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How Can Economists Contribute to Mine Action?

Updated Wednesday, 18-Sep-2013 09:09:18 EDT

This article reviews the contribution economists can make in the area of humanitarian mine clearance and describes the development of a software package and manual designed to help managers decide which combination of machine and manual methods should be used to clear minefields to the required safety standard at the lowest cost.

Millions of emplaced mines in 62 countries likely cause "between 15,000 and 20,000 new landmine/UXO casualties each year,"1 mostly in rural areas of developing countries. They reduce agricultural production and incomes by making millions of hectares unavailable for crop production or livestock grazing.2 Their impact is primarily felt by the poor, who are most likely to be forced to enter mine-affected areas in search of firewood, drinking water or grazing for their livestock.3 Refugees are often unwilling to return home when their land has not been cleared of landmines, causing a long-term burden on host communities and aid agencies. The world has responded to the humanitarian costs and economic impact of landmines and unexploded ordnance by spending $2.53 billion (U.S.) on mine and unexploded ordnance clearance since 1992,1 but little of this spending has been subject to rigorous economic analysis.

There are at least four areas of mine action in which economic analysis can assist decision-making. The first (and possibly most controversial) is whether mines should be cleared at all-do the benefits of clearance exceed the costs? Assuming that clearance is beneficial, decisions need to be made on the appropriate standard of clearance, as well as which areas should be cleared first and which methods should be used.

Should Landmines be Cleared?

At the national level, most cost-benefit evaluations of landmine clearance suggest overall costs are far greater than benefits. Geoff Harris estimates that expenditures to remove landmines from Cambodia would produce benefits-in the form of saved lives, reduced injuries and medical costs, and greater agricultural output-that are worth just 2 percent of the costs.4 In Mozambique, the benefits would be worth only 10 percent of the costs.5 For Bosnia and Herzegovina, Shannon Mitchell concludes that demining cannot be justified on development grounds.6

These cost-benefit analyses were constrained by inadequate data, which may have influenced the conclusions. In particular, they value injuries and premature death from landmines according to the present value of lost earnings (or lost gross domestic product). This foregone earnings approach is no longer popular in developed countries because it greatly underestimates the value of life.7 Instead, researchers and policymakers now use estimates of the value of statistical life, calculated from reports by survey respondents of how much they would be willing to pay to avoid risks or from market-based, revealed-preference studies. The theoretical superiority of broader measures of the value of life is recognised by Harris, but because no estimates exist for countries with landmine problems, the outdated, foregone earnings method was used.4 Perhaps as a result, saved lives and disabilities are a small part of Harris' calculated benefit of landmine clearance, whereas the value of statistical life often provides the largest benefit from environmental standards and other risk-reducing activities in developed countries.

John Gibson, et al.8 used the contingent-valuation method to investigate the value of statistical life for a rural population in northeast Thailand where the incidence rate of landmine fatalities and injuries is 34 per 100,000 in affected communities.9 Using VSL, the value of lives saved from landmine clearance is at least an order of magnitude greater than the values used in existing studies. Applying this VSL to the data used by Harris4 for Cambodia suggests the total value of benefits of mine clearance may be around 36 percent of the value of costs, compared to 2 percent of the value of costs using the foregone earnings method. But even using VSL, it appears the cost of clearing all mines would far exceed the benefits, meaning that complete clearance would result in a net loss to society, especially when it is realised that scarce development funds could have been spent on other activities that would result in a large net benefit.

The high costs of clearance and lack of net benefits from comprehensive mine clearance underline the importance of considering the benefits of alternative uses of mine clearance funds. For example, Steven Lim suggests "opening up alternative, safer income sources, such as factory work located away from landmines, may prove to be a quicker and more cost-effective way of reducing landmine casualties than traditional demining activities."10 It is realised that such an approach would be contrary to Article 5 of the Ottawa Convention in which "each State Party undertakes to destroy or ensure the destruction of all anti-personnel mines in mined areas under its jurisdiction or control, as soon as possible but not later than ten years after the entry into force of this Convention for that State Party."11

What is the Appropriate Clearance Standard?

It has been suggested that mine action agencies may overestimate the benefits of clearance, causing them to spend excessive amounts on risk reduction. Most landmines are located in poor countries, but rich country donors and non-governmental organizations pay for most landmine clearance. Gareth Elliot and Harris suggest donors may value the lives saved by clearing mines using standards from their own (rich) countries.5 This also may explain why the standards are so stringent-the goal of accredited mine action agencies is to remove all mines (and unexploded bombs) in an area.12 This standard requires expensive manual inspection of almost every inch of ground because existing machines cannot find every mine.

The economic approach suggests that the socially efficient standard is to reduce the risk from landmines only to the point where the marginal cost per life saved is the same as for other risk-reducing activities.13 Hence, in poorer countries, where people face many health risks, less stringent mine clearance standards might allow spending to be diverted to other priorities. An example may make this choice clearer. Suppose $1 million (U.S.) is available for development activities in a particular area, and clearance of a "low risk" area would cost $1 million and may save about five lives over a 10-year period. The same $1 million might also be used for safety measures along a busy stretch of road (e.g., safe crossings near schools and pedestrian barriers at busy intersections). If these improvements are expected to save more than five lives over 10 years, then the money would be better spent on road safety.

Which Areas Should be Cleared First?

Assessment of priorities for mine action is essentially little different from any other prioritisation exercise. The idea that limited funds should be concentrated on areas with the greatest need is widely accepted. Methodologies are well-developed and, with appropriate modifications, can be applied to mine action. The Geneva International Centre for Humanitarian Demining and many other agencies have carried out mine action prioritisation exercises. This often involves Landmine Impact Surveys "to provide a ranking of communities by severity of mine impact that can inform the allocation of mine action resources."14 The GICHD surveys use three main indicators to estimate a composite Mine Action Score that is used to create the ranking. The indicators are the nature of contamination (e.g., type and density of mines), the types of livelihoods and infrastructure to which mines block access, and the number of recent victims.

Which Methods Should be Used?

The growing number of purpose-built mechanical mine action machines in use and under development and the increasing variety of ways in which machines are used to support mine action suggested a need for the collection of information on the cost-effectiveness of alternative methods of mechanical mine action. Such information can serve at least two purposes. First, a greater awareness of the cost-effectiveness of various methods of mine clearance may help demining agencies use their existing resources more effectively. Second, more widely available and standardised data on the cost-effectiveness of mechanical equipment relative to other clearance methods could help planners and developers allocate support to the machines and techniques that offer the greatest promise.

Against this background, the GICHD commissioned the Management Research Centre of the University of Waikato (New Zealand) to provide advice on the appropriate methods and standards for analysing the cost-effectiveness of mechanical mine action. In support of this advice, the commission also included a requirement to provide a software tool that demining organisations could use for carrying out their own cost-effectiveness analyses. Staff from the university and the GICHD visited mine action agencies in Bosnia and the Cambodian border region in order to develop an understanding of the key variables affecting cost-effectiveness. A cost-effectiveness model was then developed as a macro that is used in Microsoft Excel. A key objective throughout this process was to develop a practical system that would require little additional data and that could be used by field management staff without additional training.

The Cost-Effectiveness Model

Model purpose and overview. Mine action is an expensive activity that can often be undertaken using a number of different methods. There is a wide range in the unit cost of these methods, even after adjusting for quality and variation in other key variables. Clearly, it is vitally important that scarce mine action resources be deployed in such a way as to achieve the best possible outcomes. Cost-effectiveness analysis has a key role to play in achieving this goal.

Cost-effectiveness analysis can be approached in two ways:

  1. By determining the least costly method of achieving a known goal-in this case, mine clearance to a level of at least 99 percent, or the fixed-effectiveness approach
  2. By finding the policy alternative that will provide the largest benefits for a given level of expenditure-the fixed-budget approach. CEMOD follows the fixed-effectiveness approach

CEMOD compares different methods of mine clearance. Analysis of alternative methods is generally more useful than comparing different machines in isolation, since each machine may make a different contribution to mine clearance. A mine clearance method is defined as any combination of techniques (e.g., machines, manual clearance, dogs, etc.) that achieves the standard goal of at least 99-percent clearance. For example, a given piece of land might be cleared to the same standard by four alternative methods:

  1. Manual mine clearance only
  2. Flail followed by manual mine clearance
  3. Vegetation cutter followed by manual mine clearance
  4. Flail followed by dog teams, supported by manual mine clearance


Figure 1: CEMOD system menu.
Graphic courtesy of Dan Marsh/MAIC

Data entry. When CEMOD is started, users are provided with the system menu (see Figure 1).

Users click on "Data Capture" if they want to enter new project data or want to edit values already entered in the model. The "Reports" button takes them to another menu where they can choose to view and print some (or all) of the standardized cost-effectiveness and cost-comparison reports.

The cost-effectiveness model requires three types of data:

  1. Basic information on the location and details of the project and on the type of analysis being conducted (e.g., past costs vs. projected costs)
  2. Information on area clearance rates and the time inputs (e.g., man-days for manual clearance and days of machine use), which might typically come from log books
  3. Information on costs, which would typically come from project accounts and budgets or equipment catalogues

The data-capture menu is used to access data entry screens for each of these three types of data (see Figure 2).


Figure 2: CEMOD data-capture menu.
Graphic courtesy of Dan Marsh/MAIC

In the "Area Cleared Data Entry Menu," users are asked to attribute areas cleared and time inputs (man-days and machine-days) to each of the various methods that their agency has used (for analyses of past costs) or is considering using (for projections).

In the "Costs Data Entry Menu," users are asked to enter data on the actual or projected costs of the mine clearance project. The costs in the model are grouped into four categories: staff salaries, staff allowances, consumables and running costs, and capital equipment. Within each of these cost categories, there is no restriction on how many cost items are specified. Thus, the model can handle analyses of both past costs, based on detailed budgets, as well as projected costs, for which there might be rather less detail available. For each cost item, the user is asked to specify a name or description for the item, the number of items used and the unit-cost per item per year.

For each cost item, the user is asked to allocate the number of units across various cost categories (e.g., management and administration, mine survey, medical support, manual mine clearance teams, dog teams and individual machines). This allocation of the number of units of each cost item allows the user to identify which costs are associated with which machines. By identifying costs with machines and other procedures, it is possible to identify, from a single budget, different costs for different mine clearance methods. Thus, the allocation of the cost items is a particularly important part of the model. Further details of CEMOD data entry and operation procedures are provided in "Mechanical Mine Action Study: Cost Effectiveness Component, Draft Final Report."15, 16

Model output and interpretation. The reports menu is used to view and print the results of the model's calculations, as well as print the worksheets that contain the input data on area cleared, days used and costs by category.

The "Standard Reports" button lets the user view and print four reports (see Table 1).

Report Key Results
S1 This report provides total cost, cost per square metre, and cost ratio/annual cost saving (compared to base case) for each mine clearance method.
S2 Annual cost, by method and cost category.
S3 Cost per square metre and potential savings by method and cost category.
S4  Machine demining, annual cost, cost per day and cost per square metre.
S5 Annual cost summary.
Table 1.

The "Key Results" report (Table 2) includes total cost, cost per square metre, cost ratio and annual cost saving. Based on the imaginary data in Table 2, use of a flail, followed by a combination of manual deminers and dog teams provides the most cost-effective clearance method. Costs per square metre (about 1.2 square yards) are $3.41 (U.S.) compared to $11.29 using fully manual methods (the base case). Use of this method over the whole area to be cleared would result in a cost savings of $7.2 million, compared to manual demining.

Method Total Cost (in U.S. dollars) Cost per sq. m Cost Ratio vs. Base Case Annual Cost Saving (in U.S. dollars)
Manual only 1,128,742 11.29 100%  
Flail + manual  1,156,574 5.78 51% 5,009,138
Flail, manual, dogs   1,365,574 3.41 30% 7,166,151
Veg. cutter, manual  365,602 7.31 65% 3,617,597
Area Reduction then manual  304,352 6.09 54% 4,732,347
Veg. cutter, manual, dogs  247,085 4.94 44% 5,774,610
Area reduction, MP, manual  587,267 11.75 104% -416,703
Table 2: Examples of Key Results Report (Report S1: Key Results). Reporting Period: 2001.

It must be stressed that cost per square metre should only be compared where all other factors are equal, i.e., for clearance of mined land of similar characteristics. Differences in cost per square metre between minefields may be a reflection of changes in minefield characteristics, rather than the cost-effectiveness of alternative mine clearance procedures.

Factors affecting cost-effectiveness. The cost-effectiveness model is designed to provide standardised calculations of the cost of mine clearance using actual or projected data. Many factors are likely to influence the cost-effectiveness of particular methods of mine clearance in particular settings. Foremost amongst these will be labour and machine costs, and the comparative productivity levels of manual-clearance teams, dog teams and mechanical-clearance machines. However, other idiosyncratic factors are also likely to be important and these are not incorporated into CEMOD even though they are likely to be relevant to the decisions agencies make about the most effective way to clear a given area.

For example, an agency may use different machines to do a similar task (say, vegetation clearance) but on land with different characteristics. While it would be possible to have a model that considers factors such as slope vs. flat, dry vs. wet, such a model would be quite complicated, and it would be more difficult to use the model for planning purposes. Instead, it is expected that when the current model gives costs for each machine, the user can work out if the higher cost for one machine is justified by the more difficult terrain.

A similar complication comes from the type of mine that is expected in a given field. Mechanical procedures feasible when working with anti-personnel mines may not be feasible when working on anti-tank mines, and the use of suitably armoured machinery is likely to affect the cost comparisons. Hence, the information provided by CEMOD cannot replace the detailed knowledge of project managers; instead, it is designed to provide additional information so they can make better-informed decisions about mine clearance.

There are at least two other factors that should be considered when interpreting the cost-effectiveness data. First, there is no explicit premium for timeliness (speed of clearance) in the calculations carried out by CEMOD. However, CEMOD reports do indicate clearance rates and cost per day, so information on the timeliness of particular methods can be extracted. It is unlikely a standardised model could provide more detail because local factors will dictate what value is placed on timeliness. Second, although cost per square metre seems to be an accepted metric for recording output, there is some argument for considering the depth of clearance. A hidden advantage of some machines may be that they clear to a greater depth than is possible with other techniques. A comparison solely on the basis of cost per square metre will miss this point and may unfairly indicate an advantage for one machine over another.

Conclusions

Many of the key issues of mine action are amenable to economic analysis. In this respect, mine action is no different from any other activity that uses scarce resources. Policy in this field has often been strongly influenced by both military and humanitarian concerns and approaches. Mine action agencies have often seen mine clearance as being a technical problem requiring technical solutions. Too often, insufficient attention has been paid to cost-effectiveness in determining the best course of action. Humanitarian concerns have brought the impact of mines to the world's attention and were one factor that led to the signing of the Ottawa Convention. However, the Convention's requirement that all mines be cleared will not always be the best way of improving the plight of those affected by mines. Likewise, the U.N. standard of 99.6-percent clearance will often be too stringent and will tend to divert funds away from other risk-reducing activities where more deaths and injuries could be avoided at lower cost.

CEMOD was developed as a practical tool that would be used by managers to assess the cost-effectiveness of alternative mine clearance methods. Feedback received so far has been positive, and some managers are reported to be making use of CEMOD. Given the large sums of money involved, potential cost savings are substantial.

Further uptake of CEMOD may be achieved if appropriate follow-up activities are carried out. Some managers will require advice and support before being convinced of the benefits of cost-effectiveness analysis. There may also be areas where managers will require input from a trained economist (e.g., in some complex cost-allocation decisions). There is also scope to further develop the model based on feedback on the first version.

This article has demonstrated the importance of economic analysis if scarce funds are to be used efficiently to assist the development of mine-affected areas. The key questions to be addressed are the following:

  • Should mine-affected areas be cleared?
  • What is the appropriate standard of clearance?
  • Which areas should be cleared first?
  • Which methods should be used?

Better answers to these questions can only help the millions of people who live and work at risk of death or injury from mines and UXO.

This paper describes work done for the GICHD as part of their Mechanical Mine Action Study that was carried out jointly with John Gibson, University of Canterbury and Geua Boe-Gibson and includes material from Marsh, Boe-Gibson and Gibson and Barns, et al.17

Biography

Dan Marsh is a senior lecturer in economics at the University of Waikato in New Zealand. He has 25 years' experience working on rural development projects in Asia, the Middle East and Africa.

References

  1. ICBL. Landmine Monitor Report 2005: Towards a Mine-Free World. New York: International Campaign to Ban Landmines. http://www.icbl.org/lm/2005/. Accessed Feb 16, 2006.
  2. Andersson, N., Palha da Sousa, C., & Paredes, S. (1995). Social Cost of Land Mines in Four countries: Afghanistan, Bosnia, Cambodia, and Mozambique. British Medical Journal, 311, 718-721.
  3. Roberts, S., & Williams, J. (1995). After the Guns Fall Silent: The Enduring Legacy of Landmines. Washington: Vietnam Veterans of America Foundation.
  4. Harris, G. (2000). The Economics of Landmine Clearance: Case Study of Cambodia. Journal of International Development, 12(2), 219-225.
  5. Elliot, G., & Harris, G. (2001). A Cost-Benefit Analysis of Landmine Clearance in Mozambique. Development Southern Africa, 18(5), 625-633.
  6. Mitchell, S. K. (2004). Death, Disability, Displaced Persons and Development: The Case of Landmines in Bosnia and Herzegovina. World Development, 32(12), 2105-2120.
  7. Rosen, S. (1988). The Value of Changes in Life Expectancy. Journal of Risk and Uncertainty, 1(3), 285-304.
  8. Gibson, J., Barns, S., Cameron, M., Lim, S., Scrimgeour, F., & Tressler, J. (2005). The Value of Statistical Life and the Economics of Landmine Clearance in Developing Countries. Department of Economics, University of Waikato. (Working Paper in Economics 4/05)
  9. Survey Action Center. (2003). Landmine Impact Survey: Kingdom of Thailand: Survey Action Center and Norwegian People's Aid. Available online at http://www.sac-na.org/pdf_text/thailand/FinalReport.pdf. Accessed Nov. 14, 2005.
  10. Lim, S. (2004). Partial Landmine Clearance: The Contribution of Manufacturing to Casualty Reduction. Department of Economics, University of Waikato. (Draft Mimeo)
  11. United Nations. Convention on the Prohibition of the Use, Stockpiling, Production and Transfer of Anti-personnel Mines and on Their Destruction. Ottawa, Canada. Sept. 18, 1997. http://www.un.org/Depts/mine/UNDocs/ban_trty.htm. Accessed Oct. 10, 2005.
  12. UNMAS (2003). International Mine Action Standards. New York: United Nations Mine Action Service.
  13. Viscusi, W. K. (2000). Risk Equity. Journal of Legal Studies, 29(2), 843-871.
  14. GICHD. (2001). A Study of Socio-Economic Approaches to Mine Action: Geneva International Centre for Humanitarian Demining.
  15. Marsh, D., Boe-Gibson, G. and Gibson, J. Mechanical Mine Action Study: Cost Effectiveness Component, Draft Final Report.
  16. Marsh, D., Boe-Gibson, G. and Gibson, J. User Guide: Cost- Effectiveness Model for Mechanical Mine Clearance CEMOD.
  17. Barns, S., Cameron, M., Gibson, J., Lim, S., Marsh, D., Scrimgeour, F. and Tressler, J. (2004). Valuing the Risk of Death and Injury From Landmines in Thailand. Paper presented at the Tenth Annual Conference of the New Zealand Agricultural and Resource Economics Society, Blenheim.

Contact Information

Dan Marsh
Department of Economics
University of Waikato
Private Bag 3105
Hamilton, New Zealand
Tel: +64 7 838 4950
Fax: +64 7 838 4331
E-mail: dmarsh@waikato.ac.nz