social dimensions of procurement

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Land Use Policy 31 (2013) 7180

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Land Use Policy

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Social dimensions ofprocurement auctions for environmental service contracts:

Evaluating tradeoffs between cost-effectiveness and participation by the poor

in rural Tanzania

RohitJindal a,John M. Kerr b,, PaulJ. Ferraro c, Brent M. Swallow d

a Haskayne School of Business and Institute for Sustainable Energy, Environment andEconomy, Scurfield Hall, University of Calgary, 2500 University Drive NW,Calgary,

AB T2N1N4, Canadab Department of Community Agriculture Recreation andResource Studies, Michigan State University, 325Natural Resources, East Lansing, MI48824, USAc Department of Economics, AndrewYoung School of Policy Studies, Georgia State University, P.O. Box3992, Atlanta, GA 30302-3992, USAd Department of Resource Economics andEnvironmental Sociology, University of Alberta, 515A General Services Building, Edmonton, AB T6G-2H1, Canada

a r t i c l e i n f o

Article history:

Received 24 March 2011

Received in revised form

17 November 2011

Accepted 20 November 2011

Keywords:

Vickrey auctions

Carbon sequestration

Poverty

Ecosystem services

Assymetric information

Africa

a b s t r a c t

Determiningthe level ofpayment and selecting participantsare important but frequently neglected issues

thataffect social, economic and environmentalperformance ofpayment for environmental services (PES)

programs. We use a pilot auction to address these issues in the context ofa PES program in Tanzanias

Uluguru Mountains. Two-hundredfifty-one local farmers submitted sealed bids in the auction. The results

reveal the supply ofPES contracts at different prices. Simulations using the auction results and household

data show large tradeoffs between achieving cost effectiveness and maximizing participation by poor

households. A monitoring survey 21 months after the auction found that most auction winners treeshad

survived, with performance uncorrelated to the farmers poverty status or bid level. Although aspects of

our auction design limit the strength ofsome ofthe conclusions we draw from the data, our study shows

how pilot auctions can assist decision makers in estimating payment levels for PES contracts. Auction

participants stated that the auction provided transparency in contract allocation and that winners felt

peer pressure to comply with contracts, which suggest areas for future research regarding the potential

advantages ofusing auctions to allocate PES contracts in developing countries.

2011 Elsevier Ltd. All rights reserved.

Introduction

Payment for environmental services (PES) is a new conserva-

tion paradigm in which conditional incentive payments encourage

land stewards to invest in land-use practices that lead to conser-

vation or production of environmental services (Ferraro and Kiss,

2002; Wunder, 2005). PES has spread rapidly over the past decade

and has become the dominant approach for securing forest-based

carbon sequestration under climate change mitigation initiatives

(Miles andKapos, 2008). There arenow numerous projects that pay

local landowners to sequester carbon by planting new forests or

protecting existing ones (Hamilton et al., 2010; Jindal et al., 2008).

As is evident from other articles in this special issue ofLandUsePol-

icy, many PESprojectsin developing countries aimto achieve social

objectives such as poverty alleviation in addition to environmental

objectives.

Corresponding author. Tel.: +1 517 3530762; fax: +1 517 353 8994.

E-mail addresses: [email protected] (R. Jindal), [email protected] (J.M. Kerr),

[email protected] (P.J. Ferraro), [email protected] (B.M. Swallow).

A key concern in PES design is to identify a payment level that

compensates landowners opportunity costs while maximizing the

impact of the conservation budget (Ferraro, 2008). Payment that is

too high or too low will not likely achieve conservation outcomes

cost-effectively (Jack et al., 2008). In long-term projects, such as

those that provide carbon sequestration through tree planting, the

payment level may need to be determined ex ante because rene-

gotiation is expensive once the project has begun.

The challenge of identifying contract prices in the absence of

competitive markets for environmental services has led to skepti-

cism of thePES approach (Kosoyand Corbera, 2010). When markets

do exist, as in the case of carbon sequestration, they are so dif-

ferentiated that a single price cannot be paid (Hamilton et al.,

2010). Moreover, it is difficult to directly transfer cost estimates

from one project to another since the cost of implementing a

new land use practice can be highly site- and farmer-specific,

with differences that are unobservable to outsiders. When mea-

suring such costs is expensive, especially in new project sites,

service providers may have littleincentive to reveal their true costs

through farm or household surveys (Ferraro, 2008). Designing a

transparent way to allocate conservation contractswith an efficient

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R. Jindal et al. / LandUse Policy31 (2013) 7180 73

Table 1

Details of study villages in Kinole catchment, Morogoro district.

Name of the village Total number of

households

Distance from nearest road

(meters)aDistance from nearest market

place (meters)a, bElevation

(meters)b

1. Tandai 375 100 200 465

2. Lukenge 72 1850 1850 766

3. Doga 69 650 660 530

4. Nyange 123 2300 2350 629

5. Chohola 90 50 3800 414

6. Kalundwa 138 100 3470 4657. Tonya 96 50 882 468

8. Kisambwa 160 50 2080 471

9.Jahimbwa 34 300 2480 469

10. Lusegwa 70 523 904 451

Total/average 1227 597.30 1867.60 512.80

a Average distance to the village center.b Elevation of the village center.

and thus may fail to bid in a way that reflects their opportu-

nity costs (Harstad, 2000). In our setting in rural Tanzania, these

concerns imply a need for careful training about the auction

process for participants. We discuss such training in the next

section.

Data andmethods

The Uluguru Mountains are a globally important biodiversity

hotspot and the source of the Ruvu River, which provides water to

Dar-es-Salaam, the largest city in Tanzania (Burgess et al., 2002).

However, rapid deforestation in many parts of the mountains

threatens the valuable environmental services that the landscape

provides. Recent studies report a disappearance of some endemic

species and a heavy flow of sediment that reduces water quality

downstream. One potential way to revitalize the local ecosystem

is by growing trees on agricultural fields, which would reduce soil

erosion and produce carbon offsets that could be sold in interna-

tional markets (Branca et al., 2011). Accordingly, the Ulugurus area focal site for PRESA (Pro-Poor Rewards for Environmental Ser-

vices in Africa), a multi-location project coordinated by the World

Agroforestry Centre (ICRAF).

Our study was undertaken in the Kinole sub-catchment of the

Uluguru Mountains in Morogoro district. The area is remote: only

onefair weather road connects to the city of Morogoro andbeyond.

Some villages are not connected to this road and residents must

trek across steep slopes just to reach it. Poverty rates are high

in this predominantly agricultural area, with many households

augmenting their income through casual labor or small busi-

nesses. Maize and cassava are the main food crops, and banana

and pineapple are the main cash crops. Mean annual rainfall

varies widely in the area, ranging from about 1200 to 4000mm

(Table 1).Data for the study were collected over twelve months in

20082009 through focus groups, a household questionnaire, and

a set of procurement or conservation auctions conducted in March

2009. The survey was administered to 400 randomly selected

households and included questions on the household profile,

agricultural profile, and the respondents timepreferences. Respon-

dents expressed a strong interest in tree planting, with many

favoring timber trees over fruit trees because of the high costs of

marketing fruit from this remote area. Planting trees on their farms

would however impose costs on farmers in the short term, requir-

ing additional incentives(e.g. PESpayments) forthem to voluntarily

do so.

We held our auctions at the main marketplace in Tandai village.

We invited all 400 of the survey respondents to attend the auction,

of whom 268attended.3 We used a second-price, uniform payment

format whereby all contract winners received the same payment,

equal to the lowest rejected bid.4

Before the auction started, we recorded the seating pattern to

enable spatial analysis of bidding patterns. We explained the con-

cept of the opportunity cost of planting trees, including the cost of

planting and caring for seedlings, cost of uprooting existing crops,

and foregone revenues in terms of crops displaced. We then guided

participants in estimating these costs for their own conditions.

Next, we conducted several mock auctions where the auctioneer

asked participants to place bids on the minimum amount of money

they would accept to sell familiar objects such as bananas and cell

phone vouchers. During these mock rounds, we answered partici-

pants questions and explained the auction process several times.

Once the participants said they were comfortable with the pro-

cess, we conducted the PES auction, which took about six hours to

complete including a refreshment break.

There were two auction rounds for two separate contracts.

Each contract required farmers to plant 80 trees on an area of

0.5 acres with spacing of 5 meters by 5 meters. This spacing

allows intercropping, so the trees interfere only minimally withother components of the farming system in the humid climate of

the Uluguru Mountains. In the first round, farmers bid to plant

40 trees of Khaya anthoteca (African mahogany) and 40 trees of

Tectona grandis (teak). In the second round, farmers bid to plant

40 trees of Khaya anthoteca and 40 trees of Faidherbia albida

(winter thorn). These species were selected after consultations

with regional experts at the Tanzania Forestry Research Institute.

Khayaanthoteca is a locally popular timber species, Tectona grandis

is a slow-growing but valuable timber species, and Faidherbia

albida provides rich organic matter to field crops when it sheds its

leaves in the rainy season. Khaya anthoteca and Faidherbia albida

are indigenous to this region of Africa (though Faidherbia albida

normally grows in nearby areas with much lower rainfall), while

Tectona grandis is native to South and Southeast Asia but is widelygrown in East Africa. Tectona grandis and Faidherbia albida have

3 A logit analysis of the determinants of auction participation shows that par-

ticipants did not differ from nonparticipants with respect to farm size, wealth or

education. They did differwith respect to:(1) familiarity with an earlier tree plant-

ing project conducted by the Wildlife Conservation Society of Tanzania (WCST),

which hada strongpositive impacton participation, and (2)age,whichhad a small

but positive impact on participation.4 The PRE SA pro je ct pro vided $50 00 f or the au ction includin g the costs of

organizing the auction (including honoraria to research assistants), participants

transportation costs; and about 4000 tree seedlings. Each auction winner received

80 seedlingsand everyoneelse received10 seedlingsas a gift(announced onlyafter

the auction). The total cost of payment to thewinningbidders was aboutUS$500 in

cash plus tree seedlingsworthabout $580.


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74 R. Jindal et al. / Land Use Policy31 (2013) 7180

Table 2

Mean values forhouseholds that participated in thefield auctions.a

Variable Mean Standard deviation Minimum Maximum

Male headed HHb (0/1) 0.69 0.46 0 1

Age of the HH head (years) 43 14.85 16 90

HH size (number of people) 7 3.1 1 17

Education of HH head (years completed) 4 3.4 0 10

HH head born in the same village (0/1) 0.79 0.40 0 1

Location of the HH in main village (0/1) 0.47 0.50 0 1

HH reported high discount rate (0/1) 0.4 0.49 0 1Participation in WCST activitiesc (0/1) 0.7 0.47 0 1

Farm ownership (number of plots) 5 2.6 0 17

Farm ownership (area in acres) 8.9 13.1 0 177.3

Total agricultural expenditure (TSH) 164,264.5 292,624.5 0 2,426,000

Expenditure of hiring labor (TSH) 67,322.7 138,937.5 0 1,500,000

Animal ownership (livestock units) 0.16 0.33 0 2.58

House contains good toilet (0/1) 0.09 0.28 0 1

Value of assets owned (thousand TSH) 250.60 1691.44 0 24,260

Head of the HH responded to survey (0/1) 0.9 0.26 0 1

Source: First authors survey, 200809.a n= 251.b HH indicates household.c In 200204 the Wildlife Conservation Society of Tanzania (WCST) implemented a tree-planting program in the area.

done well in experimental trials in nearby locations (Okorio and

Maghembe, 1994), while Khaya anthoteca can be seen growingin farmers fields all over the area. In both rounds of the auction,

farmers were told that they would receive free seedlings procured

from a reputed nursery in Morogoro. At an average price of 400

Tanzanian Shillings (TSH) per seedling, the total value of tree

seedlings per carbon contract was thus TSH 32,000 (US$25.20).5

Winning bids were not announced until after the second round,

eliminating the possibility of strategic bidding in the second round

based on theoutcome of thefirst.Participants were told that if they

won in both rounds they would have to choose between the two.

The contract specified that winning bidders would commit to

protect their trees for at least three years and they would be free

to grow crops between the trees. The three-year contract was

intended to compensate for costs during the period in which the

treeswould become wellestablished, and giveICRAF sufficienttimeto look for potential buyers of carbon offsets from the area. Farm-

ers were also toldthat they would be free to decide how to use the

trees if the contracts were not extended after three years.

All of the 268 farmers who attended the auction submitted bids

in each of the two rounds. Seventeen bids were removed from the

analysis because they were either illegible or they were an order

of magnitude higher than the other bids. Subsequent discussions

with these bidders revealed that they had mistakenly added an

extra zero to their bids. Table 2 presents household details of the

remaining 251 bidders included in the analysis.

Although ourbroader interest is in the application of auctions to

allocate carbon sequestration contracts, our cost estimates are best

viewed as the costs of tree planting. The contracts offered through

the auction were strictly for planting trees and keeping them alive,

not for producing a certain amount of carbon. Given our limited

resources and the long distances between farmer plots, we were

unable to gather data on plot characteristics that would indicate

likely rates of tree growth and thus carbon sequestration potential

on different plots. Moreover,the contractswere of too limited dura-

tion to draw strong conclusions regarding the cost of sequestering

carbon.

Furthermore, our local partner did not have the resources to

make annual payments conditional on performance. As a result,

we offered up-front payments to winning bidders. This feature is

found in other PES projects in developing countries that frontload

5

Theexchange rate in March2009 was US$1= TSH 1270.

payments in order to recruit participants (see Jindal, 2010 and

German et al., 2010 for examples in Mozambique and Ugandarespectively). The disadvantage of this approach is that payments

arrive well before the contract ends, yet participants are expected

to continue to protect the trees. Payment in advance raises threats

to the incentive compatibility of our auction because some partici-

pants might be inclined to try to win a contract without intending

to maintain the trees.6 We attempted to mitigate this problem

by informing participants that their plots would be monitored for

compliance. Also, with good compliance they would be eligible for

contract renewal if carbon buyers were found, or for future tree-

planting projects that the World Agroforestry Centre planned for

the area. Monitoring didtake place 21 monthsafterthe auction and

new tree planting projects have been introduced in the area. We

discuss issues related to up-front payments in more detail below.

Auction results

Table 3 shows that bids were similar in the two auction rounds.

This was contrary to our expectations because the two auctions

werefor differentmixes of treespecies thatinteractdifferentlywith

crops and thus should have different opportunity costs associated

with displacing existing crops. Despite similarity in bids across the

twoauction rounds, we didfind notable heterogeneity acrossfarm-

ers. The distribution of bids in both rounds was skewed to the right

with themeanbid in each round greater than therespective median

bid. The median bid of TSH 135,000 was equivalent to 90 days of

wage labor spread over three years at the wage rate prevailing in

the area.

As discussed above, payments on contracts were to be madeentirely in advance with free seedlings. Thus we had reason to

be concerned that many farmers would bid a small amount with

no intention of planting the trees because the opportunity cost

of engaging in a meaningless contract could be considered close

to zero. We find no evidence of such bidding behavior as there

were no zero or near-zero bids. Further, the lowest bids were

6 Auction theory predicts that unconditional payments give bidders an incen-

tive to lower their bids to increase the probability of receiving a contract (Spulber,

1990). Withoutenforcement,any positiveunconditionalpaymentis preferred tonot

receiving a contract. This unraveling of the auction was not observed in our study,

perhaps because contracts were enforced through social mechanisms (see section

Contractoutcomes).However, future studiesconsideringup-frontpaymentsshould

keep this potentially serious problem in mind.


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Table 3

Characteristics and summary statistics of conservation auctions.

Auction details Round 1 Round 2

Nature of contract Khayaanthoteca+Tectonagrandis Khayaanthoteca+ Faidherbiaalbida

Auction

Format Sealed bid second-price Sealed bid second-price

Reservation price No No

Succeeding rounds Sequential

BidsNumber of bids 251 247

Minimum bid TSH 1400 TSH 2000

Maximum bid TSH 450,000 TSH 450,000

Mean bid TSH 143,840 TSH 138,253

Median bid TSH 130,000 TSH 126,000

Standard deviation TSH 96,105.5 TSH 93,105.4

Salientrules

Payment criteria Uniform, lowest rejected bid Uniform, lowest rejected bid

Tie deciding rule Random Random

Auction outcomes

Number of winning bids 14 9

Payment per contract TSH 30,000 TSH 20,000

Total Area contracted 7 acres 4.5 acres

Total number of trees planted 1120 720

Source: First authorssurvey, 2009.

Note: Allbidders were eligible to bid in both rounds. Winning bids foreach round were announced only after thecompletion of both the rounds.

commensurate withthe estimatedlabor coststo plantthe seedlings

(about TSH 2160 for 80 trees), as estimated by a CARE/WWF team

that was planning a carbon sequestration project in the area (Lopa

and Jindal, 2011). As discussed further below, during a monitoring

survey 21 months after the auction, some of the winning farm-

ers indicated that they had been interested in planting trees at the

time of the auction, so a bid that covered their labor costs appears

reasonable.

Fig. 1 shows the upward sloping marginal cost curve (the sup-

ply curve). Bids from the two rounds closely overlap each other.

Starting from thelowestbidders, farmers were contracted until the

conservation budget was exhausted. The 23 lowest bidding house-

holds received three-year contracts at the end of the auction (14in round one and nine in round two). Nine of these bidders won in

both rounds, of whom eight chose to accept contracts from round

one. Following the uniform pricing rule, each of the 14 winning

bidders in round one received a payment of TSH 30,000, while the

450

500

300

350

400

150

200

250

Round 1

Round 2

0

50

100

0 20 40 60 80 100 120Bidsfroml

ocallandstewards

(inth

ousandTSH)

0 20 40 60 80 100 120Private land enrolled under tree planting

carbon contracts (in acres)

Fig. 1. Estimated supply curve for enrolling private land for tree planting carbon

contracts.

Results arefor discriminative pricing in round 1 only. Round two results are nearly

identical. Although the actual auction used uniform pricing, the case with dis-

criminative pricing lends itself well to graphical demonstration. The cost tradeoffs

between efficient targeting and pro-poor targeting are much greater with uniform

pricing as explainedin thetext.

nine winning bidders in round two received TSH 20,000 each. All

winning bidders received free seedlings; they planted 1840 trees

on 11.5 acres of land.

Fig.1 can be used to estimate thecost of promotingtree planting

in the entire Kinole sub-catchment, representing 1227 households

(Table 1, column 2).7 For a low-enrollment targetof one-thirdof the

eligible area (41 acres out of the 125.5 acres included in the auc-

tion), with each household eligible for a uniform per-acre payment

for one contract covering half an acre, a PES project would need to

pay TSH 100,000 per contract (per 0.5 acre). For the catchment as a

whole, about 184 acres (or 368 local households) would enroll at a

total costof TSH 36,800,000 (US$28,976). For a high-enrollmenttar-

getof 80%, theproject would need to payTSH 200,000 percontract8

and 491 acres of private land (982 households) would enroll at a

total cost of TSH 196,400,000.

As discussed in the methods section, we attempted to reduce

opportunities for collusion in the bidding process by using sealed

bids and discouraging communication among participants during

the bidding process. Nonetheless, prior familiarity among partic-

ipants and the seating pattern could have facilitated collusion.

Recognizing this possibility and followingJack (2010), we test for

collusionbut findno evidence that it wasa problem in ourauctions.

(See Appendix for details of the analysis.)

Participation of the poor

We analyze auction data anddemographic data from thehouse-hold survey to estimate the extent to which poor households are

low-cost tree planters. Participation of the poor is often an impor-

tant concern for PES projects in developing countries (Jindal, 2010;

Pagiola et al., 2008; Gauvin et al., 2010). Projects often are designed

7 Although only 268 households participated from the 400 that were invited for

the auction, we still assume that most households in the area would participate in

a tree planting program.This is based on twoobservations:(1) we did not find any

significant differences between households that participated in the auction from

those that did not, and (2) more than 95% of respondents in our initial survey said

they would like to plant trees on their farms.8 This total excludes the cost of supplying tree seedlings and other project

administrative costs. Administering the auction required about 20 person days and

preparing it took more than 200days.


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Table 4

Determinants of auction bids.a

Y= Log (auction bids) OLS round 1 bids OLS round 2 bids Pooled OLS both round bids Random effects both round bids

Bid round (dummy= 1) 0.07 (0.05) 0.06 (0.05)

Gender of bidder (dummy= 1) 0.09 (0.12) 0.19 (0.14) 0.15 (0.11) 0.18 (0.11)*

Age of bidder 0.006 (0.004)* 0.002 (0.004) 0.004 (0.004) 0.004 (0.004)

High time preference (dummy = 1) 0.11 (0.12) 0.08 (0.12) 0.1 (0.1) 0.06 (0.10)

HH size 0.03 (0.017) ** 0.03 (0.02) 0.03 (0.018)* 0.03 (0.017)*

Job/business (dummy= 1) 0.07 (0.13) 0.05 (0.15) 0.13 (0.12) 0.004 (0.12)

Animal ownership(livestock units) 0.43 (0.28) 0.16 (0.17) 0.3 (0.21) 0.3 (0.21)Asset value (000 TSH) 0.0001 (0.0001)*** 0.002 (0.0001)*** 0.0002(0.0001)*** 0.0001(0.0001)***

Farm area (acres) 0.01 (0.007)* 0.01 (0.008)* 0.01 (0.007)* 0.01 (0.007)*

Distance to nearest market (meters) 0.0001 (0.0001) 0.00 (0.0001) 0.00002(0.00004) 0.00002 (0.00004)

Distan ce to n ear es tr oad (meter s) 0 .0 00 3 ( 0. 00 02)* 0.00004 (0.0003) 0.0002 (0.0003) 0.0002 (0.0003)

Elevation (meters) 0.004 (0.002)** 0.001 (0.002) 0.002 (0.002) 0.002 (0.002)

Located in main village (dummy= 1) 0.25 (0.12)** 0.31 (0.16)** 0.28 (0.13)** 0.27 (0.13)**

Constant 5.99 (0.79)*** 4.73 (1.02)*** 5.33 (0.84)*** 5.34 (0.83)***

N 250 246 496 496

Prob > F 0.03 0.08 0.07 0.08

R2 0.1164 0.0807 0.0861 0.0852

***Significant at 1% **Significant at 5% *Significant at 10%.a Standard errors, robust to system heteroskedasticity in parentheses.

Fig. 2. Trade-offs in alternate targetingof carbon contracts.

To compare the outcomes of such pro-poor targeting with tar-geting based on cost-efficiency, we compare the outcomes when

bids are ranked in increasing order of the wealth of the bidding

household to the outcomes when bids are ranked from lowest to

highest. In Fig. 2, the horizontal axis indicates the acreage enrolled,

and the vertical axis represents the cumulative value of all bids.

The efficient cost curve represents an ordering of bids from lowest

to highest (Naidoo et al., 2006; Stoneham et al., 2003), as in Fig. 1.

The pro-poor curve, on the other hand, orders the bids in increas-

ing order of the bidders asset ownership. Many poor households

are high cost providers, and thus bids do not follow a monotonic

order when ordered by the bidders asset ownership. Using this fig-

ure, one can see the tradeoffs when efficient targeting is replaced

by pro-poor targeting in an auction with discriminative pricing (in

which a winning bidders payment is the amount she bid). Thecumulative cost curve for efficient targeting is significantly lower

than the pro-poor cost curve, which indicates that enrolling poorer

households first would require a larger budget to achieve the same

outcome. In thecaseof an auction using theuniformpricing Vickrey

rule, allwinning bidders would receive a payment equal to thenext

rejected bid of TSH 405,000 because the poorest group of house-

holds in the sample includes a high cost bidder (TSH 400,000). As

a result, the cost tradeoff between pro-poor targeting and cost-

effective targeting would be much greater under uniform pricing

than under discriminative pricing.11

11 The tradeoff under uniform pricing does not lend itself very well to graphic

display.

This exercise cannot shed light on the ethics of whether or

not PES projects should target poorer households first. Rather, it

demonstrates that policy makers and buyers of environmental ser-vices may need to be prepared to bear additional costs for doing

so. For instance, to enroll 25% of the potential land area under con-

tracts, the auction results suggest pro-poor targeting would raise

costs by almost threefold under discriminative pricing and almost

fivefold under uniform pricing (Table 5). Tradeoffs between max-

imizing cost-effectiveness and reaching the poor remain high at

higher enrollment levels as well.

These estimates make the tradeoffs clear but should be used

with caution when projecting to alternative targeting scenarios.

First, the estimates are derived from an auction in which bidders

were informed that contracts would be awarded on the basis of the

bids alone.If thebidderswere toknow that theirbids would instead

be ranked using a poverty score, they would have reason to strate-

gically increase their bids to maximize their gains from the scoring

criteria (Kirwanet al., 2005). That would further increase thetrade-

off between achieving cost-effectiveness and poverty alleviation

objectives. Second, we selected winners using the uniform pricing

rule (each contracted household receiving the lowest rejected bid).

Bids would likely be higher if bidders knew that contracts would

be allocated under a discriminatory payment system (Cason and

Gangadharan, 2005). Third, a recent study (Jack, 2010) suggests

that participation and compliance decisions may also be affected

by whether a farmer is allowed to formulate a bid or simply make

a decision to accept a take-it-or-leave-it fixed price.

Contract outcomes

A monitoring exercise conducted by two of the authors inJanuary 2011 found high rates of contract compliance. Of the 23

farmers who won the tree-planting contracts, we were able to con-

tact 19 and visit their farms. (The other four farmers did not live

in either of the two main villages and we lacked the resources to

locate them.) We found that 18 of the19 farmers haddulycomplied

with the contract requirements, with 63% of the trees surviving on

their farms almost two years after being planted. Unique personal

circumstances of the 19th farmer prevented contract compliance.

After theauction,this personsold theseedlings toa neighbor rather

than plant them.

The contract outcomes were similar across the two sets of tree-

planting contracts, though the survival rates varied by tree species:

83% for Khayaanthoteca, 44% for Tectona grandis, and 36% for Faid-

herbia albida. The variation in mortality is explained by higher


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Table 5

Tradeoffs between efficient and pro-poor targeting.a

Pricing method Targeting approach Cost of contracting (in thousands of TSH) under different enrollment targetsb

25% 50% 75%

Discriminative Efficient 2803 9288 18352

Pro-poor 8042

(5239)c17163

(7875)

27327

(8975.0)

Uniform Efficient 5670 17010 35814

Pro-poor 25515(19845)

56700(39690)

84600(48786)

a Results presented arefor round 1 of theauction only. Round 2 results are nearly identical.b In Round 1, there were a total of 251 valid bids, each corresponding to 0.5 acres. The total area that could potentially be contracted was 125.5 acres. Therefore, 25%

enrollment target corresponded to 31.375 acres,50% to 62.75acres, and 75%to 94.125 acres respectively.c Figures in parentheses represent the additional cost of enrollment compared to efficient targeting.

familiarity with Khaya anthoteca than Faidherbia albida,12 and a

failure of the short rains needed by Tectona grandis. Local farmers

reported that under a similar tree-planting project in the area in

200204 that offered free seedlings but not cash incentives, many

fewer trees survived due to lack of proper maintenance and care.

We analyze tree survival in relation to farmer characteristics

such as their bids and their poverty status. We report correlationcoefficients because the small sample size limits our ability to ana-

lyze these relationships using multiple regression. The correlation

coefficient between bid and survival percentage is 0.14 and it is

statistically insignificant, suggesting that there is no relationship

between a farmers bid and compliance with the contract. This

finding supports the idea that farmers took the contract seriously

and did not place low bids with the intention of gaining the pay-

ment without complying with the contract terms. The correlation

between total value of the farmers assets and tree survival is 0.009

and is also insignificant, suggesting that poverty and compliance

are not related.13

A group discussion with auction participants, including both

winners andlosers, washeld duringthe monitoring visit 21months

afterthe auction. Many bidwinnersexplained that they hadalready

been interested in planting trees at the time of the auction, which

facilitated their ability to do so. Many of these farmers made low

bids because they wanted the trees. They said that up-front pay-

ments were important in helping them cover the initial planting

costs. This finding helps explain both the low bids and the reason-

ably high survival rates.

During the group discussion, auction winners also explained

that they faced social pressure to comply with the contract. They

said that almost everyone knew who had won the auction and that

they had received payment for planting trees. Those who did not

receive payment did not want to see the auction winners take the

money without complying, apparently because they were acutely

aware that low bidders had prevented them from winning con-

tracts. We had not anticipated such pressure but it appears to be

a potentially important benefit of auctions as a way to allocatecontracts and is worthy of further study.

Many farmers also said that they liked the transparent way

in which the auction process had identified recipients of the

12 Faidherbia albida is native to lower rainfall areas of Africa includingnearbyareas

in Tanzania, butUlugurufarmers werenot familiarwith it. Severalfarmersreported

that they triedto waterit whenit lost itsleavesin therainy season, having forgotten

that this is one of its essential characteristics. The additional water would have

damaged survival prospects.13 This analysis is for the 19 farmers monitored because we have no basis for

assuming anything about the survival rates for those who were not monitored. If,

however, we assume that none of the trees survived for the other three farmers

who were notmonitored,thenonly location in themainvillage is significantly(and

positively) correlated to tree survival.

tree-planting contracts. They expressed satisfaction with the fact

that unlike in other projects prominent villagers did not receive

contracts, because the auction selected farmers only on the basis

of their bids. This potential benefit of auctions is also worthy of

further study.

Conclusion

As PES continues to spread in developing countries, pilot auc-

tions can help analysts and project planners gain a rich picture of

potential social, economic and environmental impacts. The auc-

tions in the Uluguru Mountains in Tanzania were well received by

thelocalfarmers,who appearto have bidin a mannerthat reflected

their opportunity costs of planting trees.The auction bids appearto

have beenunaffectedby collusionand yielded valuable information

on the cost of planting trees in the area. Moreover, by combin-

ing the bid data with household survey information, we were able

to explore the environmental and social implications of different

contract targeting rules.

Although determining whois poor is nota simplematter(Morris

et al., 2000), our analysis suggests that there are potentially strongtradeoffs between efficient targeting and strict pro-poor targeting

in our study site. The analysis makes these tradeoffs explicit by

estimating the additional budget that PES managers would need to

extend the carbon contracts to all the poor households. Although

some poor households participate under cost-efficient targeting,

the costof including all poor households is sohighthatit islikelyto

be much more cost-effective to achieve environmental and poverty

alleviation through separate interventions. The nature of our con-

tracts, with up-front payments for only a three-year commitment,

means that in a real carbon sequestration project such costs would

likely be even greater.14

Two key caveats apply to our conclusions. First, the use of up-

front payments for tree planting contracts implies that payments

were not conditional on performance. We do not recommendup-front payments in conservation contracts, but they were

necessary due to logistical challenges involved in making periodic

payments. The use of up-front payments (usually in combination

with subsequent conditional payments) is not unusual in PES

programs that require fixed start-up investments by farmers

in developing countries (e.g. Jindal, 2010; German et al., 2010;

Pagiola et al., 2008). It is impossible to know how conditional

payments would have changed the outcomes, but participants

appear to have taken the contracts seriously, knowing that their

14 Moreover, locking poor households into restrictive long-term contracts is not

unambiguously favorable for them, further reinforcing the idea that a PES contract

may notnecessarily be a good poverty alleviation tool.


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