Journal ofInternational Cooperation for
Agricultural Development

J Intl Cooper Agric Dev 2021; 19: 2–16

Original

The Effect of Harvesting Labor
Constraints on the Production of Robusta
Coffee Farmers in Chumphon Province,
Thailand
Kanjana Kwanmuang1) and Laddawan Lertjunthuk2)
1) Office of Agricultural Economics, Ministry of Agriculture and Cooperative, Thailand
2) Faculty of Agricultural Technology, Sakonnakhon Rajabhat University, Thailand

Received: April 19, 2020 Accepted: December 19, 2020
Abstract. Farm labor shortages are posing a challenge to the Thai agricultural sector, causing labor constraints. Farmers
who grow Robusta coffee in Chumphon Province are highly dependent on seasonal migrant labor from northeast regions
during the harvest season. However, recent changes in labor market conditions and the development of non-agricultural
sectors across the country have increased the difficulty in finding seasonal farm labor, and this acute labor constraint may
affect coffee production. This study examines the effects of this labor constraint on production outcomes and labor allocation.
To identify constraints and the allocation of inputs, especially labor input, a quadratic production function is employed toestimate marginal productivity. An augmented inverse probability weighting estimator is then utilized as a double robust to
estimate the average treatment effect. Our estimations found that the difference in the marginal productivity of labor inputs
is not significant; however, the labor hiring constraint has a negative and statistically significant effect on coffee production.
Thus, the exchange of labor information and providing information on coffee picking practice in the site are needed. Additionally, as farmer groups serve an important role in building stronger social ties and decreasing labor constraints, programs
that implement technology and tools for supporting unskilled harvesting labor, labor information, and coffee farm practices
should be implemented through farmer groups communities.
Key words: labor constraint, augmented inverse propensity weighted estimator, Robusta coffee, Thailand

1. Introduction
In the last 40 years, in Thailand, more than half of all
farm labor has shifted from employment in the agricultural
sector to other non-agricultural sectors in which production growth rates and wages are much higher1). Higher
rates of education, as well as farming’s relatively low- andinsecure-income level, have turned younger generations
away from farming and toward the industrial and service
sectors2).
Labor shortages in the farm sector are a national
Corresponding author: Kanjana Kwanmuang, e- -mail address:
mam_econ@hotmail.com

2 J Intl Cooper Agric Dev 2021

concern because labor is one of the factors that drive
agricultural output, and therefore, agricultural growthand development in Thailand2). For example, the effect oflabor shortages can be seen in Thailand’s rice production.
From 1989 to 1995, although the planted area increased
and the planting methods improved, rice production stilldecreased due to labor shortages1). The continual decreasein farm labor has also affected the production quantity ofrice, maize, and cassava, thereby affecting food security3).
This problem could be a concern for rural livelihoods if the
impact is significant for other cash crops. In Thailand, Robusta coffee was once a main source of income; in recent
decades, farmers in Chumphon province have primarily
devoted their land to planting this crop. Even though theproduction of Robusta coffee has decreased in production quantity and land area, it continues to contribute to
the local economy. Coffee production is important on thehousehold level, as it is responsible for farmers’ incomes
and indicates their farm management capacities, and on anational level in terms of competition in the global coffee
market. The new goal of the five-year coffee plan (20172021) designed by the Thai government is to maintaincoffee production and enhance coffee yield and productivity in this province. However, planted areas, production,and yield have decreased, and coffee has been replaced byother cash crops, such as rubber, palm oil, and fruit. As
a production system, coffee cultivation is labor intensive,
especially during the harvesting period4,5). Farm laborers
on coffee plantations require a particular set of skills,
and, in the light of the limited options for mechanization,
dependence on physical labor is a necessary part of theplantation system5). However, this system has traditionallydepended on seasonal migrant labor from the northeast
region for harvesting work, and difficulty in finding thisharvesting labor has become pervasive in recent years.
This study aims to examine the effects of this labor
constraint on production outcomes and labor allocation forcoffee production in Chumphon. However, to investigate
the effect, the issue of concern is that labor constraints
are not exogenously or randomly assigned to farmers,
which implies that the endogeneity of labor constraints
must be considered. Thus, this study introduces a doubly
robust estimator, augmented inverse probability weighting (AIPW) estimation, on our original farmer survey in
Chumphon province to test the hypothesis that the hiring
labor constraint has a significant impact on coffee production.   Moreover, how farmers cope with this harvestinglabor constraint or labor allocation for coffee productionis also in our interest. We hypothesize that farms under
this constraint use family labor to compensate for the lackof available hired labor. To examine this behavior, we
employed a quadratic production function to estimate themarginal productivities of family labor and hired labor.
This paper is organized as follows: Section 2 reviews labor constraint issues in Thai agriculture. Section
3 describes the labor requirements for Robusta coffee,
specifically in the main production area of Chumphon
province. Section 4 describes the methodologies in the
study: applied production function and AIPW estimator.
Section 5 presents the data collection and survey design.
Section 6 demonstrates the estimation results, and Section
7 discusses the findings.

2. Labor constraint issues in Thai
agriculture
Farm labor shortages pose a challenge to the Thai
agricultural sector. In 2017, the total labor force in Thailand was 38.099 million people, or 57.56% of the total
population, and 11.783 million people (30.9%) from thisgroup represented the farm labor force6). However, from1977 to 2017, the farm labor force in the country decreasedby more than half, from 67.2% to 30.9%, which is an annual decrease rate of 0.33%6). Meanwhile, during the same
period, the labor force employed in the non-farm sectorrose from 31.7% to 67.4%6) (Fig. 1).
This declining trend in the farm labor force (definedas those aged 15-64 years) was particularly sharp among
those aged 15-24 due to a rise in educational enrollment,
which caused many young workers to engage in other sectors as the country has become more industrialized1,7). In
addition, the decline in the number of young people whowant to work in farming has also led to agricultural laborscarcity2). Moreover, the average age of the heads of farmhouseholds reached 56.26 years by 2017. From 2005 to
2017, the percent of farm household heads over the age of

Fig. 1. Percentage changes in the labor force employment status in the period from 1977 to2017
Source: Labor Force Survey in Thailand, National Statistical Office, Ministry ofInformation and Technology, updated and published by Bank of Thailand (BOT)J Intl Cooper Agric Dev 2021 3

60 increased from 29.34%8) to 39.29%9).
This situation is common across the country; thenumber of agricultural laborers has shown a gradual decline in every region. From 1998 to 2014, the farm labor
force decreased from 22.80 million to 17.78 million, decreasing by an annual rate of 1.18%10). This trend occurred
across all regions; farm labor has decreased by 0.74% in
the north, 1.58% in northeast, 1.35% in central, and 0.19%
in the south10).
Even though Thailand imports and uses immigrant labor from neighboring countries (89.3% of this labor comesfrom Myanmar), laborers prefer to work in agro-industries
and the service sectors because the farm sector offers only
seasonal jobs, which do not provide secure incomes11).
Moreover, there are many regulations that limit the availability of alien laborers to work on farms, and farm work
is not so different from the work available in their own
countries and provides lower pay compared with non-farmjobs11). Moreover, there are long-term disadvantages—it
would be impractical to rely on foreign or immigrant laborbecause of the advanced economic progress of neighboring countries, which often tempts immigrant laborers to go
back to their homelands11).
Many studies in Thailand clearly reveal that labor is
one of the most important inputs in agricultural production.
Perennial crops, such as longan, have also been affectedby labor shortages, especially during the harvest season in
the northern regions, including Chiang Mai and Lamphunprovinces. The lack of harvest labor has affected production in term of both the quantity and quality of products.
Thus, the demand for labor to harvest longan, especiallymigrant and foreign laborers from Myanmar, has greatly
increased12,13). Moreover, labor shortages have also beenfound to significantly affect palm production in Krabi
province, the main location for palm oil in Thailand14),
and chili production in Sakhon Nakhon province15). Labor
is especially important for labor-intensive crops, suchas perennial crops like rubber, because technology andmachinery cannot help much with the production of theseperennial crops16).
Thus, labor shortages in the farm sector are a national
concern because they have not only led to an increase in
the cost of human labor, but have also affected the performance of timely farm operations, thereby affecting productivity levels and the growth of the sector2). Moreover, labor
shortages are especially problematic for seasonal crops forwhich their insufficient technological labor substitution;
Robusta coffee is one of these crops.

4 J Intl Cooper Agric Dev 2021

3. Labor requirements for Robusta
coffee production in Chumphon
province
Chumphon has produced Robusta coffee since the1980s. The general characteristics of Robusta coffee areas follows: Robusta coffee is suitable for growing in thewarm and humid climate of southern Thailand. It has ahigher level of disease resistance, quicker fruit maturing,and higher bean productivity than other coffee types17, 18).
Moreover, Robusta coffee produces a round bean that
distributes a stronger taste and provides more caffeine
compared to Arabica18). These characteristics are why coffee growers in southern Thailand prefer to grow Robusta
coffee.
Before introducing the current labor situation in theregion, the labor requirements for production should be
confirmed. The types of operations and their labor needs
are summarized in Table 1. This information is based on
discussions with farmers in the study region. Some operations require hired labor when the family cannot fully
satisfy the labor requirements. For example, fertilizers(chemical and manure) are applied around 1-3 times ayear. Family labor is mostly used for applying fertilizer;
however, if there is a of lack of family labor, local labor
will usually be hired.
However, there is a scarcity of labor for certain
operations requiring skill, especially harvesting/picking
work. Moreover, in the Robusta coffee area in Chumphon
province, there was no mechanical harvest applied byeither small or larger farms to pick coffee beans. Traditionally coffee was harvested by hand by mostly the wayof selective picking. Harvesting labor selective picking
involves making numerous passes over the coffee trees,
selecting only the ripe cherries, then returning to the tree
several times over a few weeks to pick the remaining cherries as they ripen. For the final harvesting of the remaining
coffee cherries, the coffee trees are harvested entirely in a
one time “stripping” all the beans off the branches, unripe
as well as ripe cherries.   Labor constraints in this work
is a significant factor affecting the quality and quantity ofcoffee because picking coffee berries is intensive work andmost berries mature contemporaneously across villages.
Labor constraints in this limited time period can result inboth a loss of mature coffee berries as well as the incorrect
harvesting of unmatured berries, resulting in reduced coffee production17). Coffee growers must use family laborplus seasonal labor to cope with their labor needs at thistime. The picking process cannot be skipped; thus, labor
constraints will affect the quantity and quality of coffee
production.
The evidence from other coffee studies indicate that

Table 1. the main activities on coffee operations
Wage rate

In case of shortage/ coffee
grower response byCoffee operations

time per year

Labor use

Pruning coffee tree
branch/ Shadetrimming

1-4 times a year

Mostly skill family labor.
and hiring from local

Per-day (300baht/day)No shortage but faced tightsituation of labor available
because it needs highly
skilled labor/ skip theoperation

Apply fertilizer

2-3 times a year

Family labor, and hiringfrom local

Per bag of fertilizer(40 baht/bag)No shortage/ if sometime
shortage of labor arise, using
more family labor.

Weeding(Pesticide/ herbicide)

1-4 times a year

Family labor, and hiringfrom local

Per-day (300baht/day)No shortage/skip theoperation

HarvestingOnce a year/ The
harvesting time
was from late
October until earlyFebruary

Use family, and hired
labor mostly from
northeast

Per kg. (average is
2.5 baht per kg) one
labor can harvest
250 kg cherry perday  Labor shortage is an issue inthis operation. Using familylabor/local labor/ resort tohigher payments

Drying coffee berriesOnce a year after
harvesting

Mostly use family labor-

No shortage

Transporting to themarket/buyers

Once a year after
harvesting

Mostly use family labor-

No shortage

Source: Authors’ survey and the Handbook for the Management of Main Perennial Crops, Department of Agricultural Extension,
Agricultural Statistics Yearbook, Office of Agricultural Economics.

farmers primarily hire laborers from the northeasternregion17, 19, 20). This is consistent with the interviews conducted with farmers in our study region, who reported that87% of farms hire extra labor from this region for coffee
harvesting, or they contract with northeastern laborers.
This is because most coffee farmers in Chumphon moved
to the province from the northeastern region of Thailand.
Thus, their social ties can be utilized to hire seasonal
migrant laborers from that region.
Each year farmers contact laborers either via agentsor through personal contacts, and informal contracts aredeveloped before the arrival of these laborers. These contracts are not documented; rather, they are oral agreements
reached between coffee farmers and northeastern laborers.
Typically, the contracts cover three basic items that coffee
farmers will provide for laborers: a wage, by baht per kg;
transportation costs (expenses for fuel for groups of laborers to travel in their own trucks or bus fees for those who
travel by bus); and temporary accommodations.
Employed laborers are also allowed to work at othercoffee farms on the condition that they have already finished harvesting coffee at their contract farm. Moreover,some farmers, due to the difficulties in finding harvest
labor, resort to higher payments for contract laborers tosecure their harvest. Because of the limited time period for
harvesting and limited supply of laborers to work on manycoffee farms, most laborers look for work on resource-richfarms that can provide higher incomes.

Social ties are significant in securing seasonal harvest
labor. The specific difference between coffee farms andother farms is that most coffee farmers immigrated fromthe northeast region in previous decades and have gained
experience in growing coffee over the generations. Most ofthese immigrant farmers began as laborers picking coffee.
They then started to settle down, purchase land, and later
grow coffee themselves21).
Social ties also work among villages. Many coffee
farmers formed groups, introducing cooperatives andother enterprises in the area22). Nevertheless, farmers
continue to maintain strong connections with their northeastern origins21). Thus, the connections and social support
occur not only in the area they settled but also among thenortheastern migrant groups because the laborers they use
depend primarily on employers from their region of origin,
as discussed earlier.
The strength of social relationships/networks and social capital has influenced many aspects of farmers’ operations. Research on the value of Chumphon coffee networks
by Homchum22) concluded that the strong networks among
coffee groups, corporations, or enterprises, affected not
only farming practices and technology diffusion via the
supply of information through these networks, but also
created links to marketing channels.
In this case, the strength of social relationships/
networks and social capital among coffee farmers could
also possibly contribute to available labor market informaJ Intl Cooper Agric Dev 2021 5

tion. This is because the connections and mutual support
in the group is likely to create cohesion and thus enable theunhindered flow and exchange of information, thus easing labor constraints. Based on the interviews conductedwith farmers, under strong social relationships/networks
and strong social ties, the contract laborers could also
be introduced to other coffee farms after finishing theirwork on contract farms. This evidence of introducing
labors to other farmers is also consistent with studies by
Homchum22) who showed that strong networks could also
support the exchange of information among skilled laborers through informal discussions. Strong social ties among
the groups and farmers also provided for the sharing of
information among skilled and hard-working laborers in
the area23), this evidence may facilitate effective matching
between laborers and employees. Moreover, Pokeeree,
Rangsipaht and Sriboonruang24) also supported that being
in a group of coffee farmers was related to more coffee
production. Regarding the selling income of coffee farmers, the payment for their products or coffee berry is basedon the shipped volume for each farmer, and the cherry
price is common between farmers. Even though farmers
A and B join a farmers group or cooperative, they get thesales based on the price by A or B’s shipped volume.

4. Methodologies
4.1 Pooled production function estimation

The estimating of pooled quadratic production
function using all households’ data was first analyzed.
This was a practical estimation for capturing which input
factors affect coffee bean production for all households.
This estimation was utilized as a baseline to observe theinput factors that affect the coffee production for the wholewithout the concerning labor constraint issue. Recently,
a flexible functional form is preferred for estimating the
production function. However, the translog form, whichis commonly used for this type of functional form, is not
appropriate for this study. Because some farmers havenever hired labor from outside of the family, we observedsome farms with a zero input of hired labor. The translog
requires positive input observations, so we utilized thequadratic production function in this study. Our quadratic
production model for identifying the factors affect coffee
bean production is expressed as:
Y = α0+ βA ∙ PA+ βF ∙ FL+ βH ∙ HL+ βN ∙ NT+ γAF ∙ PA×FL+ γAH ∙PA × HL + γAN ∙ PA × NT + γA2 ∙ PA2 + γF2 ∙ FL2+ γH2 ∙ HL2 + γN2 ∙NT 2 +αD ∙ DD + αslope ∙ LandSlope + αsquality ∙ SoilQuality,(1)
where Y is coffee production. PA, FL, HL, and NT are
the inputs for planted area, family labor, hired labor (themeasure of both labor inputs are recorded in man-day),
6 J Intl Cooper Agric Dev 2021

and fertilizer (nutrition), respectively. In the estimation,these variables are normalized at their means. This means
we estimate the normalized quadratic function. Moreover,some physical plot characteristics, such as the slope of
the plot land (Land Slope) and soil quality (Soil Quality),
are introduced as dummy variables, and a district dummy(DD) is included. α, β, and γ are the estimated parameters.
4.2 Augmented Inverse Propensity Weighted (AIPW)
Estimator and Average Treatment Effect (ATE)As the aim of this study is to identify the effect oflabor constraints on coffee bean production, however, thesimple comparisons of productivities and production between farmers are not appropriate because this constraint
is not randomly assigned. As we briefly discussed in the
introduction, we must consider the following issues to examine this objective. First, the scarcity of seasonal migrantlabor from the northeastern region for harvesting coffee
is a pervasive phenomenon in Chumphon province. However, the actual employment of this migrant harvest laboris contingent on contracts. On the labor side, the workers are concerned with their actual income based on thepiece-meal rate of the picking operation. Meaning the plot
conditions affecting the productivity of berries could be
an important factor in drawing the contract. Second, some
farmers, at times, offer better payment or accommodation
to the laborers. Implying that the wealth of the farmers
must be considered to understand the ease or difficultywith which they secure labor. Moreover, the farmers areconcerned with information about the migrant laborers,
and the social networks among villagers can be mobilized
to acquire this information. This background of labor contracts with migrant harvest laborers should be considered aconstraint in finding or securing harvest labor. Thus, labor
must not be considered randomly assigned; rather, it is an
endogenously determined phenomenon.
In order to control for these endogeneity problemsof the labor constraint on coffee production in estimating
its production effect, we applied the augmented inverse
propensity weighted (AIPW) estimator for the normalized
quadratic production function as an outcome equation. The
AIPW estimator has another advantage in estimating treatment effect. It is known as double robust estimator25, 26, 27),
which requires a correct specification for either the treatment model or outcome model (not both). In other words, itenables a consistent estimation of the treatment parameterswhen either the outcome model, treatment model, or bothare correctly specified26, 27). Moreover, the AIPW has beentermed the “efficient influence function”28). The AIPW
estimator has attractive theoretical properties and requires
only two things be specified: (1) a binary regression modelfor the propensity score and (2) a regression model for the

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farm labor per planted area, debt holding status), these
Recently, the AIPW estimator for ATE has been apvariables mainly reflect farmers’ endowments and a farm
plied to study the effect of adoption of farm technology
household’s ability to hire labors. The hypotheses for theor innovation on crop production in order to control for
impact of the variables are as follows.
selection in terms of both treatment as a binary variable
The physical conditions of the coffee plots and area,25)
and a multivalued variable. For example, Haile et al.
including planted area, coffee tree age, the slope of theused a double robust estimator to observation differences
coffee plot land, lack of water, ratio of coffee plants mixed
and found there to be a positive impact on maize yield andwith other crops to total coffee land are also included.
harvest value in Malawi. This AIPW estimator has been
Since the wage of hired laborers is paid by baht per kiextended to a multivalued as multinomial logit treatment.
logram of coffee production, plots with well-conditioned
Kikulwe et al.29) utilized the multinomial logit model forplots for picking reflect a relatively higher wage/income
treatment to determine the factors affecting adoption offor laborers compared to farms with poor resources. Thus,
control practices, and they employed the AIPW estimator
laborers are more interested in working on resource-rich
for ATE. They found the adoption of Banana Xanthomonas
farms, which is represented in those farms’ characteristics.
Wilt (BXW) control practices had significantly
impacted
Not only will laborers obtain more income for working
1higher values of banana production and sales in Uganda.
on resource-rich farms but working on resource-rich farms
1
While, Smale26) established an order logit for treatment ofwould make the work/harvesting easier by saving time
the adoption of sorghum seed on various outcomes. The
and energy, so the laborers could work more on other cofauthor’s results suggest that improved seed appears to befee farms, resulting in higher earnings. Thus, these farmassociated with an increased sales share.
characteristics are expected to influence labor constraints.
In this study, our analysis has two components for
As discussed above, strong networks and groups
estimating ATE. First, we specify a probit regression informed by the coffee farmers are expected to affect theorder to predict treatment status or determinants of labor
labor constraint in a positive way. These factors, including
constraints in farm households and calculate a propensity
the length of time a farm household has been settled in
J Intl Cooper Agric Dev 2021 7

1

Chumphon province and farmers’ opinions with respect tothe strength of these groups in a particular area, reflect therole of social ties. Groups/communities that tend to stayunited in particular areas are likely to be very cohesive,
enabling the unhindered flow and exchange of informationand the sharing of labor between farms. Farmers’ opinionsof the support from government and private organizations
for coffee farms are also important factors to be included.
Positive or good experiences of support from either government or the private sector could reflect valuable advice
or information. The best support they experienced could
also reduce labor constraints. (Details of the variables anddefinitions are shown in Table 3).
As we had earlier discussed the calculation of AIPW
which has yielded a doubly robust property, the importanceof coping with the issue of endogeneity in both (treatment
and outcome) equations was one major concern.  For the
production function or outcome equation, the estimation
could be biased if estimating the production function withthe basic factor inputs such as land, labor, and current
inputs (fertilizer) because we are unable to identify the difference of the coffee plot characteristics farms. However,in this estimation, we had taken into consideration of soil
condition and the land slopes for controlling/regulating
the difference in plot characteristics. The particular farming condition did not demonstrate any significant effecton hired labor constraint, but the social network was able
to pinpoint them, as noted before. Thus, in this researchstudy, the social ties variables (Length of time settled farmhousehold in Chumphon province (year), farmers’ opinionof the strength of the groups in the farmers’ area) were also
deployed in the treatment equation to elaborate on the likelihood of labor constraint. These settings in the outcome
and treatment equations could contribute to risk reductionin omission variable bias problem as much as possible
even when the double robust estimator was applied.
Moreover, there was another concern where social
ties worked well in the estimation.  That is, the spillover
effect of treatment assignment is known as a violationof Rubin’s Stable Unit Treatment Value Assumption(SUTVA). Actually, social networks are important fortransmitting knowledge or adopting technology or variety
among farmers; the production was likely to be affectedby social networks. However, the harvesting was almost
approaching final stage of production. After hiring theharvest labor, there was no room to spare for social networks or ties works on productivity. On the other hand,
if social ties variables were not included in the selection
equation, it unveils bias outcome in estimation. Thus, social ties variables were included in the selection equation,and since the harvesting season was being left at the final
stage, there was no reason to be worried about the spillover
8 J Intl Cooper Agric Dev 2021

effect of treatment assignment (labor constraints) or there
was no possibility of violating SUTVA, which requires nospillover effects from the treatment30)
4.2.2 Production function in the AIPW model

The outcome equation that consists of all the factors as in a pooled quadratic production function for all
households (eq.1) was conducted, estimated separately foreach group of farms depending on their labor constraint
situation (those with and without labor constraints) byusing the AIPW approach.
Moreover, in the present context, it should be notedthat Laufer’s study introduced this functional form to
examine the differences between the marginal productivities of male and female labor in Indian agriculture, which
is a relevant previous study31). Thus, we further utilized
a quadratic production function to estimate the marginal
productivities of family labor and hired labor to examine
how farmers were coping with this harvesting labor
constraint or labor allocation for coffee production as our
hypothesize that farms under this constraint used familylabor to compensate for the lack of available hired labor.  
For this purpose, a comparison in the marginal productivity of family labor and hired labor between farms with andwithout harvest labor constraints is useful. We followed
the basic principle that the marginal productivity of inputs
must be equal to the ratio of input price to output price. If
there are no constraints and no market imperfections, themarginal productivity of hired labor seems to be equal to
the wage and coffee price ratio. However, especially labormarket imperfections are common in developing countries, and some farmers offer higher payment to meet theirneed for hired labor. Labor shortage constraints or higher
payment/effective wage for hired labor derive the higher
marginal productivity of hired labor than the farmer underno constraints. Also, if hired labor is not sufficiently available, perhaps family labor must be introduced. Specifically,
we should examine if farmers mobilize their family labor
to compensate for the shortage of hired labor to mitigate
production; this means that the marginal productivity offamily labor is likely to be lower in hired labor constraint.
The comparison of marginal productivities and attained
production between farmers with and without hired labor
constraints provide a useful approach for understanding its
effects on the outcome and farmers’ coping behaviors.

5. Data collection and Survey design
This study was conducted in Chumphon province,
the main province for producing Robusta coffee. The
survey was carried out in mid-April until May (or after
coffee harvesting had finished) of 2016. Data on totalcoffee households were collected from registered coffee

growing households at the Chumphon extension office as alist frame. A multistage sampling approach was applied to
identify subdistricts, villages, and households. At the firststage, we purposely selected two subdistricts that produce
mainly coffee, the Rubroo and Kaotalu subdistricts, which
are the main hubs for coffee production; in these subdistricts, 44.7% and 24.7%, respectively, of all households
produce coffee. In the second stage, we selected households
from each village using proportional sampling. Finally,
160 total coffee households were selected randomly. Data
were collected through a questionnaire guiding in-depth
interviews with heads of coffee farms. The survey consisted of three parts. The first part collected information
about the socioeconomic characteristics of farmers andfarm households (sex, education, age, history of immigration, farm and nonfarm labor), household debts, andexperiences of difficulties in hiring laborers. The second
part of the survey collected information on the characteristics of each particular plot of the coffee farm, including
water supply, land slope, soil conditions, coffee crop types(single or mixed), land use, farm production, farm income,inputs used, and especially coffee production. The last partgathered information about farmers’ groups, their opinions
on the role of these groups, and farmers’ opinions on thegroup strength in their areas.
Information for all inputs, especially labors inputs
used in the production functions, were collected. Participants were asked about the use of both family labor andhired labor in all activities of coffee production on thefarm, including pruning, applying fertilizer, weeding, harvesting, and other activities (drying, transporting). Theselabor inputs are recorded in number of persons. However,both family and hired labor, in man-days, were calculated
from the number of laborers multiply by the number ofworking days for each activity.
Thus, for family labor used in all coffee growing activities, the most intensive operation is picking coffee dueto the limited period in which the berries are mature. The

amount of family labor used in the harvesting period isaround 114.17 man-days, while pruning the coffee branches, applying fertilizer, and weeding (applying pesticide/herbicides) took around 43.21 man-days. Other activities,
like drying and transporting were asked about in relation
to family labor, but these activities did not require theparticipation of all family members and took only a few
hours a day, not the whole day. When we calculated thesein man-days, they took around 47.65 man-days. Thus, thetotal family labor took around 205.03 man-days, or around68 days a year. For hired labor, outside of family labor,coffee picking required the most hired labor, especiallylaborers from the northeast region. This was followed
by applying fertilizer and pruning, but labor these jobs
was mostly within the village. In total, these activities
took around 140 man-days. The coffee planting area andfertilizer/nutrition inputs are recorded in area of rai andkilogram, respectively.
In order to identify the labor constraint context, farmers were asked about their experiences with hiring laborers. All farmers who were hiring, or not hiring, laborers (inthe survey year 2016) were asked to identify if they could
hire the amount of labor that they actually wanted to hire.
Thus, the constrained households are the farms that could
not hire the amount of labor that they actually wanted tohire. Unconstrained households are defined as the farms
that were able to hire the amount of labor they sought.
Finally, there were 121 farm households that hired laborers in the survey year (2016) and 39 farm households that
did not hire laborers; the labor constraints were defined asfollows:
Cell (1) and (3) of Table 2 show that there were 98
farm households (who were hiring and not hiring laborers in the survey year) that were able to hire the desired
amount of outside labor. These farms are defined as thehouseholds without labor constraints. Meanwhile, 62 farmhouseholds, shown in cell (2) and (4), were unable tohire the number of laborers they sought. These farms are

Table 2. Identifies labor constraint of coffee farms households in Chumphon province
items

Farm household whohire labor(in surveying year 2016)

Farm household who do
not hire labor(in surveying year 2016)

total

Farm households without labor constraints(or farms could hire the amount of labor thatthey actually wanted to hire)(1)
73 households(3)
25 households

98

Farm households with labor constraints(or farms who could not hire the amount oflabor that they actually wanted to hire)(2)
48 households(4)
14 households

62

121

39

total
Source: Authors’ Survey

J Intl Cooper Agric Dev 2021 9

defined as the households with labor constraints.
The level of group strength was ranked on a five-point
scale in order to measure the importance of networks incontributing information. A five-point scale was also used
to measure farmers’ opinions of government and private
support on coffee farms since the role of government andprivate support could, hopefully, generate information that

may mitigate labor constraints.

6. Estimation result
6.1 Descriptive analysis of characteristics
households with and without labor constraintsofThe definitions and descriptive statistics of the key

Table 3. Summary Statistics of Characteristics of household with and without labor constraint

Variables

Dependent variables
Labor constraint
production of coffee (kg.)
Explanatory variables
InputsPlanted area (rai)Family labor (man-days)
Hired Labor (man-days)
fertilizer used (chemical and bio fertilizer)(kg/rai)
Coffee farmer’s characteristics
Education of household head
(dummy variable,
0 = no education or primary,
1 = higher than primary school)
Debt holding status(1= farmers have a not completely repaid
debt at the time of the survey, 0= otherwise)
Length of time settled farm household in
Chumphon province (year)
Coffee farms’ characteristics
Coffee age tree (year) (maximum age)Land slope (0= flat land, 1= otherwise (hilland deep slope)
Lack of water (water scarcity)(1= lack of water, 0= otherwise)
Ratio of coffee plants mixed with other crops
to total coffee land
Soil quality (1=good quality, 0 = otherwise)
Farmers’ opinion of strong of the groups infarmers’ area (0= not strong, 1= relatively
strong, 2=somewhat strong, 3 = undecided
or neutral, 4=moderately strong,
5=extremely strong)
Farmers’ opinion of supporting fromgovernment and private on coffee farms (0=not satisfy, 1= relatively satisfy,
2=somewhat satisfy, 3 = undecided or
neutral, 4=moderately satisfy, 5=extremely
satisfy)
Dummy Rubroo subdistrict (1= Rubroo
subdistrict, 0= otherwise)
Source: Authors’ survey
Note: 1 rai = 0.16 hectare

10 J Intl Cooper Agric Dev 2021

Coffee household
who has labor
constraint (n=62)
L=1
mean
(SD)

Coffee household
who has no labor
constraint (n=98)
L=0
mean
(SD)

mean

(SD)

1.00
2,886.43

0.00
3,028.05

0.00
3,357.55

0.00
3,351.11

0.39
3,174.99

0.49
3,228.26

0.000018.23
209.90137.372,089.68

12.63
79.93
137.882,063.70

17.19
201.90142.982,361.22

14.30
78.14
163.202,274.85

17.59
205.00140.81
2,256.00

13.64
78.69
153.45
2,192.84

0.00000.00000.00000.0000

0.21

0.41

0.30

0.46

0.26

0.44

0.0023

0.85

0.36

0.72

0.45

0.78

0.42

0.0000

23.65

8.79

25.22

8.83

24.61

8.82

0.0000

21.32
0.85

7.34
0.36

21.09
0.86

8.00
0.35

21.18
0.86

7.73
0.35

0.00000.0000

0.45

0.50

0.20

0.41

0.30

0.46

0.0565

0.82

0.38

0.79

0.39

0.80

0.38

0.0000

0.25
2.48

0.43
1.16

0.22
3.09

0.42
1.21

0.23
2.86

0.42
1.22

0.00240.0000

3.95

1.06

3.76

1.04

3.83

1.05

0.0000

0.76

0.43

0.68

0.47

0.71

0.45

0.0000Total(n=160)P-valuevariables used in the estimation are shown in Table 3.
The average coffee output for farms that have no labor
constraints was 3,357.55 kg., which is higher than the2,886.43 kg. generated by farms that have labor constraints. For the inputs used, on average, farms withoutlabor constraints have a greater man-day for hired labor(142.98 man-days), and used more fertilizer (2,361.22kg.), while labor constrained farms depend more on familylabor (209.9 man-days) and have slightly larger plantedareas (18.23 rai). About 30% of respondents without labor
constraints had obtained a higher than primary school
degree, and this percentage is higher than 21% for labor
constrained farms. In addition, 85% of farms with labor
constraints still have debt, which is a greater percentage
than farms without labor constraints (72%). Further, 45%of labor constrained respondents faced a lack of water supply to equip their farms, compared with 20% or less forunconstrained farms. In terms of opinions, farmers withoutlabor constraints have a stronger relationship with groups
in their area.
6.2 Result of the production function of total households

Before estimating the production function for eachgroup by using the AIPW approach, the production function for all samples was estimated, the result is shown in
Table 4. Better soil quality is also a significant effect on
coffee production. Coffee farms in Rubroo subdistrict,the main area of coffee production, soil quality also significantly affect coffee production, and still has the main
effect on the coffee product. However, to derive the actual
production effect of each input, the marginal productivity
at mean was derived, as shown in Table 5. The marginal
productivities of three inputs on coffee production, plantedarea, hired labor, and fertilizer had a positive and weresignificant. The marginal productivity of family labor was
not significant.

Table 4. Estimation Results of quadratic production function of all households
variables
inputsPlanted area
Family labor
Hired labor
Fertilizers
Planted area* Family labor
Planted area* Hired labor
Planted area* Fertilizers
Family labor * Hired labor
Family labor * Fertilizers
Hired labor* Fertilizers
Squared Planted area
Squared Family labor
Squared Hired labor
Squared Fertilizers
Soil quality
Land slope
Rubroo (subdistrict dummy)
Intercept
Adjusted R-squared:  0.7811

Coffee production of total households
coefficient
S.E.
0.097    0.604    0.303    0.595**
0.182
0.309**
0.225**-0.069    
-0.017    
-0.186*-0.197    
-0.257    
-0.029    
-0.121**
    0.199    **0.084    0.204**-0.595*

0.315
0.496
0.188
0.291
0.270
0.129
0.108
0.175
0.221
0.095
0.154
0.208
0.042
0.047
0.093
0.115
0.093
0.317

Note:  *p < 0.1; **p < 0.05; ***p < 0.01
Source: Authors’ estimation

Table 5. The marginal Productivity at mean of each inputs     Input
Planted area
Family labor
Hired labor
Fertilizers

Marginal Productivity (MP)S.E.

75.828***2.865
6.732***
0.529***

21.858
  2.095
  1.718
  0.158

Note:  *p < 0.1; **p < 0.05; ***p < 0.01
Source: Authors’ estimation

J Intl Cooper Agric Dev 2021 11

6.3 AIPW estimation

However, our concern focused on the difference inthe marginal productivity of hired labor and family labor
between farmers with and without hired labor constraints.
Table 6 provides the estimated result of AIPW for thisconcern. The result of the probit model with determinantsof labor constraint is shown in the first column of Table 6.
This selection equation highlights that holding debt is an
obstacle to hiring labor. Farmers with debt may have lessability to pay for hired labor and maybe have a lack of
cash flow. Thus, they might offer fewer options for laborers compared to those who have no debt. Farms with alack of water resources are also more likely to have labor
constraints.
As we expected, the role of strong social ties seems
very important in determining the labor constraints offarm households. Both a longer length of time settled in
Chumphon province and stronger farmers’ groups increase
the probability of having no labor constraints. A longer
time being settled in Chumphon province implies that
farmers have tighter or stronger connections with the local
people and local communities as well as more experiencein dealing with northeastern labor, greater trustworthinessin terms of sharing labor with local people, or sharing

Table 6. Estimation Results of AIPW Model
Equations
Dependent variables
Labor constraint (1/0)
Education of household head
Debt holding status
Length of time settled farmhousehold in Chumphon province
Farmers’ opinion of strong of thegroups in farmers’ area
Farmers’ opinion of supportingfrom government and private on
coffee farms
Planted area
Coffee age tree
lack of water
Ratio of coffee plants mixed withother crops to total coffee land
Land slope
Rubroo (subdistrict dummy)
inputsPlanted area
Family labor
Hired labor
Fertilizers
Planted area* Family labor
Planted area* Hired labor
Planted area* Fertilizers
Family labor * Hired labor
Family labor * Fertilizers
Hired labor* Fertilizers
Squared Planted area
Squared Family labor
Squared Hired labor
Squared Fertilizers
Soil quality
Intercept

Selection equation

Outcome equation forfarmer who has hired
labor constraint

Outcome equation for farmer
who has no hired labor
constraint

Labor constraint (1/0)
coefficient
S.E.

Coffee production (kg.)
coefficient
S.E.

Coffee production (kg.)
coefficient
S.E.

-0.3330.856**

0.254
0.295

-0.029*0.013-0.280**-0.034

0.103
0.112

-0.0090.005
0.8493**
0.143

0.009
0.015
0.260
0.312

0.012
0.074

0.343
0.250

-0.115
0.133

0.203
0.164

0.143
0.324**

0.129
0.113

0.896

-0.059-1.3610.085
1.077*
0.682
0.659-0.396-0.1850.272
0.153-0.3810.250-0.145-0.254**0.324*
0.609

0.591
0.999
0.474
0.633
0.533
0.520
0.573
0.408
0.599
0.453
0.309
0.465
0.273
0.087
0.165
0.598

0.848*
1.492**
0.038
0.295-0.3010.199
0.656*
0.059
0.165
0.001
-0.396*-0.588**-0.047-0.392**0.137-1.136**

0.397
0.557
0.209
0.364
0.343
0.163
0.272
0.199
0.277
0.119
0.224
0.223
0.042
0.139
0.107
0.367

0.353

Note:  *p < 0.1; **p < 0.05; ***p < 0.01
labor constraint (1= Coffee household that has a labor constraint, 0= Coffee household that has no labor constraint), n=160.
Source: Authors’ estimation

12 J Intl Cooper Agric Dev 2021

contract laborers with other coffee farms, thus reducing
labor constraints. Additionally, stronger farmers’ groups
enabled the unhindered exchange of information. Whenstrong social relationships/networks and strong social ties
are present, the contract laborers could also be introduced
to other coffee farms after finishing their work on thecontracted farm, and this could ease labor constraints.
The result of the second component/outcome equation of the quadratic production function for both forthe treated (labor constrained households) and control(unconstrained labor households) groups are shown in thesecond and third columns of Table 6, respectively. Mainly,
the quantity of fertilizer used seems to significantly affect coffee production both linearly and quadratically forhouseholds with labor constraints. Good soil quality isalso significantly affected in coffee production. However,linearly and quadratically terms for farms with no labor
constraints showed planted area, farm labor, and fertilizer
significantly affect coffee production. In addition, the coffee farms in Rubroo subdistrict, the main area of coffee
production, also significantly affect coffee production.
The result also clarified that the planted area and fertilizer
used interaction is non-negligible. The positive interactionof planted area with fertilizer used could explain that additional coffee grows in planted areas that use fertilizer,
thereby increasing coffee production. In order to derive theactual production effects of two types of labor inputs, themarginal productivities must be derived.
6.4 Average treatment effect of labor constraint on
production

Finally, estimates of the average treatment effect(ATE) for coffee production outcomes for the three estimators are shown in Table 7. The results show that of all theestimators, the AIPW estimator provides more significant
results with either sandwich or asymptotic standard error.
Based on these results, therefore, the impact of labor constraints on outcomes is interpreted using the AIPW estimator. The effect of labor constraints on coffee production isclearly shown to be negative and statistically significant by
the AIPW estimators.
6.5 Marginal productivities of labor

The marginal productivities of labor, and their confidence intervals, result from unitizing the production model
to explain the labor allocation between family and hired
labor, as shown in Table 8. These productivities were estimated at the mean observations for each group with/without labor constraints. Showing the marginal productivities
of hired labor for the treatment group (labor constrained
farms) were higher than the control group (unconstrained
farms). This implies the farmers who faced hired labor
constraints introduce less (but insignificant) hired laborin their production compared to farmers under no hired
labor constraints. The marginal productivity of the familylabor for the treatment group was lower than that for thecontrol as we hypothesized. However, the marginal laborproductivities between the two groups of farmers were notsignificantly different. It could not support the hypothesisthat farmers mobilize their family labor to compensate forthe shortage of hired labor.

7. Discussion
The result of the ATE, found by employing AIPW,
clearly showed that labor constraints had a negative andstatistically significant effect on coffee production. However, the marginal productivity results from the quadratic

Table 7. Average treatment effect, coffee labor constraint.
estimators
AIPW
IPW Regression

ATE (Coef.)SEsandwich

T value

0.136-2.101-0.285-0.292-0.174SE
asymptotic

T value

0.118
0.118
0.117

-2.428-2.487-1.488Source: Authors’ estimation by using R package “CausalGAM” (Glynn and Quinn, 2009)

Table 8. Marginal Productivities of Labors derived from quadratic production function
Constraint household
InputsFamily labor
Hired labor

Marginal
productivity
-1.21
8.89

No constraint households

SE

confidence interval
2.5%
97.5%

3.77
4.21

-8.59
0.63

6.17
17.15

Marginal
productivity

SE

confidence interval
2.5%
97.5%

3.96
4.79

2.55
2.03

-1.05
0.81

8.97
8.78

Source: Authors’ estimation

J Intl Cooper Agric Dev 2021 13

production function revealed a difference in the marginal
productivity of the labors inputs was not significant, or
farmers mobilizing their family labor to compensate forthe shortage of hired labor was not different between thetwo groups of farmers. This could discuss that the farmers with labor constraints still had another constraint formobilizing family labor; they were unable to completely
mitigate the effect of the hired labor constraint.
The livelihood of coffee farms in Chumphon province
are based on coffee production operations. These operations face the severe problem of labor shortages, especiallyfor picking coffee—a time when these farms depend on
seasonal labor from the far northeast region to supplement
family labor. The development of the northeast region in
Thailand has decreased the agricultural labor force. Thus,
there are fewer laborers who want to work on coffee farms.
Moreover, the cost of transportation and accommodation
for laborers as well as the pay provided by coffee farmers
may increase in the future. Therefore, the issue of labor
constraints needs to be emphasized so that programs or
strategies to cope with the serious consequences are included in the government plan for the coffee industry andimplemented.
Coffee production is a key indicator of the income ofcoffee farmers, and labor constraints are shown to reduce
production—even when family labor is fully used. Moreover, it will be difficult to rely on laborers from northeast
Thailand in the future. The exchange of labor information and sharing data of labor employed from other crop
activities was needed to cope with the labor shortage for
harvesting coffee. For example, rubber tapping laborers inthe study area normally work through the night and areavailable to harvest coffee beans during the fruiting period.
However, labors working in other crop activities may lackthe skills for harvesting coffee. As a result, providing information on coffee picking practice on the site is needed.
Additionally, the exchange of labor information throughsocial ties and stronger group activities is also important.
As shown by the probit result, the key element of social
ties and stronger group activities are also important to
reducing the probability of labor constraints. Thus, one
policy recommendation is that knowledge or technologicaldiffusion, not only related to farm practices but also labor
information, can be transferred via coffee groups, cooperatives, and enterprises, growing a community of practiceamong coffee farms will also help to increase coffee
productivity.
Moreover, even though there are no mechanicalharvesting methods applied by small and larger farms forpicking coffee beans, technology or tools for analyzing
coffee bean’s integrity is required. Such as color separationand coffee bean maturity are used as appropriate indica14 J Intl Cooper Agric Dev 2021

tors for harvesting coffee for laborers who lack analyticalskills or experience in harvesting coffee. This will bebeneficial not only for saving harvesting time but also for
improving harvesting quality. Therefore, collaborations
among local and national research institutes, universities,
and stakeholders from government and non-governmentorganizations can develop those technologies.
Conflict of interest
There is no conflict of interest.
Acknowledgment
The authors express gratitude to the coffee farmers in
Chumphon province for their cooperation and responses tothe questionnaire. Every statement expressed in this article
is that of the author and does not reflect any opinions ofanyone affiliated to the authors.
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J Intl Cooper Agric Dev 2021 15

タイ王国チュンポン県におけるロブスタコーヒー
生産農家が直面する収穫労働制約が生産に及ぼす
影響
カンジャナ

クワンムワン 1)，ラダワン

レルジュンタック 2)1) タイ王国農業協同組合省農業経済局
2) タイ王国サコンナコン・ラジャバット大学農業技術学部
要旨
本研究は、タイ南部のチュンポン県のコーヒー栽培農家が近年直面している収穫労働の確保の困難性が生産に及ぼす影響を数量的に検証した。同地域では、もともと東北部から移り住んできた人々がコーヒー生産を担っており、収穫時期に必要となる労働力についてはこれまで東北部からの出稼ぎ労働力に頼ってきた。本研究は、この労働制約の起こりやすさはランダムなものではなく各農家の労働需要や収穫作業条件によって決まることを踏まえて、また雇用労働制約の有無による農家の対応をみるために、二重にロバストな AIPW 推定量によって quadratic な生産関数を推定し、雇用労働制約の有無の違いによる家族労働と雇用労働の限界生産性を比較することで、雇用労働制約がある場合の家族労働の補完的な投入の程度について検証した。結果、雇用労働制約の有無によって有意な限界生産性の差は見られないものの、雇用労働制約が有ることで有意に生産量が低くなることがあきらかとなった。

キーワード：労働制約，拡大された逆傾向重み付け推定量，ロブスタコーヒー，タイ王国

16 J Intl Cooper Agric Dev 2021