An economic framework to analyse illegal activities in the forestry sector

Adrian Whiteman 1


This paper suggests how cost-benefit analysis and game theory might be used to develop a rigorous analytical framework for the examination of illegality in the forestry sector. Using forest charge evasion as an example, it shows how this problem can be broken down into a series of probabilities and expected costs. It shows how illegal harvesting can be analysed as a combination of problems with inspection or monitoring, corruption, penalties or fines for illegal activities and the terms and conditions of employment for forestry field staff. The paper suggests how this framework might be used to analyse different types of illegal acts and describes measures that some countries are already taking to combat some of these problems. It also presents some general conclusions and recommendations about how illegality might be tackled in the forestry sector.


An effective legal system is a requirement for sustainable forest management, but illegal activities in the forestry sector are a problem in many countries. For example, in recent studies on forest charge collection in Africa (FAO, 2003), authors frequently noted the problems of corruption and illegal harvesting, which were blamed on:

Recommendations to combat problems such as these often include: increasing monitoring and control; raising the penalties for offences; and improving the terms and conditions of staff, but such recommendations are rarely based on a rigorous analysis of the problem.

The purpose of this paper is to try to improve the level of the debate on governance in the forestry sector, by proposing a relatively simple analytical framework in which these problems can be monitored and analysed. Although there are many different types of illegality in the forestry sector, it uses forest charge evasion as an example to demonstrate how this methodology might be developed. It focuses solely on the economics of this problem because this is probably a major driving force behind the problem.

The remainder of the paper is in four sections. The first describes the costs and benefits of forest charge evasion as a combination of probabilities and expected costs. The second section shows how published statistics might be used to estimate some of this information. The next section uses game theory to examine the specific problem of corruption. Finally, the paper presents some conclusions and recommendations about how this problem might be analysed further.

Cost-benefit analysis of forest charge evasion

At the simplest level, the economics of forest charge evasion comes down to the question of whether the costs of payment are higher or lower than the costs of non-payment (Equation 1). Each time forest products are harvested, the producer estimates what these costs will be and decides whether to pay charges or not.

The cost of payment (CLEG) includes all of the forest charges, plus transactions costs (e.g. the costs of measuring production, preparing all of the necessary documentation and travelling to make the payment, etc.). The cost of non-payment (E[CILL]) is an expected cost, which means that it is a function of a range of different possible costs, each multiplied by a different probability of occurrence (Equation 2).

If the producer chooses not to pay charges, it is possible that the illegal production will not be inspected, which has a cost (to the producer) of zero (i.e. (1-pINS).0 = 0). Thus, the expected cost of non-payment can be reduced to the probability of inspection and capture (pINS) multiplied by the minimum of the expected cost of bribing forestry officials to avoid prosecution (pBRB.CBRB) or the expected cost of fines or penalties ((1-pBRB).CPEN). These two latter costs are also expected costs, which will depend on a range of parameters such as: forestry officials' attitudes to corruption; their income; the likelihood of their being captured; the levels of fines or penalties for illegal acts; and the probability of successful prosecution. Although some of these parameters may be unknown at first, the producer will probably learn very quickly how to estimate them.

Data estimation

In order to analyse the costs and benefits of this decision process, it is necessary to have information about the probability of inspection (pINS) and the probability that the producer can bribe forestry officials to avoid prosecution (pBRB), plus the expected costs of bribery or prosecution. The cost of penalties can be estimated from previous prosecutions and the cost of bribes can, in theory, be derived from these figures as well (see below). The main difficulty lies in estimating the two probabilities. However, it may be possible to obtain some information about this from published statistics.

Figure 1 below shows how a probability tree can be used as a framework to analyse this information. This example uses statistics about the collection of charcoal conveyance fees, published by the Forestry Department in Zambia (Government of Zambia, 2000).2 According to these statistics, the total estimated production (TEP) of charcoal in 1999 was 1,614,391 bags.3 Total fee collection was ZKW 225million. When this is divided by the conveyance fee of ZKW360 per bag (Government of Zambia, 1997), this suggests that conveyance fees were paid for 626,000 bags of charcoal (i.e. this is the recorded legal production or RLP). The statistics also show that ZKW57 million was collected in penalty fees (equal to six times the conveyance fees) and a further ZKW13million was obtained from the sale of confiscated products. It can be estimated from these figures that about 39,000 bags of illegal charcoal production were inspected (i.e. these figures represent recorded illegal production or RIP).4

Figure 1 Using official statistics to estimate legal production, inspection and corruption in a country: the example of charcoal conveyance fees in Zambia

Assuming that the proportions of recorded legal and illegal production can be used as estimates of probability, they can be used (along with an assumption about the probability of inspection or pINS) to complete the probability tree shown in Figure 1. This figure assumes that pINS = 5% but, in reality, this probability is not known. However, with the information about total estimated production (TEP), recorded legal production (RLP) and recorded illegal production (RIP) it is possible to calculate all of the possible combinations of (pINS) and (pBRB) that are consistent with these statistics.

Equation 3 above shows that recorded illegal production (RIP) is equal to total estimated production (TEP) multiplied by the proportion (or probability) of illegal production (1-pLEG), the probability of inspection (pINS) and the probability that a bribe has not been paid to avoid a penalty or prosecution (1-pBRB). The amount of recorded legal production (RLP) can be used to derive (1-pLEG), such that this equation can be re-arranged to give the value of pINS for any value of pBRB that will be consistent with the published figures (Equation 4). Using the Zambian data, these combinations of pINS and pBRB are shown as the black line in Figure 2 and the example given in Figure 1 is shown as the dot on the line.

Figure 2 The relationship between the probabilities of inspection and corruption that are consistent with published statistics

Showing this information in this way is useful for three reasons:

Corruption and game theory

Game theory can be used to examine the costs and benefits to two (or more) parties of co-operation or non-co-operation and can show where co-operation (or collusion) is an optimal strategy. In game theory, the costs and benefits from co-operation or non-co-operation are shown in a "payoff matrix", which describes the costs and benefits to each party of the two alternative actions (Baland and Platteau, 1996). Figure 3 shows what the payoff matrix might look like for the situation of forest charge evasion. The first figure in each cell is the benefit to the producer and the second is the benefit to the forestry official.

Figure 3 A payoff matrix for corruption and forest charge evasion

Payoff matrix

Forestry official

Will not accept

Will accept


Doesn't offer bribe

-E[CPEN], 0


Offers bribe

-E[CPEN] - E[CCOR], 0


The producer has two strategies: to pay the expected penalties for the offence (E[CPEN]) or to offer a bribe to the forestry official (CBRB). The producer's expectation of the penalties for the offence (E[CPEN]) could be a separate corruption sub-game if they think that they can bribe the judiciary, but in most cases their expectation will be based on the average level of penalties in the past. In addition, there may be an additional cost (E[CCOR]) if they try and fail to offer a bribe (e.g. a fine for offering bribes).

The forestry official has two strategies: to refuse to accept a bribe or to accept a bribe. The payoff matrix above shows the costs and benefits to them of the two strategies. If they will not accept a bribe, their payoff is zero. If a bribe is offered and accepted, they receive the bribe (+CBRB) less any expected loss from disciplinary action against them if they are caught (E[CDIS]). If they ask for a bribe or signal their intention to do so, but the producer will not co-operate, they may also face this cost.

The above matrix has described the payoffs to each party in theory. In the real world, in countries where corruption is a problem, the probabilities of either the producer or official being caught and prosecuted for corruption are likely to be very small. Therefore, even where national laws might state very high fines for CCOR and CDIS, the expected costs (E[CCOR] and E[CDIS]) are likely to zero or very close to zero and the payoff matrix can be reduced to the simpler matrix shown in Figure 4.

Figure 4 shows that accepting a bribe is a weakly dominant strategy for the forestry official (i.e. they are indifferent if the producer does not offer a bribe and this is the better option for them if the producer does offer a bribe). The producer is in the same position. They have nothing to lose if the forestry official will not take a bribe and their situation is improved if they offer a bribe of an amount up to the expected penalties for the offence (i.e. -CBRB > -E[CPEN]).

Figure 4 The "real world" payoff matrix for corruption and forest charge evasion

Payoff matrix

Forestry official

Will not accept

Will accept


Doesn't offer bribe

-E[CPEN], 0

-E[CPEN], 0

Offers bribe

-E[CPEN], 0


This analysis offers the following interesting insights into the problem of corruption:


The above analysis has presented a framework for the analysis of corruption in the forestry sector. It has the following limitations: it does not take into account the non-economic factors that might affect corruption; it would be difficult to examine systemic corruption; and obtaining data for the analysis is problematic. However, the framework could be used to examine many different illegal activities and improve the level of debate on corruption. Indeed, going back to the general recommendations about corruption presented earlier, it seems to improve upon them as follows:


Baland, J, and Platteau, J, 1996, Halting degradation of natural resources: is there a role for rural communities?, FAO.

Broadhead, J, Bahdon, J, and Whiteman, A, in prep, Past trends and future prospects for the utilisation of wood for energy, GFPOS Working Paper GFPOS/WP/02, FAO.

FAO, 1996, Zambia: identification of forestry investment projects - identification report, Report No: 96/073 ADB-ZAM, FAO.

FAO, 2003, Workshop Proceedings: Reform of fiscal policies in the context of national forest programmes in Africa (13 - 16 November 2001, Abuja, Nigeria), FAO.

Government of Zambia, 1997, Statutory Instrument No 48 of 1997, Forest Licence (Amendment) Regulations 1997, Government of Zambia, Lusaka.

Government of Zambia, 2000, Forestry Department 1999 Annual Report, Ministry of Environment and Natural Resources, Lusaka.

Hamissou, G, 2001, The forest revenue system and government expenditure on forestry in Niger, Forest Finance Working Paper: FSFM/WP/05, FAO.

1 Forestry Officer, FAO, Viale delle Terme di Caracalla, 00100 Rome, Italy. Adrian.Whiteman@fao.org

2 Zambia has not been chosen because illegality is considered a problem there, but rather because of the positive fact that the Forestry Department there actually produces statistics on the capture of illegal production.

3 It should be noted that this figure is far lower than the estimates of 12 - 14 million bags in FAO (1996) and Broadhead et al (in prep). If these higher figures are more realistic, then the probability of inspection is far lower than reported here. The important point to note is that it is not difficult to estimate what the level of production in a country might be, because this can often be done by examining statistics about international trade, industry capacity and the likely size of the domestic market (e.g. based on population statistics).

4 It is assumed here that all of the penalty fees and confiscated items were taken from charcoal producers but, presumably, the Forestry Department could improve on this estimate.

5 Assuming everything else remains constant.