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Shoplifting, monitoring and price determination
The Journal of Socio-Economics 38 (2009) 608–610
Contents lists available at ScienceDirect
The Journal of Socio-Economics journal homepage: www.elsevier.com/locate/soceco
Shoplifting, monitoring and price determination Gideon Yaniv COM Academic Studies, Department of Economics, 7 Rabin Ave., 75190 Rishon Letzion (Tel-Aviv), Israel
a r t i c l e
i n f o
Article history: Received 18 January 2009 Received in revised form 14 February 2009 Accepted 24 March 2009 JEL classification: K42 D11 M31
a b s t r a c t Shoplifting is a major crime problem costing American retailers more than $10 billion per year. Surprisingly, despite the evolvement of an extensive theoretical literature on the economics of some major economic crimes, shoplifting has failed to attract economists’ attention. The present paper applies the economic toolbox to this problem, developing a principal–agent type model of shoplifting and shoplifting control. The model examines the customer’s decision of whether to shoplift or not as well as the store’s profit-maximizing price and monitoring intensity. The paper challenges the conventional wisdom that the observed rise in shoplifting calls for intensified monitoring and higher prices, showing that a rational response to increased shoplifting involves a reduction in both monitoring and prices. © 2009 Elsevier Inc. All rights reserved.
Keywords: Shoplifting Monitoring Price determination Public shame
1. Introduction Shoplifting is the act of stealing merchandise offered for sale in a retail store, usually by concealing it in a purse, pocket, bag, or under a coat. Hollinger and Davis (2002) estimate that shoplifting costs the American retailers more than $10 billion per year, which is roughly equivalent to the annual cost of auto theft. Based on their report, McGoey (2005) calculates that shoplifting occurs 330–440 million times per year, which amounts to about 1.0–1.2 million shoplift incidents every day at a loss rate of $19,000–25,300 dollars per minute. In 2007, more than 625,000 shoplifting apprehensions took place annually in just 24 large retail companies in the US which represent 19,151 stores with combined annual sales in excess of $689 billion (Hayes International, 2008). This reflects a substantial increase of 9.16% from 2006, when the number of apprehended shoplifters increased by 11.2% from the prior year. To combat the rise in shoplifting, most large retailers have increased their expenditures on retail security, installing video-surveillance cameras and employing plain-clothes agents specifically trained to observe customers as they shop, detect and apprehend shoplifters. Despite these efforts, shoplifting in the UK has risen by 70% since the year 2000 (British Retail Consortium, 2006). Consequently, whole retail store chains have gone out of business due to failure to control shoplifting, whereas those who survive attempt to pass their security costs and shoplifting losses
E-mail address: [email protected]. 1053-5357/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.socec.2009.03.005
along to customers in terms of higher prices. Hollinger and Davis (2002) estimate that an average family of four spends more than $440 per year in higher prices because of shoplifting. Traditionally, shoplifting has been a research subject of psychologists and criminologists, who have mainly investigated the characteristics and motivations of shoplifters (e.g., Bannister, 1979; Gudjonsson, 1990; Klemke, 1992; Krasnovsky and Lane, 1998; Farrington, 1999). However, because studies indicate that a significant proportion of consumers steal from shops, there has also been interest in viewing shoplifting as a type of consumer behavior rather than as a criminal act (e.g., Cox et al., 1990; Babin et al., 1994; Hayes, 1999; Tonglet, 2002). Surprisingly, despite the evolvement of an extensive theoretical literature on the economics of some major economic crimes, shoplifting has failed to attract economists’ attention. The present paper applies the economic toolbox to this problem, developing a principal–agent type model of shoplifting and shoplifting control. The model examines the customer’s decision of whether to shoplift or not as well as the store’s profitmaximizing price and monitoring intensity. The paper challenges the conventional wisdom that the observed rise in shoplifting calls for intensified monitoring and higher prices, showing that a rational response to increased shoplifting involves a reduction in both monitoring and prices. 2. The setting Consider a neighborhood of a given number of customers served by a monopolistic store. Suppose that there is just a single good that
G. Yaniv / The Journal of Socio-Economics 38 (2009) 608–610
can be shoplifted from the store. Suppose also that each customer wishes to consume just one unit of this good, deriving utility v from its consumption. Hence, a customer may either buy the good, at the price p (≤ v) set by the store, or shoplift the good by attempting to exit the store without paying for the good. Customers are assumed to be strategic in the sense that they calculate the cost and benefit involved in shoplifting and attempt to do so if it appears to be worth their while. Enforcement is shared between the government and the store: the store monitors customers with intensity m(≤1), and the government sets the fine, F, for shoplifting. If caught shoplifting, the customer is detained by the store’s security officers until the arrival of the police. He or she must then pay for the good and will be summoned to court to be charged and fined for attempted shoplifting. However, the monetary fine does not reflect the full penalty for shoplifting, since the customer also suffers shame and embarrassment due to being publicly exposed as a shoplifter. The disutility caused by public shame and embarrassment, e, varies among customers, where e ∈ [0, 1]. It is assumed to follow a uniform distribution. 3. The customer’s choice Each customer must decide whether to buy or shoplift the good. Because the utility from consuming the good is obtained in any case, the customer’s problem reduces to comparing the costs of the alternative ways of acquiring the good and choosing the less costly one. If he or she buys the good, the cost is p. If he or she shoplifts the good, the expected cost is m[p + e + F]. The customer will shoplift the good if m[p + e + F] ≤ p, or if e ≤ [(1 − m)p − mF]/m ≡ e¯ , where (1 − m)p − mF is the expected (monetary) profit from shoplifting. A prerequisite for shoplifting is, of course, that the expected profit be positive. Given the uniform distribution of e, the number of shoplifters, S, will be
S=
e¯
de = 0
(1 − m)p − mF = S(p, m, F), m
(1)
where Sp (p, m, F) > 0, Sm (p, m, F) < 0 and SF (p, m, F) < 0. Hence, a higher price, a lower monitoring intensity, or a smaller fine will increase the number of shoplifters. 4. The store’s choice Normalizing the population size to 1, the store’s potential revenue from selling the good is p. Shoplifting drives actual revenue below p. To reduce the loss from shoplifting, the store monitors customers with some intensity, m. However, monitoring is costly. The store’s problem is to choose p and m so as to maximize its expected profit, , defined as the difference between expected revenue, R, and monitoring costs, C. All other operating costs (e.g., rent, salaries of non-monitoring employees, administration, building maintenance, equipment repairs, electricity and insurance) are assumed fixed. Inventory is viewed as sunk cost. Shoplifting losses are thus captured by the loss of potential revenue. Expected revenue has two components: revenue from sales to customers who opt not to shoplift, p[1 − S(p, m, F)], and expected revenue from catching customers who opt to shoplift, mpS(p, m, F). Adding these two components yields R(p, m, F) = p[1 − (1 − m)S(p, m, F)].
(2)
Monitoring costs are assumed to rise with monitoring intensity at non-decreasing rates. That is, C = C(m), where C (m) > 0 and C (m) ≥ 0. The store’s problem is thus formulated as Max ˘(p, m, F) = R(p, m, F) − C(m).
(3)
609
Substituting (2) in (3), and noticing from (1) that pSp (p, m, F) = S(p, m, F) + F, the first-order condition determining p (for a given m) is ˘p (p, m, F) = 1 − (1 − m)[2S(p, m, F) + F] = 0.
(4)
Substituting (1) in (4) and solving for p yields p(m, F) =
m[1 + (1 − m)F] 2(1 − m)2
,
(5)
which substituting in (3) generates the expected profit as a function of m and F m[1 + (1 − m)F]2 ˜ ˘(m, F) = − C(m). 4(1 − m)2
(6)
The first-order condition determining m is 2 ˜ m (m, F) = [1 + (1 − m)F][1 + m + (1 − m) F] − C (m) = 0, ˘ 4(1 − m)3
(7)
the solution of which yields the optimal monitoring intensity for the store, m*(F), as a function of the fine. The second-order condition, ˜ mm (m, F) < 0, is assumed to hold at the optimum. Substituting ˘ m*(F) in (5) yields the optimal price for the store, p*(F), as a function of the fine. 5. Response to increased shoplifting We now turn to examine the store’s response to an exogenous increase in shoplifting. To do so, suppose that the government lowers the fine for shoplifting. Eq. (1) reveals that the number of shoplifters will increase for given p and m. Because the store does not collect the fine, the reduction in the fine has no direct effect on its expected revenue, therefore representing a pure rise in shoplifting. Proposition 1. An exogenous increase in shoplifting will lead to a reduction in monitoring intensity. Proof. dm∗ (F) dF
Totally differentiating (7) with respect to F, we have =−
˜ mF (m, F) ˘ > 0, ˜ mm (m, F) ˘
(8)
˜ mF (m, F) > 0. Hence, optimal as a quick glance at (7) reveals that ˘ monitoring falls as the fine is lowered, implying that an exogenous increase in shoplifting will reduce the intensity of monitoring. Proposition 2. An exogenous increase in shoplifting will lead to a reduction in price. Proof.
Totally differentiating (5) with respect to F yields
dp∗ (m, F) = dF
∂p∗ (m, F) ∂m
∂p∗ (m, F) dm∗ (F) + , dF ∂F
(9)
where 1 + m + (1 − m)F ∂p∗ (m, F) = > 0, ∂m 2(1 − m)3
(10)
and ∂p∗ (m, F) m > 0. = 2(1 − m) ∂F
(11)
Substituting (8), (10) and (11) in (9) reveals that its sign is unambiguously positive. Hence, the optimal price falls as the fine is lowered, implying that an exogenous increase in shoplifting will reduce the price of the good. Propositions 1 and 2 suggest that the store’s rational response to an exogenous increase in shoplifting is to reduce both its monitoring intensity and price. This suggestion contradicts the conventional
610
G. Yaniv / The Journal of Socio-Economics 38 (2009) 608–610
wisdom and evidence regarding stores’ practical monitoring and price responses to increased shoplifting. The rationale underlying it is that an exogenous increase in shoplifting increases losses that are beyond the store’s control, therefore driving the store to reduce losses which it does control (i.e., monitoring costs) through reducing monitoring intensity. This, however, encourages shoplifting. If the store were to raise the price, it would encourage shoplifting even further! To offset the adverse effect of a lower monitoring intensity on shoplifting, the store finds it worthwhile to reduce the price. Does the store’s reaction help reduce shoplifting? Substituting (5) in (1), the number of shoplifters when p is optimally adjusted to m may be expressed as a function of m and F 1 − (1 − m)F ˜ S˜ = = S(m, F). 2(1 − m)
(12)
Evidently, S˜ is positively related to m. Hence, a reduction in m (optimally compensated by a reduction in p) acts to moderate the exogenous increase in the number of shoplifters resulting from a fall in F. While the uncompensated effect of a reduction in m is to encourage shoplifting (as implied by Eq. (1)), the discouraging effect on shoplifting of the accompanying reduction in p more than offsets the former, resulting in less shoplifting than would prevail if the store did not react at all to the exogenous increase in shoplifting or reacted in the intuitively expected way by increasing both m and p. Increasing m and decreasing p would, of course, be more helpful in reducing shoplifting, but since the store seeks to maximize its expected profit rather than to minimize shoplifting, the latter response would be counter-productive as it attempts to cope with an increase in shoplifting losses by increasing related losses (monitoring costs) even further. 6. Concluding remarks The present paper makes a first step in applying economic reasoning to the problem of shoplifting, developing a simple principal–agent model with the hazard of being publicly embarrassed and humiliated if caught shoplifting playing a major role in customers’ decision of whether or not to shoplift. Challenging the conventional wisdom that the observed rise in shoplifting calls for intensified monitoring and higher prices, the paper shows that a rational response to increased shoplifting involves a reduction in both monitoring and prices. There are several ways by which the model can be extended to examine the robustness of this result in a more complex setting as well as to address additional questions. First, the customer population can be expanded to include honest customers who never shoplift regardless of the potential benefit, as well as kleptomaniacs who always shoplift regardless of the potential cost. The latter group may include not just customers
who feel compelled to steel from the store, but also those who do so because of the thrill and excitement involved. Second, part (or all) of the fine for shoplifting can be assumed to be transferred to the store as a compensation for incurring the costs of monitoring. While this does not seem to be common practice in the case of shoplifting, it is sometimes assumed to be so in the literature on economic crimes such as commercial piracy. Notice that if a compensatory fine is transferred to the store, it is no longer possible to examine its response to increased shoplifting by reducing the fine, because a lower fine would directly affect the store’s expected revenue, therefore failing to reflect a pure increase in shoplifting. A possible alternative would be to examine the store’s reaction to an increase in the size of the kleptomaniac population. Third, the government can be introduced to the arena as an active player who determines the optimal fine for shoplifting with the purpose of maximizing social welfare (or minimizing social loss). Fourth, the monopolistic setting can be replaced with a competitive one, leaving the price-taking stores with the problem of determining their monitoring intensity only. An interesting question is whether the competitive price responds differently to increased shoplifting than the monopolistic price. Finally, the closely related issue of employee theft, which constitutes the major component of retail inventory shrinkage, can be incorporated in the analysis. References Babin, B.J., Robin, D.P., Pike, K., 1994. To steal or not to steal? Consumer ethics and shoplifting. In: Park, C.W., Smith, D.C. (Eds.), Marketing Theory and Applications. American Marketing Association, Chicago, IL, USA, pp. 200–205. Bannister, J.P., 1979. Illegal consumer activity: an exploratory study of shoplifting. Quarterly Review of Marketing (Summer), 13–22. British Retail Consortium, 2006. 2006 Retail Crime Survey., http://news.bbc.co. uk/1/hi/uk/6039074.stm. Cox, D., Cox, A.D., Moschis, G.P., 1990. When consumer behavior goes bad: an investigation of adolescent shoplifting. Journal of Consumer Research 17 (2), 149–159. Farrington, D.P., 1999. Measuring, explaining and preventing shoplifting: a review of British research. Security Journal 12 (1), 9–27. Gudjonsson, G.H., 1990. Psychological and psychiatric aspects of shoplifting. Medicine, Science and Law 30 (1), 45–51. Hayes International, 2008. 20th Annual Retail Theft Survey., http://www. Hayesinternational.com/ts shplftng.html. Hayes, R., 1999. Shop theft: an analysis of shoplifter perceptions and situational factors. Security Journal 12 (2), 7–18. Hollinger, R.C., Davis, J.L., 2002. 2001 National Retail Security Survey: Final Report, Security Research Project. University of Florida, Gainesville, FL, USA. Klemke, L.W., 1992. The Sociology of Shoplifting: Boosters and Snitches Today. Praeger, Westport, CT, USA. Krasnovsky, T., Lane, R., 1998. Shoplifting: a review of the literature. Aggression and Violent Behavior 3 (3), 219–235. McGoey, C.E., 2005. Shoplifting Facts., http://www.crimedoctor.com/shopliftingfacts.htm. Tonglet, M., 2002. Consumer misbehaviour: an exploratory study of shoplifting. Journal of Consumer Behavior 1 (4), 336–355.
Contents lists available at ScienceDirect
The Journal of Socio-Economics journal homepage: www.elsevier.com/locate/soceco
Shoplifting, monitoring and price determination Gideon Yaniv COM Academic Studies, Department of Economics, 7 Rabin Ave., 75190 Rishon Letzion (Tel-Aviv), Israel
a r t i c l e
i n f o
Article history: Received 18 January 2009 Received in revised form 14 February 2009 Accepted 24 March 2009 JEL classification: K42 D11 M31
a b s t r a c t Shoplifting is a major crime problem costing American retailers more than $10 billion per year. Surprisingly, despite the evolvement of an extensive theoretical literature on the economics of some major economic crimes, shoplifting has failed to attract economists’ attention. The present paper applies the economic toolbox to this problem, developing a principal–agent type model of shoplifting and shoplifting control. The model examines the customer’s decision of whether to shoplift or not as well as the store’s profit-maximizing price and monitoring intensity. The paper challenges the conventional wisdom that the observed rise in shoplifting calls for intensified monitoring and higher prices, showing that a rational response to increased shoplifting involves a reduction in both monitoring and prices. © 2009 Elsevier Inc. All rights reserved.
Keywords: Shoplifting Monitoring Price determination Public shame
1. Introduction Shoplifting is the act of stealing merchandise offered for sale in a retail store, usually by concealing it in a purse, pocket, bag, or under a coat. Hollinger and Davis (2002) estimate that shoplifting costs the American retailers more than $10 billion per year, which is roughly equivalent to the annual cost of auto theft. Based on their report, McGoey (2005) calculates that shoplifting occurs 330–440 million times per year, which amounts to about 1.0–1.2 million shoplift incidents every day at a loss rate of $19,000–25,300 dollars per minute. In 2007, more than 625,000 shoplifting apprehensions took place annually in just 24 large retail companies in the US which represent 19,151 stores with combined annual sales in excess of $689 billion (Hayes International, 2008). This reflects a substantial increase of 9.16% from 2006, when the number of apprehended shoplifters increased by 11.2% from the prior year. To combat the rise in shoplifting, most large retailers have increased their expenditures on retail security, installing video-surveillance cameras and employing plain-clothes agents specifically trained to observe customers as they shop, detect and apprehend shoplifters. Despite these efforts, shoplifting in the UK has risen by 70% since the year 2000 (British Retail Consortium, 2006). Consequently, whole retail store chains have gone out of business due to failure to control shoplifting, whereas those who survive attempt to pass their security costs and shoplifting losses
E-mail address: [email protected]. 1053-5357/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.socec.2009.03.005
along to customers in terms of higher prices. Hollinger and Davis (2002) estimate that an average family of four spends more than $440 per year in higher prices because of shoplifting. Traditionally, shoplifting has been a research subject of psychologists and criminologists, who have mainly investigated the characteristics and motivations of shoplifters (e.g., Bannister, 1979; Gudjonsson, 1990; Klemke, 1992; Krasnovsky and Lane, 1998; Farrington, 1999). However, because studies indicate that a significant proportion of consumers steal from shops, there has also been interest in viewing shoplifting as a type of consumer behavior rather than as a criminal act (e.g., Cox et al., 1990; Babin et al., 1994; Hayes, 1999; Tonglet, 2002). Surprisingly, despite the evolvement of an extensive theoretical literature on the economics of some major economic crimes, shoplifting has failed to attract economists’ attention. The present paper applies the economic toolbox to this problem, developing a principal–agent type model of shoplifting and shoplifting control. The model examines the customer’s decision of whether to shoplift or not as well as the store’s profitmaximizing price and monitoring intensity. The paper challenges the conventional wisdom that the observed rise in shoplifting calls for intensified monitoring and higher prices, showing that a rational response to increased shoplifting involves a reduction in both monitoring and prices. 2. The setting Consider a neighborhood of a given number of customers served by a monopolistic store. Suppose that there is just a single good that
G. Yaniv / The Journal of Socio-Economics 38 (2009) 608–610
can be shoplifted from the store. Suppose also that each customer wishes to consume just one unit of this good, deriving utility v from its consumption. Hence, a customer may either buy the good, at the price p (≤ v) set by the store, or shoplift the good by attempting to exit the store without paying for the good. Customers are assumed to be strategic in the sense that they calculate the cost and benefit involved in shoplifting and attempt to do so if it appears to be worth their while. Enforcement is shared between the government and the store: the store monitors customers with intensity m(≤1), and the government sets the fine, F, for shoplifting. If caught shoplifting, the customer is detained by the store’s security officers until the arrival of the police. He or she must then pay for the good and will be summoned to court to be charged and fined for attempted shoplifting. However, the monetary fine does not reflect the full penalty for shoplifting, since the customer also suffers shame and embarrassment due to being publicly exposed as a shoplifter. The disutility caused by public shame and embarrassment, e, varies among customers, where e ∈ [0, 1]. It is assumed to follow a uniform distribution. 3. The customer’s choice Each customer must decide whether to buy or shoplift the good. Because the utility from consuming the good is obtained in any case, the customer’s problem reduces to comparing the costs of the alternative ways of acquiring the good and choosing the less costly one. If he or she buys the good, the cost is p. If he or she shoplifts the good, the expected cost is m[p + e + F]. The customer will shoplift the good if m[p + e + F] ≤ p, or if e ≤ [(1 − m)p − mF]/m ≡ e¯ , where (1 − m)p − mF is the expected (monetary) profit from shoplifting. A prerequisite for shoplifting is, of course, that the expected profit be positive. Given the uniform distribution of e, the number of shoplifters, S, will be
S=
e¯
de = 0
(1 − m)p − mF = S(p, m, F), m
(1)
where Sp (p, m, F) > 0, Sm (p, m, F) < 0 and SF (p, m, F) < 0. Hence, a higher price, a lower monitoring intensity, or a smaller fine will increase the number of shoplifters. 4. The store’s choice Normalizing the population size to 1, the store’s potential revenue from selling the good is p. Shoplifting drives actual revenue below p. To reduce the loss from shoplifting, the store monitors customers with some intensity, m. However, monitoring is costly. The store’s problem is to choose p and m so as to maximize its expected profit, , defined as the difference between expected revenue, R, and monitoring costs, C. All other operating costs (e.g., rent, salaries of non-monitoring employees, administration, building maintenance, equipment repairs, electricity and insurance) are assumed fixed. Inventory is viewed as sunk cost. Shoplifting losses are thus captured by the loss of potential revenue. Expected revenue has two components: revenue from sales to customers who opt not to shoplift, p[1 − S(p, m, F)], and expected revenue from catching customers who opt to shoplift, mpS(p, m, F). Adding these two components yields R(p, m, F) = p[1 − (1 − m)S(p, m, F)].
(2)
Monitoring costs are assumed to rise with monitoring intensity at non-decreasing rates. That is, C = C(m), where C (m) > 0 and C (m) ≥ 0. The store’s problem is thus formulated as Max ˘(p, m, F) = R(p, m, F) − C(m).
(3)
609
Substituting (2) in (3), and noticing from (1) that pSp (p, m, F) = S(p, m, F) + F, the first-order condition determining p (for a given m) is ˘p (p, m, F) = 1 − (1 − m)[2S(p, m, F) + F] = 0.
(4)
Substituting (1) in (4) and solving for p yields p(m, F) =
m[1 + (1 − m)F] 2(1 − m)2
,
(5)
which substituting in (3) generates the expected profit as a function of m and F m[1 + (1 − m)F]2 ˜ ˘(m, F) = − C(m). 4(1 − m)2
(6)
The first-order condition determining m is 2 ˜ m (m, F) = [1 + (1 − m)F][1 + m + (1 − m) F] − C (m) = 0, ˘ 4(1 − m)3
(7)
the solution of which yields the optimal monitoring intensity for the store, m*(F), as a function of the fine. The second-order condition, ˜ mm (m, F) < 0, is assumed to hold at the optimum. Substituting ˘ m*(F) in (5) yields the optimal price for the store, p*(F), as a function of the fine. 5. Response to increased shoplifting We now turn to examine the store’s response to an exogenous increase in shoplifting. To do so, suppose that the government lowers the fine for shoplifting. Eq. (1) reveals that the number of shoplifters will increase for given p and m. Because the store does not collect the fine, the reduction in the fine has no direct effect on its expected revenue, therefore representing a pure rise in shoplifting. Proposition 1. An exogenous increase in shoplifting will lead to a reduction in monitoring intensity. Proof. dm∗ (F) dF
Totally differentiating (7) with respect to F, we have =−
˜ mF (m, F) ˘ > 0, ˜ mm (m, F) ˘
(8)
˜ mF (m, F) > 0. Hence, optimal as a quick glance at (7) reveals that ˘ monitoring falls as the fine is lowered, implying that an exogenous increase in shoplifting will reduce the intensity of monitoring. Proposition 2. An exogenous increase in shoplifting will lead to a reduction in price. Proof.
Totally differentiating (5) with respect to F yields
dp∗ (m, F) = dF
∂p∗ (m, F) ∂m
∂p∗ (m, F) dm∗ (F) + , dF ∂F
(9)
where 1 + m + (1 − m)F ∂p∗ (m, F) = > 0, ∂m 2(1 − m)3
(10)
and ∂p∗ (m, F) m > 0. = 2(1 − m) ∂F
(11)
Substituting (8), (10) and (11) in (9) reveals that its sign is unambiguously positive. Hence, the optimal price falls as the fine is lowered, implying that an exogenous increase in shoplifting will reduce the price of the good. Propositions 1 and 2 suggest that the store’s rational response to an exogenous increase in shoplifting is to reduce both its monitoring intensity and price. This suggestion contradicts the conventional
610
G. Yaniv / The Journal of Socio-Economics 38 (2009) 608–610
wisdom and evidence regarding stores’ practical monitoring and price responses to increased shoplifting. The rationale underlying it is that an exogenous increase in shoplifting increases losses that are beyond the store’s control, therefore driving the store to reduce losses which it does control (i.e., monitoring costs) through reducing monitoring intensity. This, however, encourages shoplifting. If the store were to raise the price, it would encourage shoplifting even further! To offset the adverse effect of a lower monitoring intensity on shoplifting, the store finds it worthwhile to reduce the price. Does the store’s reaction help reduce shoplifting? Substituting (5) in (1), the number of shoplifters when p is optimally adjusted to m may be expressed as a function of m and F 1 − (1 − m)F ˜ S˜ = = S(m, F). 2(1 − m)
(12)
Evidently, S˜ is positively related to m. Hence, a reduction in m (optimally compensated by a reduction in p) acts to moderate the exogenous increase in the number of shoplifters resulting from a fall in F. While the uncompensated effect of a reduction in m is to encourage shoplifting (as implied by Eq. (1)), the discouraging effect on shoplifting of the accompanying reduction in p more than offsets the former, resulting in less shoplifting than would prevail if the store did not react at all to the exogenous increase in shoplifting or reacted in the intuitively expected way by increasing both m and p. Increasing m and decreasing p would, of course, be more helpful in reducing shoplifting, but since the store seeks to maximize its expected profit rather than to minimize shoplifting, the latter response would be counter-productive as it attempts to cope with an increase in shoplifting losses by increasing related losses (monitoring costs) even further. 6. Concluding remarks The present paper makes a first step in applying economic reasoning to the problem of shoplifting, developing a simple principal–agent model with the hazard of being publicly embarrassed and humiliated if caught shoplifting playing a major role in customers’ decision of whether or not to shoplift. Challenging the conventional wisdom that the observed rise in shoplifting calls for intensified monitoring and higher prices, the paper shows that a rational response to increased shoplifting involves a reduction in both monitoring and prices. There are several ways by which the model can be extended to examine the robustness of this result in a more complex setting as well as to address additional questions. First, the customer population can be expanded to include honest customers who never shoplift regardless of the potential benefit, as well as kleptomaniacs who always shoplift regardless of the potential cost. The latter group may include not just customers
who feel compelled to steel from the store, but also those who do so because of the thrill and excitement involved. Second, part (or all) of the fine for shoplifting can be assumed to be transferred to the store as a compensation for incurring the costs of monitoring. While this does not seem to be common practice in the case of shoplifting, it is sometimes assumed to be so in the literature on economic crimes such as commercial piracy. Notice that if a compensatory fine is transferred to the store, it is no longer possible to examine its response to increased shoplifting by reducing the fine, because a lower fine would directly affect the store’s expected revenue, therefore failing to reflect a pure increase in shoplifting. A possible alternative would be to examine the store’s reaction to an increase in the size of the kleptomaniac population. Third, the government can be introduced to the arena as an active player who determines the optimal fine for shoplifting with the purpose of maximizing social welfare (or minimizing social loss). Fourth, the monopolistic setting can be replaced with a competitive one, leaving the price-taking stores with the problem of determining their monitoring intensity only. An interesting question is whether the competitive price responds differently to increased shoplifting than the monopolistic price. Finally, the closely related issue of employee theft, which constitutes the major component of retail inventory shrinkage, can be incorporated in the analysis. References Babin, B.J., Robin, D.P., Pike, K., 1994. To steal or not to steal? Consumer ethics and shoplifting. In: Park, C.W., Smith, D.C. (Eds.), Marketing Theory and Applications. American Marketing Association, Chicago, IL, USA, pp. 200–205. Bannister, J.P., 1979. Illegal consumer activity: an exploratory study of shoplifting. Quarterly Review of Marketing (Summer), 13–22. British Retail Consortium, 2006. 2006 Retail Crime Survey., http://news.bbc.co. uk/1/hi/uk/6039074.stm. Cox, D., Cox, A.D., Moschis, G.P., 1990. When consumer behavior goes bad: an investigation of adolescent shoplifting. Journal of Consumer Research 17 (2), 149–159. Farrington, D.P., 1999. Measuring, explaining and preventing shoplifting: a review of British research. Security Journal 12 (1), 9–27. Gudjonsson, G.H., 1990. Psychological and psychiatric aspects of shoplifting. Medicine, Science and Law 30 (1), 45–51. Hayes International, 2008. 20th Annual Retail Theft Survey., http://www. Hayesinternational.com/ts shplftng.html. Hayes, R., 1999. Shop theft: an analysis of shoplifter perceptions and situational factors. Security Journal 12 (2), 7–18. Hollinger, R.C., Davis, J.L., 2002. 2001 National Retail Security Survey: Final Report, Security Research Project. University of Florida, Gainesville, FL, USA. Klemke, L.W., 1992. The Sociology of Shoplifting: Boosters and Snitches Today. Praeger, Westport, CT, USA. Krasnovsky, T., Lane, R., 1998. Shoplifting: a review of the literature. Aggression and Violent Behavior 3 (3), 219–235. McGoey, C.E., 2005. Shoplifting Facts., http://www.crimedoctor.com/shopliftingfacts.htm. Tonglet, M., 2002. Consumer misbehaviour: an exploratory study of shoplifting. Journal of Consumer Behavior 1 (4), 336–355.