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2011年1月26日 星期三

Paper:association between business cycle and workplace injuries

The association between the business cycle and the incidence of workplace injuries:
Evidence from the U.S.A.
景氣週期與工傷事件間的關連性:美國的實證結果
Authors: Abay Asfaw , Regina Pana-Cryan, Roger RosaCDCNIOSH的官員)
Source: Journal of Safety Research xxx (2010) xxx–xxx(等待出版發行)

Abstract
Introduction:
The current study explored the association between the business cycle and the incidence of workplace injuries to identify cyclically sensitive industries and the relative contribution of physical capital and labor utilization within industries.
Method:
Bureau of Labor Statistics nonfatal injury rates from 1976 through 2007 were examined across five industry sectors with respect to several macroeconomic indicators. Within industries, injury associations with utilization of labor and physical capital over time were tested using time series regression methods.
利用時間序列迴歸方法,檢定礦業、營造業、製造業、農業與貿易等五個行業於1976200732年)間,其傷害率與總體經濟指標(GDP、失業率、工業生產指數、建造許可)間的關聯性
Results:
Pro-cyclical associations between business cycle indicators and injury incidence were observed in mining, construction, and manufacturing but not in agriculture or trade. Physical capital utilization was the highest potential contributor to injuries in mining while labor utilization was the highest potential contributor in construction. In manufacturing each effect had a similar association with injuries.
結果顯示在製造業、礦業與營造業,傷害率與總體經濟指標之間有同步的關連性;而農業與貿易業則無關聯性。在礦業,設備運用與工傷的變化關聯性最高;在營造業則是勞力運用;而製造業的工傷和兩者都有關
Conclusion:
The results suggest that firms in the construction, manufacturing, and mining industries should take additional precautionary safety measures during cyclical upturns. Potential differences among industries in the mechanisms through which the business cycle affects injury incidence suggest different protective strategies for those industries. For example, in construction, additional efforts might be undertaken to ensure workers are adequately trained and not excessively fatigued, while safety procedures continue to be followed even during boom times.
製造業、礦業與營造業在景氣繁榮階段應該格外小心與採取相關措施,而這些措施則須隨著產業差異而調整;例如營造業必須注意讓勞工獲得適當的訓練與不要過度操勞。(礦業則是要盡量減少不安全設備的使用)

想做的題目又再一次被歪國人捷足先登了…..而且也做得比我想像的還要好

1.      Introduction(摘錄)
Aggregate fluctuations in the economy, commonly known as the business cycle, affect a variety of factors that may directly or indirectly influence occupational safety and health. Several empirical studies support this assertion, demonstrating increases in work-related injuries and illnesses associated with increases in various business cycle indicators, such as the Gross Domestic Product (GDP; e.g., Davies, Jones, & Nuňez,
50 2009), capacity utilization (e.g., Bowers, 1981), and unemployment rate (e.g., Brooker, Frank, & Tarasuk, 1997). Workplace injuries measured directly as incidence rates (e.g., Boone & van Ours, 2006), or indirectly as workers’ compensation claims and costs (e.g., Shuford, 2008), showed similar associations.
In addition mechanisms through which the business cycle affects workplace injuries have been examined. Shea (1990) reported that injury rates were more cyclical in low labor turnover industries. Fairris (1998) observed that low worker bargaining power in manufacturing was associated with increased injury rates. Leigh (1985) and Boone and van Ours (2006) showed that injury rates increase during economic expansions because workers over-report.
這種關連性一些進一步發現:員工流動率低的行業這種現象特別明顯(Shea)、製造業中談判力量處於弱勢的勞方(Fairris)、景氣好的時候勞工比較敢提報工傷假(Leigh)
Potential factors contributing to these trends during recessions include fewer inexperienced workers remaining employed, especially those in hazardous industries, and injured workers, who fear losing their jobs, deferring filing for workers’ compensation benefits. The pace of work also is slower and unsafe equipment may be used less frequently (IWH, 2009).
而關於此一現象的可能解釋:景氣不好的時候,比較不可能僱用技術不成熟的勞工,勞工也於害怕失業所也比較不敢請工傷假,亦或產能力利用率不高,所以不安全的機器設備較少開機使用
Surprisingly few studies have directly examined injury rates by industry sector on a national level, despite the fact that certain industries are more sensitive to the business cycle than others (e.g., Shuford, 2008). Among nine sectors examined by Davies et al. (2009), construction and manufacturing injury rates were most sensitive to the business cycle.
We know of no other such studies using national-level data. Consequently, we used national-level time series data from the United States to compare the impact of the business cycle on the incidence rate of workplace injuries among industries. We further examined human and physical capital utilization as broad mechanisms through which the business cycle affects the rate of injuries in those industries.
以上這一段寫出了本文和其他文獻的差別與賣點(用整個國家所有產業的資料來做、也檢定勞工與設備運在各項產業發生影響的機制)
另外有要做這個研究的話:Boone & van Ours, 2006 這篇要找來看

2. Theoretical Framework
作者的研究架構與對於此一現象解釋的超讚彙整(Boone & van Ours, 2006; Kossoris, 1938; Robinson, 1988; Root & Hoefer, 1979).如圖一:



分成勞工面(Labor)與機台設備面(Physical Capital
上面是談景氣好的的狀況,下半部則是景氣不好的情況
勞工面之報告(reporting:不敢報工傷,因為怕被認為不適任(景氣好時,則較無此一心理顧慮)
勞工面之勞力組成(Labor Composition:較少新人、主要也是有經驗的勞工來進行作業
勞工面之勞動環境條件(Working condition:較多的訓練與休息時間、較少的高風險作業、單位時間的工作強度較低/較不疲勞、主管比較有空監督作業安全
機台設備面之產能利用(Working Below Capacity:有較多時間好好保養機臺、機台沒有操到極限、強調設備操作安全
機台設備面之機台使用(Use of New Machinery:請向於使用較安全、良好保養與熟悉的設備,同時也較少堆貨/趕出貨的狀況

所以按照以上推論,在景氣衰退期間,應該有較少的事故率(單位工時的工作傷害次數;意味著不是看總工作傷害次數=>已經把景氣好工時與產能較多的影響銷弭掉)
總體經濟景氣指標和事故率呈現正相關(景氣越好,事故率越高;景氣越差,事故率越低)
另一派學者((Beale & Nethercott, 1988; Jenkins, MacDonald, Murray, & Strathdee, 1982; Mattiasson, Lindgfirde, Nilsson, & Theorell, 1990).的說法剛好相反:
總體經濟景氣指標和事故率呈現負相關
景氣越差,事故率越高;理由與解釋為Fear of layoffs during recession might also increase the stress of employees, which could increase the probability of injuries員工擔心被資遣的心理壓力,反而會造成不專心進而塑造出較高的事故傷害率,
firms have more resources during economic expansion to buy new and efficient equipment, train workers, and maintain equipment than during periods of economic contraction公司在景氣好,才行有餘力能購買新機臺、訓練員工與好好保養設備
During recession, troubled firms might also force layoffs, which could result in more hours, higher workloads, and more exposures for those who remain.
不景氣的時候,為了縮減控制成本,留下來的勞工工作負荷反而更大
因此反而容易出事(事故率高)
備註:
以上正反兩方的想法與邏輯都沒有破綻,所以才要搞實地驗證,確認何者的看法較接近事實;而另一個延伸的想法是:景氣變化只是影響外生變數,而公司面對相同不景氣的因應對策才是獨立變數(為什麼有的公司懂得在不景氣的時候練兵先蹲後跳,有的在不景氣的時候卻飲鴆止渴,優先開除薪資水平高/但有經驗的資深員工與主管?=>反應出公司管理的好壞?如何量測一家公司管理的好或不好?)薪資/營收穩健?
另外就實證研究而言,一個可以試試看的idea是:
樣本選用景氣循環明顯的產業=>DRAMTFTLCD都是不錯的選項,從中選擇幾家公司,用景氣好和景氣差的事故傷害率資料來跑paired的比較;或許更能釐清以上爭議, 也消弭掉不同產業間的特性差異

3. Method and Measurement of Variables



Eqs. (1) to (3) were not directly estimated since most economic time series variables are not stationary. Regressing non-stationary variables produces spurious results even if the sample size is very large (Granger & Newbold, 1974). To determine whether the variables were stationary, we used the augmented Dickey-Fuller (ADF) test. The ADF (with constant) test revealed that the hypothesis of stationarity was rejected for most but not all of the variables. However, the first difference (the change score) of most variables was stationary. The ADF test results presented in Appendix 1 show that the first difference of the variables was stationary.
上面這段看不懂該來去學時間序列了

4. Results
4.1. The business cycle and workplace injuries
1顯示出兩者關聯性的回歸與檢定

實值GDP增加1%,事故率增加1.645%;失業率增加1%,事故率減少0.216%
GDP、失業率與工業生產指數對工傷率變化的關連性係數非常顯著,R-Square解釋變異約20-30%;一對一作圖與回歸線如圖2




提升解釋力的想法:
用主成分分析找出由GDPUnemploymentindustrial production index的主成分。
用此一主成分做為解釋變數,各產業的工傷率一樣為被解釋變數;另外加入產業別的平均薪資水平與中小企業比例=>間接測量企業管理的好壞與反應人數規模的影響。

4.2. Cyclicity of workplace injuries by industry
接著表2 By不同行業來檢定四項總經指標與工傷率間的相關性


顯然製造業與營造業關聯性最明顯,礦業也還可以,農業與貿易業關聯性不顯著
營造業與製造業的關聯,如圖3





想法:
石化業的部份不知道會不會顯著(猜測以工傷率當依變數,會不顯著,EX:六輕。或許改用事故率為被解釋變數,與景氣循環間的關聯性才會顯著!)

4.3. Business cycle mechanisms affecting workplace injuries
景氣週期影響工作傷害率的機制探討
表3改用勞動力與實體資本利用率來看待與詮釋此一相關性


備註:
作者大概是借用經濟學裏生產要素的觀念來加以詮釋,
個人認為,以因果關聯的詮釋而言,或許企業的管理水平才是真正的關鍵因素,只是難以衡量不同產業的管理水平差異來做為解釋變數。
                                                        
5. Discussion and conclusion(摘錄)
        Between1976 and 2007, a 1% increase in real GDP was associated with a 1.6% increase in the incidence rate of workplace injuries. During the same time period, a similar increase in the industrial production index was associated with a 0.8% increase in the incidence rate of workplace injuries, while a 1% increase in the rate of unemployment was associated with a 0.2% decrease in the incidence rate of workplace injuries.
Additional analyses demonstrated an association of injuries with our estimate of physical capital utilization in mining and labor utilization in construction. In manufacturing, injury rates were sensitive to both physical capital and labor utilization. A 1% increase in physical capital utilization in mining was associated with a 1.49% increase in the incidence of workplace injuries.
研究限制:
Overall, the results appear consistent with previous studies but potential biases may limit their interpretation. First, physical capital input was measured indirectly. Second, due to lack of data and multicollinearity problem, demographic, regulatory, and socio-economic variables that may affect the incidence of workplace injuries were not analyzed. Third, we only examined nonfatal injuries that may be influenced by reporting effects.
管理意涵:
First, employers in construction, manufacturing, and mining should be aware of the strong association between the business cycle and the incidence of injuries, which would point to the need for additional safety measures during expansions. Second, the mechanism through which the business cycle affects the incidence of workplace injuries was not the same across different industries, which points to the need for different prevention measures in each of the industries we examined.
未來研究方向:
In future research, fatal injuries could be examined to understand their sensitivity to the business cycle. Additional analyses could be conducted using industry-specific rather than economy-wide variables as business cycle indicators. This might be especially important to better understand industries, such as agriculture, that seem to be insensitive to economy-wide fluctuations. Capacity utilization could be modeled using more specific variables such as new equipment purchases. Lastly, but importantly, the validity of the mechanisms we portrayed in Fig. 1 could be empirically tested.


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