Abstract
In fisheries, regime shifts from common-pool to tradable individual quota rights have led to a reduction in race-to-fish behavior—in particular under adverse weather conditions, and thus to a reduction in the rate of fatal injuries. At the same time, tradability of quota rights has led to some fleet consolidation, along with a shift to larger vessels, which may have also affected safety. We isolate the role of changing fleet composition and size in the overall reduction of fatal injuries by simulating a counterfactual scenario: what weather conditions fishermen would brave if quota rights were individual but not tradable and thus could not have been a factor in fleet consolidation. Looking at the specific case of the U.S. reef fish fishery in the Gulf of Mexico to calibrate our model, we find that consolidation boosts the safety gains by 10–15%: while the remaining active vessels are inherently less safe and target much larger annual catches (thus finding it more difficult to avoid poor weather conditions), they are also more efficient, thereby reducing overall labor-hours and exposure to risk.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
For example, if an operator acquires enough allocations for, say, ten 5-day trips, then the operator will attempt to time the trips over the (weather-based) best 50 or so days in the year. Based on the known cumulative distribution function of wind speed, they can achieve this by opting to take a trip whenever the weather forecast predicts wind speeds below 4 m/s (or 10 mph).
Alvarez and Schmidt (2006) and Solís et al. (2014) find some evidence that poor weather conditions can adversely impact fishing activities. Both studies use binary variables for weather conditions. Alvarez and Schmidt (2006) study productive efficiency for the hake fishery in northern Spain, where both the weather and fishery are different from our study. The authors find that good and average sea conditions have a 26% and 22% positive effect on landings, compared to bad sea conditions. Solís et al., on the other hand, estimate a stochastic distance frontier production function for red snapper in the GoM and find a 10% adverse effect of poor weather conditions on production levels across vessels, but do not find any evidence of adverse effect of poor weather conditions when vertical line gear (a technology that is predominantly used in the red snapper fishery) is used.
Note that this probability refers to the event of a single human casualty during a trip. Accidents with multiple casualties are rare and are thus not taken into account.
Our focus on deaths—as opposed to non-fatal injuries—is mainly driven by the fact that the latter are not systematically reported, whereas Centers for Disease Control (CDC) data on fatal injuries are more reliable. However, available U.S. Coast Guard data on non-fatal injuries appear to be correlated to fatalities data (a correlation coefficient of 0.54).
On the presumption that higher income might encourage more risk-taking behavior, we also tested total revenues per crew as an explanatory variable. The coefficient turned out to be statistically insignificant—even though it had the expected positive sign—and we therefore omitted it.
We initially considered the Poisson maximum likelihood method, which requires the equality of mean and variance. Examination of the histogram and tests of normality for the dependent variable show that the distribution of the dependent variable has overdispersion with a long left tail. Also, the LM test statistic is 17.24 (prob. 0.0001), which thus rejects the null hypothesis of no overdispersion. Since the Poisson normality assumption is violated, the estimated fatality function parameters based on the standard Poisson maximum likelihood model are not robust due to the tendency to underestimate the standard errors. We therefore use the negative binomial method, which considers cross-sectional heterogeneities.
There is a sizable literature on the relationship between firm size and occupational injury, although it is not clear to what extent insights that are garnered from manufacturing industries apply to the fisheries sector. Leigh (1989), for instance, finds that very small firms (those with less than 20 employees) and very large firms (those with more than 1000 employees) are safer than medium sized ones. In our GoM fisheries context, however, the smallest vessels employ two crewmembers, while the largest ones rarely employ more than six. Studies of injury frequency in the construction industry, such as in McVittie et al. (1997) or Kines and Mikkelsen (2003), might be more relevant here, given that the construction industry is also dominated by small firms. In line with Jin and Thunberg (2005), both of these studies also find a negative correlation between firm size and rates of serious injury.
It is also worth pointing out that cost data items that are provided by owner-captains—the set of trips where the owner was on board—tend to be highly spread, often far above or far below the mean for all trips that are taken on comparable vessels. Such inconsistency in reporting is much less prevalent among trips that are led by hired captains: when the owner was not on board.
For Class 1 license holders, who account for 91% of historic landings, ITQ shares for red snapper were based on the best 10 consecutive years of landings from 1990–2004 (NOAA 2016a). ITQ shares for grouper-tilefish species were based on 1999–2004 landings, with an allowance for dropping one year of data (NOAA 2016b).
For instance, the price of gag grouper was not significantly affected by the sharp 90% decrease in TAC in 2011—presumably because South-Atlantic gag, whose quota is set separately by another council, is an almost perfect substitute. Furthermore, the same wholesalers operate in both regions, while retailers and consumers may find it easy to substitute one species for another, or to substitute imports for domestic catches.
When the annual aggregate quota was equal to 2.3 million lbs. in June of 2010, the wholesale price of red snapper was about $4/lb. The July 2010 quota increase to 3.2 million lbs. led to a 20% price decrease. If we assume that by the end of June, vessels had caught half of their initial quota (1.15 million lbs.), then they would have had an extra 2.05 million lbs. to catch over the second half of the year. In other words, the quantity supplied, and thus the quantity purchased, almost doubled, which suggests an elasticity of substitution of \(\varepsilon = (dQ/Q)/(dp/p) = 1/-0.2 = -5\).
This is also true for the estimates of the production function for other (less prevalent) gears, although the results are not reported here for brevity.
References
Adelaja, A., McCay, B., & Menzo, J. (1998). Market share, capacity utilization, resource conservation, and tradable quotas. Marine Resource Economics, 13, 115–134.
Alvarez, A., & Schmidt, P. (2006). Is skill more important than luck in explaining fish catches? Journal of Productivity Analysis, 26(1), 15–25.
Ashenfelter, O., & Greenstone, M. (2004). Using mandated speed limits to measure the value of a statistical life. Journal of Political Economy, 112(1), S226–S267.
Bergland, H., & Pedersen, P. A. (1997). Catch regulation and accident risk: The moral hazard of fisheries management. Marine Resource Economics, 12(4), 281–92.
Bockstael, N. E., & Opaluch, P. (1983). Discrete modeling of supply response under uncertainty: The case of fishery. Journal of Environmental Economics and Management, 10(2), 125–37.
Brandt, S. (2007). Evaluating tradable property rights for natural resources: The role of strategic entry and exit. Journal of Economic Behavior & Organization, 63, 158–176.
Clark, C. W., Clarke, F. H., & Munro, G. R. (1979). The optimal exploitation of renewable resource stocks: Problems of irreversible investment. Econometrica, 47(1), 25–47.
Davis, M. E. (2012). Perceptions of occupational risk by U.S. commercial fishermen. Marine Policy, 36, 28–33.
Decker, C. S., & Flynn, D. T. (2008). Work-related accidents and the level of market competition: An analysis of worker injury rates at U.S. Steel Corporation, 1907–1939. Economic Inquiry, 46(3), 438–53.
Decker, C. S., & Wohar, M. (2006). Environmental accidents and industry structure. Journal of Economic Research, 11, 17–47.
Eggert, H., & Martinsson, P. (2004). Are commercial fisheries risk-lovers? Land Economics, 80(4), 550–60.
GMFMC. (2006). Final Amendment 26 to the Gulf of Mexico Reef Fish Fishery Management Plan to establish a red snapper individual fishing quota program. Tampa, FL: Gulf of Mexico Fishery Management Council.
Golbe, D. (1986). Safety and profits in the airline industry. Journal of Industrial Economics, 34(3), 305–318.
Jin, D., & Thunberg, E. (2005). An analysis of fishing vessel accidents in fishing areas off the northeastern United States. Safety Science, 43(8), 523–40.
Kennet, D. M. (1993). Did deregulation affect aircraft engine maintenance? An empirical policy analysis. The RAND Journal of Economics, 24(4), 542–558.
Kines, P., & Mikkelsen, K. L. (2003). Effects of firm size on risks and reporting of elevation fall injury in construction trades. Journal of Occupational and Environmental Medicine, 45(10), 1074–1078.
Lavetti, K. (2015). Estimating preferences in hedonic wage models: Lessons from the deadliest catch. Ohio State University Working Paper.
Leigh, J. P. (1989). Firm size and occupational injury and illness incidence rates in manufacturing industries. Journal of Community Health, 14(1), 44–52.
Lian, C., Singh, R., & Weninger, Q. (2010). Fleet restructuring, rent generation, and the design of individual fishing quota programs: Empirical evidence from the Pacific coast ground fishery. Marine Resource Economics, 24(2), 329–359.
Marvasti, A. (2017). Value of life and injury: Evidence from a U.S. fishery. Working Paper.
Marvasti, A., & Dakhlia, S. (2017). Occupational safety and the shift from common to individual fishing quotas in the Gulf of Mexico. Southern Economic Journal, 83(3), 705–20.
McVittie, D., Banikin, H., & Brocklebank, W. (1997). The effects of firm size on injury frequency in construction. Safety Science, 27(1), 19–23.
Mistiaen, J. E., & Strand, I. E. (2000). Location choice of commercial fishermen with heterogenous risk preferences. American Journal of Agricultural Economics, 82(5), 1184–90.
NOAA National Marine Fisheries Service, Southeast Regional Office. (2016a). 2015 Gulf of Mexico Red Snapper individual fishing quota annual report.
NOAA National Marine Fisheries Service, Southeast Regional Office. (2016b). 2015 Gulf of Mexico Grouper-Tilefish individual fishing quota annual report.
Overstreet, E., Perruso, L., & Liese, C. (2017). Economics of the Gulf of Mexico reef fish fishery—2014. NOAA Technical Memorandum. NMFS-SEFSV-716.
Schnier, K. E., Horrace, W. C., & Felthoven, R. G. (2009). The value of statistical life: Pursuing the deadliest catch (p. 49). Center for Policy Research, No: Syracuse University.
Smith, M. D., & Wilen, J. E. (2005). Heterogeneous and correlated risk preferences in commercial fishermen: The perfect storm dilemma. The Journal of Risk and Uncertainty, 31(1), 53–71.
Solís, D., del Corral, J., Perruso, L., & Agar, J. (2014). Evaluating the impact of individual fishing quotas (ITQs) on the technical efficiency and composition of the US Gulf of Mexico red snapper commercial fishing fleet. Food Policy, 46, 74–83.
Squires, D., & Kirkley, J. (1996). Individual transferable quota in a multiproduct common property industry. Canadian Journal of Economics, 29(2), 318–42.
Viscusi, W. K. (1993). The value of risks to life and health. Journal of Economic Literature, 31, 1912–1946.
Acknowledgements
The authors thank Devin Lucas of the Centers for Disease Control and James G. Law of the U.S. Coast Guard for data and Eric Thunberg for valuable feedback on an earlier version.
Funding
The opinions expressed herein are those of the authors and do not necessarily reflect the views of NOAA, whose support Grant EA-133F-12-BA-0034 is gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
1.1 Estimating the Impact of Weather on Per-Trip Output
To examine the effect of weather conditions on landings, we estimated the production function for trips that are taken by vessels that land a positive amount of red snapper; we distinguish among different fishing technologies—in particular the so-called “long line” and “vertical line” gears. We consider the aggregate annual output for all fish species that are caught by these vessels. We tried various specifications, but regressing the log of output on the log transformations of wind speed, labor-hours, and vessel length seems to offer the best fit, and those results are reported here. In Table 10, we first report the parameter estimates for all vessels and all types of gears combined. To account for (linear) technological progress, we add a time variable to the model. Autoregressive (AR) parameters are used to correct for the first-order serial correlation in the models. Like Solís et al. (2014), we find a negative correlation between wind speed and output in the all-gears model.
We examine the data to explore whether pooling of vessels—regardless of gear type—is appropriate. For this purpose, we distinguish vessels by the two dominant gear types: 92% of the vessels are equipped with vertical lines, while 6% use long lines. In the more restricted data set, which is limited to only vertical and long line gears, the wind speed coefficient loses some statistical significance. We next estimate the production function for the vertical line and long line gears separately. The F-statistic for the Chow test is 73.5, relative to the critical value of 3.32 (DF: 4, 51,580) at the 1-percent level, rejecting the null hypothesis of equality of coefficients between the two gears. Therefore, estimated coefficients of wind speed in columns 4 and 5 are more robust, showing no statistically significant evidence of a relation between wind speed and the level of landings.
We further explore the effect of wind speed on output by estimating the production function for each vessel size separately for the entire sample (Table 11) and for the long lines and vertical line gears sub-sample (Table 12). There is no consistent and/or robust relation between wind speed and the level of output in the models that are here,Footnote 13 which justifies our simplifying assumption that wind speed has no impact on per-trip catch.
1.2 Simulation Flowcharts
See Fig. 8.
Flowcharts
Rights and permissions
About this article
Cite this article
Dakhlia, S., Marvasti, A. Regulatory Change, Market Structure, and Fatalities: The Case of the Gulf of Mexico Reef Fish Fishery. Rev Ind Organ 57, 1–26 (2020). https://doi.org/10.1007/s11151-019-09712-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11151-019-09712-7