Effects of the Economic Cycle on Workplace Injury Rates: A Time Series Analysis of RIDDOR Data

Summary of key findings

• Previous analysis of the effect of the economic cycle on workplace injury rates has been updated to include the latest recession
• Modelling confirms the earlier finding that there is a positive correlation between injury rates and economic activity ie when the economy is growing, injury rates tend to increase and when it is falling they tend to reduce
• For the first time, we have attempted to quantify this impact and estimate that, for the latest recession, approximately 10% of the fall in injury rates is likely to have been due to the economy. For construction, this figure may be closer to 20%

Aim of the analysis

To develop a time series methodology using regression with ARIMA errors, that explores the relationship between economic activity and rates of workplace injury.

Background

A number of analyses have been produced that explore the effect; the most recent published analysis for Great Britain covers a period from 1986 to 2005. Currently there is no time series analysis that covers the 2008 recession. This analysis extends the currently available data up to 2012 and uses a time-series regression technique to examine the relationship between economic activity and workplace injury.

Methodology

In order to produce RIDDOR injury data that covers the period of interest (1986/87 - 2012/13) a number of data sources have been combined to form a single injury series. As the period covers a number of different industry classifications (SIC80, 92 & 07) a probabilistic method for translating the older codes into the latest classification has been developed. The analysis is restricted to employees, mainly due to the high levels of under-reporting by self-employed workers in the RIDDOR data. To produce quarterly employee injury rates, quarterly employee jobs data from the Workforce Jobs dataset was used from Nomis¹ official labour market statistics.

For Gross Domestic Product (GDP) data, the time series published with the second estimate for Q1 2013² was used. For the all industry figures the log of overall quarterly GDP data was used. To produce historical sector Gross Value Added (GVA) figures, the GVA by sector figures were combined with earlier data from Economic Trends Annual Supplement 2006³. The Economic Trends data was adjusted so that the two series match when they first overlap in 1997.

The time series method used for the analysis was a regression model with ARIMA errors, where a regression model is fitted to the data and an ARIMA model removes any remaining auto-correlation within the residuals. The GDP term was tested with up to a year lag and the model with the best GDP fit was chosen. All the series used in the analysis are differenced in order to make the data stationary and therefore the ARIMA methods used are applicable.

Major injuries (the largest component being slips & trips) are more likely to occur when it is colder ie in the winter. To allow for this effect, a quarterly temperature series was constructed, that used the minimum quarterly temperature from the Met Office's historical weather data (Durham weather station⁴). This series was used as a proxy for temperature and seasonal effects.

Three step functions have been defined:

• April 1996 to take account of a change in reporting due to the introduction of RIDDOR95.
• April 2012 to take account of a change in reporting due to the introduction of over-7-day reporting
• Between April 2003 and September 2011 to allow for differences in reporting due to the introduction of the Incident Contact Centre (ICC).

All these step functions were tested with each injury series, but only functions with significant coefficients where left in the final models.

Results

Table 1 shows the results of the analysis. Because the whole economy models use a different independent economic variable in the regression than the industry breakdown models (ie log (GDP) rather than Indexed GVA) the value of the coefficients are much larger for these models.

^a GDP variable for all industry: log(GDP)
** Significant at 95% level
--- No significant temperature term
† All industry refers to the entire economy

Discussion

The methodology developed within this analysis seems to produce results that are consistent with previous analyses. Davies, R & Jones, P (2006). Trends and context to rates of workplace injury detected a pro-cyclical relationship between major injury rates and GDP while in Davies, Rhys et al (2009) The impact of the business cycle on occupational injuries in the UK. Social science & medicine. 69 (2), pp. 178-182 a pro-cyclical relationship was detected between over-3-day injury rates and GDP. This analysis detects both of these effects. As well as the overall effect, the analysis also detects stronger effects within the construction and manufacturing sectors than in services (which actually produces a small significant negative coefficient for major injuries), which again is consistent with previous analysis. [It is worth noting that major injuries are not necessarily more serious than over-3-day injuries. The major injury definition covering the majority of the analysis period is given in Annex A].

Further analysis was performed on individual service industries, although the results are not clear, producing a mixture of positive and negative GDP coefficients that are not easy to interpret.

The analysis for agricultural employees indicated a negative correlation between injury and GDP. However, the recession may have had an effect on the employee/self-employed breakdown in agriculture and could have affected these results.

An attempt to analyse the two recessions separately using two dummy variables failed to produce any significant GDP terms. Performing analysis with dummy GDP variables that split the analysis period into two equal halves, gave similar results to those quoted in Table 1 with a single GDP term.

Table 2 calculates an estimate of the effect on the reported injury rates due to changes in GDP between 2007/08 and 2009/10. This is calculated for:

1. All industry
2. Construction
3. Manufacturing

The percentage is calculated by multiplying the change in the GDP variable between Q1 2008 and Q1 2010 by the relevant coefficient from Table 1; this is then divided by the change in injury rates between 2007/08 and 2009/10 for the injury series being modelled. This calculation can be applied to any industry/severity combination that contains a significant GDP term.

Further work

This analysis has assumed a consistent reporting level across the period and no adjustment has been made for under-reporting. Now the methodology has been developed, the next step would be to introduce an adjustment for under-reporting to see what effect, if any, this has on the results. It may then be possible to construct models for workers (ie employees + self-employed) which may help with the analysis in agriculture.

Annex A

Reportable major injury definition:

• Fracture, other than to fingers, thumbs and toes;
• Amputation;
• Dislocation of the shoulder, hip, knee or spine;
• Loss of sight (temporary or permanent);
• Chemical or hot metal burn to the eye or any penetrating injury to the eye;
• Injury resulting from an electric shock or electrical burn leading to unconsciousness, or requiring resuscitation or admittance to hospital for more than 24 hours;
• Any other injury leading to hypothermia, heat-induced illness or unconsciousness, or requiring resuscitation, or requiring admittance to hospital for more than 24 hours;
• Unconsciousness caused by asphyxia or exposure to a harmful substance or biological agent;
• Acute illness requiring medical treatment, or loss of consciousness arising from absorption of any substance by inhalation, ingestion or through the skin;
• Acute illness requiring medical treatment where there is reason to believe that this resulted from exposure to a biological agent or its toxins or infected material.

1. Nomis - official labour market statistics. Back to reference of footnote 1
2. Second estimate of GDP, Q1 2013. Back to reference of footnote 2
3. Economic Trends Annual Supplement 2006. Back to reference of footnote 3
4. Met Office historical weather data (Durham weather station). Back to reference of footnote 4