Tsukuba Economics Working Papers
No. 2011-007

An efficient nonmarket institution under imperfect markets:
Labor sharing for tropical forest clearing
by
Yoshito Takasaki, Oliver T. Coomes, Christian Abizaid, and Stéphanie Brisson
December 2011
This version: January 2012

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANAn efficient nonmarket institution under imperfect markets:
Labor sharing for tropical forest clearing
Yoshito Takasakia*, Oliver T. Coomesb, Christian Abizaidc, and Stéphanie BrissonbaUniversity of Tsukuba, bMcGill University, cUniversity of Toronto
January 17, 2012

Abstract
This article examines the substitutability/productivity, efficiency, and evolution of animportant nonmarket institution in agrarian societies – labor sharing. From analyses offield-level data on forest clearing through time among shifting cultivators in the Peruvian
Amazon, we find: (1) that family, hired, and cooperative labor are perfect substitutes;
although hired and cooperative labor are as productive as one another, their productivity
is higher than that of family labor; (2) the combination of labor market and sharing makes
productivity-adjusted total labor use unconstrained by household and network
endowments, i.e., efficient labor allocation; and (3) as labor composition is constrainedby network endowments and liquidity, credit policies alter not only labor composition,
but also network formation.
Keywords: cooperative labor sharing, labor market, substitutability, productivity,
efficiency, network, tropical forest clearing.
JEL codes: O13, O15, O17.
Acknowledgements
The authors gratefully acknowledge the people of our two study villages for their kindcollaboration that made this project possible. Rinda Mozombite, Limber Barbarán, andCarlos Rengifo Ipushima provided valuable assistance in the field. Funding support wasprovided by the Social Science and Humanities Research Council of Canada, The Fonds
Quebeçois pour la Formation de Chercheurs et l’Aide a la Recherche (FCAR), McGill
University, University of Toronto, and the Japan Society for the Promotion of Science.
This article benefited significantly from the insightful comments and suggestions of
Clément Imbert, Takashi Kurosaki, and conference participants on an earlier version ofthis work presented at NEUDC (2011, Yale).
*Corresponding author. Graduate School of Humanities and Social Sciences, Universityof Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8571 Japan, Tel./fax: +81 29 853
6280. E-mail address: takasaki@sk.tsukuba.ac.jp.

1

In rural developing areas where market institutions are missing or imperfect, how
informal nonmarket institutions serve as market substitutes for better resource allocation
is a central question. In peasant agriculture, credit market imperfections often preclude
poor farmers with limited working capital from hiring sufficient labor, although theyneed extra-family labor to accomplish certain tasks such as weeding, harvesting, and field
opening. Farmers resort to labor-sharing arrangements, which are given various names,
depending on locales and contexts, such as cooperative labor, reciprocal labor, collective
labor, communal labor, exchanged labor, festive labor, work group, and work party(Erasmus 1956, Moore 1975). Distinct from hired labor for which buyers pay market
wages, the cost of cooperative labor reflects the opportunity cost of labor to bereciprocated and the costs of drink/food to be offered. In-depth ethnographic studies
describe labor-sharing institutions which persist alongside labor markets in rural areas
around the world (e.g., Chibnik and de Jong 1989, Donaldson 2011, Downey 2010,*HVFKLHUH*XLOOHW6DMRUùDXO:RUE\ . Althoughdevelopment economists have extensively studied agrarian contracts such as share
cropping (e.g., Bardhan 1989), labor sharing has not been given comparable treatment 1;
this is in contrast to extensive economic analyses of reciprocal nonmarket institutions
such as risk sharing (Coate and Ravallion 1993, Ligon, Thomas, and Worrall 2002) andcredit arrangements (Besley, Coate, and Loury 1993).
Consider reciprocal labor-sharing arrangements with limited commitment as
being analogous to informal risk sharing (Coate and Ravallion 1993, Ligon, Thomas, andWorrall 2002). Incentive and/or enforcement problems associated with securing

2

cooperative labor for one’s farm over time may be mitigated through reputational
mechanisms (Ernst, Gächter, and Kirchsteiger 1997), social norms (Bandiera, Barankay,
and Rasul 2010), mutual monitoring, peer pressure (Kandel and Lazear 1992, Mas andMoretti 2009), and high returns to teamwork (Hamilton, Nickerson, and Owan 2003), as
found in other work settings; these and other factors may even make cooperative labor
more productive than family labor. Similarly, if wage labor contracts are not based on an
anonymous spot market, but on personalized, repeated interaction, reputational
mechanisms for future employment may mitigate incentive problems in hired labor even
without reciprocity (i.e., analogous to permanent labor contract in contrast to casual labor
contract, Dutta, Ray, and Sengupta 1989); this is especially so when employment
opportunities are limited in poor communities.
Using original field-level data on forest clearing through time among shifting
cultivators in the Peruvian Amazon, where land is claimed by clearing and held in
usufruct – i.e., land market is missing – and credit is scarce, this article examines thesubstitutability/productivity, efficiency, and institutional interaction/evolution ofcooperative labor sharing. Specifically, we pursue three lines of enquiry. First, are family,
hired, and cooperative labor perfect substitutes, and are they as productive as one
another? The extant literature shows mixed empirical findings on substitutability andproductivity of hired and family labor for agriculture, though not explicitly for field
opening (e.g., Bardhan 1973, Brown and Salkin 1974, Deolalikar and Vijvergerg 1983,
Deolalikar and Vijvergerg 1987, Desai and Mazumdar 1970, Frisvold 1994, Pitt andRosenzweig 1986). We estimate two-level constant-elasticity-of-substitution (CES)

3production functions for forest clearing with family, hired, and cooperative labor as
separate inputs, controlling for household fixed effects; our results show infinite
elasticities of substitution (i.e., perfect substitutes), equal productivity of hired andcooperative labor, and their higher productivity compared to family labor. We examine
why extra-family labor is more productive than family labor.
Second, does the combination of labor market and sharing enable efficient labor
allocation, despite market imperfections? When markets are perfect, household
consumption (labor supply) and production (labor demand) decisions are separated(Singh, Squire, and Strauss 1986, Taylor and Adelman 2003). The well-established test
for this separability hypothesis is to see whether total labor use follows market wages,
unconstrained by household demographic characteristics (Benjamin 1992); inseparability
makes labor demand dependent on the household shadow wage determined by supply
factors. Whereas extant empirical results on separability are mixed (e.g., Benjamin 1992,
Kevane 1996, López 1986, Pitt and Rosenzweig 1986), agricultural household models
have not yet been extended to incorporate nonmarket labor institutions.2 Theoretically,
efficient labor allocation is possible as far as the unit cost of labor sharing – laborreciprocation and inkind – is endogenously set at the level of market wage. Our article
augments the separability test: a null hypothesis is that labor demand is unconstrained not
only by household demographic characteristics, but also by network characteristics, giventhat labor-sharing arrangements are organized by household networks (as risk-sharing
arrangements are, e.g., Dercon and De Weerdt 2006, Fafchamps and Gubert 2007,
Fafchamps and Lund 2003).3 Importantly, both the conventional and augmented

4

separability tests assume perfect substitutability of labor inputs; distinct from most extant
works, we first test this assumption. We develop an empirical model focusing on kin
network endowments. Our results do not reject separability, suggesting that forest
clearing is labor efficient and responsive to market prices (e.g., de Janvry, Fafchamps,
and Sadoulet 1991).
Third, how do market and nonmarket institutions for labor allocation co-evolve in
response to economic policies? We make use of credit policies which were introduced in
one of our sample villages as a natural experiment. Kranton (1996) demonstrates
theoretically that market and nonmarket reciprocal institutions interact with each otherthrough external costs imposed on each other – search costs in anonymous market andenforcement in personalized reciprocal exchange – and which institution persists depends
on the initial distributions of participants in these two institutions. Although both market
and nonmarket institutions are personalized in our context (and in many other ruraldeveloping areas), the key implications of Kranton’s (1996) work still hold; as credit
relaxes liquidity constraints and labor market exchange expands, it becomes difficult to
recruit cooperative labor (i.e., external cost). We conjecture that households not only
reallocate hired and cooperative labor with equal productivity (i.e., substitution, or
quantity adjustment), but also alter the formation of labor-sharing networks for stronger
reciprocity (i.e., network tightening, or quality adjustment) (Downey 2010 finds similar
flexibility in labor-sharing networks among Amerindians in Belize). Our analyses reveal
supportive evidence for both.

5This article begins with a description of the study area and the data. We thenreport descriptive statistics of land, forest clearing labor, and labor sharing. Afterdeveloping econometric specifications to test our hypotheses discussed above, estimation
results are reported. The last section presents a summary of the main results and aconclusion.
Study Area and Data
This study is based on household survey data gathered in 2001 from traditional mestizo
peasant (ribereño) households in two communities located on the Marañón River, one ofthe primary Andean tributaries of the Amazon River in Peru (about one-day’s travel byriver boat to Iquitos). One village is located on the lowland, which is susceptible toflooding (each year the river rises and falls over a range of 8-10 metres), and the other ison the upland, which is never flooded. Although all households in the lowland village
were sampled, in the larger upland village households were randomly sampled in each of
three strata defined by land holdings. The analysis is based on 74 households withcomplete data (34 in the lowland village; 40 in the upland village).4
Forests are de facto open access within community territory. Once cleared, land isheld by usufruct (i.e., without title), privately used, and transferred principally alongfamily lines; land markets are absent. Forest clearing is a highly laborious task,
undertaken by males using only machetes and axes (no chainsaws). We collected fieldlevel data on acquisition (cleared or transferred) and forest clearing labor (if cleared) in a
retrospective manner, for each field held at the time of survey in 2001 – when it was
cleared first for claiming – and each field acquired at the time of household formation in6

the current village. Respondents were readily able to recall their forest clearing practices(we discuss potential recall bias later).5 The database does not contain fields acquired(cleared or not) after household formation and not held in 2001 (i.e., transferred to others)(we discuss potential attrition bias later); thus, we lack complete data of land holdings
through time.
All live births, deaths, and departures from each household were also recorded,
allowing the tracking of demographic composition of households through time. As we
can identify members of each kin group, we were also able to construct the demographic
composition of kin groups through time, and thus, at the time of forest clearing for eachcleared field. These demographic variables, however, underestimate true historical kingroup size, because the data do not capture those few households that migrated out andare based on the sample, not census, in the upland village (we discuss potential
systematic measurement errors later). In field data at the time of forest clearing, there isno significant difference in demographic characteristics – at the household and kin-grouplevels – between two villages (see Table A); this is also true in household-level data in2001 (results not shown).
Land and Labor
Land Holdings and Portfolio
At the time of interviews in 2001, on average, a household in the lowland village held 6.2
fields with 6.3 ha and in the upland village, 4.6 fields with 9.5 ha (see Table 1). In
addition to upland, four distinct types of lowland exist depending on land form andelevation: high levee is flooded only by high floods in some years; low levee is flooded

7each year; and, mudflat and sandbar appear only for a limited time during the low-waterseason. Lowland alluvial soils are significantly more fertile than upland soils. Land
portfolios are quite distinct between two villages: upland is available only in the uplandvillage (upland portfolio size is typically larger than other lands because of fallowing);
fertile high levee and mudflat are scarce (mudflat is scarce, especially near the uplandvillage); low levee holdings are almost uniform (locally abundant); and, sandbar is an
uncommon land type. A typical household agricultural portfolio includes food crops on
low levee along with a combination of cash crops (especially rice) on fertile mudflat
and/or food/tree crops on secure upland and relatively secure/fertile high levee.6 Current
land holdings consist of cleared land and non-cleared land which includes transferred
land and claimed mudflats/sandbars without clearing (typically mudflats/sandbars
without woody vegetation do not require clearing).
Forest Clearing Labor
Our analyses on forest clearing focus on cleared upland/high levee/low levee fields withcomplete labor use data (n=260). In the lowland village (n=121), low levee is dominant
in terms of the number and area of fields cleared; in the upland village (n=139), upland
and low levee are the main land types, followed by high levee (see panel A of Table 2).
The mean size of cleared field is about 1 ha in both villages, regardless of land types;
total labor use for clearing per hectare is also similar across land types in each village(about 29 labor days) (panel B of Table 2).
In addition to family labor and hired labor (fixed wage contract), households
commonly host cooperative labor events, whereby they recruit people in the same village

8

to clear forests for a day or less and offer drink (usually a fermented drink made frommanioc) and occasionally food to the guests in exchange. Distinct from exchange labor ora work group, in which reciprocity among group members is explicit (failure toreciprocation often leads to the termination of the contract), attendance is voluntary andthus reciprocity is implicit (as in informal risk sharing). It is not uncommon for someone
to decline an invitation or fail to show up, and for others to attend, even if they were notformally invited.7 This type of cooperative labor is well suited for forest clearing, because
it involves massive laborious work to be done quickly (cleared field needs to be burned
before weed invasion), it can be done irregularly (lack of synchronization, cf. harvesting),
and the task is so simple that work quality may not be a major concern (even with drink)(Chibnik and de Jong 1989). Cooperative labor events in the sample are categorized into
two major types: minga, which involves a full day of work (7 hours) with two meals aswell as generous drink, and mañaneo, which entails a half day of work (3 hours in themorning) with drink but no meal (Brisson 2003).8
In each village, family and cooperative labor were much more commonly
deployed for clearing than hired labor was (about two thirds and a half of fields vs. about
14% of fields). Although mañaneo was more common than minga in the lowland village,
minga was much more common than mañaneo in the upland village. The number ofworkers per event was larger in the lowland village than the upland village (22.3 vs. 16.2),
with no significant difference between minga and mañaneo; in labor amount (labor-day),minga was larger than mañaneo in the lowland village and minga dominated cooperativelabor in the upland village.

9

Hired labor played a major role in the upland village: it was common (29% offields) and large in amount (44% of labor days) to clear high levee (most high levee fields
are cleared, not transferred); hired labor was uncommon to clear upland (9% of fields)
but large in amount (27% of labor days) (cf. 16% of labor days to clear low levee) (weexplain why shortly). For upland/high levee/low levee, hired labor in the upland village
was larger in proportion than the lowland village (24 vs. 11% of labor days); the converse
holds true for family labor, whereas the proportion of cooperative labor was similar in
two villages.
The most common labor arrangements were family labor alone (36-40% of fields),
cooperative labor alone (25-29% of fields), and their combination (19-23% of fields).
Cooperative labor and hired labor are almost mutually exclusive; i.e., households
typically deployed either cooperative or hired labor, not both, when they needed extrafamily labor. Hired labor alone and hired labor combined with family labor wererelatively common to clear high levee in the upland village (18 and 12% of fields).
Evolution of Forest Clearing LaborIn this field-level dataset, fields were cleared from 1949 through 2001: 18% before 1980,
23% during the 1980s, and 59% in 1990s or later (panel C of Table 2). Labor
composition significantly changed through time mainly because of local credit
opportunities as follows. In the 1970s, credit for jute on lowland was available in bothvillages; during the 1980s, under Garcia’s populist regime the region experienced a credit
boom targeted through the National Agrarian Bank toward upland crops (only in the
upland village); in the early 1990s credit virtually disappeared under Fujimori’s regime

10(Coomes 1996), followed by private credit expansion for lowland rice production in thelate 1990s (especially in the lowland village with large mudflats). Correspondingly,
although hired labor was relatively common at the same level in both villages in the
1970s, hired labor became more common and then less common in the upland village; theopposite pattern occurred in the lowland village. In the upland village, hired labor was
especially commonly used to clear high levee in the 1980s and remained relatively
common afterwards, indicating that credit for upland agriculture during the 1980s may
have been used also for clearing high levee (with the small numbers of observations of
upland/high levee in each period, except for upland in the 1990s, interpreting theirevolution requires caution).
These changes in labor markets through time are associated with corresponding
changes in family and cooperative labor use, in distinct manners across land types: on
one hand, cooperative labor decreased and family labor increased over periods to clear
low levee in both villages; on the other hand, cooperative labor use to clear upland
became less common during the1980s and then more common by the 1990s, and theconverse holds true for high levee in the upland village. Notable institutional changes
occurred in cooperative labor: in the lowland village minga was replaced by mañaneo
through time, and in the upland village mañaneo emerged to clear low levee in the 1980s
and upland and high levee in the 1990s. As such, the labor-sharing institution strongly
responded to the market expansion: the more hired labor, the less cooperative labor inincidence and scale.

11

Econometric Specification
Substitutability and Productivity
We assume the following two-level CES production function (Sato 1967) in which twoforms of extra-family labor – hired and cooperative labor which are almost mutually
exclusive as found above – are nested.
థΤఏ‫ ߙ = ݕ‬ቄߟൣߢ݄ఏ + (1 െ ߢ)ܿ ఏ ൧ଵΤథ+ (1 െ ߟ)݂ థ ቅ,(1)where y is the size of field cleared; f, h, and c are family, hired, and cooperative labor
input, respectively; Į is a positive efficiency parameter; ț and Ș (0  ț, Ș  1) aredistribution parameters; ș and ʔ ( 1) are the intra-class and interclass elasticity-ofsubstitution parameters, respectively (ș, ʔ = 1 imply perfect substitutability; ș, ʔ = 0
imply the Cobb-Douglas specification; and ș, ʔ = - imply perfect complementarity). In
equation (1), although the elasticities of substitution between hired and family labor andbetween cooperative and family labor are the same, the elasticity of substitution between
hired and cooperative labor can be different from them. In the case where ș ʔ, equation(1) becomes a CES production function‫݄ߜ{ ߙ = ݕ‬ఘ + ߣܿ ఘ + (1 െ ߜ െ ߣ)݂ ఘ }ଵΤఘ ,(2)where ȡ ș ʔ( , į Șț, and Ȝ Ș(1 – ț). The elasticity of substitution between
any two inputs is equal to 1/(1 – ȡ), and ȡ = 1 implies the infinite elasticity of substitution(i.e., perfect substitutability).
The two-level CES production function equation that will be empirically tested isଵఏఏln‫ݕ‬௜௝௧ = lnߙ + థ ln ቄߟൣߢ݄௜௝௧
+ (1 െ ߢ)ܿ௜௝௧
൧

థΤఏ

థ

+ (1 െ ߟ)݂௜௝௧ ቅ + ࣇ࢚ + ߱௝ + ߝ௜௝௧ , (3)12where i, j, and t stand for field, household, and year, respectively; vt is a vector of villageperiod dummies (1980s, and 1990s or later; 1970s or before as a base); Ȧj is unobservable
household fixed effects (e.g., skills); and İijt is an error term. Village-period dummies
control for time-variant/-invariant village factors, such as technological progress, forest
stock, labor market conditions, and policies including credit ones; they also roughly
control for cooperative labor type (minga or mañaneo) which varies across periods ineach village. As our field-level data are measured in different years for each household in
most cases, across-field variations in the same household also capture across-time
variations. First differencing equation (3) gets rid of the household fixed effects, yielding௬೔ᇲ ೕ೟ᇲ

ln ௬

೔ೕ೟

ଵ

= థ ln൜ఎቂ఑௛ഇᇲ ᇲ ା(ଵି఑)௖ ഇᇲ ᇲ ቃ
೔ ೕ೟
೔ ೕ೟
ഇ
ഇ
൜ఎቂ఑௛೔ೕ೟
ା(ଵି఑)௖೔ೕ೟
ቃ

ഝΤഇ

ഝΤഇ

ഝ

ା (ଵିఎ)௙ ᇲ ᇲ ൠ೔ ೕ೟ഝା (ଵିఎ)௙೔ೕ೟ ൠ+ ࣇ࢚ᇲ െ ࣇ࢚ + ߝ௜ ᇲ ௝௧ ᇲ െ ߝ௜௝௧ .

(4)

We estimate equations (3) and (4) using nonlinear least squares (NLLS) (the same
estimation strategies are employed by Duffy, Parageorgiou, and Perez-Sebastian 2004).
The CES production function equation that will be empirically tested is the constrained
equation (3) with ș ʔ (=ȡ), and its first-difference specification is analogously defined(as done by Duffy and Parageorgiou 2000). In each specification, we estimate both theunconstrained model and the constrained model with ș, ʔ  or ʌ  to test
Hypothesis 1 (perfect substitutability): ș ʔ = 1 and ȡ =1.
In the two-level CES production function (1), ț measures the productivity of hired
labor relative to cooperative labor and Ș measures the productivity of nested extra-familylabor relative to family labor. In the CES production function (2), įand Ȝmeasures the
productivity of hired and cooperative labor, respectively, relative to family labor; with13

perfect substitutability (ȡ =1), equation (2) becomes the linear production function with aconstant marginal product of each input.
We first consider upland/high levee/low levee together, ignoring across-type
heterogeneity, and then low levee only (other land types are too uncommon for thisdisaggregate analysis). Although within-type heterogeneity is still not controlled for, it
should be much smaller than across-type heterogeneity (the first-difference specification
does not eliminate unobservable land quality because our field data are not panel). The
first-difference specification also controls for potential attrition bias in our field data,
unless household past land transfers are systematically related with forest-clearing
productivity within the same land type and such correlations significantly change overtime. To check the robustness of our results to recall bias, we repeated the analysis
dropping old fields (those cleared before 1980) which tend to contain larger recall errors,
finding very similar results to those presented below.
Separability
Separation of the household production and consumption decisions suggests that the unit
cost of cooperative labor – i.e., the opportunity cost of reciprocated labor evaluated at the
shadow wage and the costs of drink/food to be offered – is endogenously set at the levelof market wage by adjusting reciprocation and the type of labor sharing (minga vs.
mañaneo) as well as quantities and quality of drink/food.9 Then, even if labor market isconstrained by liquidity, households can efficiently use total labor as cooperative laborcomplements hired labor. Put differently, although labor sharing is constrained by
network endowments as households with stronger networks can better mobilize

14cooperative labor, hired labor can complement cooperative labor. That is, thecombination of labor market and labor sharing can enable efficient labor allocation.
Given that family, hired, and cooperative labor are perfect substitutes, we test theaugmented separability by estimating the following labor demand equation:ln݈௜௝௧ = ߚ଴ + ࢼ૚ ࢞࢏࢐࢚ + ࢼ૛ ࢠ࢏࢐࢚ + ࢚࢜ + ‫ݑ‬௜௝௧ ,(5)

where lijt is productivity-adjusted total labor use to clear forests (labor-day); xijt is a vectorof household demographic characteristics (detailed below); zijt is a vector of network
characteristics (defined shortly); and uijt is an error term. As how flexibly a household canchoose minga or mañaneo varies across periods in each village, the village-period
dummies control for exogenously determined (accepted) cost of drink/food. We consider
upland/high levee/low levee together and then low levee separately.
Hypothesis 2 (separability): Productivity-adjusted total labor use for forest
clearing is not affected by household and network endowments, i.e., ȕ1 ȕ2 = 0.
A key empirical question is how we can capture exogenous network
characteristics. Our strategy is to employ a reduced-form approach by using exogenous
determinants of labor-sharing networks in order to avoid potential identification problems
associated with network formation. Although historical labor network data are lacking,Abizaid et al. (2012) conducted social network analyses on cooperative labor for all
agricultural tasks (mostly forest clearing, plus weeding and harvesting) in 2001 in thelowland village with data from a complete census and examined the determinants of
network formation using the dyadic regression framework (Fafchamps and Gubert 2007).
Two major findings stem from that work. First, cooperative labor networks are formed by

15

kin-group affiliation and female kin relationship – within and outside kin group. That is,cooperative male labor is organized by female networks, not by male networks. Second,kin-group structure (connections/relationships of households in the group), not size
(number of households in the group), matters in the within-group networks.10 Based onthese results, we focus on kin networks, employing the following three specifications.
First, we focus on within-group networks using kin-group dummies (fixed effects),
which fully control for their characteristics: specifically, we employ OLS using sevendummies for major kin groups (four in the lowland village; three in the upland village)
with minor kin groups/no kin-group affiliation as a base. Within-group networks are notsufficient for cooperative labor; the number of male working adults (age 15-64) in thesame kin group (excluding own male adults) – including both major and minor groups –is much smaller than the number of workers per event on average (5.0 vs. 22.3 in thelowland village; 5.1 vs. 16.1 in the upland village) (Tables A and 2). Thus, households
must also rely on across-group networks which are wider than within-group networks.
Second, we capture across-group networks while controlling for within-group
network size. Specifically, we use number of male working adults in the same kin group(excluding own endowment) to capture within-group network size and number of female
working adults in the same kin group (excluding own) to capture across-group networks
and the characteristics other than size of within-group networks, both formed by female
connections (both major and minor kin groups are considered). Our working assumption
is that the more female adults in the kin group, the more female relatives in thevillage/group and thus the better connections. Since the number of adults in the kin group16change through time, we employ the fixed-effects specification, fully controlling fortime-/field-invariant group factors, such as social capital and fixed components of all
networks (within- and across-group), as well as unobservable household fixed effects.
For the reasons listed above, the fixed-effects model controls for potential attrition bias in
our field data. As discussed, these two variables underestimate true kin-group size.
Whether such measurement errors cause bias after controlling for group fixed effects
depends on whether changes in such errors are systematically correlated with changes inthese variables and household demographic variables (discussed below).
Third, we use the share of male/female working adults in the same kin groupwithin the village. They do not cause bias, unless household attrition patterns – becauseof migration and sampling – change in distinct manners across kin groups. In any case,qualitatively the same results of the level and share variables suggest the robustness ofthe estimation results. We also repeated the analysis dropping old fields which tend tocontain larger attrition errors, as well as larger recall errors, finding very similar results.
Household demographic factors other than labor endowments measured bynumber of male/female working adults include number of children (almost no households
had elderly members at the time of forest clearing), household age in the village (which
can affect labor-sharing network), age of household head (lifecycle), and two dummies
for male and female heads’ birth in the village (which also can affect network)(descriptive statistics of these variables are shown in Table A). In the fixed-effects
specification, the last two dummies vanish and effects of household age and age ofhousehold head cannot be distinguished.11

17

Although the market wage in each period in each village is fully controlled for by
the village-period dummies, within-period (yearly) variations are not controlled for(historical wage data are lacking). If an unobservable change in wage within the period issystematically correlated with a change in household/network endowments, our estimateswould be biased. Such potential bias is unlikely to be a major concern for the following
reasons. First, the largest shocks on local labor markets were credit policies which arecontrolled for by the village-period dummies, and compared to across-period wage
variations, within-period fluctuations should be much smaller. Second, local labor
employment plays a minimal role in households’ livelihoods, and thus, local wage isunlikely to be strongly related to migration which influences demographic size.
Evolution
Our conjecture is that households respond to credit policies by altering not only
labor composition as found above, but also the formation of labor-sharing networks. The
former quantitative substitution suggests that network endowments affect two forms ofextra-family labor in the opposite ways.
Hypothesis 3-1 (network-endowment effects): The stronger networks, the morecooperative labor and the less hired labor.
We also conjecture that as the recruitment pool for cooperative labor shrinks, households
rely more on close connections with a high degree of reciprocity. This quality adjustment(network tightening) can be tested using the upland credit boom as a natural experiment.
Hypothesis 3-2 (network formation): Cooperative labor networks to clear
upland/high levee are tighter than networks to clear low levee.

18

To test hypotheses 3-1 and 3-2, we estimate equation (5) for family, hired, andcooperative labor separately, examining how distinctively within- and across-group
network endowments affect upland/high levee and low levee clearing.12 This also allows
us to indirectly examine how labor composition is affected by liquidity (lack of historical
wealth data precludes us from capturing liquidity constraints).
We estimate determinants of employment (dkijt = 1(lkijt > 0), where 1(ͻ) is anindicator function which takes 1 if the argument holds and 0 otherwise and lkijt is type klabor input) using linear probability models first and then those of log of amount(dkijtlnlkijt) to examine the combination of employment and intensity using OLS/fixed
effects models. To capture potentially distinct network-endowment effects in twovillages, we use male/female labor endowments in the kin group (level or share)interacted with village dummies for upland/high levee/low levee and low levee; kin group
dummies defined within each village already capture across-village heterogeneity. We
also analyze upland/high levee clearing in the upland village separately.
Estimation results
Substitutability and Productivity
NLLS estimation results of forest clearing functions for upland/high levee/low levee andlow levee are reported in Table 3.13 In the unconstrained models for the two-level CES
production function (panel A) and the CES production function (panel B), respectively,
all estimated șand ʔ, but one for ș taking .91, and all estimated ʌ, but one taking .93, area little bit larger than unity (up to 1.5) and none of them are statistically different fromunity (a null hypothesis that ș = ʔ is not rejected at all); in the constrained models, all19results corresponding to estimates of greater than one take the boundary value 1. These
results strongly support Hypothesis 1 – family, hired, and cooperative labor are perfect
substitutes.
In each specification, estimation results for ț and ɻ (panel A) and for į and Ȝ(panel B) are very similar to each other between the unconstrained and constrainedmodels. Although estimated ț is not statistically different from 1/2, estimated ɻ issignificantly greater than 1/2 in all cases. Estimated į and Ȝ are not statistically different
from each other, though they are significantly greater than 1 – į – Ȝ (= ȝ) in all cases.
Further, a null hypothesis that hired and cooperative labor are twice as productive as
family labor, i.e., į = Ȝ = 2ȝ, is not rejected. Very similar results are obtained for the
linear production function (columns 1, 3, 5, and 7 of panel C).14 These results suggestthat although hired and cooperative labor are equally productive, they are moreproductive than family labor (we discuss a possible reason later). Combining hired andcooperative labor as extra-family labor, we also estimate the linear production functions
with two inputs, i.e., į = Ȝ (columns 2, 3, 6, and 8 of panel C); estimated į aresignificantly greater than 1/2 and are not statistically different from 2/3.
Separability
Estimation results of labor demand functions for upland/high levee/low levee and low
levee are reported in Table 4 – OLS with kin group dummies in panel A, fixed-effects
models with the number of male/female adults in the kin group in panel B, and fixedeffects models with the share of male/female adults in the kin group in panel C (onlyhousehold labor endowments and network variables are shown; the results of household

20labor endowments not reported in panel C are almost the same as those in panel B). In
each panel, columns (1) and (3) show results for productivity-unadjusted total labor andcolumns (2) and (4) are for productivity-adjusted total labor in family labor unit – familylabor plus hired/cooperative labor weighted by two (results are robust to other weights
near two). No household demographic factors (including those not shown in the table) are
statistically significant across models; similarly, most kin group dummies and network
endowments are not statistically significant.15 Thus, the augmented separability,
Hypothesis 2, holds.
Evolution
Estimation results of labor composition (employment and amount) are reported in Table 5(OLS with kin group dummies) and Table 6 (fixed-effects models with network
endowments) – upland/high levee/low levee and low levee in both villages in panels A
and B, respectively, and upland/high levee in the upland village in panel C; in Table 6
only household labor endowments and network variables are shown (the results ofhousehold labor endowments not reported in panels A2 and B2 are nearly the same as
those in panels A1 and B1, respectively). First, within-group network endowments,
captured by kin group dummies, in the lowland village negatively affect hired labor toclear low levee (the dominant type), and the results for upland/high levee/low levee aresimilar and jointly statistically significant; in contrast, within-group network endowmentsin the upland village negatively affect hired labor to clear upland/high levee, not low
levee (the results are jointly statistically significant). Across-group network endowments,
captured by female adult size/share, in the lowland village positively (and negatively)

21

affect cooperative (and hired labor) to clear low levee, and the results for upland/highlevee/low levee are similar. In contrast, across-group network endowments in the uplandvillage alter neither upland/high levee nor low levee clearing in a significant manner.
Hence, network endowments affect two forms of extra-family labor in the opposite
manners and cooperative labor networks for upland/high levee clearing are tighter thanthose for low levee clearing, i.e., Hypotheses 3-1 and 3-2 hold. On the other hand, withingroup network size, measured by male adult size/share, has a very limited influence
across land types/villages, confirming that network size is not a key factor for labor
sharing (Abizaid, et al. 2012). Significant results for family labor are also very limited.
Most household labor endowments (including those not reported in panels C1 and
C2) are statistically nonsignificant; as the only exception, households with more female
adults hire more labor for clearing (this holds across land types, though results only for
upland/high levee/low levee are statistically significant; household male laborendowment is also statistically significant with the opposite sign and we return to thisresult later). This relationship may indicate a tight link of market and nonmarket
institutions, whereby male adults who could build good reputation in cooperative labor
events could get employment (as documented by Pierre 2005 in Haiti); then, female
connections in nonmarket reciprocal exchange extend to personalized market exchange(another channel of institutional interaction).16
Why Is Extra-Family Labor More Productive Than Family Labor?
Rain forest clearing with rudimentary technology is highly laborious and we conjecturethat clearing is more productively done by young male adults working together. As male

22

working adults in the household become older over the lifecycle (and when households
are very young, with small children), the household must rely more on extra-family labor.
Under the tightly linked market and nonmarket institutions, young male adults may well
work harder not only as wage laborers, but also in cooperative labor events, for future
employment and reciprocity for land accumulation than they do on their own fields(Abizaid, et al. 2012 show supportive evidence in the pivotal roles of composite
households – those comprised of parents and the families of adult children – in labor
sharing in 2001). Moore (1975) offers other potential reasons why young adults may
work more productively in groups in his extensive review of cooperative labor. As such,
extra-family laborers may be younger and more productive in forest clearing than familylaborers.
With lack of individual-level data on forest clearing labor, we indirectly test ourconjecture by repeating the analyses of labor composition using young adults (age 15-29)
and older working adults (age 30-64) in the household/kin group separately (this division
is based on the distribution of adults through time; many adults were relatively young at
the time of clearing). Although aggregate male labor endowments in the household/kin
group do not alter labor composition as found above, if young male adults are moreproductive especially as extra-family laborers, disaggregated male labor endowments
should influence the use of extra-family labor (as network endowments do).
Hypothesis 4 (adult-lifecycle effects): The more young (older) male laborendowments in the household and the kin group, the less (more) hired labor andthe more (less) cooperative labor, respectively.

23With the small number of observations in our field data, we focus on upland/highlevee/low levee and low levee in both villages with within-group labor endowments notinteracted with village dummies.
Fixed-effects estimation results are reported in Table 7 with the same format as
Table 6. The following findings support Hypothesis 4: (1) households with a larger
number of own older male working adults are more likely to hire labor to clear low levee;(2) households with fewer young male adults hire a larger amount of labor to clear
upland/high levee/low levee and low levee only (the results sustain in aggregate malelabor endowment as found above); (3) households with a greater number/share of older
male adults in the kin group use less cooperative labor – in both employment and amount– to clear upland/high levee/low levee; and, (4) no results for family labor are statisticallysignificant.17
At the same time, both young and older female working adults in thehousehold/kin group influence labor composition in the same way as found above (evenif statistical significance varies between them, estimated coefficients are largely
comparable); thus, labor networks are formed based on the connections of both females.
Put differently, individual female kin networks are relatively stable (after marriage).
Conclusion
Using original field-level data on forest clearing through time among shifting
cultivators in the Peruvian Amazon, this article examined their nonmarket labor-sharing
institution under imperfect markets. This is the first economic work that examines the24substitutability, productivity, and efficiency of agricultural labor sharing. The following
three major findings were obtained from our analyses of forest clearing labor:
1) Family, hired, and cooperative labor are perfect substitutes. Although hired andcooperative labor are as productive as one another, their productivity is higher thanthat of family labor, probably because extra-family laborers are younger and perhaps
more highly motivated during agricultural work events than family laborers.
2) Labor allocation is efficient as the combination of labor market and labor sharing
makes productivity-adjusted total labor use unconstrained by household and network
endowments. Labor reciprocation allows cooperative labor to complement hired labor.
3) Labor composition, however, is constrained by network endowments and liquidity;
hence, credit policies alter not only labor composition, but also network formation(network tightening for stronger reciprocity).
These findings suggest the following three implications for research and policy.
First, nonmarket labor-sharing institutions can serve as market substitutes for efficientlabor allocation. Although empirical findings on the separability hypothesis in theliterature are mixed, results may change if labor sharing, where present, is explicitly
incorporated in theoretical and empirical analyses. Inter-household labor reciprocation
can also facilitate labor substitution. As such, labor sharing may play a much moresignificant role in peasant agriculture and peasants may be more responsive to market
signals than currently thought. More attention is needed to better understand theeconomics of agricultural labor sharing and its potential role in and implications for ruraldevelopment. For example, labor sharing may be complementary to other social networks

25

among peasants such as ones for technology adoption (Conley and Udry 2010) and may
significantly influence local public goods provisioning in community-based development(World Bank 2002).
Second, as economic development and policies relax market constraints, market
exchange expands and nonmarket exchange contracts (North 1990). Labor-sharing
institutions, for example, can persist not only because of their efficiency and productivity
comparable to market exchange, but also through their flexibility in making quantitativeand qualitative adjustments, i.e., substitution and network formation. The co-evolution ofmarket and nonmarket institutions is a broad theme which is critical in studying
development process for better policymaking. Exploring labor-sharing institutions along
this line is promising and important because of their prevalence, significance, anddynamism as documented in many ethnographic studies.
Finally, efficient forest clearing among shifting cultivators does not necessarily
lead to efficient allocation of lands under missing/imperfect land markets, because land
accumulation is also determined by land transfer, another network-based nonmarket
institution which reallocates cleared lands. Better understanding forest-clearing decisions
and consequences – land accumulation and distribution and broad environmental
outcomes, such as secondary forest regrowth and ecosystem service provisioning – intropical forests requires the examination of the interaction between labor transfer(sharing) and land transfer. These issues are to be addressed in a separate article.

26

References
Abizaid, C., O.T. Coomes, Y. Takasaki, and S. Brisson. 2012. "Social network analysis
and peasant agriculture: cooperative labor as gendered relational networks."
Working Paper. Toronto: University of Toronto.
Bandiera, O., I. Barankay, and I. Rasul. 2010. "Social incentives in the workplace."
Review of Economic Studies 77(2):417-458.
Bandiera, O., and I. Rasul. 2006. "Social networks and technology adoption in northern
Mozambique." Economic Journal 116(514):869-902.
Bardhan, P.K., ed. 1989. The Economic Theory of Agrarian Institutions. Oxford: Oxford
University Press.
---. 1973. "Size, productivity, and returns to scale: an analysis of farm-level data in Indian
agriculture." Journal of Political Economy 81(6):1370-1386.
Benjamin, D. 1992. "Household composition, labor markets and labor demand: testingfor separation in agricultural household models." Econometrica 60(2):287-322.
Besley, T., S. Coate, and G. Loury. 1993. "The economics of rotating savings and credit
associations." American Economic Review 83(4):792-810.
Brisson, S. 2003. "Labor access and unequal land holdings among peasant farmers in alowland and upland community of the Peruvian Amazon." Master Thesis, McGill
University.
Brown, J.A.J., and J.S. Salkin. 1974. "Underemployment in Rural South Vietnam: A
Comment and a Discussion of Family Labor." Economic Development andCultural Change 23(1):151-160.

27

Chibnik, M., and W. de Jong. 1989. "Agricultural labor organization in ribereño
communities of the Peruvian Amazon." Ethnology 28(1):75-95.
Coate, S., and M. Ravallion. 1993. "Reciprocity without commitment: characterizationand performance of informal insurance arrangements." Journal of Development
Economics 40(1):1-24.
Conley, T.G., and C.R. Udry. 2010. "Learning about new technology: pineapple in
Ghana." American Economic Review 100(1):35-69.
Coomes, O.T. 1996. "State credit programs and the peasantry under populist regimes:
lessons from the APRA experience in the Peruvian Amazon." World Development24(8):1333-1346.
Coomes, O.T., F. Grimard, and G.J. Burt. 2000. "Tropical forests and shifting cultivation:
secondary forest fallow dynamics among traditional farmers of the Peruvian
Amazon." Ecological Economics 32(1):109-124.
Coomes, O.T., Y. Takasaki, and J. Rhemtulla. 2011. "Land-use poverty traps identified in
shifting cultivation systems shape long-term tropical forest cover." Proceedings of
the National Academy of Sciences of the United States of America 108(34):1392513930.
de Janvry, A., M. Fafchamps, and E. Sadoulet. 1991. "Peasant household behavior withmissing markets: some paradoxes explained." The Economic Journal101(409):1400-1417.
De Weerdt, J., and S. Dercon. 2006. "Risk-Sharing Networks and Insurance against
Illness." Journal of Development Economics 81(2):337-356.

28

Deolalikar, A.B., and W.P. Vijvergerg. 1983. "The heterogeneity of family and hired
labor in agricultural production: a test using direct-level data from India." Journalof Economic Development 8(2):45-69.
---. 1987. "A test of heterogeneity of family and hired labour in Asian agriculture."
Oxford Bulletin of Economics and Statistics 49(3):291-305.
Dercon, S., and J. De Weerdt. 2006. "Risk-sharing networks and insurance against
illness." Journal of Development Economics 81(2):337-356.
Desai, M., and D. Mazumdar. 1970. "A test of the hypothesis of disguised
unemployment." Economica 37(145):39-53.
Donaldson, S.M. 2011. "From neighbors to migrants: the shifting organization ofagricultural labor in East Tennessee." Anthropology of Work Review XXXII(1):920.
Downey, S.S. 2010. "Can properties of labor-exchange networks explain the resilience ofswidden agriculture?" Ecology and Society 15(4):15 [on line: URL:
www.ecologyandsociety.org/vol15/iss14/art15].
Duffy, J., and C. Parageorgiou. 2000. "A cross-country empirical investigation of theaggregate production function specification." Journal of Economic Growth5(1):87-120.
Duffy, J., C. Parageorgiou, and F. Perez-Sebastian. 2004. "Capital-skill complementarity?
Evidence from a panel of countries." Review of Economics and Statistics
86(1):327-344.

29

Dutta, B., D. Ray, and K. Sengupta. 1989. "Contracts with eviction in infinitely repeatedprincipal-agent relationships", In P. Bardhan, ed. The economic theory ofagrarian institutions. Oxford: Clarendon Press.
Erasmus, C.J. 1956. "Culture Structure and Process: The Occurrence and Disappearanceof Reciprocal Farm Labor." Southwestern Journal of Anthropology 12(4):444-469.
Ernst, F., S. Gächter, and G. Kirchsteiger. 1997. "Reciprocity as a contract enforcement
device: experimental evidence." Econometrica 65(4):833-860.
Fafchamps, M., and F. Gubert. 2007. "The formation of risk sharing networks." Journalof Development Economics 83(2):326-350.
Fafchamps, M., and S. Lund. 2003. "Risk-sharing networks in rural Philippines." Journalof Development Economics 71(2):261-287.
Frisvold, G.B. 1994. "Does supervision matter? Some hypothesis tests using Indian farmlevel data." Jounal of Development Economics 43(2):217-238.
Geschiere, P. 1995. "Working groups or wage labour? Cash-crops, reciprocity and money
among the Maka of Southeastern Cameroon." Development and Change
26(3):503-523.
Gilligan, D.O. 2004. "The economics of agricultural labor exchange with evidence fromIndonesia." Ph.D. Dissertation, University of Maryland.
Guillet, D. 1980. "Reciprocal labor and peripheral capitalism in the Central Andes."Ethnology 19(2):151-167.

30

Hamilton, B.H., J.H. Nickerson, and H. Owan. 2003. "Team incentives and worker
heterogeneity: an empirical analysis of the impact of teams on productivity andparticipation." Journal of Political Economy 111(3):465-497.
Jacoby, H.G. 1991. "Productivity of men and women and the sexual division of labor inpeasant agriculture of the Peruvian Sierra." Journal of Development Economics
37(1-2):265-287.
---. 1993. "Shadow wages and peasant family labour supply: an econometric application
to the Peruvian Sierra." Review of Economic Studies 60(4):903-921.
Kandel, E., and E.P. Lazear. 1992. "Peer pressure and partnerships." Journal of Political
Economy 100(4):801-817.
Kevane, M. 1996. "Agrarian structure and agricultural practice: typology and application
to Western Sudan." American Journal of Agricultural Economics 78(1):236-245.
---. 1994. "Village labor markets in Shikan District, Sudan." World Development22(6):839-857.
Kranton, R.E. 1996. "Reciprocal Exchange: A Self-Sustaining System." American
Economic Review 86(4):830-851.
Krishnan, P., and E. Sciubba. 2009. "Links and architecture in village networks " The
Economic Journal 119(537):917-949.
Ligon, E., J.P. Thomas, and T. Worrall. 2002. "Informal Insurance Arrangements with
Limited Commitment: Theory and Evidence from Village Economies." Review of
Economic Studies 69(1):209-244.

31

López, R.E. 1986. "Structural models of the farm household that allow for interdependentutility and profit maximization decisions", In I. Singh, L. Squire, and J. Strauss,eds. Agricultural Household Models: Extensions, Applications, and Policies.
Baltimore: Johns Hopkins University Press.
Mas, A., and E. Moretti. 2009. "Peer at work." American Economic Review 99(1):112145.
Moore, M.P. 1975. "Co-operative labour in peasant agriculture." Journal of PeasantStudies 2(3):270-291.
Munshi, K., and M. Rosenzweig. 2006. "Traditional Institutions Meet the Modern World:
Caste, Gender, and Schooling Choice in a Globalizing Economy." American
Economic Review 96(4):1225-1252.
North, D.C. 1990. Institutions, institutional change, and economic performance.
Cambridge: Cambridge University Press.
Pierre, Y.-F. 2005. "A test of some hypotheses on agrarian exchange labour in Haiti."
Social and Economic Studies 54(1):43-66.
Pitt, M.M., and M.R. Rosenzweig. 1986. "Agricultural prices, food consumption, and thehealth and productivity of farmers", In I. Singh, L. Squire, and J. Strauss, eds.
Agricultural Household Models: Extensions, Applications, and Policies.
Baltimore: Johns Hopkins University Press.
Sajor, E., E. 2000. "Are they incompatible? - Modern farming and non-market labour inthe northern Philippine uplands." CLARA Working Paper, 9. Amsterdam:
IIAS/IISG.

32

Sato, K. 1967. "A two-level constant-elasticity-of-substitution production function."
Review of Economic Studies 34(2):201-218.
ùDXO0:RUNSDUWLHVZDJHVDQGDFFXPXODWLRQLQD9ROWDLFYLOODJHAmerican
Ethnologist 10(1):77-96.
Singh, I., L. Squire, and J. Strauss, eds. 1986. Agricultural Household Models:
Extensions, Applications, and Policy. Baltimore: The John Hopkins University
Press.
Skoufias, E. 1994. "Using shadow wages to estimate labor supply of agricultural
households." American Journal of Agricultural Economics 76(2):215-227.
Taylor, J.E., and I. Adelman. 2003. "Agricultural household models: genesis, evolution,and extensions." Review of Economics of the Household 1(1/2):33-58.
Worby, E. 1995. "What does agrarian wage-labour signify? Cotton, commoditisation andsocial form in Gokwe, Zimbabwe." Journal of Peasant Studies 23(1):1-29.
World Bank. 2002. Reaching the poor: a renewed strategy for rural development.
Washington, D.C.: World Bank.
Notes
1

As exceptions, Kevane (1994) describes work parties in comparison to labor markets in

Sudan, Gilligan (2004) examines determinants of exchange labor in Indonesia, andKrishnan and Sciubba (2009) analyze the structure of labor-sharing networks in Ethiopia;
none of them systematically compare labor sharing with other forms of labor or examine
the substitutability, productivity, or efficiency of labor sharing.

33

2

Although Jacoby (1991) compares family labor with extra-family labor which consistsof hired and shared labor in the Peruvian Andes, he does not distinguish between the two.
3

Social networks in development have recently received considerable attention,

especially in technology adoption (Bandiera and Rasul 2006, Conley and Udry 2010) andrisk sharing (De Weerdt and Dercon 2006, Fafchamps and Gubert 2007, Fafchamps andLund 2003). Munshi and Rosenzweig (2006) examine caste labor network in India.
Social network has not yet been combined with agricultural household models.
4

In both villages agriculture and fishing are two main livelihood activities, counting forabout 90% of total income. Local labor markets are thin, and thus, wage income is
minimal; although the largest employment opportunities emerge for harvesting rice (withliquidity), due to the synchronicity rice farmers hire labor mostly from other villages. The
mean annual income per capita is $314 (3.5S/. = US$1); most households are poor
(almost 90% according to $2-per-day poverty line).
5

Coomes, Grimard, and Burt (2000) and Coomes, Takasaki, and Rhemtulla (2011)

employ the same retrospective data collection in another village in the area to examine
the evolution of land holdings/portfolios and land use.
6

Depending on land types which determine possible cropping duration, flood

vulnerability, and soil fertility, farmers employ distinct agricultural strategies using very
rudimentary technologies. In upland agroforestry, plantain and manioc (main food crops)
are planted first, followed by tree crops; at any moment in the crop rotation the plot may
be left in fallow. Lowland agroforestry sequences on the high and low levees depend on

34

soil conditions determined by the annual flood; manioc as well as maize to a lesser extent
is cropped annually, whereas plantain may be harvested over several seasons. On the
mudflats and sandbars, farmers grow rice and cowpea, respectively.
7

Although early works by Erasmus (1956) and Moore (1975) sharply distinguishbetween exchange and festive labor according to the existence/absence of reciprocity, theform of cooperative labor examined here presents some elements of the two. Whenfestive labor events are organized by any households for regular activities (cf. special
event organized by the wealthy), they serve as flexible exchange-labor contract in which
recruitment and participation are endogenously determined based on implicit reciprocity.
8

Mañaneo is cheaper and easier to recruit workers than minga. Rough estimates of direct

cost per cooperative worker – value of food/drink evaluated at the market price, plus
opportunity cost of labor for preparation evaluated at market wage, ignoring reciprocation
cost – for all agricultural tasks in 2001 are $2.22 for minga and $.91 for mañaneo (cf.
$.2.86 market wage rate without food) (Brisson 2003). Two other types – media-minga
with a half day of work (4 hours) with one meal and drink and ayuda, a variation of theminga with smaller size and without drink – also exist but are very rare.
9With lack of complete data on labor offered to others’ forest clearing, we are unable tomeasure the degree of reciprocity. Lack of data about crop production and income
through time also precludes us from estimating the value marginal product of clearing
labor, i.e., shadow wage, as Jacoby (1993) and Skoufias (1994) do.
10

Krishnan and Sciubba (2009) find that both size and structure of networks matter.

35

11

Different from extant empirical work, our model does not control for land size because

it is endogenous as a determinant of forest clearing; land holdings in year t are thecumulative outcome of historical forest clearing as well as land transfers.
12

How household labor endowments affect labor supply (family labor) depends on its

utility function and is generally ambiguous (Benjamin 1992). Similarly, how network
endowments affect family labor is also ambiguous.
13

Zero inputs are not permitted in the two-level CES production function (1) with non-positive ș and ʔ and in the CES production function (2) with non-positive ȡ. The NLLS
results reported in Table 3 are robust to empirically plausible initial parameter values.
14

With fixed-effects controlled for, no village-period dummies are statistically significant,suggesting lack of technological progress and limited productivity difference between
minga and mañaneo.
15

There are two weak exceptions for the weighted total labor to clear low levee. First, kingroup dummies in the lowland village are jointly, not individually, statistically significant
at a 10% significance level. Second, the share, not number, of female adults in the kingroup is statistically significant at a 10% significance level.
16

The estimated impacts of village-period dummies are largely consistent with ourdescriptive findings above: cooperative labor is less common and smaller during the
1980s and the 1990s or later, with no significant across-village difference (results are
statistically significant for upland/high levee/low levee); correspondingly, family labor

36

becomes larger especially in the 1990s, though hired labor does not show significant
change.
17

In almost all cases, disaggregated labor endowments are neutral toweighted/unweighted total labor use (results not shown), buttressing the separability. The
only exception is that the share, not number, of older male working adults in the kingroup negatively affects weighted total labor for clearing upland/high levee/low levee.

37
Table 1. Land holdings and portfolios in 2001
Lowland village (n = 34)
Holds
Total at least
no.
one
fieldsfield(0/1)

Upland village (n = 40)

Mean no.
fieldsMean area
(ha)

Holds
Total at least
no.
one
fieldsfield(0/1)

All land212
1.00
6.24 (2.72)
Upland00.00
0.00 n.a.
High levee
11
0.32
0.32 (0.47)
Low levee123
1.00
3.62 (2.24)
Mudflat61
0.94
1.79 (0.98)
Sandbar9
0.24
0.26 (0.51)
Cleared land
124
0.97
3.65 (1.97)
Non-cleared land74
0.85
2.18 (1.70)
Note: Standard deviations are in parentheses.

6.25 (3.6)
0.00 n.a.
0.14 (0.25)
3.74 (2.61)
1.78 (1.45)
0.27 (0.65)
3.49 (2.08)
2.53 (2.5)

187
59
25
78
16
5
130
51

1.00
0.68
0.33
0.83
0.33
0.10
1.00
0.63

Mean no.
fieldsMean area(ha)

4.55 (2.55)
1.45 (1.43)
0.60 (1.17)
1.90 (1.41)
0.40 (0.63)
0.13 (0.40)
3.18 (1.57)
1.25 (1.61)

9.49 (15.6)
4.91 (12.9)
0.78 (2.10)
2.89 (4.52)
0.39 (0.89)
0.11 (0.50)
3.43 (3.00)
5.75 (13.6)

38
Table 2. Cleared fields and forest clearing labor since household formation
Lowland village

A. Cleared fields.
No. fields
Mean area (ha)B. Forest clearing labor.
Employment (0/1):
Family labor
Hired labor
Cooperative labor
Minga
MañaneoLabor composition (proportion):
Family labor only
Hired labor only
Cooperative labor only
Family and hired labor
Hired and cooperative labor
Cooperative and family labor
Family, hired, and cooperative labor
Mean amount (labor-day):Total
Per hectare
Family labor
Hired labor
Cooperative labor
Minga
Mañaneo
Mean no. workers per event
Minga
MañaneoC. Employment (0/1) by period.
1949-1979:
Family labor
Hired labor
Cooperative labor
Minga
Mañaneo
No. fields
1980-1989:
Family labor
Hired labor
Cooperative labor
Minga
Mañaneo
No. fields
1990-2001:
Family labor
Hired labor
Cooperative labor
Minga
Mañaneo
No. fields

Upland villageHighlevee/
low levee

High
levee

Low levee

Upland/highlevee/
low levee

121
1.02(0.79)

80.74(0.74)

113
1.04(0.81)

139
0.99(0.90)

56
1.13(0.86)

17
1.02(1.13)

66
0.87(0.87)

0.68
0.16
0.54
0.20
0.35

0.63
0.13
0.38
0.25
0.13

0.68
0.16
0.55
0.19
0.36

0.63
0.12
0.50
0.40
0.07

0.57
0.09
0.54
0.45
0.09

0.59
0.29
0.29
0.24
0.06

0.68
0.11
0.52
0.39
0.06

0.36
0.06
0.25
0.04
0.02
0.23
0.04

0.50
0.13
0.25
0.00
0.00
0.13
0.00

0.35
0.05
0.25
0.04
0.02
0.24
0.04

0.40
0.07
0.29
0.04
0.01
0.19
0.01

0.38
0.09
0.34
0.00
0.00
0.20
0.00

0.41
0.18
0.24
0.12
0.00
0.06
0.00

0.41
0.03
0.27
0.05
0.02
0.21
0.02

27.3
(20.8)
26.7
15.2(20.5)
2.9
(9.9)
9.1
(12.8)
5.8
3.3

19.0
(13.0)
25.7
4.9
(5.4)
3.8
(10.6)
10.4(16.1)
8.8
1.6

27.9
(21.2)
26.7
15.9(20.9)
2.9
(9.9)
9.0
(12.6)
5.6
3.4

30.4
(27.8)
30.7
11.6(15.6)
7.4(28.2)
11.3(16.1)
9.9
0.4

35.3
(32.6)
31.3
12.1(17.6)
9.6(34.0)
13.7(19.0)
12.4
0.5

30.7
(28.3)
30.1
9.6(14.9)
13.5(32.2)
7.6(13.3)
7.2
0.4

26.1
(22.5)
30.2
11.7(14.2)
4.1(20.7)
10.3(13.7)
8.5
0.3

25.4
20.6

35.0
30.0

24.5
20.4

16.8
12.7

16.6
13.2

19.0
15.0

16.7
11.5

0.48
0.16
0.72
0.52
0.20
25

0.50
0.17
0.71
0.50
0.21
24

0.57
0.17
0.57
0.57
0.00
23

0.50
0.17
0.67
0.67
0.00
6

0.83
0.17
0.17
0.17
0.00
6

0.45
0.18
0.73
0.73
0.00
11

0.62
0.08
0.54
0.38
0.15
26

0.65
0.04
0.57
0.39
0.17
23

0.55
0.21
0.39
0.27
0.06
33

0.67
0.22
0.11
0.11
0.00
9

0.00
0.50
0.50
0.50
0.00
6

0.67
0.11
0.50
0.28
0.11
18

0.77
0.19
0.47
0.01
0.47
70

0.76
0.20
0.48
0.02
0.48
66

0.67
0.07
0.52
0.40
0.10
83

0.56
0.05
0.61
0.49
0.12
41

1.00
0.20
0.20
0.00
0.20
5

0.76
0.08
0.46
0.35
0.05
37

UplandHigh
levee

Low levee

Note: The sample consists of cleared fields held in 2001 and fields cleared at the time of household formation and notheld in 2001, excluding fields cleared after household formation and not held in 2001. Fields with incomplete labor data
are also excluded. Panel C does not report data for high levee in the lowland village due to the small number ofobservations. Standard deviations are in parentheses.

39
Table 3. Forest clearing functions
Upland/high levee/low levee
NLLS
First-difference NLLS
(n=260)(n=186)
(2)
(3)
(4)

(1)
A. Two-level CES
1.508 **
1.000 aș
1.136 ** 1.000 a(0.461)(0.731)ʔ
0.906 *** 0.907 *** 1.154 *** 1.000 a(0.145)(0.145)(0.276)
ț
0.493 *** 0.492 *** 0.381 *** 0.420 ***(0.053)(0.046)(0.102)(0.074)
Ș
0.755 *** 0.768 *** 0.783 *** 0.788 ***(0.055)(0.040)(0.058)(0.025)șʔ 
șʔ 
Constraints
No
No
R squared
0.187
0.187
0.288
0.285
F (p-value):
ș 
0.769
0.488
ʔ=1
0.517
0.520
0.577
ț 
0.901
0.867
0.243
0.276
Ș 
0.000
0.000
0.000
0.000
B. CESȡ
0.930 *** 0.930 *** 1.212 *** 1.000 a(0.137)(0.137)(0.296)
į
0.384 *** 0.384 *** 0.321 *** 0.330 ***(0.040)(0.040)(0.075)(0.063)
Ȝ
0.398 *** 0.398 *** 0.495 *** 0.457 ***(0.032)(0.032)(0.084)(0.053)ȡȡConstraints
No
No
R squared
0.187
0.187
0.292
0.285
F (p-value):
ȡ 
0.606
0.606
0.475
į Ȝ
0.840
0.840
0.253
0.269
į ȝ
0.005
0.005
0.149
0.142
Ȝ ȝ
0.000
0.000
0.007
0.000
į Ȝ ȝ
0.780
0.780
0.516
0.542
C. Linearį
0.388 *** 0.657 *** 0.328 *** 0.671 ***(0.042)(0.026)(0.062)(0.03)
Ȝ
0.403 ***
0.461 ***
(0.033)(0.053)Ȝ įȜ įConstraints
No
No
R squared
0.186
0.186
0.288
0.280
F (p-value):
į Ȝ
0.848
0.236
į ȝ
0.002
0.134
Ȝ ȝ
0.000
0.000
į Ȝ ȝ
0.913
0.494
į 
0.000
0.000
į 
0.701
0.880

(5)

NLLS
(n=179)(6)

Low leveeFirst-difference NLLS
(n=114)(7)(8)

1.267 **
1.000 a1.479 ** 1.000 a(0.544)(0.736)
1.094 *** 1.000 a
1.175 *** 1.000 a(0.248)(0.368)0.507 *** 0.500 *** 0.579 *** 0.548 ***(0.063)(0.049)(0.171)(0.086)0.814 *** 0.813 *** 0.852 *** 0.841 ***(0.066)(0.023)(0.073)(0.027)
șʔ 
șʔ 
No
No
0.237
0.236
0.383
0.378
0.625
0.705
0.917
0.000

0.998
0.000

0.516
0.635
0.646
0.000

0.582
0.000

1.119 *** 1.000 a
1.194 *** 1.000 a(0.227)(0.384)
0.416 *** 0.406 *** 0.465 *** 0.461 ***(0.053)(0.045)(0.106)(0.083)0.413 *** 0.406 *** 0.406 *** 0.380 ***(0.046)(0.038)(0.089)(0.065)
ȡ
ȡ
No
No
0.237
0.236
0.391
0.378
0.600
0.972
0.002
0.000
0.734

0.998
0.000
0.000
0.849

0.614
0.752
0.025
0.014
0.521

0.586
0.005
0.000
0.260

0.406 *** 0.684 *** 0.446 *** 0.718 ***(0.045)(0.032)(0.087)(0.032)0.406 ***
0.396 ***
(0.038)(0.069)
Ȝ į
Ȝ į
No
No
0.236
0.236
0.388
0.387
0.998
0.000
0.000
0.849

0.745
0.009
0.000
0.203
0.000
0.576

0.000
0.110

*10% significance, **5% significance, ***1% significance. aboundary value. Note: Dependent variables are log
size of field cleared (m2) for NLLS and its first difference for first-difference NLLS. Robust standard errors are inparentheses. Other controls not shown here are period dummies and village-period dummies in all columns, plus
village dummy and constant in columns (1), (2), (5), and (6).

40
Table 4. Labor demand for forest clearing
Upland/high levee/low levee(n=258)Unweighted
Weighted(1)(2)
A. OLS with kin-group dummies.
Male adults in household
Female adults in household
Kin group in lowland village (0/1)
Group 1
Group 2
Group 3
Group 4
Kin group in upland village (0/1)
Group 5
Group 6
Group 7Low levee(n=179)Unweighted
Weighted
(3)
(4)

0.051
(0.062)-0.043(0.095)0.087(0.071)-0.022(0.099)0.058(0.066)-0.197(0.146)0.112(0.076)-0.215(0.138)-0.056(0.204)0.252(0.247)-0.250(0.238)0.282(0.245)-0.062(0.205)0.240(0.267)-0.253(0.248)0.177(0.236)-0.183(0.226)0.338(0.279)-0.265(0.256)0.377(0.279)-0.262(0.224)0.352(0.264)-0.278(0.252)0.274(0.262)-0.098(0.189)-0.214(0.175)0.193(0.194)-0.043(0.191)-0.310(0.206)0.178(0.235)-0.162(0.250)-0.256(0.238)0.414(0.295)-0.015(0.247)-0.312(0.277)0.483(0.354)

F for kin groups (p-value)
Both lowland and upland villages
0.240
0.351
0.130
Lowland village
0.291
0.425
0.159
Upland village
0.237
0.228
0.172
R squared
0.134
0.165
0.173
B. Fixed-effects models with no. of male/female adults (15-64 yrs) in kin group.
Male adults in household
0.006
0.009
-0.033(0.085)(0.101)(0.094)
Female adults in household
0.090
0.127
-0.063(0.119)(0.123)(0.231)
Male adults in kin group
0.036
0.036
0.053
(0.040)(0.042)(0.042)
Female adults in kin group-0.061-0.052-0.088(0.056)(0.052)(0.062)R squared
0.05180.045
0.067
C. Fixed-effects models with share of male/female adults (15-64 yrs) in kin group.
Male adults in kin group
-0.38
-0.46
-0.48
(1.46)(1.61)(1.54)
Female adults in kin group
-1.65
-1.66
-3.57
(1.33)(1.48)(2.24)

0.099
0.086
0.201
0.232
0.003
(0.106)0.065(0.188)0.042(0.044)-0.074(0.053)0.072-0.73(1.53)-3.90 *
(2.09)*10% significance, **5% significance, ***1% significance. Note: Depedent variables are log amount ofunweighted/weighted total labor (labor-day). Robust standard errors are in parentheses. Other controls
not shown here are no. male/female children (<15 yrs), household age, period dummies, village-period
dummies, and constant. Age of household head, male/female head born in the vilage, and village
dummy are also included in panel A. Male/female adults (15-64 yrs) in household are also included inpanel C. All labor endowments in panels B and C are measured at the time of forest clearing.

41
Table 5. Labor composition for forest clearing - OLS with kin-group dummies
Employment (0/1)
FamilyHired
Cooperative
(1)
(2)
(3)
A. Upland/high levee/low levee in lowland & upland villages (n=258).
Kin group in lowland village (0/1)
Group 1
0.045
0.013
-0.021(0.120)(0.103)(0.132)
Group 20.085
-0.158 **0.126(0.195)(0.073)(0.183)
Group 30.017
-0.1420.129(0.162)(0.106)(0.166)
Group 40.235 *
-0.0620.149(0.133)(0.113)(0.139)Kin group in upland village (0/1)
Group 5-0.135-0.147 **0.161(0.127)(0.059)(0.113)
Group 60.138
0.009
-0.075(0.104)(0.074)(0.110)Group 7
-0.115-0.045-0.029(0.136)(0.096)(0.140)F for kin groups (p-value)
Lowland village
0.476
0.074
0.648
Upland village
0.082
0.020
0.303
R squared
0.088
0.097
0.000
B. Low levee in lowland & upland villages (n=179).
Kin group in lowland village (0/1)
Group 1
0.143
0.012
-0.117(0.127)(0.108)(0.143)
Group 20.108
-0.1000.106(0.200)(0.084)(0.210)Group 30.049
-0.1130.095(0.177)(0.113)(0.173)
Group 40.228
-0.0700.158(0.139)(0.112)(0.140)
Kin group in upland village (0/1)
Group 5-0.249-0.0550.157(0.215)(0.065)(0.170)Group 60.062
0.069
-0.127(0.129)(0.098)(0.156)
Group 7-0.1650.226-0.120(0.237)(0.181)(0.251)F for kin groups (p-value)
Lowland village
0.563
0.452
0.361
Upland village
0.409
0.222
0.489
R squared
0.183
0.093
0.000
C. Upland & high leve in upland village (n=72).
Kin group in upland village (0/1)
Group 5-0.051-0.297 **0.313 **
(0.161)(0.126)(0.148)Group 6
0.261 *
-0.1400.062(0.149)(0.116)(0.149)Group 70.079
-0.335 ***0.168(0.202)(0.120)(0.206)
F for kin groups (p-value)
0.151
0.029
0.191
R squared
0.285
0.394
0.000

Family(4)

Log amount (labor-day)
Hired
Cooperative
(5)
(6)

0.143
(0.393)0.223(0.615)-0.086(0.456)
1.041 **
(0.446)0.080(0.298)-0.619 ***(0.238)-0.425(0.284)-0.242(0.328)-0.106(0.348)0.759(0.539)0.322(0.418)0.286(0.380)-0.121(0.364)0.310(0.305)-0.051(0.424)-0.361 **(0.176)0.105(0.236)-0.190(0.305)0.195(0.351)-0.503(0.318)0.124(0.470)

0.181
0.593
0.113

0.008
0.048
0.094

0.454
0.199
0.135

0.388
(0.417)0.197(0.682)-0.028(0.501)
1.094 **
(0.472)0.026(0.306)-0.381 *(0.225)-0.381(0.286)-0.179(0.339)-0.371(0.363)0.682(0.591)
0.242(0.450)0.371(0.378)-0.578(0.583)0.059(0.393)-0.160(0.722)0.060(0.134)0.349(0.266)0.724(0.603)0.123(0.531)-0.520(0.475)-0.325(0.763)

0.212
0.740
0.133

0.121
0.357
0.161

0.216
0.638
0.143

0.199
(0.518)0.649(0.415)0.482(0.627)
0.452
0.338

-1.116 **(0.489)-0.525(0.448)-1.395 ***(0.501)
0.038
0.342

0.634
(0.451)-0.378(0.388)0.539(0.657)
0.116
0.416

*10% significance, **5% significance, ***1% significance. Note: Robust standard errors are in parentheses. Other
controls not shown here are male/female children, household age, age of household head, male/female head born in thevilage, village dummy, period dummies, village-period dummies, and constant.

42

Table 6. Labor compositon for forest clearing - fixed-effects models with network endowments
Employment (0/1)
Log amount (labor-day)
FamilyHired
Cooperative
FamilyHired
Cooperative
(1)
(2)
(3)
(4)
(5)
(6)
A1. Upland/high levee/low levee in lowland & upland villages (n=258) - no. male/female adults in kin group.
Male adults (15-64 yrs) in household
0.021
0.002
0.056
-0.036-0.209 *0.212(0.057)(0.041)(0.068)(0.167)(0.125)(0.186)
Female adults (15-64 yrs) in household
0.001
0.119 **
-0.062-0.081
0.532 *** -0.209(0.101)(0.054)(0.086)(0.259)(0.189)(0.262)
Male adults in kin group of lowland village-0.017
0.002-0.0330.033
0.075-0.045(0.033)(0.035)(0.031)(0.102)(0.103)(0.085)
Male adults in kin group of upland village
0.017
0.000
-0.025-0.004-0.009-0.046(0.052)(0.022)(0.032)(0.129)(0.056)(0.107)
Female adults in kin group of lowland village
0.043-0.016
0.099 **
-0.078-0.1500.186 *
(0.045)(0.037)(0.043)(0.129)(0.115)(0.107)
Female adults in kin group of upland village-0.004-0.0090.053 *
0.012
-0.0490.106(0.047)(0.019)(0.031)(0.120)(0.064)(0.087)R squared
0.046
0.086
0.097
0.068
0.075
0.094
A2. Upland/high levee/low levee in lowland & upland villages (n=258) - share of male/female adults in kin group.
Male adults in kin group of lowland village
-1.27
0.16
-1.39
1.30
1.17
-4.16
(1.04)(1.00)(1.36)(2.79)(2.84)(3.83)
Male adults in kin group of upland village
-1.10
-1.07
0.44
-2.06
-3.58
1.99
(1.97)(1.48)(1.48)(5.47)(5.56)(4.42)
Female adults in kin group of lowland village
2.96 *-2.27 *
2.83 **
1.75-9.80 **
5.77
(1.66)(1.31)(1.39)(3.79)(4.15)(3.76)
Female adults in kin group of upland village
-0.64
0.66
0.30
-2.37
2.88
-0.69
(1.86)(0.72)(1.52)(5.76)(3.24)(4.94)
B1. Low levee in lowland & upland villages (n=179) - no. male/female adults in kin group.
Male adults in household-0.0080.057
0.059-0.239-0.0740.188(0.064)(0.038)(0.079)(0.197)(0.123)(0.198)
Female adults in household-0.0560.138
0.120-0.5270.570
0.211(0.188)(0.108)(0.183)(0.635)(0.357)(0.505)
Male adults in kin group of lowland village-0.018-0.001-0.0320.053
0.065-0.051(0.034)(0.032)(0.031)(0.110)(0.103)(0.078)
Male adults in kin group of upland village
0.047-0.049-0.0290.134-0.130 *-0.013(0.069)(0.038)(0.052)(0.136)(0.067)(0.175)
Female adults in kin group of lowland village
0.043-0.0300.092 *
-0.011-0.1920.178(0.053)(0.040)(0.055)(0.171)(0.132)(0.136)
Female adults in kin group of upland village-0.038-0.0060.100-0.119-0.0550.172(0.080)(0.033)(0.060)(0.177)(0.084)(0.182)R squared
0.135
0.173
0.113
0.134
0.126
0.115
B2. Low levee in lowland & upland villages (n=179) - share of male/female adults in kin group.
Male adults in kin group of lowland village
-1.27
0.11
-0.97
1.44
1.03
-3.54
(0.98)(0.84)(1.24)(3.07)(2.43)(3.70)
Male adults in kin group of upland village
-2.42
-2.05
-0.04
-8.91
-4.81
1.32
(2.45)(1.98)(2.46)(5.62)(3.47)(7.19)
Female adults in kin group of lowland village
3.17 **-2.59 *
2.45 *
2.67-10.87 **4.76(1.52)(1.42)(1.36)(4.42)(4.40)(3.96)
Female adults in kin group of upland village
-2.52
-1.49
4.34 *-11.62 *-4.60
8.11(3.05)(1.63)(2.33)(6.03)(4.28)(6.41)
C1. Upland & high levee in upland village (n=73) - no. male/female adults in kin group.
Male adults in kin group
0.022-0.001-0.073 **
0.032
0.034
-0.213 **(0.038)(0.027)(0.028)(0.154)(0.109)(0.098)
Female adults in kin group0.055 **
-0.003
0.016
0.209
0.002
0.034
(0.024)(0.027)(0.026)(0.125)(0.106)(0.087)R squared
0.224
0.087
0.336
0.196
0.120
0.316
C2. Upland & high levee in upland village (n=72) - share of male/female adults in kin group.
Male adults in kin group
1.68
-1.39
2.15
5.43
-8.53
6.29
(1.75)(1.87)(2.33)(7.22)(8.21)(6.64)
Female adults in kin group-2.24 *
1.42
0.56
-7.57 *
7.58
1.82
(1.22)(1.39)(1.44)(3.72)(6.00)(3.50)*10% significance, **5% significance, ***1% significance. Note: Robust standard errors are in parentheses. Other controls
not shown here are no. male/female children (<15 yrs), household age, period dummies, village-period dummies, andconstant. All labor endowments are measured at the time of forest clearing.

43
Table 7. Adult-lifecycle effects on labor compositon for forest clearing - fixed-effects models with disaggregatedlabor endowments
Employment (0/1)
Log amount (labor-day)
FamilyHired
Cooperative
FamilyHired
Cooperative
(1)
(2)
(3)
(4)
(5)
(6)
A1. Upland/high levee/low levee in lowland & upland villages (n=258) - no. male/female adults in kin group.
Young male adults (15-29 yrs) in household
0.020-0.0090.068-0.034-0.250 **0.252(0.062)(0.039)(0.072)(0.187)(0.117)(0.193)
Older male adults (30-64 yrs) in household-0.0750.106-0.032-0.2070.111
0.100(0.105)(0.082)(0.088)(0.295)(0.233)(0.276)Young female adults (15-29 yrs) in household
0.030
0.098 **
-0.018-0.1040.421 **
-0.105(0.097)(0.045)(0.079)(0.253)(0.164)(0.251)
Older female adults (30-64 yrs) in household-0.0290.108
0.032-0.0930.575 *
-0.150(0.153)(0.092)(0.183)(0.501)(0.325)(0.528)Young male adults in kin group-0.017-0.0060.000-0.032-0.0020.038(0.037)(0.021)(0.031)(0.105)(0.063)(0.092)
Older male adults in kin group
0.055
0.002
-0.100 **
0.148
0.064
-0.257 *(0.062)(0.035)(0.045)(0.151)(0.085)(0.136)Young female adults in kin group
0.019-0.0250.080 **
-0.026-0.1190.147(0.051)(0.024)(0.036)(0.124)(0.077)(0.097)
Older female adults in kin group-0.0260.007
0.121 **
-0.115-0.0840.294 *
(0.046)(0.039)(0.056)(0.133)(0.095)(0.152)R squared
0.062
0.103
0.105
0.075
0.090
0.104
A2. Upland/high levee/low levee in lowland & upland villages (n=258) - share of male/female adults in kin group.
Young male adults in kin group
-0.56
-0.13
0.02
-0.31
-0.36
0.29
(0.43)(0.39)(0.43)(1.17)(1.25)(1.23)
Older male adults in kin group
0.59
-0.33
-2.24 *
4.77
-1.75
-6.91 **
(1.18)(1.02)(1.13)(3.46)(3.76)(3.04)
Young female adults in kin group
0.66
-0.60
1.12 **-0.25-2.16
2.61 *(0.95)(0.44)(0.52)(1.94)(1.59)(1.47)
Older female adults in kin group
-0.41
0.40
0.85
-2.36
1.59
2.00
(0.57)(0.53)(0.67)(1.91)(2.19)(1.80)
B1. Low levee in lowland & upland villages (n=179) - no. male/female adults in kin group.
Young male adults in household
0.005
0.030
0.068
-0.231-0.200 *0.244(0.071)(0.037)(0.084)(0.224)(0.115)(0.211)
Older male adults in household-0.1030.224 **
-0.121-0.3030.319-0.005(0.121)(0.096)(0.117)(0.392)(0.254)(0.373)Young female adults in household
0.030
0.014
0.204
-0.4480.133
0.385(0.190)(0.102)(0.204)(0.679)(0.298)(0.563)
Older female adults in household-0.0920.049
0.430-0.8630.517
0.625(0.242)(0.136)(0.319)(0.865)(0.407)(0.875)Young male adults in kin group-0.003-0.037-0.0050.024-0.1140.066(0.038)(0.027)(0.038)(0.103)(0.079)(0.104)
Older male adults in kin group
0.062-0.024-0.0560.256
0.097-0.232(0.072)(0.053)(0.065)(0.176)(0.128)(0.206)Young female adults in kin group
0.022-0.011
0.095 **
-0.055-0.1030.153(0.061)(0.029)(0.045)(0.157)(0.100)(0.133)
Older female adults in kin group-0.044-0.0220.114-0.128-0.1350.276(0.058)(0.043)(0.076)(0.179)(0.123)(0.191)R squared
0.152
0.200
0.137
0.147
0.150
0.129
B2. Low levee in lowland & upland villages (n=179) - share of male/female adults in kin group.
Young male adults in kin group
-0.08
-0.24
-0.42
1.15
-1.06
-1.26
(0.44)(0.41)(0.45)(1.60)(1.08)(1.36)
Older male adults in kin group
-0.90
-0.16
-2.01
2.81
2.87
-7.28 **
(1.21)(1.26)(1.26)(4.34)(3.44)(3.39)
Young female adults in kin group
1.05
-0.96
1.78 ***-0.17-4.26 **
4.04 **(1.05)(0.65)(0.60)(2.38)(1.91)(1.97)
Older female adults in kin group
-0.75
-1.43
3.92 ***
-4.96
-4.19
9.71 ***(1.06)(0.94)(1.23)(3.30)(2.62)(3.41)*10% significance, **5% significance, ***1% significance. Note: Robust standard errors are in parentheses. Other controls
not shown here are no. male/female children (<15 yrs), household age, period dummies, village-period dummies, andconstant. All labor endowments are measured at the time of forest clearing.

44

Table A. Cleared field means of labor endowments and other household
demographic characteristics at the time of forest clearing

Labor endowments (15-64 yrs)
No. male adults in household
No. female adults in household
No. male adults in kin group
No. female adults in kin group
Within-village share of male adults in kin group
Within-village share of female adults in kin group
Other household demographic characteristics
No. male children (<15 yrs)
No. female children (<15 yrs)
No. male elderly (65 yrs or above)
No. female elderly (65 yrs or above)
Household age
Age of household headNo. observations
Note: Standard deviations are in parentheses.

Lowland village

Upland village

1.55 (1.09)
1.32 (0.89)
5.03 (4.53)
4.25 (3.90)
0.11 (0.08)
0.12 (0.09)

1.29 (0.72)
1.25 (0.68)
5.12 (5.05)
4.78 (4.56)
0.13 (0.11)
0.13 (0.10)

1.21 (1.29)
1.13 (1.26)
0.03 (0.18)
0.00 (0.00)
9.9 (12.6)
35.0 (12.9)
121

1.10 (0.99)
1.17 (1.10)
0.03 (0.17)
0.01 (0.12)
8.3 (10.9)
33.4 (11.8)
139