HIAS-E-55

Earnings, Savings, and Job Satisfaction in a
Labor-intensive Export Sector:
Evidence from the Cut Flower Industry in Ethiopia
Aya Suzuki
University of Tokyo

Yukichi Mano
Hitotsubashi University

Girum Abebe
Ethiopian Development Research Institute

October 2017

Hitotsubashi Institute for Advanced Study, Hitotsubashi University
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Earnings, Savings, and Job Satisfaction in a Labor-intensive Export Sector:
Evidence from the Cut Flower Industry in Ethiopia

Aya Suzuki 1, Yukichi Mano 2, and Girum Abebe 3

Abstract:
While labor-intensive export-oriented industries typically bring positive economic
benefits to countries through employment generation, the effects of these industries on
various aspects of workers’ welfare have not been formally studied very well. This
paper considers the case of the cut flower industry in Ethiopia to provide such
quantitative evidences. Based on the propensity-score matching and doubly robust
estimations to facilitate rigorous comparisons, we find that production workers in the
cut flower sector earn significantly more than similar workers in other sectors, mostprobably due to the flower farms’ interest to reduce costly worker turnovers. In
addition, the cut flower industry workers save more regularly than workers in othersectors who have similar characteristics, and the amount saved relative to the income
level is also higher, after controlling for the frequency of wage payment andemployment status. The subjective valuation of their jobs is also higher in the cut
flower sector, particularly in terms of the income level, stability, and future prospect,
but they are not necessarily more satisfied with the type of work they do. Risk-averse
individuals are more satisfied in the cut flower sector and age is rewarded more, while
work experience and math skills tend to reduce satisfaction levels more in the sector at
this level of unskilled production workers.
Keywords:
Labor-intensive sector, Wage differential, Savings, Job satisfaction, Ethiopia
JEL Codes:
F63, F66, J31, O12, O14, O19

1
University of Tokyo
2

Hitotsubashi University

3 Ethiopian Development Research Institute11. Introduction
The high-value export-oriented horticulture sector in developing countries has received
attention in the past decades for its potential role in improving the welfare of rural
population. Some earlier studies focused on farmers involved in the sector as producers(Jaffee and Masakure 2005, Minten et al. 2009, Maertens and Swinnen 2009, Suzuki etal. 2011), while more recent studies examine the impacts of employment in these
sectors on the welfare of individual workers (Maertens et al. 2011, Mano et. al. 2011,
Hermann and Grote 2015, Van den Broeck et al. 2017). Their results are mixed–someargue that such effects do not benefit the poor (Barron and Rello 2000, Trifkovic 2014,
Ulrich 2014), while others argue that the sector contributes to reducing poverty(Maertens and Swinnen 2009, Maertens et al. 2011, Getahun and Villanger,
forthcoming).
On another strand of literature, mainly from international economics,
emergence of labor-intensive export sector, such as agro-processing and lightmanufacturing industry, are known to bring higher wages and other positive economic
results to receiving countries via employment generation, earning foreign exchange or
positive external economies (Borensztein et al. 1998, Markusen and Venables 1999,
Barrios et al. 2005, Bwalya 2005, Li and Liu 2005, Gohou 2012, Gui-Diby 2014). One
prominent effect is a wage increase specific to this export sector, exporter wage
premia, found in the seminal work by Bernard and Jensen (1995) and others. The
suggested explanations for this wage premia vary. Exporting firms may be moreproductive and able to offer higher wages (Tybout and Westbrook 1995, Isgut 2001),
export premium may actually be the returns for the higher educational qualification ofworkers (Almeida 2007, Munch and Skaksen 2008), or may be paid in order tomaintain a low turnover rate (Schank et al. 2007).
However, existing studies lack the following important elements. First, mostof them fail to control several important worker characteristics, leaving a large roomfor unobserved heterogeneity behind the evidence of higher wages. Literatures frominternational economics mostly use firm-level aggregate data, and thus do not have any
variables that control for workers’ characteristics. Literatures from development
studies use worker-level data, but do not consider some important variables that predict
job acquisition and occupational choices, such as workers’ ability and psychologicalfactors, nor do they analyze the reasons why these sectors offer different opportunities
for their workers. Second, most studies only examine worker incomes and do notconsider other aspects of welfare, such as savings, remittances, or job satisfactionlevels, which are also important from the development economics’ perspectives. Third,2although these sectors play a significant role in the early stages of economic
development, rigorous empirical studies on developing countries remain limited. 4 It is
important to examine the cases of developing countries as poverty reduction is a key
development objective and the industry structures may be different from those ofdeveloped countries.
This paper aims to understand the impact of participating in the laborintensive export sector on the welfare of individual workers, considering the case of
the cut flower sector in Ethiopia. We particularly focus on the effects on workers’ pay
and saving behavior as well as their subjective valuations of quality of work in this
sector. Using primary data collected from production workers in the cut flower farms
and workers who have similar characteristics but are in other sectors, we examine
whether financial performances, such as income, saving, remittance, contribution tohouseholds income, and subjective valuation of the quality of work are higher for the
cut flower workers. In order to correct the possible selection bias, we employ
propensity-score matching and doubly robust estimation methods. We find that the
production workers in the cut flower sector earn significantly more than theircounterparts in other sectors, both in terms of income and wage. We also find certain
evidence that farms pay this wage premia in order to reduce worker turnover rates.
Further, we find that workers in the cut flower sector save more regularly, and theirsaving rate, the ratio of the amount saved to the income, is also higher in comparison totheir counterparts. The results are robust, including the hours worked, employment
status, and frequency of payment. The total job satisfaction level is also higher for cut
flower workers, particularly considering income level, stability of income, and future
prospect.
In further analyses, we find that age is rewarded more in the cut flower sector
in comparison to other sectors, but the effect of the number of years worked to the job
satisfaction level decreases more rapidly for the cut flower industry workers incomparison to others. Risk-averse individuals are more satisfied with their jobs in the
cut flower sector relative to people with the same level of risk averseness in othersectors, although they are paid less in comparison to other sectors.
Our contribution is threefold. First, we extend the current literature onexporter wage premium by providing evidence based on worker-level data, whichallowed us to control for workers’ qualities to the extent possible. We show that the
4 Exceptions are Herrmann and Grote (2015), Hermann (2017), Van den Broeck et al. (2017), andGetahun and Villanger (forthcoming).

3

export-oriented labor intensive industry, as represented by the cut flower sector in
Ethiopia, offers higher income opportunities to workers in comparison to other sectors,
holding the workers’ qualities constant, even for the level of production workers who
are at the bottom of the employment pyramid in the sector. We also provide someevidence for the reasons for this wage premia, relating the firm-level turnover rates andworker-level wages. Second, we find that participation in the high-value horticulture
sector induces workers to save more often and larger amounts in comparison to theirincome level. To our knowledge, the impact of high-value horticulture sector on thesaving behavior of workers has not been studied, and while saving behavior is a muchstudied topic in development literature (e.g., Karlan et al. 2014), its linkage to theworking environment has not been examined. While the exact mechanism on what
drives this behavior needs to be examined in further studies, anecdotal evidence
suggests the role of peer effects within these labor-intensive environments. Third, we
extend the study of the impact of export-oriented industry on subjective satisfaction ofworkers, going beyond the traditionally-studied outcome of income levels. The
findings illuminate the type of workers who benefit (aged and more risk-averse) andthe satisfaction with the work provided by these sectors.
The next section explains our hypotheses to be tested. Section 3 elaborately
describes the cut flower sector in Ethiopia. Section 4 explains the data collection
methodology. Section 5 discusses the estimation strategies employed and theestimation results are contained in section 6. Conclusions follow in section 7.
2. Hypotheses
In order to examine the impacts of working in the cut flower sector, we pose thefollowing five hypotheses. First, as the export wage premia holds, we predict that the
cut flower sector offers higher wages to its workers in comparison to the workers’
quality. The main reason for believing in the existence of wage premia is based on thefact that a high turnover rate among production workers is one of the main issues thatcut flower farms encounter. From our interviews with managing directors of cut flower
farms in 2013, we find that the turnover rate among permanent production workers was
14.4% in 2012 and increased to 20% in 2013. Note that these figures actually
understate the true turnover rate of production workers as it only considers those who
managed to remain for at least 45 days with the firm, those who became permanent
workers. Anecdotes suggest that a considerable number of workers depart the farms
within the first 2 or 3 weeks of employment. This high turnover rate among factory
workers is also confirmed by Blattman and Dercon (2016), who find that a third of4

workers quit within the first month of employment in industrial jobs within theirsample firms in Ethiopia.
Wage premia may be explained by a standard demand and supply model if thereasons for the premia are related to workers’ utility, such as union effects, short-run
immobility of labor, or compensating the bad working conditions of the job(“compensating differentials”) (Krueger and Summers 1988). However, efficiency
wage theories, modeled notably and formerly by Stiglitz (1984) and Katz (1986), apply
when firms find it profitable to pay wages higher than the market wage relative to theworkers’ qualifications. Krueger and Summers (1988) explain four factors that induce
firms to consider efficiency wage in particular: to minimize turnover costs, to raise
workers’ effort levels, to increase workers’ sense of loyalty to firms, and to attract
workers of higher quality. Among these possible reasons, we suspect that the
hypothesis of minimizing turnover costs applies to the efficiency wage employed in the
cut flower industry in Ethiopia, based on our anecdotal evidence of our fieldwork.5
Essentially, during our interview with the Ethiopian Horticulture and Agricultural
Investment Authority, it was specifically mentioned that “To reduce turnovers, farms
pay higher wages.” We find that 9% of the farms surveyed in the flower census
highlighted that labor turnover is the most important business problem that they faced.
These firms chose turnover to be a more pressing problem than access to capital,
market, or production technology. In order to minimize the costly turnover for the cut
flower farms, we predict that the cut flower farms offer economic rents to theirworkers. As discussed later, this is also observed in other studies (Krueger andSummers 1988, Schank et al. 2007).
Hypothesis 1: The cut flower sector offer wage premia to its workers in comparison toworkers of similar qualifications in other sectors.
Second, we predict that the previous turnover rate of flower farms would
affect the wage as farms attempt to attract and retain workers by offering higher wages.
The result may also confirm the relevance of efficiency wage theory in this sector.
Hypothesis 2: Among the cut flower farms, wage is higher in farms which previously
had higher turnover rates, holding the quality of workers constant.
5 There are supervisors in every section closely monitoring the production workers, and shirking is

not a major issue (Mano et al. 2011).

5

Third, as the cut flower sector provides stable employment opportunities, thismay allow workers to plan for the longer run (Mullainathan and Shafir 2013) or form
an informal savings club with colleagues, while taking advantage of the reduced
transaction cost and peer pressure. Thus, we predict that cut flower workers tend tosave more. Fourth, exporters operating in developing countries generally offer betterworking conditions in comparison to local firms.6 We predict that the level of job
satisfaction is higher for the cut flower industry workers.
Hypothesis 3: Working in the modern stable sector, cut flower industry workers tend tosave more than their counterparts.
Hypothesis 4: The cut flower industry workers are more satisfied with their jobs,
particularly in terms of stability.
Finally, job opportunities are often limited in developing countries and many
people are self-employed. While employed workers tend to earn stable and relatively
high incomes, self-employed workers may enjoy other aspects of their jobs, such as
self-independence in the equilibrium. For instance, emerging evidence from labormarket studies in Ethiopia has shown that formal employment in industrial jobs is lesspreferred to self-employment (Franklin 2014, Blattman and Dercon 2016).
Hypothesis 5: Self-employed workers are more satisfied with their jobs, particularly interms of self-independence. Employed workers earn stable and relatively high income,partially compensating for the lack of self-independence.
We test these empirical hypotheses on the primary data of workers that we collected in
Ethiopia.
3. Cut Flower Sector in Ethiopia
The cut flower sector in Ethiopia is one of the typical labor-intensive export-oriented
industries brought to developing countries by globalization seeking lower wages. It has
6 Cut-flower production is exclusively destined for the export market in developed countries. In thedestination markets, consumers are increasingly wary of the conditions under which labor intensive
products are being produced in developing countries. The growing awareness of customers and the
vocal presence of labor and environmental campaigners have significantly induced multinationalfirms to voluntarily introduce high levels of labor and environmental standards in their production
sites in developing countries (e.g., Henson and Rearcon 2005, Riisgaard 2009).

6

experienced a significant growth starting from the first decade of the 2000s, following
its neighbor Kenya, which ranks first in supplying cut flowers to the European markets.
The export value of Ethiopia increased from 0.4 million USD in 2000 to 245 million
USD in 2013/14 (Ethiopian Horticulture Development Agency 2015), and the numberof firms surged from 5 in 2002 to 75 in 2013. The rapid growth was due to a strong
government support, which provided many incentives for the investors in this sector,
such as exemptions from taxes for inputs, lower land rental fees with basic utilities,
facilitation of trade logistics and subsidy of air freight to main markets using thenational carrier, and special loans provided by the Development Bank of Ethiopia(Mano and Suzuki 2015, Schaefer and Abebe 2015).
The development of this sector created a new labor market (Mano et al. 2011).
Figure 1 shows the change in the average size of a cut flower firm in Ethiopia, using
our primary census data of cut flower firms over the years. On an average, each firmhires approximately 500 workers. As a comparison, we also plotted the average
number of workers for general Ethiopian firms and large firms (more than 100
employees) based on the World Bank Investment Climate data of 2011. It shows thecontribution of the flower sector in employment opportunities. Production workers,
who are unskilled workers, share 92.1% of the total employment in 2013. Consideringthat majority of the production workers are female workers with very limited education
and alternative job opportunities, this sector appears to have contributed significantly
to poverty reduction.7 The unemployment rate for female workers was 32.5%, whilethat for all workers was 15.9% in Ethiopia in 2012 (Central Statistical Agency, 2013).
The percentage of full time female workers in a general Ethiopian firm was 33.9%,
while it was 71% for the production workers in the cut flower sector (using our firm
survey of 2007).
(Figure 1 around here)
In a typical firm, production workers, generally employed locally, lie at thebottom of the worker pyramid, and are in charge of planting, spraying, harvesting,grading, and packing flowers for export. These tasks are not challenging but often
require team work. To be adept in the specific tasks and fast in completing the work,
time and experience is required. Losing one production worker equates to losing more
7 In Ethiopia, all the export flowers are produced by large-scale flower exporting firms. As of 2015,

no major role was played by smallholder farmers, unlike Kenya, where smallholders planted
various flowers using a part of their agricultural plots for sales.

7

worker and it lowers the team’s production efficiency. Hjort (2014) demonstrated
various levels of production efficiencies of team work using the cut flower industry of
Kenya. The production workers are paid fixed wages per day or per month and are also
constantly monitored by supervisors, reducing room for moral hazards. These workers
are paid in cash and not through banks. Above them, supervisors oversee the work
done by these production workers. These supervisors may be internally promoted fromthe role of production workers or hired by the firms directly. Several non-production
workers who are employed from outside have bachelors’ or masters’ degrees inhorticulture sciences. Above them are farm managers, who are more educated, andother white collar workers, such as accountants and administrators who work in theoffice. Labor poaching is not observed at the level of production workers, while it iscommon at the level of supervisors and managers. We focus on production workers inthis study as they account for the majority of workers and are more likely to be
impoverished.
4. Data Collection
We collected census of the flower industry in 2007, 2010, 2012, and 2013. Further,from January to March 2013, we conducted primary survey on workers of the cut
flower sector. Cut flower farms are located around the capital city of Addis Ababa dueto the proximity to the international airport and the high altitude, which is suitable forgrowing flower varieties with large buds. In order to draw random samples of
production workers, we used a stratified sampling technique. First, we divided all thecut flower farms in Ethiopia into five groups, based on the index we created according
to the socio-economic conditions of the nearby wereda (district), which are population,
percentage of urban area, percentage of economically active people, and percentage of
housing with water access, using the wereda-level information from the 2007
Population and Housing Census of Ethiopia with each element receiving the same
weight. In each group, there were approximately 15 farms. Further, we randomly
selected a few farms from each group, totaling to 10 farms. We visited these farms,
requested a list of workers, and randomly selected approximately 15 production
workers for the interview from the list. We ensured that the gender ratio corresponds to
the real situation in the respective farm. The total number of production workers was
142.
Selecting samples for the comparison group was a challenging task. As our
purpose was to examine the effect of working in the cut flower sector, in order to selectpeople in the comparison group, we were required to choose individuals who had

8socio-economic characteristics very similar to the production workers in the cut flower
sector, but were not in the same sector. Selecting people randomly from the nearby
locality was not appropriate as those people may not have attributes similar to the
production workers. In the rural area, people may be fairly homogeneous in terms of
their potential of being employed, but some of the farms were located in small towns
where there were various job opportunities, and thus different types of people. Thus,
we relied on the production workers’ own network. We requested production workers
to introduce us to some of their friends who were not in the flower sector but had
characteristics similar to them, particularly in terms of their education level, which is
known to be an important determinant in the job market, as well as age and gender. 8
From the list of friends, we randomly selected people for the interview. When none of
their friends were available for the interview, we additionally selected people from thelocality and ensured that they had similar observable attributes to our production
worker group. The number of samples collected from the locality, rather than throughthe friends’ network, was only 11 out of the total 159 samples in the comparison group.
Among the comparison group, we excluded 19 samples who were
unemployed and 5 samples who were in school. Although they may represent thecounterfactual state of production workers in the cut flower sector accurately, as manyof our variables of interest are related to income, using many samples without incomein the control group may yield biased results. Therefore, our estimates of welfare
effects of the presence of the cut flower sector are likely to be modest, and should beconsidered as lower bounds. Among the remaining 135 samples, 76 were hiredworkers, 12 were farmers, 39 were self-employed, and 8 had multiple jobs (3 hiredworkers and farmers, 2 hired-workers and self-employed, and 3 farmers and selfemployed). The typical jobs for the hired-workers were house maid, beauty salon
worker, waiter, laundry worker, factory worker, hired agricultural laborer, construction
worker, government organization employee, guard, hotel receptionist, café cashier, andso forth. There were 13 factory workers who were most comparable to the production
workers in the cut flower sector in terms of the type of work. This shows that ourcontrol group comprises of people with various types of jobs available in the locality.
In addition to the standard socio-economic characteristics, such as age,gender, schooling, and income levels of these respondents, we also requested the
respondents a subjective valuation of their jobs. We asked the respondents questions on
8 To control for the variation in abilities, which are not fully captured by the education level, we

also use the math test score in the following analyses.

9five aspects: 1) income level, 2) stability of income, 3) type of work engaged, 4) future
prospect, and 5) self-independence. The respondents were expected to rate theirsatisfaction levels using 5 scales, from 1 to 5, where 1 referred to being very unhappy
and 5 referred to being very happy. We also computed the total score with a maximumof 25.
Further, we collected information about the respondents’ psychological traits,
namely risk preferences, competitiveness, and overconfidence. These psychologicaltraits appear particularly relevant for stability of income or in promoting competition
among employees, which typically characterize labor-intensive mass-production
enterprises. In order to elicit risk preferences, we conducted a game with real payoffs,
following the popular method pioneered by Tanaka et al. (2010). We asked the
respondents to choose either project A, which receives a certain payoff, project B,
which yields a probabilistic payoff of a certain amount (8 Birr), or 0 each at 50%
probability. We prepared eight games, and the certain payoff in project A increases as
we go down the list of eight games (Figure 2). The expected payoff for project B
remains the same in all the eight games. The expected payoffs of these projects areequalized at game 4, and from then onward, it is higher for project A. Thus, earlier the
respondent switches from project B to A, the more risk averse the person is. We createdan index between one and nine, nine being the most risk averse.
(Figure 2 around here)
In order to measure how competitive a person is, we undertook a simple mathtest, following a popular method (Niederle and Vesterlund 2007, and Balafoutas et al.
2012). The respondents were asked to solve the addition of two 2-digit numbers within
two minutes and were graded according to the number of correct answers. We chose
this simple test such that our respondents, who had few years of schooling, were able
to solve at least some of the easiest questions. We conducted these tests three times,
and each time a different set of problems with real payoffs were provided.
In the first round, the respondents were provided piece-rate rewards of 1 Birr
per correct answer, which is approximately 5% of their daily wage. In the second
round, the respondents were provided relative rewards of 3 Birr per correct answer if
their score was the highest among the group of five people, but none if they ranked
second or lower. The four other members were chosen randomly from the samples and10had previously completed the interview. 9 The respondents did not know who the othermembers were. They were only informed that the other members were also the
respondents of this survey (i.e., thus had traits similar to the respondent). In the thirdround, the respondent was provided a choice of three alternative reward methods,
without being informed of their performance in the previous rounds. The first method
follows the first round reward (absolute piece-rate grading), the second method follows
the second round reward (relative grading and winner-takes-all), and the third method
is a more moderate version of the relative grading (the top receives 2 Birr per correct
answer, 2nd and the 3rd receive 1 Birr per correct answer, and otherwise receives zero).
We used the respondent’s choice of payment method in the third round to measure
their competitiveness. If the person chose the first method of absolute grading, theperson was considered “not very competitive” as they preferred to be graded only by
their own score, not relative to other people. Similarly, if the person chose the second
method (winner-takes-all), they were considered “the most competitive.” The personchoosing the third method was in between. Their experience of solving these problems
from the previous rounds could influence their choice of payment. For example, thoseworkers who thought that their scores were high may choose the most competitive
payment method, while those who thought to have scored low may choose a piece-rate
payment method. Here, it is important to note that we did not inform the respondents
on how well they actually scored (neither in absolute scores nor in ranking) after eachround. Thus, the effect of previous experience on the payment choice of the third roundis only based on their own self-evaluation and not reflecting any new information
about their actual ability in answering these problems.
In order to control for the difference in this type of self-evaluation, we alsomeasured the degree of overconfidence. Prior to conducting the math test, we asked the
respondents about their prediction on how many correct answers they would mark andwhat would be their rank among the group of five in the relative grading rounds. We
used the difference between the former prediction and the actual number of correct
answers to indicate overconfidence in score estimation, while we used the difference
between the latter prediction and the actual ranking to measure the overconfidence inthe rank estimation. The former can be considered as overconfidence in absoluteevaluation and the latter as overconfidence in relative evaluation (Kamas and Preston2012).
9 For the previous respondents, we conducted the tests for several workers to first obtain their testscores and used these scores to complete their questionnaires subsequently.

11

Further, we utilized our primal data which was separately collected from all
the cut flower farms in Ethiopia in 2012 to control for the characteristics of farms thatworkers belonged to, in particular, the turnover rate and the number of workers. In
addition, we also use public data, such as the 2007 Population and Housing Census tocontrol for wereda-level information.
5. Estimation Strategy
Our aim is to examine how the emergence of the cut flower industry has impacted thewelfare of workers. Individual workers’ status of whether they are involved in the cut
flower sector may also be subject to selection bias. Although we have carefully
selected the comparison group such that they have similar observable characteristics as
the production workers, some selection bias may remain. For example, production
workers in the cut flower sector may perform better financially as they are morediligent. Similarly, cut flower farms may select and retain workers who are morecapable and dedicated. We control for the potential unobservable ability of workers by
including the score of the math test we conducted during the interview. Solving a
number of cumbersome math questions require not only numeracy but also diligence.
Further, in order to reduce the differences between the treatment and control groups
based on observable characteristics, we employ several methods.
First, we conduct a propensity score matching (PSM). Under the PSM, we
first estimate the probability that each observation belongs to the cut flower sector
based on the worker’s characteristics and match a production worker with a worker inthe comparison group who has a similar probability. The average of the differences in
the outcome variables between the matched samples is the treatment effect. In order toestimate the propensity score, we included variables which were considered to beimportant for job acquisition, such as the worker’s background, ability, and personal
characteristics, and which are found to be statistically and significantly different
between the control and treatment groups. To match, we attempted several different
methods and report the results of two methods among them, Caliper matching and
Kernel matching, as they reduce the bias the most in our case. Caliper matches anobservation in the treatment group with an observation in the comparison group which
has the closest propensity score within the specified maximum tolerance distance,
while Kernel matching creates hypothetical observations using the information fromthe control group to match the observation in the treatment group. Each matching
method has both advantages and disadvantages, and we report how significant bias was
reduced through these matching methods.

12Second, while the PSM removes systematic observable differences between
the treatment and control groups, it reduces efficiency in estimation (Hahn 1998,
Heckman, Ichimura, and Todd 1998). Thus, we also use an inverse propensity-scoreweighting regression, which was originally proposed by Robins and Rotnitzkey (1995)
and developed further by Hirano, Imbens, and Ridder (2003) among others. In thismethod, the inverse of the propensity score is used as weights to run regressions of theoutcome variable. This is also known as a “doubly-robust” estimator as only one of themodels (treatment or outcome) needs to be correctly specified to achieve consistency
in estimation (Wooldridge 2007). As our aim is to estimate the treatment effects on thetreated, we use the weight which is equal to one for treated observations and, where ps refers to the propensity score for the control observations(Hirano and Imbens 2001).
Further, in order to guarantee a sufficient overlap in propensity scores of thetwo groups, we also trim our samples to conduct the inverse propensity-scoreweighting regressions. Among the various methods to trim the samples (e.g., Dehejia
and Wahba 1999, and Heckman et al. 1997, 1998), we rely on Crump et al. (2006)
which compute the optimal cut-off point from estimated propensity scores as their
method is derived from rigorous theory. 10 Based on this, we use samples withpropensity scores in the interval of [0.1609, 0.8391], which meant excluding 26
observations. 11
6. Estimation Results
Table 1 shows the summary characteristics of the two groups. While we observe
similarities, we also find some variables that are statistically different between the twogroups. We control for these differences in the following analyses. We find that on anaverage, the respondents are in their mid to late 20s. Approximately 80% of them arefemale, which is a characteristic of the cut flower sector, particularly at the production
worker level. The workers have approximately 6 years of education, while theirmothers and fathers have approximately 1.5 and 2.5 years of education, respectively.
As aimed in our sampling method, the number of years of schooling is not different
10 Other studies, such as Chen et al. (2009) and Deininger and Liu (2013) rely on this trimming

method.
11 For a robustness check, we also conducted the same estimations using the full sample and thetrimmed sample based on the “maxima-minima” criteria (Caliendo and Kopeinig 2008). These
yielded very similar results in comparison to what was reported below both, in terms of statisticalsignificance and magnitudes of coefficients.

13

between the workers in the treatment and control groups. However, we also find thatthe average math score is higher for the control group. Approximately 73% of the
production workers are from farming households. The average number of years
worked in the current job is 3.5 years for the production workers, while it is 5.3 years
for the comparison group. Whether the person is born in the village and the number ofyears they are living in the current village is not different across the two groups.
Psychological traits, such as risk averseness, competitiveness, and overconfidence,show interesting differences. While the two groups are not different in terms of risk
preferences, production workers in the cut flower sector are more competitive, but lessoverconfident in terms of relative ranking estimations.
(Table 1 around here)
On an average, we find that financial performance between these groups is not
significantly different (Table 2). Typically, these workers earn approximately 730 Birr
(about 34.7 USD) per month. The daily income is approximately 24 Birr (1.15 USD)
for the cut flower worker, while it is 23.9 Birr (1.13 USD) for the comparison group.
The daily household income per capita is 16 Birr (0.76 USD) for the cut flower worker
and 18.3 Birr (0.87 USD) for the other group, indicating that most of them live below
the international poverty line of 1.25 USD per day. The cut flower production workers
tend to save more often than their counterparts and the difference is statisticallysignificant. Their saving rate, defined as the amount saved over the income level, is 3%
higher for the cut flower workers in comparison to their counterparts, but thedifference is not statistically significant. The frequency of remittance, householdincome, contribution to household income, and percentage of food expenditures are notdifferent between these groups. Distribution of income is depicted for each group in
Figure 3. We observe that although the average income is comparable, the production
workers’ distribution of income is tighter around the mean, while the income of thecomparison group has a wider spread, most likely owing to the comparison group
including a variety of sectors with varying amounts of economic rents.
(Table 2 around here)(Figure 3 around here)(Figure 4 around here)
The diamond-diagram in Figure 4 shows that the average job satisfaction level14

is higher for the production worker group than the comparison group in all aspects.
Particularly, cut flower production workers seem to be more satisfied in terms of thestability of their job and self-independence.
PSM Results
In order to analyze their performance more closely, we conducted a propensity
score matching and inverse propensity-score weighting regressions. To estimate thepropensity score of working in the cut flower sector, we included variables that matterin job acquisition, such as schooling, ability, family background, and certain
psychological traits and used the model in Table 3. Some variables, such as age,gender, math score, whether parents are farmers, whether they were born in the village,
and certain psychological factors, are significantly different between the two groups. In
order to choose a matching method that reduces the bias the most, we adopted several
matching methods as presented in Table 4. As shown after matching, the number ofcovariates that are statistically significant between the two groups becomes zero in all
the methods. However, when we examine the extent to which the covariates explain
the treatment status, we find that Caliper matching minimizes the difference incovariates between the two groups (low pseudo R2 and high p-value). Further, themean bias is the smallest for the Caliper matching method and is less than 5%, which
is a commonly used threshold (Caliendo and Kopeinig 2008). Thus, Caliper matchingprovides the best balance. We report the results of Caliper matching and Kernel
matching in Tables 5 and 6 respectively, and depict the histogram of the propensity
scores between these groups (Figure 5). 12 Considering these, our sample appears to bevery well balanced after matching.
(Table 3 around here)(Table 4 around here)(Table 5 around here)(Table 6 around here)(Figure 5 around here)
Our PSM results show that while the financial performance between the twogroups was not different before the workers obtained the current job, their income level
12 For models using the logged income, farmers with negative incomes (six observations) were

excluded.

15

and saving status significantly differ currently (Table 5). Under Caliper matching, noneof the variables are significant in panel A, which reports the status before their current
jobs. Considering Kernel matching, the annual income and whether they save arenegative for the cut flower production workers in panel A. However, in panel B, which
reports the current status, the log of annual income is positive and statisticallysignificant at 5% level for Caliper matching and at 1 % level for Kernel matching.
Whether the workers save is also statistically significant for both the matching
methods, and the saving rate, defined as the amount saved monthly divided by themonthly income of the respondent, is positive and statistically significant for both.
These findings suggest that assuming that only observable differences are important,13
if we randomly allocate workers to the cut flower sector, they will earn more and savemore often.
Table 6 shows the PSM results for the job satisfaction level for these workers.
We find that most of these coefficients are not statistically significant but are positive.
In the aspect of stability, the Kernel matching result shows that the cut flower workers
are more satisfied than their counterparts in other sectors.
On Income and Wage
For more robust analyses, we conducted an inverse propensity-score weighting
regression. Here, we limited our interest to the log of income, log of wage, whether
they save, and the saving rate, which were found to be statistically significant in the
PSM analyses, and the various job satisfaction levels. In Columns (1) to (4) of Table 7,
we show four models for the log of income and log of wage, with and without thestatus of employment (i.e., hired or self-employed). We show the results from trimmed
samples for brevity, but the results using the full sample and trimmed using the
maxima-minima criteria show similar results.
(Table 7 around here)
First, we observe positive effects of cut flower dummy on all the models,
which indicate the presence of exporter-wage premia. This supports Hypothesis 1. In
terms of income, the cut flower workers earn 44% more than their counterparts in othersectors, and 26% more than the hired workers in other sectors. In terms of wage, the
13 Note that we also control for certain potentially unobserved psychological characteristics andabilities in our propensity score estimation.

16

cut flower workers earn 20% higher than the hired workers in other sectors. It isnoteworthy that this wage premia was confirmed, holding other variables constant thatare important for wage, such as schooling, ability, experience, psychological factors,
payment methods, and wereda-level labor market conditions. This finding is consistent
with existing literatures which examined the existence of exporter wage premia(Schank et al. 2007).
While examining the reason why the cut flower sector is offering wages
higher than the market wage relative to workers’ qualities, we used data from our firmlevel survey to estimate whether the past turnover rates of farms have any effects on
the wages of production workers. In the survey, we asked the managing directors of cut
flower farms for their past turnover rates and this ranged from 2% to 70% in 2010. The
result in Column (5) of Table 7 shows that indeed, the past turnover rate is positive andstatistically significant at 1% level. In terms of magnitude, a 1% increase in theturnover rate leads to a wage increase by 3.3%. This supports Hypothesis 2. Although
the tasks that these production workers are assigned are relatively simple, such as
cutting, sorting, or packaging roses, to be adept at these tasks and to increase
efficiency, time and experience is required. As team work is often required, losing one
experienced production worker lowers the production efficiency of the entire team. It is
generally the case that during the first several months of employment, production
workers go through on-the-job training. It is costly for the farms to train the workers as
their supervisors and some of the co-workers are required to spend time with the newworkers. The negative and statistically significant coefficient on the number of cut
flower workers in the wereda also suggests that the farms can afford to pay smaller
wage premia in areas where a large pool of experienced workers are available owing tolower replacement costs.
Several significant results were also obtained from other control variables. We
find that more years worked and higher math scores lead to higher income and wages.
While the number of years of schooling does not appear to be important indifferentiating the wage level for the production workers, the actual skills (math score)does matter for the wages. We also find that when payments are made more frequently,
higher is the annual income. The hours worked is negatively related to wages.
Considering the employment status, the base category is a group of workers who are
self-employed, including farmers. We find that income and wages are higher for
employed workers in comparison to self-employed workers, by as much as 57.6%(Column (2)) and 50.4% (Column (4)), respectively.

17

On Saving Behavior
In Table 8-1, we examined the effects of the cut flower sector on saving behaviors. The
probability of saving is higher for the cut flower workers by approximately 21.1–
25.0% than their counterparts in other sectors (Columns (1) and (2)). We find that thesaving rate, defined as the amount saved over the income level, is also significantly
higher for the cut flower sector by approximately 4.6–6.3% (Columns (3) and (4)).
These results confirm Hypothesis 3. If an individual works in the cut flower sector,
they tend to save more often and a more amount relative to the income level,
controlling for the frequency of payment. The probability of saving and the saving rate
are higher for younger workers, which is consistent with the life-cycle model(Modigliani and Brumberg 1954, 1979, Deaton 1992). Those with more years ofworking experience tend to save more often. The more number of hours people worked
per month, more often people saved. We also find that the saving rate of the selfemployed workers is higher by 6.2%. However, in interpreting this result, one needs totake into account the observation that the self-employed workers in developing
countries often manage household and business expenses together and do not separate
accounting (Drexler et al. 2014, Abebe, Tekle, and Mano 2016). Thus, it is possiblethat those self-employed workers may attribute positive business profits as theirpersonal savings. It is notable that among the hired workers, the saving rate of the cut
flower industry workers is higher by 6.3%.
As this higher saving behavior among the cut flower industry workers may be
due to their higher income levels, we conducted the same analyses for the subsamples
of lower and higher income groups, using the threshold of the mean income (Table 82). It is possible that cut flower workers can afford to save as they have higher
incomes. However, we find that the cut flower dummy is significant only in the lower
income group (Columns (1)-(2)). This suggests that the cut flower sector is inducing
their lower-income workers to save more relative to their counterparts in other sectors.
We also find that the number of years of working experience positively affects thesaving behavior of only the lower-income group.
(Tables 8-1 and 8-2 around here)
A higher frequency of saving in the cut flower sector is consistent with the
PSM results and is rather puzzling why people tend to save more if they are allocatedto this sector. Higher probabilities to save and higher saving rates are obtained byholding income levels, frequency of payment, and employment status constant. Thus,

18

there appears to be something about the cut flower sector which motivates people tosave more. Based on our field observation about their working environment, we
suspect that this is partly due to the high degree of social interaction among workers in
the cut flower sector. The workers meet each other every day from 8am to 4pm, work
side-by-side through all the working hours, eat lunch together, commute together, andpossibly live in close proximity with each other. This type of communication is typicalof a labor-intensive mass production industry, while in other sectors, people tend towork in smaller groups or independently. Recent studies found that social interaction
within firms affect the productivity of workers (e.g., Bandiera et al. 2005, 2010, 2011,
Hjort 2014) and we suspect that it also affects their behavior outside the workplace.
We can consider several potential mechanisms of how these social interactions
lead to higher saving rates. First, higher social interactions may increase trust amongpeople, facilitating the Rotating-Savings-and-Credit-Association (ROSCA) type ofsavings. In Ethiopia, a ROSCA called Ekub is very common. People form groups tosave a certain amount of money in a common pool and rotate the turn to receive thesavings among the members. For this type of saving to functionally work, it is notdifficult to imagine that social interactions among workers are essential. Indeed,
Getahun and Villanger (forthcoming) find that cut flower employment facilitatednetwork formation, including Ekub membership, among workers within the farms.
Second, high social interactions may work as a good reminder to save (e.g., Karlan etal. 2014, Abebe et al. 2016). As workers receive payments together on the same day
and in cash, it is likely that they talk about the payment and how they expect to spend
it. In many farms, workers need to sign payroll sheets to receive wages and they canobserve how much others receive through these sheets. Individuals may be reminded tosave if they realize that their friends save as well. Third, social interaction may go
beyond a simple reminder and impose certain pressures on people to save if saving is
considered to be a good behavior socially (e.g., Mas and Morretti 2009). Given thatmost of these workers are female and mothers of households, social pressures to act as
good mothers may be high. In fact, in our interview, one of the workers mentionedthat “Ekub is the best way to save because I feel equal to other colleagues.” Saving thesame amount with other colleagues gave the interviewee a sense of confidence that she
is doing the right thing. Though these need to be tested formally to mention anything
conclusive, social interactions between workers appear to have certain effects on thehigher saving behaviors in the cut flower sector.

19

On Job Satisfaction Level
Considering the impact on job satisfaction levels, we find that the total score is higher
and statistically significant for the cut flower workers by approximately 2.1 point outof the full mark of 25 points (8.4%) (Column (6) in Table 9), confirming Hypothesis 4.
More specifically, workers are more satisfied in terms of their income level, stability ofincome, and future prospect (Columns (1), (2), and (4), respectively). Particularly, themagnitude and statistical significance level are high for the aspect of stability. Stabilityof income may be related to the saving rate as the workers’ regular income exceeds
some threshold level above which (they feel) the workers can afford to save constantly(Friedman 1957).
(Table 9 around here)
In addition to the cut flower dummy, we find that as individuals work formore number of years in one place, they tend to be less satisfied with selfindependence (Column (5)). The number of hours worked per month is positively
correlated with many of the aspects of job satisfaction.
Another notable finding is that being employed reduces job satisfaction levels
in most of these aspects. The total satisfaction level is lower for these hired workers by
2.8 points out of 25 points (11.4%) than self-employed workers (Column (6)). It isstriking to compare these results with the income regression in Table 7, which indicatesthat income levels are higher for these hired workers in comparison to self-employedworkers. Thus, self-employed workers, though earn lesser than their counterparts,
appear to be happier with their jobs, supporting Hypothesis 5. Combining thesefindings, this higher income by 57.6% (Column (2) in Table 7) may be considered as
the compensating differential of disutility from being employed by someone else andgiving up independence.
Heterogeneous Effects
Finally, in order to examine the heterogeneous effects of the cut flower sector on
income and saving behavior, we report the coefficients of interaction terms between
the cut flower dummy and corresponding variables from inverse propensity-scoreweighting regressions in Table 10. We find that older people tend to be rewarded morein the cut flower sector than other sectors. The contribution of age to income isapproximately 3.2% more in the cut flower sector relative to other sectors. This result
survives after controlling for firm-specific experience, holding the years of worked at20

the same place constant. This suggests that the cut flower sector also provides animportant earning opportunity for older people.
(Table 10 around here)
We also find that risk averseness is rewarded less in the cut flower sector
relative to the other sectors. This suggests that risk-averse people particularly prefer
the stability of this sector and accept the jobs in other sectors only when they are
sufficiently compensated for the instability of other sectors. Considering the saving
behavior, we find that the contribution of years worked at one place and math score tosaving rate is less in the cut flower sector relative to other sectors. The number ofhousehold members, which negatively affected income and wage in Table 7, arepositive for the cut flower industry workers. This possibly suggests that the cut flower
farms offer a better working environment for mothers with children or more
dependents.
The heterogeneous effects of the cut flower sector on job satisfaction levels
are reported in Table 11. We find that the contribution of age and years worked to thesatisfaction level on future prospect is lower in the cut flower sector in comparison toother sectors. Thus, longer the worker continues to work in the cut flower sector, they
become less satisfied with the future prospect relative to workers in other sectors,
probably as the tasks of the production workers are simple and tedious, and as they are
not satisfied with the fact that they did not get promoted to the supervisor’s level. Thismay be another explanation for the high turnover rate in this industry. We also find thatamong the cut flower workers, those who have higher math ability are less satisfied
with their income level. This is also possible considering the type of work that they
engaged in at the production worker’s level.
(Table 11 around here)
Among the psychological traits, the contribution of risk averseness to job
satisfaction level is higher in the cut flower sector. Particularly, its contribution issignificantly higher in the aspects of income level, stability of income, and selfindependence. As the cut flower sector provides higher income and stable employment
opportunity, risk-averse individuals may be happier in this sector in comparison toother sectors. It is interesting to compare this result with the previous table, whichshows that risk-averseness is awarded less in the cut flower sector. Although risk-

21

averse individuals are paid less in this sector, they are more satisfied with the job
owing to its stability. The frequency of payment also contributes more to thesatisfaction on income level and stability in the cut flower sector than in other sectors.
These findings on heterogeneous effects show that the cut flower sector provides
benefits, particularly for older workers and those with more household members, andoffers good working environments for more risk-averse workers. However, workers
with higher skills or more experience appear to be less satisfied in comparison to theircounterparts in other sectors.
7. Conclusion
In this paper, we analyzed the impacts of participating in the cut flower sector as an
employee relative to working in the other sectors on individual workers’ financial
welfare. Utilizing individual workers’ data, we find that the cut flower sector offers
higher wages relative to other sectors in the locality, holding the workers’ qualities
constant. Further, we find that cut flower wage premia increases as the past turnoverrates increase, suggesting that efficiency wage is employed in this sector in order toreduce worker turnover rates. The cut flower industry workers also save more regularly
than their counterparts in other sectors, and the amount saved relative to the income
level is also higher for the cut flower workers. We also find that this positive impact on
saving behavior can be observed only for the lower-income group, indicating thesector’s contribution to the welfare of the poor segment of the society. The workers’
subjective valuation of their work is also higher for the cut flower workers, particularlyon their income level, stability of income, and future prospect. However, they are notnecessarily more satisfied with the type of work that they engage in when compared totheir counterparts in other sectors. The high wage appeared to be driven by the need forflower farms to maintain a stable group of workers in order to sustain supply to theglobal market. On the other hand, the more number of years worked in the cut flower
sector has greater impact of reducing the job satisfaction level relative to other sectors.
In order to retain the experienced yet less satisfied production workers, farms offer
higher wages relative to other sectors.
While our study showed that an export-oriented labor-intensive industry can
improve the welfare of the poor and change their saving behavior, there are several
areas that need to be further studied. First, our analysis is limited only to the welfare ofworkers themselves, and may thus underestimate the full impacts. For example, we
could expect to see impacts on the resource allocation within the worker’s household,such as investment in children’s education or changes in the bargaining power of wives

22

within households. As this sector is dominated by female workers, it is likely that
employment opportunities in this sector have influenced the welfare of households aswell as individual workers. Second, we did not examine what determines the survivalof workers in firms in this sector. It would also be of interest to examine whether there
are any systematic differences between those who exited the sector and those who
continue to work in this sector. A certain level of worker turnover is healthy, andproduction workers rarely prefer to stay as production workers throughout theirworking life. In order to avoid persistent wage increments, firms are also reluctant tocontinue to employ aging workers in the same low-value adding tasks for a long periodof time. The finding that flower workers save a higher proportion of their income incomparison to workers in other sectors and that older workers are less satisfied withworking in the flower industry indicate that saving from the sector could present an
opportunity for mutually beneficial separation between workers and firms. Indeed, inmany low income countries where the financial sector is underdeveloped, personalsaving is the main source of initial capital for workers to set up their own businesses.
Further study is warranted to explore the extent to which saving from the sector has
induced the formation of successful small businesses by stimulating workers to moveout of wage-employment. Finally, it will be valuable to particularly examine theeffectiveness of wage premium in reducing worker turnover and improving the
productivity of the flower farms. These issues warrant further investigation in order tounderstand the full impact of the emergence of these sectors in developing countries.

23

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Table 1: Descriptive Statistics (before matching)
Production
Worker(142)
Age (years)
25.1(7.08)
Female (=1)
0.796
(0.40)
Years of schooling (years)
5.96(3.65)
Math Score
5.96(5.08)
Mothers Edu (years)
1.34(2.73)
Father Edu (years)
2.45(3.96)
Parents farmer (=1)
0.725
(0.45)
Parents keep livestock (=1)
0.648
(0.48)
Number of HH members (including self)
3.18(1.90)
Number of dependents in HH
1.11(1.33)
Years worked at current place (years)
3.59(2.44)
Born in the current village (=1)
0.42
(0.49)
Years living in the village (years)
16.3(11.2)
Risk Averse (1-9; 9 most risk averse)
5.35(2.34)
Competitive (1-3; 3 most competitive)
1.94(0.75)
Absolute overconfidence
9.33(14.77)
Relative overconfidence
0.07(1.53)Note) Standard deviations are in parentheses. ** p<0.05; *** p<0.01.

29

Comp
Group
(135)
27.1
(8.18)
0.726(0.45)
6.2(3.77)
7.87(6.76)
1.46(3.03)
2.75(4.05)
0.60(0.49)
0.578(0.50)
3.21(1.94)
1.28(1.50)
5.31(6.96)
0.36(0.48)
16.3(12.3)
5.30(2.34)
1.73(0.76)
6.97(13.86)0.45(1.46)

p-value ofdifference
0.03**
0.17
0.59
0.01***
0.72
0.54
0.03**
0.23
0.89
0.30
0.01***
0.37
0.99
0.86
0.03**
0.17
0.04**

Table 2: Average Financial Performance (before matching)
Production
Worker(142)
Monthly income of respondent (Birr)
735.2(168.6)
Wage per hour of respondent (Birr)
3.70(0.98)
Hours worked per month
201.8(20.9)=1 if save regularly or unregularly
0.64(0.48)
Saving rate (saving amount / income)
0.13(0.15)
Monthly saving if saved (Birr)
95.56(117.4)=1 if send remittance regularly or
0.30
unregularly(0.46)
Monthly remittance sent if remit(Birr)
24.1(58.7)
Monthly HH income
1221.1(775.3)Monthly HH income per capita
481.2(301.9)% of contribution to HH income
54.6(36.8)% of food purchase in HH expenditure
57.4(50.3)Note) Standard deviations are in parentheses. . *** p<0.01

30

Comp
Group
(135)
726.9
(583.3)4.32(4.58)
196.5(86.6)
0.46(0.50)
0.10(0.16)
131.48(252.10.30(0.46)
35.6(71.9)
1388.2(922.4)550.2(409.2)55.5(37.1)
58.2(77.2)

p-value ofdifference
0.87
0.12
0.48
0.00***
0.13
0.13
0.94
0.14
0.10
0.11
0.84
0.93

Table 3: Probit estimation for matching (Marginal Effect)=1 if in Cut Flower
Age-0.013**(0.006)=1 if female
0.133*(0.077)
Years of schooling
0.005(0.012)Math score-0.024***(0.007)=1 if parents are farmers
0.189***(0.072)=1 if born in this village
0.118*(0.073)
Risk averse
0.005(0.014)Competitive
0.126***(0.045)Absolute overconfidence
0.002(0.002)
Relative overconfidence-0.072***(0.022)2
Pseudo R
0.115
LR chi2
41.79***
N
265
Note) Robust Z-stats are in parentheses. . * p<0.1; ** p<0.05; *** p<0.01

Table 4: Matching Methods and Balancing# significant
Pseudo R2
variables
Before matching4
0.115
Nearest Neighbor
00.018
Caliper (radius=0.01)
00.003
Kernel0
0.009

31

P-value LR
test
0.000
0.749
1.000
0.973

Mean Bias
19.9
9.3
4.3
6.9

Table 5: Effects of Cut Flower on Financial Performances
PSM
Caliper(1)
A. Before the current jobln(worker’s yearly income+1)*-0.62(0.61)
ln(worker’s yearly income)
0.22(0.20)=1 if save
-0.09
(0.08)
Saving rate
0.003
(0.05)=1 if send money
0.01(0.05)ln(HH monthly income per capita)
0.16(0.22)% contribution to HH-3.74(6.92)
B. Currentlyln(my yearly income)
0.31**(0.15)ln(my hourly wage)
0.14(0.15)=1 if save
0.19**(0.08)
Saving rate
0.06**(0.03)=1 if send money-0.02(0.09)ln(HH monthly income per capita)-0.09(0.10)% contribution to HH-2.14(5.45)

Kernel(2)-1.39**(0.59)
0.18(0.16)-0.10*(0.06)-0.03(0.04)
0.03(0.05)
0.14(0.13)-6.26(6.39)
0.29***(0.11)
0.17(0.12)
0.18*(0.09)
0.04*(0.02)
0.03(0.08)-0.14*(0.08)
1.65(6.68)Note) Bootstrapped z-statistics are in parentheses. . * p<0.1; ** p<0.05; *** p<0.01.
*: 1 is added to the yearly income in order not to lose the observations with no income.

32

Table 6: Effects of Cut Flower on Job Satisfaction
PSM
Income level
Stability
Type of work
Future prospect
Self-independence
Total

Caliper(1)-0.07(0.20)
0.23(0.22)
0.26(0.22)
0.12(0.26)
0.16(0.24)
0.70(0.95)Note) Bootstrapped z-statistics are in parentheses. . * p<0.1.

33

Kernel(2)
0.004(0.19)
0.38*(0.20)
0.23(0.17)
0.14(0.22)
0.16(0.23)
0.92(0.79)

Table 7: Effects of Cut Flower on Income and Wage (Inverse P-score weighting
regression, Treatment effect on the treated, Trimmed sample)
ln(worker’s yearly income)(1)(2)
Worker-level Characteristics=1 if in Cut FlowerAge=1 if female
Years of schoolingYears worked here
Math score# HH members
Risk averse
Competitive
Absolute overconfidence
Relative overconfidence
Hours worked per monthFrequency of payment=1 if hired

0.443***(5.28)-0.017**(2.00)-0.018(0.20)-0.008(0.52)
0.062***(5.43)
0.021**(2.22)-0.038*(1.77)
0.030(1.54)-0.007(0.11)
0.003(1.11)-0.012(0.40)
0.001(0.80)
0.308**(2.06)

CF firm’s turnover rate
2010
Wereda-level Characteristics# CF workers# working population
Constant
R2
N-0.000(0.82)-0.000(0.59)
8.440***(19.44)0.32
244

ln(worker’s hourly wage)
(3)
(4)
(5)

0.260***(3.65)-0.014*(1.79)-0.063(0.71)-0.010(0.69)
0.065***(5.64)
0.017*(1.88)-0.041**(2.00)
0.032*(1.73)
0.025(0.43)
0.004(1.65)-0.008(0.28)
0.001(0.63)
0.447***(3.64)
0.576***(3.29)

0.363***(4.41)-0.012(1.62)-0.028(0.33)-0.010(0.66)
0.055***(5.03)
0.022**(2.50)-0.036*(1.88)
0.028(1.47)-0.012(0.22)
0.004*(1.74)-0.008(0.27)-0.005***(4.72)
0.308**(2.13)-0.000(0.75)-0.000(1.58)
7.999***(17.40)0.39
244-0.000(0.27)-0.000(0.75)
1.976***(4.87)
0.41
244

0.202***(3.07)-0.009(1.38)-0.068(0.80)-0.011(0.83)
0.058***(5.05)
0.019**(2.19)-0.039**(2.11)
0.029(1.63)
0.016(0.29)
0.005**(2.35)-0.004(0.15)-0.005***(5.05)
0.429***(3.50)
0.504***(2.99)

0.001(0.31)-0.106***(2.65)-0.001(0.19)
0.042***(4.59)-0.000(0.00)-0.005(0.53)
0.004(0.67)-0.019(1.11)
0.001(1.30)-0.010(1.28)-0.003(1.39)
0.042(0.55)-0.000(0.22)-0.000(1.63)
1.590***(3.65)
0.47
244-0.000***(4.09)
0.000**(2.18)
1.719***(4.11)
0.74
115

0.033***(3.69)Note) All includes zone fixed effects. Weights are from the propensity score to work in the CF sector. Robust tstatistics in parentheses. Column (5) is for workers in the CF sector only. * p<0.1; ** p<0.05; *** p<0.01

34

Table 8-1: Effects of Cut Flower on Saving Behavior (Inverse P-score weighting
regression, Treatment effect on the treated, Trimmed sample)(1)
Worker-level Characteristics=1 if in Cut FlowerAge=1 if female
Years of schoolingYears worked here
Math score# HH members
Risk averse
Competitive
Absolute Overconfidence
Relative Overconfidence
Hours worked per monthFrequency of payment=1 if hired
Wereda-level Characteristics# CF workers# working population
Constant
R2
N=1 if save

Saving rate

(2)

(3)

(4)

0.211***(2.85)-0.013**(2.23)
0.010(0.11)
0.019(1.36)
0.034***(4.83)-0.007(0.89)
0.000(0.02)-0.012(0.82)
0.010(0.21)
0.003(1.28)-0.011(0.42)
0.001**(2.38)
0.116(1.29)

0.250***(2.97)-0.014**(2.33)
0.017(0.20)
0.019(1.35)
0.033***(4.46)-0.006(0.80)
0.001(0.03)-0.013(0.85)
0.004(0.09)
0.003(1.17)-0.012(0.47)
0.002**(2.50)
0.091(0.98)-0.118(1.08)

0.046**(2.47)-0.003*(1.76)-0.015(0.53)
0.005(1.10)
0.002(0.75)-0.003(1.26)-0.003(0.53)-0.003(0.83)
0.020(1.49)
0.001(1.21)
0.008(1.18)
0.000***(2.77)
0.045*(1.90)

0.063***(3.39)-0.003*(1.95)-0.013(0.46)
0.005(1.18)
0.002(0.68)-0.003(1.13)-0.003(0.42)-0.004(0.88)
0.015(1.10)
0.001(1.05)
0.008(1.15)
0.001***(2.95)
0.032(1.31)-0.062*(1.94)

0.000(0.55)
0.000(0.56)
0.375(1.32)
0.19
248

0.000
(0.55)
0.000(0.80)
0.461(1.54)
0.19
248

0.000
(1.43)-0.000(0.34)
0.037(0.48)
0.20
235

0.000
(1.42)-0.000(0.05)
0.084(1.03)
0.22
235Note) All includes zone fixed effects. Weights are from the propensity score to work in the CF sector. Robust tstatistics in parentheses. * p<0.1; ** p<0.05; *** p<0.01

35

Table 8-2: Effects of Cut Flower on Saving Behavior Using Sub-Samples (Inverse Pscore weighting regression, Treatment effect on the treated, Trimmed sample)

Worker-level Characteristics=1 if in Cut Flower

Lower income=1 if save
Saving rate
(1)(2)

Higher income=1 if save
Saving rate
(3)(4)

0.418***

0.090***-0.134(3.74)(3.57)(0.79)

Age-0.004=1 if female-0.146

Years of schooling

0.026

Years worked here

0.035***(0.56)(1.26)(0.14)(4.10)(1.41)

0.006(1.36)

Math score

0.001# HH members-0.007-0.002

Risk averse-0.008Competitive

0.015

Absolute Overconfidence

0.005

Relative Overconfidence

0.008

Frequency of payment

0.002***

Hours worked per month-0.008(0.09)(0.25)(0.43)(0.69)

0.001(0.08)-0.002(0.30)

0.016(0.25)(0.95)

0.001(1.60)(1.43)

0.004(0.23)(4.01)(0.09)

Wereda-level Characteristics# CF workers(0.99)-0.0050.008(1.39)=1 if hired-0.002(0.48)

0.001***(2.75)

0.008(0.25)-0.027***(3.24)

0.127(1.09)

0.002(0.10)

0.026(1.31)-0.016(1.24)

0.016(0.46)-0.024(1.11)

0.041(0.50)

0.001(0.38)-0.047(1.17)

0.001(0.45)

0.118(0.57)-0.001(0.02)-0.005**(2.27)-0.035(0.81)-0.000(0.01)

0.001(0.13)-0.004(0.82)-0.002(0.19)-0.007(1.22)

0.015(0.65)

0.001(0.51)

0.008(0.77)

0.000(0.38)

0.015(0.30)-0.343***-0.123***0.210-0.027(2.82)(2.88)(1.04)(0.51)

0.000**

0.000

0.000

0.000

(2.17)(1.52)(0.02)

0.000*

Constant

0.289

0.031

0.570

0.193

(0.83)(0.31)(1.07)(1.32)(1.86)

R2
N

0.000(1.26)

0.38
143

0.36
130

-0.000(1.12)# working population(0.73)

0.23
105-0.000(1.17)

0.21
105Note) All includes zone fixed effects. Weights are from the propensity score to work in the CF sector. Robust tstatistics in parentheses. * p<0.1; ** p<0.05; *** p<0.01

36

Table 9: Effects of Cut Flower on Job Satisfaction (Inverse P-score weighting
regression, Treatment effect on the treated, Trimmed sample)

IncomeStability

Type ofwork

Futureprospect

(1)

(2)

(3)

(4)

Selfindependence(5)Worker-level Characteristics=1 if in Cut Flower
0.385**(2.15)
Age
0.001(0.10)=1 if female
0.123(0.72)
Years of schooling
0.004(0.13)
Years worked here
0.004
(0.22)
Math score-0.006(0.34)# HH members
0.026(0.63)
Risk averse
0.011(0.32)
Competitive
0.148(1.52)
Absolute
0.001
Overconfidence(0.14)
Relative-0.026
Overconfidence(0.45)
Hours worked per
0.005***
month(4.01)
Frequency of
0.231
payment(1.29)=1 if hired-0.623***(2.74)

0.748***(4.19)-0.003(0.27)
0.148(0.86)
0.027(0.94)
0.020(1.10)-0.007(0.39)
0.040(1.00)
0.029(0.84)
0.117(1.15)-0.002(0.25)-0.031(0.56)
0.003**(2.39)
0.050(0.26)-0.680***(3.22)

0.233(1.14)
0.003(0.20)
0.138(0.58)
0.021(0.63)-0.014(0.52)-0.009(0.41)-0.028(0.57)
0.021(0.49)
0.097(0.81)
0.002(0.34)-0.045(0.66)
0.002(1.48)-0.382(1.50)-0.615**(2.12)

0.481**(2.17)
0.015(0.96)-0.111(0.54)
0.002(0.06)
0.005(0.24)
0.004(0.19)-0.080(1.62)
0.016(0.37)
0.135(1.12)
0.006(0.81)
0.051(0.79)
0.003*(1.81)-0.020(0.09)-0.619**(1.98)

0.232(1.35)-0.001(0.07)
0.013(0.07)-0.021(0.63)-0.049*(1.65)
0.009(0.44)-0.033(0.76)-0.009(0.24)
0.010(0.09)-0.000(0.03)
0.024(0.43)
0.003*(1.81)-0.128(0.59)-0.286(0.92)

2.079***(2.95)
0.015(0.27)
0.312(0.43)
0.033(0.27)-0.034(0.38)-0.009(0.11)-0.075(0.43)
0.067(0.50)
0.507(1.21)
0.007(0.29)-0.027(0.12)
0.018***(2.94)-0.249(0.31)-2.823**(2.60)

Wereda-level Characteristics# CF workers-0.000(1.25)# working pop.
-0.000(0.69)
Constant
1.040(1.43)
R2
0.38
N
248

-0.000(0.96)-0.000(0.82)
1.267*(1.84)
0.36
248

0.000
(0.55)
0.000(1.37)
2.970***(3.80)
0.15
248-0.000**(2.50)
0.000(1.62)
1.614(1.62)
0.25
248-0.000***(4.53)
0.000*(1.78)
2.757***(2.92)
0.29
248-0.000**(2.23)
0.000(0.94)
9.648***(2.97)
0.33
248

Total
(6)

Note) Dependent variables are indexes of scale 1 (least satisfied) to 5 (most satisfied) except for columns (11) and (12), whose
maximum score is 25. All includes zone fixed effects. Weights are from the propensity score to work in the CF sector. Robust tstatistics in parentheses. * p<0.1; ** p<0.05; *** p<0.01.

37

Table 10: Heterogeneous Effects of Cut Flower on Income and Saving rate (Inverse Pscore weighting regression, Treatment effect on the treated, Trimmed sample)

CF dummy x:FemaleAgeSchooling
Years worked
Math score
Risk Averse
Competitive
Absolute overconfidence
Relative overconfidence# HH members
Frequency of paymentln(myyearlyincome)(1)ln(myhourly
wage)
(2)

0.076
(0.44)
0.032***(2.62)-0.028(1.31)
0.000(0.01)-0.023(1.55)-0.073*(1.77)-0.106(1.02)
0.000(0.04)-0.031(0.50)
0.086*(1.88)
0.381(1.30)

0.067(0.41)
0.023**(1.99)-0.017(0.89)-0.005(0.25)-0.021(1.51)-0.064(1.65)-0.073(0.77)-0.002(0.41)-0.027(0.44)
0.075*(1.81)
0.346(1.25)=1 if save

Saving rate

(3)

(4)

0.094
(0.57)-0.006(0.60)-0.013(0.68)-0.031(1.33)-0.014(1.15)
0.025(0.86)
0.047(0.44)
0.000(0.01)
0.008(0.15)
0.071**(1.99)-0.211(0.97)

0.029(0.58)-0.002(0.82)-0.005(0.99)-0.015**(2.26)-0.007*(1.95)
0.002(0.27)
0.009(0.38)-0.000(0.25)
0.001(0.04)
0.014(1.23)
0.008(0.14)Note) Above presents coefficients on interaction terms between a dummy variable which equals to 1 if the respondentis in the Cut Flower sector and respective characteristics. Each coefficient is from separate regression. Independent
variables included in estimation but not reported here are: wereda-level population of CF workers, wereda-levelworking population, age, female dummy, years of schooling, years worked here, math score, # HH members, risk
averse, competitive, overconfidence, hours worked per month, frequency of payment, and whether hired. All includes
zone fixed effects. Robust t-statistics in parentheses. * p<0.1; ** p<0.05; *** p<0.01.

38

Table 11: Heterogeneous Effects of Cut Flower on Job Satisfaction (Inverse P-scoreweighting regression, Treatment effect on the treated, Trimmed sample)

CF dummy x:FemaleAgeSchooling
Years worked
Math score
Risk Averse
Competitive
Absolute
overconfidence
Relative
overconfidence# HH members
Freq. of payment

Incomelevel

Stability

Work
type

FutureProspect

Self
independ
ence

Total

0.051
(0.15)-0.001(0.05)-0.035(0.90)-0.031(0.64)-0.045*(1.69)
0.192***(2.74)-0.309(1.51)
0.013(1.22)-0.135(1.17)-0.045(0.53)
0.908**(2.00)

0.015(0.05)
0.001(0.06)-0.025(0.61)-0.036(0.82)-0.036(1.27)
0.163**(2.30)-0.301(1.63)
0.015(1.21)-0.139(1.26)-0.157*(1.97)
0.762*(1.81)-0.046(0.11)
0.017(0.77)-0.066(1.36)
0.008(0.13)-0.017(0.51)
0.030(0.34)
0.155(0.66)-0.000(0.02)
0.005(0.04)
0.132(1.35)
0.664(1.40)

0.430(1.13)-0.043*(1.89)-0.003(0.06)-0.105**(2.17)
0.024(0.70)
0.085(0.94)-0.327(1.42)-0.012(0.98)-0.065(0.49)-0.014(0.14)
0.244(0.43)-0.359(0.96)-0.020(0.87)
0.003(0.07)-0.023(0.38)-0.010(0.34)
0.143*(1.80)-0.405*(1.74)-0.010(0.98)-0.120(1.02)-0.026(0.30)-0.364(0.57)

0.090(0.07)-0.045(0.57)-0.127(0.78)-0.186(1.03)-0.083(0.73)
0.613**(2.15)-1.187(1.47)
0.005(0.12)-0.455(0.98)-0.110(0.31)
2.214(1.16)Note) Above presents coefficients on interaction terms between a dummy variable which equals to 1 if the respondentis in the Cut Flower sector and respective characteristics. Each coefficient is from separate regression. Independent
variables included in estimation but not reported here are: wereda-level population of CF workers, wereda-levelworking population, age, female dummy, years of schooling, years worked here, math score, # HH members, risk
averse, competitive, overconfidence, hours worked per month, frequency of payment, and whether hired. All includes
zone fixed effects. Robust t-statistics in parentheses. * p<0.1; ** p<0.05; *** p<0.01.

39

Average Employment Size of a Cut Flower Firm
600
500
400
300

Total

200

Prod

100

Ave of Ethiopian
firms
Ave of large firms in
Ethiopia

0
2004

2006

2008

2010

2012

2014

Figure 1. Number of workers employed in the cut flower sector

Project A

Project B

A or B?

You obtain for sure:

50% chance of obtaining:

50% chance of obtaining

RG1

1 Birr

8 Birr

0 BirrRG22 Birr

8 Birr

0 BirrRG3

3 Birr

8 Birr

0 Birr

RG44 Birr

8 Birr

0 BirrRG5

5 Birr

8 Birr

0 Birr

RG6

6 Birr

8 Birr

0 Birr

RG7

7 Birr

8 Birr

0 Birr

RG88 Birr

8 Birr

0 BirrFigure 2. Payoff of the risk preference game40

Distribution of ln(Monthly Income)CompGroup1
0.5

Density

1.5

ProdWorker

4

6

8

4

6

8

log of my total monthly income (tminc)
Densitykdensity lntminc
Graphs by Production Worker (flower) =1, Comparison Group=2

Figure 3. Distribution of log of yearly income by group

Self
indep.

ProdWorker

Income543210CompGroup

Stability

Futureprospect

Work
type

Figure 4. Means of subjective valuation of work by group

41

Figure 5. Balancing test: Propensity scores by group

Histogram of Propensity Scores by Groups

0

.2

.4
.6
Propensity Score
Untreated
Treated: Off support.8
Treated: On support421