioeconomic factors, which may be influenced by governmental intervention, are also important. Socioeconomic inequality may also have some influence on occupational hazards in the workplace. Psychosocial stress from relative deprivation, disrupted social cohesion, insufficient investment in social capital and underinvestment in human resources have all been suggested as factors involved in ill-health at population level (Chiang, 19 99).

Women are represented in almost equal numbers as men on the labor market in Stockholm. However, women work fewer hours (Inregia, 1995; Statistics Sweden, 1994). Women suffer more from occupational diseases, whereas men have more occupational injuries (Statistics Sweden, 1995). Difference in applicability of regulation to judge the injury cases, i.e., between diseases and accidents, may explain the difference in reimbursements between women and men. Job segregation also contributes to the differential between the genders. Amount of reimbursement is directly related to level of income and prevalence of occupational hazards. Since women work fewer hours, their income level is much lower than that of men. In Stockholm county the income of men is 23% higher than that of women (Inregia, 1996). The proportion of women is higher in lower income groups, and decreases as income rises. This income effect can result in lower reimbursements to women. However, this is reflected in the analysis by controlling for socioecon omic conditions. One reason for the gender differential with regard to reimbursements is that occupational-health insurance is established on the basis of full-time employment. Accordingly, part-time workers, who are more likely to be female, will receive less compensation because of their lower income. This means that the level of risk for a full-time worker is better reflected in his/her reimbursements than that of a part-time worker. It might be argued that the probability of injury is higher for a full-time worker than for a part-time worker. When female workers are exposed to equal hazards as male at work, women, due to lower income are not fully compensated according to their occupational risk.

(*.) Corresponding author. Tel.: + 1-46-8-5177-79-89;

fax: + 1-46-8-30-73-51.

E-mail address: jahangir.khan@phs.ki.se (J. Khan).

References

Bobak, M., Pikhart, H., Hertzman, C., Rose, R., Marmot, M., 1998. Socioeconomic factors, perceived control and self-reported health in Russia. A cross-sectional survey. Social Science and Medicine 47, 269-279.

Chiang, T.L., 1999. Economic transition and changing relation between income inequality and mortality in Taiwan: regression analysis. British Medical Journal 319, 1162-1165.

Friis, R., Yngve, A., Person, V., 1998. Review of social epidemiological research on migrants' health: findings, methodological cautions, and theoretical perspectives. Scandinavian Journal of Social Medicine 26, 173-80.

Inregia, 1995. Statistik om Stockholms Lan: Forvarvsarbetande Befolkning 1993 efter Naringsgren och Kon, (Statistic about Stockholm County: Gainful Working Population 1993 according to Industrial Classification and Sex, Report number 9).

Inregia, 1996. Arsstatistik for Stockholms Lan och Landsting 1994-1996 (Statistical Yearbook of Stockholm County, 1994-1996).

Janlert, U., 1991. Work Deprivation and Health: Consequences of Job Loss and Unemployment. PhD thesis. Karolinska Institutet, Department of Social Medicine. Sundbyberg, Stockholm.

Kawachi, I., Kennedy, B.P., 1999. The relationship of economic inequality to mortality: does the choice of indicator matter? In: Kawachi, I. et al. (Eds.), The Society and Population Health Reader. The New Press: New York.

Smith, G.D., Hart, C., Watt, G., Hole, D., Hawhorne, V., 1998. Individual social class, area-based deprivation, cardiovascular diseases risk factors, and mortality: The Renfrew and Paisley study. Journal of Epidemiology & Community Health, 52, 399-405.

SNI 92, 1994. Standard for svensh naringsgrens-indelning 1992: innehalbbeskrivningar and nychlar (Swedish standard for classification of occupational industries: contents and keys), SCB, Stockholm.

Statistic Sweden, 1994. Arbetskraftsundersokningen, Arsmedeltal 1993 (Labor Force Investigation, yearly average 1993), SCB. Stockholm.

Statistics Sweden, 1995. Occupational Diseases and Occupational Accidents 1993. National Board of Occupational Health. SCB, Stockholm.
Table 1

Socio-economic variables distributed across three groups of
municipalities (deprived, intermediate and affluent) in Stockholm
county, mean and two standard deviations. 1993

Socio-economic         Deprived municipalities
variables              (n (*) = 79,422)
                       mean                          SD

Average income (a)     21.48                       1.08
Dwelling space (b)      1.41                       0.04
Education (c)          12.04                       1.46
Immigrants (d)         18.94                      10.69
Welfare recipient (e)  10.00                       2.74
Unemployed (f)          6.68                       0.80

Socio-economic         Intermediate municipalits
variables              (n (*) = 828,559)
                        mean                          SD

Average income (a)     23.96                        0.97
Dwelling space (b)      1.40                        0.07
Education (c)          14.74                        0.98
Immigrants (d)         16.24                        4.09
Welfare recipient (e)   8.82                        1.67
Unemployed (f)          5.50                        0.83

Socio-economic         Affluent municipalities
variables              (n (*) = 158,095)
                        mean                        SD

Average income (a)     29.74                      2.03
Dwelling space (b)      1.57                      0.04
Education (c)          17.83                      0.31
Immigrants (d)         12.63                      0.58
Welfare recipient (e)   3.67                      0.58
Unemployed (f)          3.88                      0.68

(*)Refers to number of persons eligible for sickness compensation in
each municipality.

(a)Average income from gainful work in USD 1000 per person and year.

(b)Number of rooms per person,

(c)Percentage share of inhabitants with at least 3-year high school
education.

(d)(e)Percentage share in total population.

(f)Percentage share of registered job seekers in total population.
Table 2

Reimbursements (USD) of sickness compensation and disability annuity
from occupational injury insurance, by total, per 10,000 inhabitants
with gainful work and ratios between observed (O) and expected (E)
reimbusements, 1993

Municipalities   Sickness compensation

                 Men                     Women
                 Total       per 10,000  Total      per 10,000

Deprived
 Botkyrka         1,093,546    711,342     806,282  527,568
 Norrtalje        1,055,629    945,396     437,411  425,828
 Nynashamn          194,906    358,217      98,147  195,941
 Sundbyberg         475,973    675,331     146,327  202,137
 Sodertalje       1,583,513    845,126     819,976  464,208
Intermediate
 Ekero              240,655    465,034     102,561  199,807
 Haninge          2,158,955  1,391,976     812,428  531,833
 Huddinge         2,611,237  1,454,323     751,609  411,773
 Jarfalla         1,036,962    725,402     397,203  275,281
 Nacka              827,556    531,336     524,992  320,743
 Salem              174,469    554,221      88,580  275,608
 Sigtuna            553,243    688,028     151,312  191,631
 Sollentuna         699,977    551,510     536,967  415,416
 Solna              320,911    277,821     250,207  200,840
 Stockholm       10,692,277    723,468   5,888,743  371,651
 Tyreso             870,284    993,248     272,949  305,210
 Upplands Bro       192,825    396,107     103,476  219,835
 Upplands Vasby     845,365    952,524     371,189  413,581
 Vallentuna         256,768    441,409     114,989  203,844
 Vaxholm             81,644    452,572      90,981  515,765
 Varmdo             527,729    814,396     137,706  229,242
 Osteraker          886,266  1,089,047     516,214  671,979
Affluent
 Danderyd           127,372    203,015      32,065   49,490
 Lidingo            240,335    279,981     166,594  185,620
 Taby               389,206    264,784     151,312  103,532

Total            28,137,603    733,132   13,770,20  349,803

Municipalities   Disability annuity

                 Men                       Women
                 Total         per 10,000  Total       per 10,000

Deprived
 Botkyrka         1,375,297      894,618      760,839    497,834
 Norrtalje        2,133,659    1,910,854      814,409    792,844
 Nynashamn          698,393    1,283,574      262,613    524,283
 Sundbyberg         787,815    1,117,786      565,792    781,589
 Sodertalje       3,402,976    1,816,180    2,367,878  1,340,511
Intermediate
 Ekero              407,196      786,853      181,530    353,653
 Haninge          2,121,864    1,368,062      851,396    557,342
 Huddinge         2,681,387    1,493,393      910,249    498,685
 Jarfalla         1,460,714    1,021,836      800,906    555,067
 Nacka              943,132      605,542      601,964    367,769
 Salem              296,854      942,992      173,476    539,752
 Sigtuna            746,664      928,571      380,522    481,917
 Sollentuna       1,362,368    1,073,407      602,822    466,364
 Solna              652,108      564,546      422,755    339,344
 Stockholm       10,934,452      739,854    6,811,528    429,890
 Tyreso           1,058,242    1,207,763      342,280    382,735
 Upplands Bro       378,298      777,111      178,258    378,709
 Upplands Vasby   1,008,741    1,136,609      466,811    520,123
 Vallentuna         566,317      973,556      212,267    376,294
 Vaxholm            121,335      672,586       67,013    379,895
 Varmdo             839,955    1,296,227      336,036    559,407
 Osteraker          590,151      725,179      308,574    401,684
Affluent
 Danderyd           163,926      261,279      145,290    224,247
 Lidingo            353,641      411,977      358,517    399,461
 Taby               646,307      439,694      382,312    261,589

Total            35,731,792      931,000   19,306,037    490,429

Municipalities   O/E




Deprived
 Botkyrka        1,06
 Norrtalje       1,66
 Nynashamn       0,96
 Sundbyberg      1,11
 Sodertalje      1,80
Intermediate
 Ekero           0,73
 Haninge         1,55
 Huddinge        1,54
 Jarfalla        1,03
 Nacka           0,73
 Salem           0,92
 Sigtuna         0,92
 Sollentuna      1,00
 Solna           0,55
 Stockholm       0,90
 Tyreso          1,15
 Upplands Bro    0,71
 Upplands Vasby  1,21
 Vallentuna      0,81
 Vaxholm         0,81
 Varmdo          1,18
 Osteraker       1,17
Affluent
 Danderyd        0,29
 Lidingo         0,51
 Taby            0,43

Total            1,00
Table 3

Correlation coefficients and outcomes of regression analyses (a) of
socioeconomic differences in payments (b) of sickness compensation and
disability annuity from occupational injury insurance, distributed
across all municipalities in Stockholm county, by gender, 1993

             Male                                    Female
Explanatory  [R.sup.2] = 57.5  t-value   p-value   [R.sup.2] = 55.3
variables    Beta                                  Beta

Model 1
 SEF1 (c,f)  -0.740            -5.395    0.000     -0.679
 SEF2 (d,f)   0.503             3.607    0.001      0.503

             Female                Total
Explanatory  t-value   p-value   [R.sup.2] = 61.2  t-value   p-value
variables                        Beta

Model 1
 SEF1 (c,f)  -4.815    0.000     -0.750            -5.707    0.000
 SEF2 (d,f)   3.670    0.001      0.533             4.051    0.001
             Male                                    Female
Explanatory  [R.sup.2] = 62.4  t-value   p-value   [R.sup.2] = 27.4
variables    Beta                                  Beta

Model 2
 ORL (e)     0.800             6.391     0.000     0.552

             Female                Total
Explanatory  t-value   p-value   [R.sup.2] = 55.3  t-value   p-value
variables                        Beta

Model 2
 ORL (e)     3.172     0.004     0.756             5.536     0.000
             Male                                    Female
Explanatory  [R.sup.2] = 65.7  t-value   p-value   [R.sup.2] = 55.0
variables    Beta                                  Beta

Model 3
 SEF1 (c,f)  -0.312            -1.465    0.158     -0.605
 SEF2 (d,f)   0.300             2.016    0.057      0.493
 ORL (e)      0.518             2.472    0.022      0.157

             Female                Total
Explanatory  t-value   p-value   [R.sup.2] = 64.1  t-value   p-value
variables                        Beta

Model 3
 SEF1 (c,f)  -3.719    0.001     -0.478            -2.322    0.030
 SEF2 (d,f)   2.997    0.007      0.391             2.566    0.018
 ORL (e)      0.923    0.366      0.341             1.674    0.109

(a)Standardized regression coefficients and explained variation.

(b)Payments age-standardized per 10,000 insured.

(c)Comprising mean income, dwelling space and educational level.

(d)Comprising proportion of immigrants, welfare recipients and
unemployed in all municipalities.

(e)Occupational risk level (ORL) calculated on the basis of occupational
injury incidences in the Swedish industries and occupational structure
of the municipalities.

(f)99% of variation in the original socio-economic variables jointly
explained by socioeconomic deprivation factors, SEF1 and SEF2.
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Author:Khan, Jahangir; Jansson, Bjarne
Publication:The Journal of Socio-Economics
Article Type:Statistical Data Included
Geographic Code:4EUSW
Date:Nov 1, 2001
Words:3862
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