A household model of opium-poppy cultivation in Afghanistan

Accepted Manuscript Title: A Household Model of Opium-Poppy Cultivation in Afghanistan: Results and Lessons for Policymaking (Household Model of Opium-Poppy Cultivation) Authors: Victoria A. Greenfield, Craig A. Bond, Keith Crane PII: DOI: Reference:

S0161-8938(17)30064-9 http://dx.doi.org/doi:10.1016/j.jpolmod.2017.06.002 JPO 6371

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Please cite this article as: Greenfield, Victoria A., Bond, Craig A., & Crane, Keith., A Household Model of Opium-Poppy Cultivation in Afghanistan: Results and Lessons for Policymaking (Household Model of Opium-Poppy Cultivation).Journal of Policy Modeling http://dx.doi.org/10.1016/j.jpolmod.2017.06.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

A Household Model of Opium-Poppy Cultivation in Afghanistan: Results and Lessons for Policymaking (Household Model of Opium-Poppy Cultivation) January 2017 Victoria A. Greenfield (Corresponding author) George Mason University Department of Criminology, Law and Society 4400 University Drive Fairfax, VA 22030 USA Craig A. Bond RAND Corporation 1200 South Hayes Street Arlington, VA 22203 USA Keith Crane Science and Technology Policy Institute 1899 Pennsylvania Ave. NW Washington, DC 20006 USA

Authors’ note: This article draws from research presented in Greenfield, V.A., K. Crane, C.A. Bond, N. Chandler, J. Luoto, and O. Oliker (2015), “Reducing the Cultivation of Opium Poppies in Southern Afghanistan,” RR-1075-INL, Santa Monica, CA: RAND Corporation, which was funded by the U.S. Department of State, Bureau of International Narcotics and Law Enforcement Affairs. The report, including technical appendixes, is available as of January 17, 2017, at http://www.rand.org/pubs/research_reports/RR1075.html. The views expressed in this article are those of the authors and should not be attributed to their employers or the project sponsor.

Abstract

This article presents and implements a theoretical foundation for exploring Afghan farmers’ decisions to cultivate opium poppy that can be used to develop supply-control policy and guide future empirical research. To our knowledge, we are the first to employ a household production model that allows farmers to choose between opium-poppy and licit-crop cultivation to maximize their expected utility, while weighing the costs, benefits, and risks of agricultural production and consumption jointly. We derive policy lessons from the theoretical analysis and empirical evidence that reflect the complexity of farmers’ decision-making and the socioeconomic and environmental factors that influence it.

I. Introduction Each year, farmers in Afghanistan harvest thousands of tons of opium from opium poppy, “papaver somniferum,” which flow eventually to illicit markets in Asia, Europe, and, to a lesser extent, North America. That tonnage, much of which originates in Afghanistan’s southern provinces, travels as opium and its derivative products (morphine and heroin) and accounts for a large majority of global production (Figure 1).

[Figure 1 about here.]

Opium and its derivatives add substantially to Afghanistan’s national income, represent Afghanistan’s leading export, and provide 100s of thousands of full-time equivalent jobs (International Monetary Fund 2014, Byrd and Mansfield 2014, 6), but they also take a toll on the country’s governance and security, by fueling corruption, undermining rule of law, and supplying financial support to insurgents.

Over the past decade or more, the international community has spent billions of dollars on policies to reduce Afghan opium-poppy cultivation, calling on troops and civilians to implement them. These efforts have involved carrots and sticks, found, for example, in rural development and eradication programs, respectively. The former include narrowly defined crop-substitution programs and more-broadly defined alternative-livelihoods programs that might promote a mix of alternative agricultural and non-agricultural pursuits, educational opportunities, and infrastructure. Each approach, be it carrot or stick, attempts to target farmers’ incentives to cultivate opium poppy by reducing the relative appeal of the crop. 1

Arguably, the success or failure of such efforts will depend partly on how well they target farmers’ incentives, and, by extension, on the policy community’s understanding of the underlying decision-making process and the socio-economic and environmental factors that drive it. Greenfield et al. (2015, 46-49), for example, identify over a dozen such factors (hereafter, “cultivation factors”) that play a part in decision-making, relating largely to governance and security, risks of eradication, drought, and disease; various household-specific characteristics, including landholdings and wealth; access to off-farm income; the use of agricultural inputs and technology; and commodity prices. At the start of each growing season, a farmer must decide whether to cultivate opium poppy or engage in other economic activities, given whatever information he has about the status of each factor.

The main contribution of this article is to provide a theoretical foundation for exploring the drivers of opium-poppy cultivation in Afghanistan and, on that basis, developing supply-control and related policy. 1 To date, the literature does not provide a model or set of models that comprehensively or consistently explains why Afghan farmers do what they do or how they will respond to changes in ground conditions, whether policy-induced or otherwise. Although true “one-stop shopping” might be unattainable, we believe that our framework represents a step toward a more holistic understanding of farm-level decision-making that can benefit policymaking directly and suggest avenues for future empirical research.

1

This article draws from research presented in the RAND report, “Reducing the Cultivation of Opium Poppies in

Southern Afghanistan,” Greenfield et al. (2015), which was funded by the U.S. Department of State, Bureau of International Narcotics and Law Enforcement Affairs. The report, including technical appendixes, is available as of January 17, 2017, at http://www.rand.org/pubs/research_reports/RR1075.html.

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Our approach is among the few (e.g., Andersson 2013, Clemens 2008, Greenfield 1991, Ibanez and Carlsson 2010, Ibanez and Martinsson 2013, Lind, Moene, and Willumsen 2014, and Palacios 2012) that treats the grower’s decision to cultivate a drug crop (opium poppy, coca, or cannabis) as the focal point of a formal behavioral model. Others, such as Caulkins and Hao (2008), Costa Storti and De Grauwe (2009), Kennedy, Reuter, and Riley (1993), Mejia and Restrepo (2016), and Oladi and Gilbert (2015), have developed structural representations of drug-crop cultivation, but they are nested in market equilibrium models and lack sufficient detail to support a close examination of decision-making. In addition, Angrist and Kugler (2008), Davalos (2016), Dion and Russler (2008), Garcia-Yi (2014), Moreno-Sanchez, Kraybill, and Thompson (2003), Reyes (2014), and Rouse and Arce (2006), and other econometricallyoriented studies provide empirical insight to the cultivation decision. 2

To our knowledge, we are the first to employ a household production model in which farmers choose between cultivating opium poppy and licit crops to maximize their expected utility, while weighing the costs, benefits, and risks of agricultural production and consumption jointly. 3 The coupling of production and consumption decisions, which development economists refer to as “non-separability,” may occur when a household can produce and consume a commodity, oftentimes a food crop, but might do so at different prices because of market failures, transaction

2

Casting a wider disciplinary net, Bradley and Millington (2008), Davalos et al. (2011), Medel and Lu (2015),

Rincón-Ruiz, Corea, Leon, and Williams (2016), Salisbury and Fagan (2013), and others use mapping, spatial analysis, and related empirical methods to assess the relevance of cultivation factors and implications of cultivation. Still others across disciplines proceed more descriptively (e.g., Farrell 1998, Lupu 2004, Mansfield 2014, Mansfield and Fishstein 2013, and UNODC 2003), sometimes drawing on extensive fieldwork. 3

Garcia-Yi (2014) draws from the literature, but does not develop a structural model

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costs, or households’ perceptions of substitutability, each of which might be characterized as a side effect of institutional dysfunctionality ( de Janvry et al.1991; de Janvry and Sadoulet 2006; Lofgren and Robinson 1999; Singh, Squire, and Strauss 1986; Taylor and Adelman 2003). Under such

circumstances changes in commodity prices can present conflicting incentives—price increases tend to be “good” from the producer perspective and “bad” from the consumer perspective. Given that farmers, including those who grow opium poppy, tend to allocate some land to marketable and consumable food crops (see, e.g., Mansfield and Fishstein 2013, 10-12); that a not-insubstantial share of farmers’ face subsistence concerns (Greenfield et al. 2015, 25-36); and that intermittent violence and weak infrastructure can disrupt markets and impose substantial transaction costs, we argue for the relevance of non-separability.

Lastly, our conceptualization of the tradeoff between illicit and licit opportunities is consistent with, but elaborates upon Becker’s (1968) approach to the economics of crime. We and others, e.g., Andersson (2013), focus on economic incentives that emerge from surrounding socioeconomic and environmental conditions, including imperfect institutions, and the implications of those incentives for utility maximization.

This article proceeds as follows. In section II, we present the household production model, which we adapt from Fafchamps (1992), and comparative static results for each model parameter. In section III, we consider the effects of incremental changes in cultivation factors on the landallocation decisions of households of different types, distinguishing among them on the basis of landholdings, wealth, and related characteristics. Many factors depend on more than one model parameter and in those cases we consider the combined effects of changes in each parameter. To

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the extent possible, we supplement the theoretical analysis with empirical evidence. (See Greenfield et al. 2015, 42-43, for an overview of data limitations.). In section IV, we discuss policy implications and present concluding remarks.

II. The Household Production Model

This section documents the model adapted from Fafchamps (1992). Table 1 provides the underlying model assumptions.

[Table 1 about here.]

We assume that households are endowed with a fixed quantity of land and must decide how much of their land to allocate to each potentially cultivatable crop, such as opium poppy or a food crop. After production, households can use the income from cropping, plus any off-farm income, to purchase a set of consumption goods (possibly including the same crops that they can produce) in accordance with their preferences. We also assume that returns to crops, consumer prices, and off-farm income are stochastic and possibly correlated.

Households are endowed with heterogeneous risk preferences, plausibly ranging from risk neutral to risk averse, as defined by the curvature of the implied indirect utility function V ( y, p) , where y is total income and p is the vector of consumer prices of length K. The optimization problem facing an Afghan farmer is thus specified as:

5

max E V ( y, p) Li

s.t. y    i Li  y *

[1]

i

L

i

L ,

i

where  i represents (random) per-acre returns for crop i, reflective of producer prices and net of input costs, Li is the amount of land allocated to each, y * , is random off-farm income, and L is the (fixed) amount of land held by the household. The random off-farm income term, y*, is a novel adaptation; the rest of the specification purposefully follows Fafchamps (1992). Expectations are over the random consumption prices, crop returns, and off-farm income. We assume all land is cropped, and thus the first-order conditions for an interior solution to the problem are E Vy i   0 i and

L

i

 L.

i

Following Fafchamps (1992), a second-order Taylor series expansion of the indirect utility function around the means of prices and income can be written as M

Vy  Vy  V ypk ( pk  pk )  Vyy ( y  y ). [2] k 1

Using Roy’s identity, denoting qk as demand for consumer good k, and differentiating and substituting results in

  M  q V y q y q   V y V y  Vy 1     k yy  k   k ( pk  pk )  yy ( y  y )   , [3]  y qk y  Vy y   k 1  y V y 

6

where  

Vyy y Vy

 0 is the (negative of the) coefficient of relative risk aversion towards income

risk, evaluated at expected income.

Letting k 

qk y  be the income elasticity of good qk evaluated at mean pric es and income, y qk

multiplying by  i , and taking expectations yields  M      p y E V y i   Vy  E  i     sk     k  E  i ( k  1)     E i (  1)    , [4]  k 1 pk y        

where the budget share sk 

qk  0 is conceptualized as the share of the value of mean income pk y

from crop production (via returns) plus off-farm income dedicated to consumption of good k, M

valued at local consumption prices. Of particular note, the term

 p  s      E  ( p

k

k

k 1

k



i

k

  1)  

represents the link between consumption and production, as a lack of correlation between returns and consumption prices results in this term equaling zero. Optimal land shares are derived by setting the first order conditions of the maximization problem equal to zero for all crops i ( E V y i   0 i ).

We do this for the specific Afghan context. In a stylized representation, we study the case of opium poppy and a land-competing crop; namely, wheat (the numeraire, indexed by 0). For simplicity, we assume that wheat is both produced and consumed locally, for cash or sustenance, and that opium poppy is a cash crop that is not consumed locally and thus has a consumption share of zero ( s1  0) . We further assume that households are not risk loving (   0) , that the 7

mean returns for opium poppy are greater than the mean returns to wheat (m 



1  1) , that the 0



correlation in returns between crops is positive  01  0 , and that the correlation between the





consumption price of wheat and returns to wheat is positive  p0 0  0 but the correlation





between the consumption price of wheat and returns to opium is zero  p01  0 .

Using these maintained assumptions, define P *  (1  m)(1   ) , and





Q *  s0      CV p0   0 , p0 CV 0 , with the coefficient of variation for variable x defined as the

ratio of variance to mean, or CVx 

cov( x, z ) x , and  xz  as the correlation coefficient x  x z

between random variables x and z. Substituting the previous expression into the first order conditions of the optimization problem, and then manipulating and solving for the land share of opium poppy results in

 y*    P*  Q*  1    ( S *  Y * )  0  l1*  , [5]  P*  Q*   m  1  T *    where S *  mCV0 CV1 0 ,1  CV20  m  1 , T *  CV20  CV21 m2  2CV0 CV1 0 ,1 m  (m 1)2 ,

and Y

*

 

1 , y*



 1   , y*  0  y*  y *  1   0  0



2 0

. This solution is not valid for corner solutions,

which would imply mono-cropping.

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If production and consumption between goods are separable, then Q* equals zero; otherwise, the sign of Q* corresponds to the sign of Ψ+η.4 If wheat is a necessity, then 0<η<1. Thus, Q* ≥ 0 applies for households with relative risk aversion coefficients with absolute values greater than 1 when consumer prices and market returns are positively correlated. For this case, the greater the value of Q* , the less opium poppy will be planted, all else equal. ( Q* will increase with greater degrees of risk aversion and higher consumption shares of wheat, as likely would be associated with poorer farmers with smaller landholdings.) Furthermore, the greater the correlation between consumption prices and returns from wheat or the greater the variance in the consumption price of wheat, the more land is planted to wheat. These findings are consistent with empirical observations in the developing world that conditions of non-separability result in the “oversupply” of the jointly produced and consumed crop, relative to the maximization of the expected utility of profit (Barrett 1993). However, Q* is decreasing in the income elasticity of wheat, so an increase in this parameter will tend to increase the share in opium poppy as a result of the consumption linkage.

Table 2 presents the results of the comparative static analysis of the share of land allocated to opium poppy by model parameter. The analysis allows us to predict behavioral changes— specifically, changes in land-allocation decisions—in relation to each model parameter and, eventually, in relation to each factor of interest. However, as we show in section III, a single socio-economic or environmental factor can involve multiple model parameters, so that a change in a factor can have mixed effects.

4

Note that –Ψ=η would also imply Q*=0, but the equality would be mere coincidence.

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[Table 2 about here.]

III. Factor Analysis

In this section, we use the comparative static results and model adaptations to explore how changes in cultivation factors affect land-allocation decisions. Specifically, we consider the roles of landholdings, environmental and eradication risks, security concerns and remoteness, outside income, indebtedness, land-tenure arrangements, agricultural inputs and technology, and commodity prices. When a cultivation factor involves multiple model parameters, we can use the household model to identify channels of influence and potential sources of tension in policymaking. In some instance, empirical evidence sheds light on the net effects of conflicting parameters; in others, it reaffirms ambiguities.

Throughout the factor analysis, we typify landholding households of different means as “very small,” “small,” and “medium to large” on the basis of three model parameters, namely, risk preferences, consumption shares, and income elasticities. The phrasing refers to the size of the household’s landholdings, treated both literally and as emblematic of wealth and other household characteristics, not to the number of individuals in the household.

We characterize: 

“very small” landholding households (interpreted as very poor) as being highly risk averse, with a large share of consumption in wheat and a large income elasticity of

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wheat.5 These households would have too little land to meet an average household’s nutritional needs solely through food-crop cultivation and would tend to couple their production and consumption decisions. 

“small” landholding households (interpreted as neither very poor nor wealthy) as being somewhat risk averse, with a smaller share of consumption in wheat and a smaller income elasticity of wheat than very small landholding households. These households might have just enough land to meet their nutritional needs, but would still make their production and consumption decisions jointly.



“medium-to-large” landholding households (interpreted as relatively wealthy) as being progressively less risk averse, with a negligible share of consumption in wheat and a negligible income elasticity of wheat. These households would have more than enough land to meet their nutritional needs, if they attempted to do so, and would make their production and consumption decisions separately.

To implement our conceptualization of the relatively wealthy, medium-to-large landholding household, we set the consumption share of (local) wheat equal to zero, such that Q* = 0, which implies maximization of the expected utility of profit in a “pure” production decision. For a household of average size and nutritional requirements, Greenfield et al. (2015) demonstrate that very small landholdings would amount to less than one-half-to-one hectare, small landholdings would amount to about one-to-two hectares, and medium-to-large landholdings would amount to about two hectares or more. On the basis of national survey data

5

As discussed in section II, it seems likely that poorer households with smaller landholdings would be characterized

by −Ψ > η, with η < 1 (but relatively large) and large consumption shares.

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(Maletta 2007), they find that most opium poppy might be grown on medium-to-large landholdings, but that about half of all landholders have smaller plots.

In the subsections that follow, we discuss how a change in each cultivation factor affects each of the three household types (see Table 3.) While this typology is convenient expositionally, we recognize that households do not exist in discrete categories, but along a continuum of behavioral dimensions and that various “tipping points” might exist where the effect of a change in a cultivation factor on the land share of opium poppy could reverse sign. Furthermore, although we argue that landholdings are generally correlated with the three behavioral parameters, substantial heterogeneity is likely within each group.

[Table 3 about here.]

Landholdings Notwithstanding our application of a “landholding” typology, households can still own or control different amounts of land within a category or gain or lose land and cross into a different category. The constant-returns-to-scale assumption of our model implies that crop shares are invariant to plot size; thus, we cannot explore the effect of a change in the amount of land that a household owns or controls on the cropping decision directly. To proxy the effect, we, like Fafchamps (1992), focus on the behavioral parameters that comprise our typology, risk preferences, consumption shares, and income elasticities.

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For farmers with small and medium-to-large landholdings, the approach suggests a positive relationship between landholdings and opium-poppy cultivation; that is, the larger the landholding, the larger the share of land allocated to opium poppy.

For farmers with very small landholdings, the implications of the approach are less definitive and depend on the existence of a tipping point—specifically, the dividing line between very small and small landholders—below which the household does not have enough land to meet its food consumption needs and above which it does. A “very small” household will grow more opium poppy on more land in an effort to generate enough cash to purchase food. However, if the household acquires enough land to bridge the tipping point, the household would switch into food production. While the discussion in section II suggests that non-separabilty would reduce incentives to grow opium poppy relative to a pure production decision, the introduction of risk aversion and consumption shares has the potential to drive a land-poor household toward opium poppy to better assure its survival. This result also finds general support in the literature on subsistence modeling (e.g., Arslan and Taylor, 2009).

All-told, the analysis supports Mansfield and Fishstein’s (2013, 10-12) description of concurrent wheat and opium-poppy cultivation and Greenfield et al.’s (2015, 33-36) discussion of monocropping. According to the latter, which draws partly from Mansfield and Fishstein, monocropping has been observed among land-poor farmers in parts of southern Afghanistan, but it is unclear whether it is a function of plot size, land-tenure arrangement, or both.

Environmental (Yield) Risk

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We conceptualize changes in environmental risk, e.g., the risks of intermittent drought or disease, through changes in the standard deviation of crop returns. We direct readers to the comparative static analysis for   0 and  1 in Table 2 and briefly summarize our logic here. An increase in the environmental riskiness of opium poppy,  1 , unambiguously decreases the share of land allocated to opium poppy because the crop does not enter the demand-side of the model and, in this analysis, we assume at least some amount of risk aversion among all households. However, an increase in the riskiness of wheat,   0 , could play out either way because of the potential for non-separability. On balance, poorer households with smaller landholdings are more likely to plant less opium poppy when wheat becomes riskier and households with progressively greater wealth and larger landholdings are more likely to plant more.

Eradication Risk We conceptualize eradication risk as a discrete probability, in addition to any other variation in returns, that the household’s opium-poppy crop will not generate any income. 6 In contrast to the possibilities of intermittent drought or disease, here we are contemplating the possibility of an ongoing eradication program and, thus, consider the potential for changes in the overall distribution of opium poppy returns.

If the environmental risk remains unchanged and there is a given probability, 0 ≤γ ≤1, in each growing season that eradication will eliminate opium-poppy returns, then the overall distribution of those returns can be modeled with a mixture distribution. As derived in Greenfield, et al.

6

In practice, partial losses are also possible.

14

2 (2015), let  env and  env be the mean and variance of returns to opium poppy under

environmental risk only. Under eradication risk, the new mean of the opium poppy return 2 2 distribution will be E 1   (1   ) env , while the associated variance is  12  (1   )( env   env ).

As such, an increase in eradication risk will unambiguously decrease mean opium poppy returns, but may increase or decrease the variance term if   0.5.

Table 2 shows that, in the case where a change in eradication risk decreases the mean and increases the variance of opium-poppy returns, the share of land allocated to opium poppy will unambiguously decrease. If, however, the change in eradication risk decreases the mean returns but also decreases the variance in returns, the effect is ambiguous. It is possible that, especially for extremely risk-averse producers, the incentive to increase opium-poppy cultivation given the decrease in variance will dominate the incentive to decrease opium poppy from the decreased mean returns. Although counterintuitive, the result complements that of Andersson (2013), which shows that an increase in eradication risk can encourage an increase the share of land allocated to opium-poppy cultivation in the presence of imperfect credit markets. Andersson argues (2013, 8) that imperfect credit drives the outcome (proposition 1), irrespective of the degree of risk aversion, but we find that this institutional condition is not necessary to create the perverse incentive. Empirical evidence from interviews and polling (Greenfield et al. 2015, 6061) neither supports nor refutes the possibility.

Security Concerns and Remoteness Changes in security conditions and differentials in remoteness across farmers can manifest in changes to the same model parameters, albeit in potentially differing magnitudes. In particular, 15

both degradation of security and greater remoteness could reduce access to inputs, impede output marketing, and increase associated transaction costs, thus reducing mean crop returns; increase the variance in agricultural output, thus affecting the variances of returns; increase the differential between producer and consumer prices; and reduce eradication risk.

The net result of reductions in mean crop returns on the incentive to plant opium poppy or wheat depends on the relative magnitude of the change, with a decrease in returns creating a disincentive towards the crop affected. If input price changes disproportionally affect one crop through relative differences in input intensity (e.g., opium poppy is more labor intensive than wheat, but wheat is more fertilizer intensive than opium poppy), this might favor cultivation of the other. On the output side, differences in marketing opportunities could favor opium poppy over wheat. Traders collect opium at the farm gate, even under adverse conditions, because it is relatively compact (easier to move and hide), robust (easier to store), and readily salable. As such, the transaction cost gradient for wheat with respect to degraded security conditions and remoteness might be steeper than for opium poppy, thus increasing the relative mean returns for the latter under risky or remote conditions.

If security degradation results in market disruptions that increase the variance of crop returns, or remoteness results in less access to a yield–stabilizing input, such as a pesticide, then the variance of crop returns might change too. If the variance in opium-poppy returns increases, the disincentive to plant opium poppy is unambiguous and the share of land allocated to opium poppy will decline. However, the disincentive to plant opium poppy might be fully or partially

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offset by a change in the variance of wheat returns, especially for households that are relatively risk-tolerant and have relatively small consumption shares in wheat (see Table 2).

Both remoteness and security concerns can serve to increase gaps between producer and consumer prices because of new security-related market failures, higher transaction costs, and new risks associated with uncertain prices (de Janvry et al., 1991). As shown in Greenfield et al. (2015, Appendix C, 63-64), these effects could cause: a decrease in mean wheat returns (an incentive to plant more opium poppy); a decrease in the variance of wheat returns (an incentive to plant less opium poppy for all but poorer, very risk-averse households); and/or an increase in the correlation between wheat consumption prices and wheat returns (an ambiguous incentive, but more likely to promote opium-poppy cultivation for larger, risk-tolerant households and irrelevant for households with separable production and consumption decisions). It seems likely that the mean effect would dominate for wealthier households with larger landholdings, but for others, the outcome would depend on the relative magnitudes of the effects.

Remoteness, more so than security concerns, might also be correlated with eradication risk. Government officials, troops, and others cannot eradicate what they cannot find or reach, suggesting a negative relationship between remoteness and eradication risk. If eradication risk and opium-poppy cultivation are negatively related, as they might be among wealthier households with larger landholdings, then remoteness and cultivation could be positively related through this channel of influence for the same households.

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In sum, the effects of security degradations and remoteness on the land-allocation decision are ambiguous and sensitive to the assumptions about the specific market effects of the disruption, their relative magnitudes, and the type of household under consideration. This interplay of incentives helps to highlight the difficulties associated with predicting the effects of complex factors that alter multiple channels of influence, especially among smaller households with nonseparable consumption and production decisions. For wealthier households with larger landholdings, more of the analysis of means, variances, price differentials, and eradication risk seems to tilt toward opium poppy than wheat, but not definitively so.

Empirical evidence also suggests conflicting land-allocation incentives. According to Greenfield et al. (2015, 53), UNODC data show that opium-poppy cultivation in Afghanistan has become concentrated increasingly in provinces with “poor” or “very poor” security environments, but the presence of international forces appears to discourage cultivation in the immediate vicinity of the violence. 7 UNODC survey data, which do not distinguish parcel size, suggest that remoteness might encourage opium-poppy cultivation, but inconsistently. UNODC (2010-2013) finds a statistically significant and positive relationship between the average distance to market and poppy-cultivating status nationally in each of the 2009-2010, 2010-2011, and 2011-2012, growing seasons, but not in the 2012-2013 growing season.

Outside Income

7

Notably, in the time since UNODC began publishing its socio-economic polling data (2005), security-related

concerns have not registered as a motive for either growing or ceasing to grow opium poppy.

18

Outside income, which we treat as costless, enters our analysis through the term y * . An increase in y * represents an increase in outside income opportunities and can either encourage or discourage opium-poppy cultivation, depending on the value of P* and Q* and their associated parameters, including mean crop returns, the variance of wheat returns, the correlation between wheat returns and consumption prices, the household’s relative risk aversion, the income elasticity of wheat, and the wheat consumption share.

Nevertheless, we can analyze the underlying incentives for conditions of separability, nonseparability, and correlated income and crop returns.

Predictably, the analysis is clearer under conditions of separability, as might be associated with wealthier households with larger landholdings, than under those of non-separability. If households make production and consumption decisions independently, they will treat the increase in outside income as an opportunity to take advantage of the relative profitability of opium poppy and plant more of the crop, regardless of their risk preferences. If household decision-making is non-separable, an increasing degree of jointness can work to offset the profit incentive for sufficiently risk-averse households (such that Q*>0) and quite possibly reverse it (if Q*>-P*). Intuitively, the increase in outside income creates two, competing incentives: it can lead such a household to increase the share of land allocated to opium poppy to increase profits due to the insurance mechanism (similar to growing wheat)8 or to increase the share of land allocated to wheat to account for consumption risk.

8

It serves a similar function, but without use of scarce resources because outside income is costless.

19

If outside income and crop returns are positively correlated, the comparative statics with respect to a change in y * are the same, but the correlations enter the Y* term in the numerator of [5] and can affect the share of opium poppy planted. Given some amount of risk aversion, we can see diversification effects at play, in that an increase in the correlation between a crop’s return and outside income tends to discourage planting of that crop. A positive correlation between off-farm income and opium-poppy returns could offset the propensity of wealthier households to plant more opium poppy when outside income increases and reinforce the propensity of poorer farmers to grow less. If outside income and wheat returns are positively correlated, then the tendency would be to increase the share of land in opium poppy (relative to the uncorrelated case), for the same reasons, which could reinforce the propensity of wealthier farmers to grow more opium poppy and offset the propensity of poorer farmers to grow less.

Finally, we note that the outside income term in our model is exogenous to the cropping decision. That is, it entails no opportunity costs, e.g., by drawing on family labor or other household resources, as would an alternative crop. Admittedly, the assumption that off-farm activities are entirely “non-competing” is strong, but relaxing it would not reverse the sign for wealthier households; rather, it would generate indeterminacy.

The empirical evidence does not address our question directly, but it lends some credence to conventional wisdom, which treats outside income as a mechanism for opium-poppy reductions. The 2010 and 2011 UNODC Afghanistan Opium Surveys asked respondents about their coping strategies after ceasing to grow opium poppy and ‘income from off-farm employment’ was the top response in both years. This would be consistent with conditions of non-separable production

20

and consumption with relatively high degrees of risk aversion and, possibly, those of positivelycorrelated opium-poppy returns and off-farm income opportunities.

Indebtedness Debt accumulation is a dynamic process and is difficult to replicate using a simplified static model, but proxies and simple thought exercises permit some consideration of effects. In our analysis, we use a decrease in the outside income term, y * , to proxy an increase in the stock of household debt, but we assume a negative correlation with both crop returns and

  , y *  1    1

0,y

*

  0  0 because debt is oftentimes denominated in opium poppy.

An increase in debt, all else equal, will tend to lower income through repayment, but the level of this repayment likely covaries negatively with returns—as returns increase, the level of debt decreases and income increases. The reduction in mean outside income could discourage opiumpoppy cultivation among wealthier farmers, depending on the extent of the diversification effect, which could, instead, favor opium poppy, and encourage it among non-separable, risk averse poorer households. In addition, the assumed correlation structure tends to incent opium-poppy cultivation relative to an uncorrelated (or positively correlated) case.

For insight to the process of dealing with debt as a process of income flows, we can walk through a simple example of consumption smoothing. If, prior to harvest, a household were to secure an opium-denominated loan, repayable with interest in a future period, then the contract effectively would bind the agent to plant at least some portion of opium poppy (or obtain it via the market, thus decreasing expected income). Such an agreement may also change the mean and variance of 21

returns for opium poppy that is not part of the agreement; for example, if, as in Andersson (2013), the price fully capitalized risk. In that case, the variance of returns to opium poppy could shrink to zero, although returns to opium poppy would also be lower relative to a non-contracting arrangement. As previously discussed, because these two forces create conflicting incentives for opium-poppy cultivation, the net result is parameter-specific.

The empirical evidence neither addresses our question directly nor suggests a particular direct of outcome. A substantial share of households has cited ‘getting loans,’ but not ‘paying off loans,’ as reason for growing opium poppy; at the same time, loans appear to be more prevalent among opium-poppy farmers than among other farmers (Greenfield et al. 2015, 70).

Land-Tenure Arrangements Our model, like that of Fafchamps (1992), depicts farmers as owner operators, but can be used to explore the implications of alternative land-tenure arrangements that are common in Afghanistan. In our conceptualization of sharecropping, tenants provide labor in exchange for a share of the eventual harvest, but have little or no control over the cropping decision. Our assignment of control to the landholder departs from Andersson (2013), but it accords with empirical evidence (e.g., Mansfield 2014) and supports a plausible narrative in which landholders who engage sharecroppers are likely to look like the medium-to-large landholders in our typology and to maximize the expected utility of profit in production decisions. Coupled with an abundant supply of potential sharecroppers in rural Afghanistan, the greater returns to opium poppy would ensure that such a landholding household would favor opium-poppy cultivation in the sharecropping contract.

22

Alternatively, we could frame the arrangement as a means of earning income from landholdings without applying family labor, which might be scarce relative to landholdings, particularly among households with large landholdings and a moderate household size. As such, the sharecropped land is a capital good earning a return on the basis of non-family labor and is not part of on-farm income. Thus, we can model the sharecropping arrangement as an increase in y * , with a positive correlation between this off-farm income and the returns to opium poppy. As noted, an increase in y * would tend to encourage opium-poppy cultivation among wealthier farmers, such as those likely to engage sharecroppers, but the correlation could have a partially offsetting diversification effect which would be greater with increased risk aversion. Although not certain, sharecropping arrangements seem likely to favor opium-poppy cultivation and we find nothing empirical—in Mansfield’s or others’ field reports—to suggest otherwise.

Agricultural Inputs and Technology Agricultural inputs and technology do not lend themselves to generalization, but the effects of input prices and technology investments are implicit in the returns to agricultural activities and apparent in the attractiveness of outside income opportunities.

Hinting at the analytical complexities, we sketch the potential paths of influence from an increase in the price of a particular input, labor, to the cultivation decision. On the production side, higher rural wages will reduce the profitability of agricultural activities in accordance with their labor intensity and, thus, would discourage opium-poppy cultivation, because it is a relatively labor-intensive agricultural activity. On the consumption side, the rising wage could

23

also imply access to better paying outside-income opportunities in which case the increase in y * , could discourage or encourage opium-poppy cultivation as per the discussion of outside income. The net results are ambiguous, depending partly on the relative importance of the immediate effect on profitability, which would point to less opium poppy, and the potential for a contemporaneous increase in outside income that might—or might not—mitigate. On balance, we might expect a reduction in opium-poppy cultivation relative to other options, if we treat the reduction in returns as the first-order effect.

Although we do not model technology investments explicitly, we can explore potential effects through the comparative static results in Table 2. Investments will tend to affect both the returns to agricultural activities and their relative variances and, depending on how they are financed, could affect indebtedness, which we can proxy, as above, with a change in y * . For example, a new irrigation system might lead to relatively higher and less variable wheat yields—insomuch as wheat is less drought resistant than opium poppy, it might benefit to a greater extent—and a debt-incurred reduction in y * , with differing implications, by household type.

Greenfield et al. (2015, 75-76 and Appendix B) uses crop budgets to confirm the relative laborintensity of opium-poppy cultivation and the disproportionate impact of wage increases on opium-poppy returns, but also demonstrates the difficulty of motivating substantial opium-poppy reductions through higher wages or yield-affecting technologies. Among households that contribute some of their own labor to agricultural activities, as is typical, the wage rate might need to quadruple to motivate a shift out of opium poppy. Moreover, the abundance of sharecroppers and availability of un-priced or under-priced labor—namely, women and children— 24

could provide offsetting incentives. Similarly, a doubling of wheat yields would increase the crop’s profitability, but would not assure its cultivation.

Mean Commodity Prices The analysis of output prices, which are embedded in crop returns,  i , is less complex than that of consumption prices, p, which can present conflicting incentives. A rise in a crop’s output price, all else constant, will encourage cultivation of that crop, regardless of a household’s landholdings or wealth; a rise in the consumption price can have differing effects, by household type. For farmers with scant landholdings, an increase in the consumption price of wheat—or another staple commodity—could promote a shift toward opium poppy to maintain consumption. For others, the price increase might induce substitution toward self-supply and away from opium-poppy cultivation. So long as opium poppy is just a cash crop, higher opium prices will tend to favor more opium-poppy cultivation for most landholding households.

As a practical matter, Greenfield et al. (2015, Appendix B) demonstrate that wheat prices would need to increase fourfold, all else constant, before wheat would out-perform opium poppy as a cash crop. Such an increase would imply substantially higher wheat prices than were observed in Afghanistan in 2008–2009, in the midst of a global grain crisis.

The effects of changes in the prices of other commodities will depend on whether the commodities “look” more like wheat, opium poppy, or outside income in the cultivation decision. If, like wheat, the commodity is a staple and it competes directly with opium poppy for land use, we would expect a household to respond to changes in prices similarly and with at least

25

as much complexity. If, like opium poppy, it is a cash crop that is not consumed locally and it competes directly with opium poppy for land use, we would expect farmers to plant more of it, as its price rises. If, instead, the commodity is a cash crop that is neither consumed locally nor directly competing with opium poppy for land use—it might be planted at a different time of year—we would expect farmers to respond as they would to an increase in y * .

Kuhn’s (2010, 6-7) comparison of the net returns of various field and perennial orchard crops sheds light on the viability of other agricultural options. Not all field crops compete directly with opium poppy for land use because of differences in seasonality, but perennial orchard crops tend to compete at least partly because they remain in place year-round. Few of the land-competing field crops (i.e., white onions and potatoes) offer net returns near to opium poppy’s, but most of the perennials (especially, grapes and almonds) look more attractive. However, in calculating the net returns to the perennials, Kuhn (2010, 9) assumes “vibrant trees and best practices for orchard care” and does not account for the costs of saplings or the implications of substantial lags between planting and maturity. A farmer would need to weigh the tradeoff between the initial capital outlay, which might require financing, and the eventual flow of discounted returns. Even still, the high-value perennials look more promising than many other options.

IV. Lessons for Policymakers and Concluding Remarks

In this paper, we fleshed-out a household production model and used it to consider the effects of changes in cultivation factors on land-allocation decisions in Afghanistan. In effect, we responded to Werb et al.’s (2008) call for a scientific, evidence based approach to policy

26

development in Afghanistan with a framework that can be used to probe the decision-making process, support policy analysis, and guide future empirical research.

The analysis demonstrates the complexity of decision-making and the potential for conflicting incentives. A factor can simultaneously discourage and encourage opium-poppy cultivation within households and between households of different types. Broadly speaking, we might expect wealthier farmers with medium-to-large landholdings to place a higher priority on concerns about crop returns than poorer farmers, implying a greater degree of determinacy for the former than the latter. Producer prices represent the small minority of cultivation factors that comes closest to eliciting determinate effects for all households.

What lessons can we draw for policymakers?

First, policymakers cannot expect to find a “magic bullet.” Editorials and media reports (e.g., Applebaum 2007 and Gennett 2009) point to pharmaceutical morphine, biodiesel fuels, and bulkcrop purchases, as pragmatic and readily-attainable solutions, but “voila!” proposals merit skepticism (Chouvy 2008, Greenfield et al. 2009). The heterogeneity of situations across households and the interconnectedness of production and consumption decisions make it unlikely that such solutions will deliver as promised.

Second, policymakers should expect mixed results. Almost any program, whether founded in principles of rural development or counternarcotics, seems likely to include mechanisms that will present conflicting incentives and, thus, push and pull opium-poppy cultivation in different

27

directions. Greenfield et al. (2015) examine ten representative programs (seven focusing on rural development, three involving eradication) through the lens of the household model—and additional empirical evidence—and find conflicting incentives throughout.

Third, despite the likelihood of conflicting incentives, some programs might be more apt to nudge households, especially those with sufficient wealth to focus primarily on crop returns, away from opium poppy than others. Largely because of the implications for returns, we suggest focusing on fruits, nuts, and other traditional high-value agricultural products that have ready markets and compete directly with opium-poppy for land use. 9 Complementary measures to strengthen the farm-to-market pipeline could also serve to mitigate the effects of remoteness, both real and metaphorical. Moreover, programs—or longer-term development strategies—that contribute to wage growth could discourage opium-poppy cultivation, but higher rural wages might have greater effect if accompanied by broader social changes that improve options for would-be sharecroppers, ascribe greater value to women’s labor, and draw children from the workforce. Lastly, we suggest steering clear of widespread eradication campaigns. Our caution stems not just from our analysis of “eradication risk,” but from that of actual eradication, potentially entailing shifts of production into new locales.10

9

Greenfield et al. (2015) provide additional and more-detailed recommendations, drawing partly from their

assessment of programmatic efforts in Afghanistan. 10

Greenfield et al. (2015) discuss the effects of eradication risk (pp. 59-61) and actual eradication (pp. 80-81)

separately and identify circumstances in when eradication, per se, could result in subsequent increases in cultivation, e.g. to repay debt from crop losses or because it favors the Taliban’s position. The authors also present evidence of a “balloon effect,” suggesting that eradication has contributed to a shift in cultivation from one part of southern Afghanistan, the “Helmand Food Zone,” to another, the “dasht.”

28

Our fourth lesson requires further contemplation of the longer term and the dynamics of development. If wealthier households tend to respond more directly and predictably to profit motives than poorer households, it might be desirable to focus on programs that can serve to elevate poorer households’ wealth, even if the immediate implications for opium-poppy cultivation are unclear. For example, a program that creates opportunities for households to earn outside income might encourage some households to allocate more of their land to opium poppy, but it might also free some households, eventually, from over-riding concerns about nutritional adequacy. In our analysis, we tend to treat “very small,” “small,” and “medium-to-large” as elements of a static rural system, but they are not. Thus, the appearance of short-term failures might be worth—or even be necessary to—a long-term success.

Our analysis offers little hope for a near-term, program-led decline in aggregate opium-poppy cultivation in Afghanistan, but incremental change might be better than no change, even if some change appears to run counter to immediate intentions. Arguably, policy would also benefit from future empirical assessments of the magnitude and balance of “pushes” and “pulls” of cultivation factors across supply-control program. Armed with a better understanding of the underlying decision-making process and the roles of factors that play into that process, the policy community might be better-able to formulate polices that can—eventually—guide farmers in Afghanistan toward licit options or, at least, “do no harm” (see, e.g., Chouvy 2011 and 2013, Greenfield and Paoli 2012, and Reuter 2008).

29

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Figure 1. Afghan and Rest-of-World Opium Production (With Afghanistan’s share of world production) 9000 0.91 8000 7000 6000

Tons

0.83

Taliban ban on production 0.79

0.61

5000

0.52 0.58 0.52 0.62

0.51

0.46 0.48 4000 0.42

0.70

0.83

0.86

0.91 0.87 0.75 0.89 0.75

0.81 0.76

0.81

0.77

0.69

3000 2000

0.11

1000

Afghan Production

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

0

Rest of World Production

Sources: United Nations Office on Drugs and Crime (2005, 2016).

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Table 1. Underlying Household Production Model Assumptions Key Concept Land endowment Production technologies Crop returns Income

Risk preferences Consumption goods Consumption good prices Decision timing

Assumption Fixed ( L ) Leontief with constant returns to scale1 Exogenous, random, and possibly correlated with consumption prices and off-farm income2 Endogenous crop income Exogenous, random off-farm income, possibly correlated with crop returns and consumption good prices Risk-neutral to risk averse May or may not be produced Exogenous, random, and possibly correlated with crop returns and off-farm income Cropping decisions made prior to realization of random variables; consumption decisions made after realization

NOTES: All assumptions, except those on off-farm income, are from Fafchamps (1992). 1 Different production technologies can be represented by adding crops to the model. 2 The specification of non-zero correlation is the mechanism by which consumption and production decisions are made non-separable in the model.

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Table 2. Results of Comparative Static Analysis of Opium-Poppy Crop Share with Respect to Model Parameters Parameter Symbol Sign Condition Mean returns to + opium poppy Mean returns to wheat Variance in returns to opium poppy Variance in + or More likely to be negative for poor, riskreturns to wheat averse households with large wheat consumption share Correlation of crop returns , Relative Risk + or More likely to be negative for highly Ψ Aversion correlated returns and consumer prices, small differences in mean returns, high income elasticity of wheat, and large wheat consumption shares Consumption + or Negative when −Ψ > share of wheat Mean + or Negative when −Ψ < consumption price ̅ of wheat Income elasticity + of wheat Variance in + or Negative when    consumer price of wheat Correlation of + or Negative when −Ψ > consumer price of , wheat and returns to wheat ∗ Mean off-farm + or Depends on model parameters, but positive income when consumption and production are separable; less incentive for opium poppy as non-separability increases and −Ψ > NOTE: Comparative static signs for consumption parameters are valid if Q * does not equal zero. SOURCE: Authors’ adaptation of Greenfield et al. (2015), Appendix C, Table C.1. For a detailed discussion of the comparative statics, see ibid., Appendix C, 48-56.

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Table 3. Factor Analysis in Terms of Model Parameters Factor

Change in Model Parameters (likely sign)

Likely Impact on Opium poppy Share by Household Type ( l1 ) Very small

Small

Comments

Landholdings

 (+), s 0 (-),  (-)

Decrease or Increase

Increase

Medium-tolarge Increase

Environmental (Yield) Risk Opium poppy risk increases Wheat risk increases

 1 (+)

Decrease

Decrease

Decrease

----

  0 (+)

Decrease

Decrease or Increase

Increase

Eradication Risk Increases for Opium Poppy

 (+), or eq.,  1 (-) and  

Decrease or Increase

Decrease or Increase

Decrease (if sufficiently risk-tolerant)

Security Risk Increases

 1 (+),  0 (+),  1 /  0 (+)

Decrease or Increase

Decrease or Increase

Increase (if sufficiently risk-tolerant)

Decrease or Increase

Decrease or Increase

Increase (if sufficiently risk-tolerant)

For poorer households, the positive correlation between prices and returns can induce consumption risk for wheat, thus increasing its on-farm production For risk averse households, depends on level of eradication risk, environmental risk, mean returns, and correlation of returns Mean return effects tend to increase opium poppy share. Consumer price of wheat effect tends to decrease share of opium poppy when risk aversion is relatively high. Correlation effect increases share in opium poppy when risk aversion high, but may decrease it otherwise. Variance in opium poppy effect decreases share in opium poppy; variance in wheat effect increases or decreases share in opium poppy depending on consumption risk, risk aversion, and correlation between market and producer returns and total effect Assume remoteness creates a gap in production and consumption prices due to transaction costs in the wheat market. Opium poppy is unaffected due to

1

changes

 1 (+),   0 (+),  1 /   0 (+)  (+), or eq.,  0 (-), p1 (+),   0 (-) and  p1 , 0 (+)

Remoteness Increases

 (+), or eq.,  0 (-), p0 (+),   0 (-) and  p1 , 0 (+)

Tipping point for farmers with very small landholdings

39

buying at farm gate. Wheat return effect increases share in opium poppy, wheat consumer price effect decreases share in opium poppy if risk aversion high, variance of wheat effect increases share in opium poppy for subsistence, decreases share otherwise, and correlation effect decreases share in opium poppy if risk aversion high Landholding Arrangement (Impact of sharecropping on land allocation decision)

y * (+), 1 , y*  0

n/a

Decrease or Increase

Increase (if sufficiently risk tolerant)

Assumes positive correlation between “outside income” from sharecropping activity and opium poppy returns. Income effect tends to increase share of opium poppy; correlation structure and risk aversion tend to decrease share of opium poppy.

Accumulated Debt Stock of debt increases

y * (-), 

Decrease or Increase

Decrease or Increase

Decrease (if sufficiently risk tolerant), but requiring further investigation

Decrease or Increase

Decrease or Increase

Decrease (if sufficiently risk tolerant), but requiring further investigation

Assumes outside income with negative correlation with opium poppy returns. Income effect tends to decrease share of opium poppy; correlation structure tends to increase share of opium poppy. Assumes consumption smoothing denominated in opium poppy, lender assumes risk. Borrower will allocate agreed-to share of land to opium poppy. Results shown for remaining land not part of agreement.

Decrease or Increase

Decrease or Increase

Increase

Flow of debt (Borrowing)

1,y

*

0

 1 (-)

 1 (-)

Outside Income Opportunities increase, uncorrelated with returns

y * (+)



* 0,y

 0,  , y*  0 1

Sign depends on strength of consumption/production linkage; share in opium poppy more likely to decrease if risk aversion is high and share of consumption in wheat is high.

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Opportunities increase, positive correlation with opium poppy returns Opportunities increase, positive correlation with wheat returns

Decrease or Increase

Decrease or Increase

Increase (if sufficiently risk tolerant)

y * (+)  0 , y*  0

Decrease or Increase

Decrease or Increase

Increase

 1 (-),  0 (-)

Decrease

Decrease

Decrease

Labor wage costs increase Off-farm income increases

 1 (-),  0 (-) y * (+)

Decrease or Increase

Decrease or Increase

Decrease or increase

Investment increases

 0 (+)

Decrease or Increase

Decrease or Increase

Decrease or Increase

y * (-)  1 (+)

Increase

Increase

Increase

 0 (+)

Decrease

Decrease

Decrease

Agricultural Inputs and Technology Labor wage costs increase

y * (+), 

1,y

*

0

 1 (+)

 1 (-)

Correlation provides additional incentive in each case to decrease share in opium poppy relative to uncorrelated case Correlation provides additional incentive in each case to increase share in opium poppy relative to uncorrelated case

Assumes opium poppy more labor intensive. Assume decrease in wheat returns less than opium poppy. Assumes opium poppy more labor intensive. Assume decrease in wheat returns less than opium poppy. Production-side effect (returns) decrease share in opium poppy. Income effects, if they exist, can increase or decrease share in opium poppy depending on correlation structures and consumption behavior. Opposite case to other variable input costs. Need for investment may also change debt structure.

 1 (-)

Opium poppy output price increase Wheat output price increase

Magnitude depends on linkages between production and consumption. Magnitude depends on linkages between production and consumption.

NOTES: eq. = equivalently. For security risk increases and remoteness,  represents the gap between producer and consumer prices.  (+) implies  0 (-), p1 (+),   (-), and  p , (+). Eradication risk is  , which implies a change in mean returns and variance of opium poppy. SOURCE: Authors’ adaptation of Greenfield et al. (2015), Appendix C, Table C.3. 0

1

0

41