05.02.2013 - Jonathan Robinson

Daily Needs, Income Targets and Labor Supply:
Evidence from Kenya
Pascaline Dupas Jon Robinson
Stanford UCSC
May 2, 2013
Dupas and Robinson Needs, Targets and Labor Supply May 2, 2013

Introduction
The majority of people in developing countries are self-employed
Typically means that they can set their own work hours.
While this has many advantages, it also has the fundamental
disadvantage of potentially generating vulnerability to self-control
issues.
In particular, many of these jobs are physically demanding and tedious,
so it might be tempting for workers to quit earlier in the day than they
would have planned

Introduction
Recent work shows that workers with self-control problems demand
external constraints to help them work as hard as they would like
Indian data processors (Kaur, Kremer and Mullainathan, 2010, 2013)
Berkeley undergraduates (Augenblick, Niederle and Sprenger, 2013)
But such external commitment devices are not typically present
outside formal work arrangements or a laboratory setting.
How do individuals free to set their own hours in low-skill, repetitive
occupations motivate themselves to work hard day after day?

This Paper
Makes use of a unique dataset collected from daily passenger-level
logbooks among 257 Kenyan bicycle taxi drivers
context: physically demanding to to be carrying passengers in the hot
sun - especially since many respondents self-report bad health
Like previous taxi cab papers, the log includes the pickup time, dropo
time, and fare for each passenger
But also includes other key variables
Most importantly, whether the respondent had a particular need that
day and, if so, the monetary amount required to meet the need.
Shocks

Preview of Results
1. Cash needs vary substantially across days
Very sensitive to shocks
Some of these are unexpected (illness, funerals)
Some are expected (ROSCA payments or school fees due)
Yet people put these o until the last day
Suggests that people are present-biased in eort
Importantly, the needs are uncorrelated with the (realized) wage rate
2. Labor supply is sensitive to the needs
At day level, people work more on days when they have greater needs
Using passenger-level data, people are more likely to quit just after
reaching the need

Preview of Results
3. Random cash payout
Provided random cash payments on randomly selected days
After work had started
No eect on labor supply → on its own, completely consistent with
neoclassical labor supply model
But combined with other results, suggests that people set targets over
labor income specically
Any explanation for overall results must reconcile this
Many alternatives would predict quitting (subsistence constraints, pure
hyperbolic discounting, limited attention, etc).
We conjecture that people are either (1) loss averse around a target
point; (2) keeping to a personal rule of meeting targets
Can't be easily undone

Preview of Results
4. Welfare costs
Implications of behavior
Negative wage elasticity → people make less total income for the same
hours
Will work some very long hour days → may harm health if very long
hour days depletes health capital
Some eect on income, and some speculative evidence of eect on
health

Related Literature
Measuring income targets
hard to measure - only other direct evidence comes from a lab
experiment by Abeler et al. (2011)
Taxi cab literature
NYC cabs (Camerer et al. 1997; Farber 2005, 2008; Crawford and
Meng 2011; Doran 2012), Singapore cabs (Chou 2002), Swiss bike
messengers (Fehr and Goette 2007), US fruit packers (Chang and
Gross 2012)
Main dierence is direct need approaches
Can incorporate need with some of these earlier approaches
Intensive and extensive margins (Oettinger 1999; Goldberg 2012; Giné
et al. 2009)

Related Literature
Self-control at work
Augenblick et al. (2013) in lab, Kaur et al. (2013) with Indian data
entry workers
as Kaur et al. (2013) note, self-control at work dierent than over
consumption or income
Results for one very specic population, but is useful to consider since
so many people in developing countries are self employed
High eort costs, low ability to save, etc. might be similar in other
settings

Outline
1. Conceptual Framework
2. Sample and Data
3. Results
4. Alternative Hypotheses
5. Welfare Consequences
6. Discussion Conclusion

Conceptual Framework
Conceptual framework is meant to just be for motivation
Two parts
Workers choose daily targets
Given those targets, workers make labor supply decisions
Why do workers have daily income targets?
That targets are daily is likely driven by narrow bracketing
Workers focus on daily targets rather than optimizing subject to a
lifetime budget constraint as in MaCurdy (1981)

Targets
Several possible interpretations of targets
Target may be a reference point
Camerer et al. (1997), Köszegi and Rabin (2006) Crawford and Meng
(2011), etc.
Workers loss averse around this point so marginal utility of additional
income decreases discontinuously
Alternatively, once set, people use their target as a personal rule or
internal commitment device
i.e. Ainslie 1992; Bénabou and Tirole 2004
If people know that they won't reach their goal, might as well not even
try
Imperfect recall about whether they made the goal on previous days
Want to keep to rule to avoid setting a precedent

Dealing with needs
Some needs are unexpected, others are foreseeable
School fees have xed due date in advance
So do ROSCA payments
If workers face no other constraints, should save up to deal with these
over some time period
If, however, they are present-biased in eort, they will procrastinate on
these for as long as possible

Implications
1. Workers will put o needs until they can no longer do so.
2. Workers will work less on days in which their income target is lower.
3. The hazard of quitting will increase after reaching the income target
4. As in Camerer et al. (1997) and subsequent papers, workers will
exhibit a negative wage elasticity
In addition, we would expect heterogeneity by certain baseline
characteristics
Eort costs
Ability to save

Study Design Overview
Project took place from September to December 2009 in Busia
District, Western Kenya
Sample of 257 bicycle-taxi drivers, locally called boda-bodas

Sampling Frame
August 2009: Census of all bicycle-taxi drivers in 14 market centers
scattered around the district
Since we needed respondents to keep logs, we excluded those who
couldn't write or who had less than 3 years of education (24% of
census)
Left with 303 respondents
Successfully enrolled 257 (84%) into the nal sample
Remainder had moved, quit bike taxiing, or didn't consent to the heavy
data collection requirements

Data
Participants enrolled on a rolling basis from September to November
2009
Stayed in the study for up to three months
Background Survey
demographics, SES, health, time and risk preferences, loss aversion
Daily diaries Daily Diary
respondents self-reported their labor supply, income, health status
crucially, rst question about special need for the day
Weekly survey Weekly Survey
day-by-day 1-week recall survey to the respondent
transfers to spouses, relatives or friends, savings, labor supply in other
jobs, more details on health shocks

Data
Median and mean number of log days lled per respondent: 47 days
(min: 7, max: 90); total of 10,835 person-days in main specications
Accompanying 1-week recall survey for 72% of the days
not 100% because some weeks enumerators were not able to nd the
respondent (e.g., if the respondent was away)
Full data available for an average of 34 (mostly consecutive) days per
study participants

Baseline Characteristics
(1) (2)
Mean Std. Dev.
Panel A. Demographic Information
Age 33.06 8.11
Married 0.96 0.19
Number of Children 3.40 2.27
Education 6.78 2.22
Value of Durable Goods Owned (in Ksh) 11097.76 8381.72
Value of Animals Owned (in Ksh) 6933.44 9858.87
Acres of land owned 1.42 1.44
Total Bike-Taxi Income in Week Prior to Survey (in Ksh) 573.52 340.41
Has another regular source of income 0.15 0.36
If yes, income in average week from other income 576.43 524.80
Has seasonal income 0.20 0.40
If yes, income in normal season 6631.84 10702.20
Number of observations 244

(1) (2)
Mean Std. Dev.
Panel B. Financial Access
Participates in ROSCA 0.75 0.43
If yes, number of ROSCAs 1.06 0.84
If yes, ROSCA contributions in last year (in Ksh) 5972.35 7880.52
Owns Bank Account 0.32 0.47
Received gift/loan in past 3 months 0.24 0.43
If yes, amount 0.29 0.45
Gave gift/loan in past 3 months 2204.22 2348.50
If yes, amount 1195.88 1877.05
If needed 1,000 Ksh right away, would:
Use savings 0.10 0.30
Sell asset(s) 0.34 0.48
Work more 0.12 0.32
Get gift/loan from friends/ relatives 0.47 0.50
Get loan from ROSCA 0.21 0.41

(1) (2)
Mean Std. Dev.
Panel C. Health
Health Problems Index (scale 1-5)
1
1.97 0.68
Average Score on Activities of Daily Living (scale 1-5)
2
1.51 0.39
Overall, how would you rate your health (scale 1-5)?
3
2.59 0.73
Missed work due to illness in past month 0.39 0.49
If yes, number of days missed 2.20 1.80
Early Bird: Starts work before 8am on median work day 0.51 0.50
Notes: Exchange rate was roughly 70 Ksh to US $1 during the study period.
1
The index refers to severity of pain and difficulty in performing activities of daily living.
The index ranges between 1 and 5, where 1=none, 2=mild, 3=moderate, 4=severe and
5=extreme. It is composed of 4 self-assessed measures shown in Table A1.
2
Average score across all activities shown in Table A1. Codes are the same as above.
3
Codes: 1-excellent, 2-good, 3-OK, 4-poor, 5-very poor.
4
The risky asset paid off 4 times the amount invested with probability 0.5, and 0 with
probability 0.5.
5
Time Consistent is a dummy equal to 1 if the respondent exhibits the same discount
rate between today and 7 days from today. Present-Biased is a dummy equal to 1 if the
respondent exhibits a higher discount rate between today and 2 days from today thanDupas and Robinson Needs, Targets and Labor Supply May 2, 2013

(1) (2)
Mean Std. Dev.
Panel D. Preferences for Risk, Time, and Loss Aversion
Amount invested (out of 100 Ksh) in Risky Asset
4
56.19 26.02
Time-consistent
5
0.32 0.47
Present-biased 0.13 0.34
More Patient in Future than in Present 0.27 0.44
Maximal Discount Rate in Present and in Future 0.29 0.45
More loss averse: Refuses the 50-50 gamble (win 30 or lose 10) 0.28 0.45
More loss averse: Refuses the 50-50 gamble (win 120 or lose 50) 0.58 0.50
Notes: Exchange rate was roughly 70 Ksh to US $1 during the study period.
4
The risky asset paid off 4 times the amount invested with probability 0.5, and 0 with probability
0.5.
5
Time Consistent is a dummy equal to 1 if the respondent exhibits the same discount rate
between today and 1 month from today. Present-Biased is a dummy equal to 1 if the respondent
exhibits a higher discount rate today, and More Patient in Future than in Present is a dummy
equal to 1 if the respondent is more patient in 1 month. Maximum Discount Rate in the Present
and in the Future is a dummy equal to 1 if a respondent always prefers 40 Ksh in the nearest
period to 200 Ksh 2 days later.

Baseline Health
Table A1. Baseline Health Level: Activities of Daily Living
(1) (2) (3) (4) (5) (6)
None Mild Moderate Severe Extreme No answer
In the past 30 days, did you have any bodily 0.30 0.40 0.19 0.04 0.02 0.05
aches or pains?
How much difficulty do you have in your daily 0.11 0.39 0.18 0.03 0.00 0.29
life due to pain?
In the past 30 days, how much discomfort 0.10 0.39 0.19 0.02 0.30 0.30
did you have?
Observations 244

Summary Statistics from Logs
(1) (2) (3) (4)
Mean Std. Dev.
# of
Observations
(Individual-days)
# of Individuals
A. Labor Supply
Worked today 0.74 0.44 12466 257
If yes, total income (Ksh) 144.70 93.72 9192 257
If yes, total hours 8.95 2.77 8583 256
Received income from some other source 0.24 0.43 8159 248
If yes, amount earned (Ksh) 79.59 485.39 1971 217
If yes, hours 3.32 2.26 1990 217
B. Is there something in particular that you need money for today?
Yes 0.88 0.32 12466 257
If yes, amount (Ksh) 203.74 340.44 10560 257
C. Cash payouts
Respondent Sick 0.18 0.38 12461 257
Somebody in household sick 0.10 0.29 12466 257
School fees due 0.02 0.13 9732 256
Funeral 0.05 0.21 9783 256
Had to make repairs to bike 0.21 0.41 9658 255
If yes, amount spent on repairs (Ksh) 77.63 92.65 2012 253
Made a ROSCA contribution 0.15 0.36 10674 257
Received a ROSCA payout 0.01 0.11 9759 256

Summary Statistics from Logs
(1) (2) (3) (4)
Mean Std. Dev.
# of
Observations
(Individual-days)
# of Individuals
D. Other Cash Flows
Somebody asked for money 0.02 0.14 9765 256
If yes, respondent gave money 1.00 0.07 201 109
Got money from somebody 0.02 0.14 9779 256
Got money from spouse 0.01 0.10 9726 256
Gave money to spouse 0.12 0.32 9721 256
Made withdrawal from home savings 0.04 0.20 8469 249
Made withdrawal from bank savings 0.01 0.09 3141 77
Received lump sum payment from regular cus 0.01 0.11 9751 256
E. Individual-level variables
Ever rented bike 0.18 0.38 13417 255
Ever got lump sum payment from regular cus 0.27 0.45 13443 256

Outline
1. Conceptual Framework
2. Sample and Data
3. Results
a. Main Results
b. Heterogeneity
c. Lottery
d. Robustness
4. Alternative Hypotheses
5. Welfare Consequences
6. Discussion Conclusion

Determinants of Needs
Nit = βwit + Su
itγu
+ Se
itγe
+ Ditθ + µi + it (1)
Nit is the daily need,
Su
it are unexpected shocks (such as sickness or funeral expenses)
Se
it are expected events which require cash (such as ROSCA payments
or school fees coming due).
We also include a measure of the realized wage rate (wit).
We follow Camerer et al. (1997) and construct a realized wage that is
exogenous to the individual by taking the average wage of all of the
other respondents in that market center.
Restrict to observations where we can construct this
Include day of the week (Dit) dummies in the specication, since these
are predictable determinants of the wage.
Worker xed eects, clustered at individual level

Results: Determinant of Daily Need
(1) (2) (3) (4)
Amount of cash
need in Ksh (0 if
none reported)
Reports cash
need for the
day
If reports need:
Cash amount
Worked
Wage
Realized log local wage rate1
-0.034 0.040 -0.045 0.137
(0.020)* (0.022)* (0.023)* (0.027)***
Cash Needs
School fees due 0.036 0.105 0.015 0.043
(0.025) (0.023)*** (0.027) (0.031)
ROSCA contribution due 0.011 0.090 -0.005 0.051
(0.012) (0.015)*** (0.014) (0.016)***
Funeral to attend 0.102 0.060 0.098 -0.108
(0.049)** (0.015)*** (0.051)* (0.027)***
Somebody in household is sick 0.059 0.038 0.058 -0.017
(0.014)*** (0.010)*** (0.015)*** (0.013)
Respondent sick 0.019 0.014 0.019 -0.359
(0.012) (0.011) (0.014) (0.025)***
Day of Week
Monday 0.018 0.058 0.011 0.038
(0.013) (0.010)*** (0.014) (0.014)***
Sunday -0.021 -0.101 -0.004 -0.417
(0.014) (0.017)*** (0.016) (0.024)***
Observations (individual-days) 10835 10835 9495 10835
Number of IDs 257 257 257 257
Mean of Dep. Var. 0.177 0.876 0.202 0.737
Std. Dev. of Dep. Var 0.331 0.329 0.347 0.440
Note: other day dummies omitted for space.

When do people deal with needs?
Some of the needs are unexpected (sickness, funerals)
But some should be fully anticipated
School fees due on a standard schedule
ROSCA meeting schedule is xed
People could deal with these needs over several days
If they procrastinate, though, they may leave these until the day they
need the money

When do people deal with needs?
(1) (2) (3) (4) (5) (6)
Made ROSCA deposit 0.55 0.55
(0.026)*** (0.027)***
Will make ROSCA deposit -0.04
tomorrow (0.018)**
Will make ROSCA deposit -0.05
in 2 days (0.014)***
Paid school fees 0.56 0.59
(0.044)*** (0.043)***
Will pay school fees tomorrow 0.03
(0.03)
Will pay school fees in 2 days 0.02
(0.02)
Repaid loan 0.26 0.25
(0.072)*** (0.068)***
Will repay loan tomorrow 0.07
(0.05)
Will repay loan in 2 days 0.04
(0.04)
Observations 8773 7473 8802 7504 7821 6692
Number of IDs 253 252 253 252 243 242
Mean of dependent variable 0.19 0.19 0.03 0.03 0.01 0.01
Reported need of:
ROSCA payment School Fees Repaid a Loan

Needs and Labor Supply
To be transparent, rst look cross-sectionally, then at day level, and
then nally within-day

Results: Cross-Sectional (Hours)
7.588.599.5
#ofhoursworked
0 100 200 300 400
Cash need for the day (Ksh)
Mean Quadratic fit
180

Results: Cross-Sectional (Income)
0 100 200 300 400
Mean Quadratic fit
100120140160180
DailyIncome
0 100 200 300 400
Notes: Each circle corresponds to an average across at least
50 man-days. Size of circle indicates number of man-days.

Across days
Across day specication
Lit = αNit + βwit + Sitγ + Ditθ + µi + it (2)
where Lit is a measure of labor supply
control for other cash needs Sit
Labor Supply by Baseline characteristics

Results: Labor Supply
(1) (2) (3) (4)
Has a need 0.19 0.023
(0.022)*** (0.004)***
If has a need: log (cash need) -0.009 0.012
(0.008) (0.002)***
Log (local wage rate) 0.116 0.113 0.058 0.060
(0.026)*** (0.029)*** (0.007)*** (0.007)***
Number of IDs 256 256 256 256
ID fixed effects Yes Yes Yes Yes
R-squared 0.20 0.16 0.12 0.10
Mean of Dep. Var. 0.741 0.776 0.107 0.112
Std. Dev. of Dep. Var 0.438 0.417 0.103 0.101
Worked Today Total Income (in 1,000 Ksh)

Results: Labor Supply
(5) (6) (7) (8) (9) (10) (11) (12)
Has a need -0.006 -0.002 -0.097 -0.005
(0.004) (0.092) (0.113) (0.007)
If has a need: log (cash need) 0.017 0.230 0.274 0.013
(0.002)*** (0.039)*** (0.054)*** (0.004)***
Log (local wage rate) 0.057 0.057 0.913 0.985 -0.598 -0.539 0.096 0.092
(0.007)*** (0.007)*** (0.146)*** (0.141)*** (0.248)** (0.213)** (0.010)*** (0.012)***
Observations (indiv-days) 9120 8099 9120 8099 8561 7633 8539 7613
Number of IDs 256 256 256 256 256 256 256 256
ID fixed effects Yes Yes Yes Yes Yes Yes Yes Yes
R-squared 0.03 0.05 0.04 0.05 0.03 0.03 0.02 0.03
Mean of Dep. Var. 0.145 0.144 4.417 4.420 8.953 8.936 0.285 0.283
Std. Dev. of Dep. Var 0.094 0.092 2.152 2.146 2.768 2.762 0.161 0.158
Percent of day spent
riding
If worked:
Total income (in 1,000
Ksh)
Number of passengers Total hours

Hazard regressions
qipt =
10
∑
b=−10
γb
Dibt + δHipt + ψH2
ipt + ηNit + µi + ηt + ipt (3)
qipt is a dummy for quitting after passenger p on date t
Hpt is hours worked up to that passenger
Nit is the goal amount
Db is a dummy for being in income bin b (of width 20 Ksh)
Minimum fare usually 10 Ksh, not very sensitive to bin width
Includes individual and day xed eects, and errors are clustered at the
individual level

Results: Hazard
0.04.08.12.16.2
Pr(quitting)
-200 -160 -120 -80 -40 0 40 80 120 160
Ksh from need
Coefficient
95% CI
All Cash Need Amounts
By Goal

Parametrization
qipt = α + γ1Oipt + β1Dipt + θ1Dipt ∗ Oipt + δHipt + ψH2
ipt
+ηNit + µi + ηt + ipt (4)
Dipt is the distance from the target
Oipt is a dummy for being over the target
Also control for hours Hipt and H2
ipt

Results: Regression
Dependent Variable = Quit After Passenger
(1) (2) (3) (4) (5) (6) (7) (8)
Coefficient on:
Cumulative
Hours Worked
Cumulative
hours worked2
Distance
from need
Over need
Distance
from need *
over need
Mean of dep.
var.
N # IDs
Panel A. Entire Sample
-0.08 0.36 0.11 0.04 0.02 0.09 34984 257
(0.04)** (0.04)*** (0.09) (0.01)*** (0.12)

Hazard
Need to check for composition eects since need amounts vary over
days
Looks similar for a given need amount
Also qualitatively similar when including respondent-day xed eects
By denition, controls for the need
Much less power however

Needs, Income Expectations, and Hours Expectations
As discussed in Köszegi and Rabin (2006) and Crawford and Meng
(2011), target is likely determined in part by income expectations
Appears also to have a need component
To examine both together, we integrate our approach with Crawford
and Meng (2011)
Proxy income expectations with average amount earned by respondent
on that day of the week
Same for hours

(1) (2) (3) (4)
Dependent variable = 1 if quit work for the day
Cumulative Hours Worked -0.03 -0.07 -0.10 -0.12
(Units = Hours / 10) (0.04) (0.04)* (0.04)*** (0.04)***
Cumulative Hours Worked Squared 0.32 0.35
(0.04)*** (0.04)***
Cumulative Income Earned 0.16 0.06 0.63 0.46
(Units = Ksh / 1,000) (0.10) (0.10) (0.11)*** (0.11)***
Cumulative Income Earned Squared -0.78 -0.65
(0.19)*** (0.19)***
Cumulative Hours Estimated Target 0.08 0.07 0.08 0.07
(0.01)*** (0.01)*** (0.01)*** (0.01)***
Cumulative Income Estimated Target 0.03 0.03 0.02 0.02
(0.01)*** (0.01)*** (0.01)*** (0.01)***
Over need 0.04 0.03
(0.01)*** (0.01)***
Observations 40670 36070 40670 36070
Number of bodas 257 257 257 257
R-squared 0.15 0.16 0.15 0.16
Mean of dependent variable 0.09 0.09 0.09 0.09

Expectations appear to matter as well
Why then do we observe such a sharp break at the need?
Needs and expectations are not correlated

Needs and Targets
(1) (2) (3) (4)
Need amount (conditional on need) 0.003 0.000
(0.003) (0.009)
Need amount (imputing as 0 days without a need) 0.003 0.003
(0.003) (0.009)
Observations 31266 34556 30650 33879
Number of bodas 257 257 256 256
R-squared 0.04 0.04 0.03 0.03
Mean of dependent variable 0.15 0.15 0.92 0.92
Proxy Income Target Proxy Hours Target

Using History of Hours / Income as Target Proxy
0.05.1.15.2.25
Pr(quitting)
-200 -160 -120 -80 -40 0 40 80 120 160
Ksh from Proxied Target
Coefficient
95% CI
Proxying target with history of realized income on that week day

Using History of Hours / Income as Target Proxy
0.2.4.6
Pr(quitting)
-5 -4 -3 -2 -1 0 1 2 3 4
Hours from Proxied Target
Coefficient
95% CI
Proxying target with history of realized hours on that week day

Results: Heterogeneity
Basic framework suggests heterogeneity based on several
characteristics
Loss aversion
Would you take gamble if you had a 50% chance of winning 30 Ksh
and a 50% chance at losing 10 Ksh?
Present-bias
More patient in future than in present in laboratory-type time
preference questions

Savings diculties
If you needed 1,000 Ksh, how would you come up with money?
Look at people who can use savings for at least part of this
Marginal eort costs
No direct measure
Look at people who start work later in the day. Idea is that they are
the ones least able/willing to get started in morning
No evidence that needs vary by these characteristics
Needs by Baseline Characteristics

Figure 3. Hazard Regressions by Subgroups-.10.1.2.3
Pr(quitting)
-200 -160 -120 -80 -40 0 40 80 120 160
Ksh from need
Can use savings to get 1k 95% CI
Can't use savings to get 1k 95% CI
Hazard by using savings to get 1k
0.1.2.3.4
Pr(quitting)
-200 -160 -120 -80 -40 0 40 80 120 160
Ksh from need
Starts work before 8 on average 95% CI
Starts work after 8 on average 95% CI
Hazard by starting work before 8am on average
-.10.1.2.3.4
Pr(quitting)
-200 -160 -120 -80 -40 0 40 80 120 160
Ksh from need
Time consistent 95% CI
Present biased 95% CI
Hazard by Time Consistency
0.1.2.3.4
Pr(quitting)
-200 -160 -120 -80 -40 0 40 80 120 160
Ksh from need
More loss averse 95% CI
Less loss averse 95% CI
Hazard by loss aversion level

(1) (2) (3) (4) (5) (6) (7) (8)
Coefficient
on:
Cumulative
Hours Worked
Cumulative
hours worked2
Distance
from need
Over need
Distance
from need *
over need
Mean of dep.
var.
N # IDs
Panel B. Heterogeneity Analysis based on Model Predictions
Can use savings if needed 1,000 Ksh
No -0.11 0.38 0.22 0.03 0.30 0.09 29839 213
(0.04)** (0.04)*** (0.11)* (0.01)*** (0.16)*
Yes -0.01 0.24 -0.07 0.03 -0.13 0.06 3365 24
(0.10) (0.11)** (0.08) (0.02)* (0.11)
[0.61] [0.26] [0.5] [0.92] [0.01***]
Early bird
No -0.09 0.40 0.34 0.04 0.19 0.09 15987 126
(0.05)* (0.05)*** (0.12)*** (0.01)*** (0.21)
Yes -0.16 0.41 0.04 0.03 -0.03 0.09 18997 131
(0.05)*** (0.05)*** (0.10) (0.01)*** (0.13)
[0.06*] [0.71] [0.44] [0.76] [0.08*]
Notes: For each dummy background characteristic, separate regressions are run for those who have the characteristic and for
those who don't. See text for more details on the specification. All regressions include individual fixed effects and controls for
the week the day of week. For each characteristic, the p-value for the test that coefficients are equal for those with and without
the characteristics are in square brackets.

(1) (2) (3) (4) (5) (6) (7) (8)
Coefficient
on:
Cumulative
Hours Worked
Cumulative
hours worked2
Distance
from need
Over need
Distance
from need *
over need
Mean of dep.
var.
N # IDs
Panel B. Heterogeneity Analysis based on Model Predictions
Present biased
No -0.10 0.36 0.09 0.04 0.04 0.09 29485 210
(0.04)** (0.04)*** (0.09) (0.01)*** (0.13)
Yes -0.09 0.45 0.25 0.04 -0.30 0.09 4285 31
(0.05) (0.06)*** (0.15) (0.02)* (0.28)
[0.65] [0.26] [0.48] [0.88] [0.29]
More loss averse
No -0.12 0.39 0.12 0.05 -0.03 0.09 23877 172
(0.05)** (0.04)*** (0.10) (0.01)*** (0.13)
Yes -0.02 0.32 0.12 0.03 0.08 0.10 9544 68
(0.08) (0.08)*** (0.18) (0.02)* (0.28)
[0.26] [0.46] [0.13] [0.57] [0.83]
Notes: For each dummy background characteristic, separate regressions are run for those who have the characteristic and for
those who don't. See text for more details on the specification. All regressions include individual fixed effects and controls for
the week the day of week. For each characteristic, the p-value for the test that coefficients are equal for those with and without
the characteristics are in square brackets.

Lottery
We created exogenous variation in non-labor income by holding
unannounced lotteries on random days
Respondents informed about lottery on same day
Lottery = pick a prize card from a bag, at market center
Odds:
50% chance 20 Ksh (control prize compensated for opportunity
cost of time)
25% chance 200 Ksh
12.5% chance 250 Ksh
12.5% chance 300 Ksh

Lottery
Sizeable compared to median daily income from bicycle-taxi driving of
around 150 Ksh
Caveat: power somewhat limited - have only 563 lottery payouts over
236 respondents (2.4 per respondent)

Lottery Results
(1) (2) (3) (4)
Worked
Today
Total
income
Total
hours
Time
ended work
Won big lottery prize today 0.011 0.005 0.093 -0.06
(0.024) (0.005) (0.153) (0.105)
Won big lottery prize yesterday 0.025 0.002 0.044 0.110
(0.024) (0.003) (0.148) (0.102)
Number of IDs 256 256 256 256
R-squared 0.24 0.21 0.04 0.01
Mean of Dep. Var. 0.74 0.146 8.92 17.433
Std. Dev. of Dep. Var 0.438 0.095 2.761 1.882
Notes: Regressions are at the individual-day level. Standard errors are in
parentheses, clustered at the individual level. Regressions include individual fixed
effects, all the variables shown in Table 3, and controls for the week the day of
week.. ***, **, * indicates significance at 1, 5 and 10%. All monetary values in
1,000 Ksh.
If worked:

Lottery
Lottery results suggest that, once set, targets are not undone by
unexpected cash payouts
If this heuristic really is how people solve a self-control issue, maybe
this isn't so surprising
Would be very easily undone
For example, a day with a ROSCA payment might be a day people quit
early

Robustness - Experimenter Eects
Experimenter eects: did asking about needs make the need more
salient?
Ideally have sample not asked about needs during same time period
We don't have that, but did collect similar logs in 2006-2008 in Dupas
and Robinson (2013)
If asking about needs made them salient, expect greater variance in
days worked in this sample
But, nd comparable (and if anything, larger) within-worker variance in
hours worked across days in that earlier sample: 2.74 compared to 2.16
in the sample considered in the present paper.

Robustness - Persistence
Another approach: how persistent are eects?
If having to record a daily cash need made otherwise perfectly
optimizing people income targeting, we'd expect this to be strongest at
the beginning of sample period
Eventually people should go back to normal since there is an income
loss to this kind of targeting
Hazard gives same pattern of results, with the same magnitude, at the
beginning and end of the data collection period.

Robustness - Timing of needs
Possible endogenous timing of needs: at some level, workers can
choose when to deal with some of these needs. Is this an issue?
First, even if chosen, the needs would still mean something, from the
hazard more about interpreting the mechanism
Second, some needs are not anticipated, yet behavior is the same for
those (sickness, funerals)
However, there is strong evidence of procrastination
Fewer needs on Sundays, more on Mondays
Putting o expenses until the last possible moment
(the timing though does suggest the reported needs match reality)
Needs are not everything earning expectations matter too

Alternative Hypotheses
They are other possible explanation
Labor supply under subsistence constraints (i.e. Barzel and MacDonald
1973; Basu and Van 1999; Jayachandran 2006; Bhalotra 2007; Halliday
2012)
Limited attention
Risk sharing
Others?
We consider several such variants

Labor supply with subsistence constraint
1. People may use bike taxiing as a way to cover immediate cash needs,
but may get higher returns to some other activity but the returns may
not be realized for some time (i.e. farming, other regular source of
income)
Unlikely since only 15% have other regular income and 20% have
seasonal income
Nevertheless, can also examine heterogeneity by these factors
-.10.1.2.3.4
Pr(quitting)
-200 -160 -120 -80 -40 0 40 80 120 160
Ksh from need
Has other regular income 95% CI
Does not have other regular income 95% CI
Hazard by having other regular income

Labor supply with subsistence constraint
2. People may have such severe savings problems that they drop o after
reaching need because marginal value of additional income is minimal
People with savings problems do drop o more quickly
But, having this type of extreme savings problem over even a day is
inconsistent with other work, for example Dupas and Robinson 2013
3. Same as above but eort costs might be so extreme that people quit
immediately after
Would likely have to interact with present-bias or savings diculties
since otherwise better in the long run to work more when wage is high
and less overall

Limited attention
Another alternative is that people have a limited attention constraint
Forget to deal with these things until the last possible moment
Possible, but people do report wanting to save for these sorts of needs
Also not consistent with lottery payments

Risk sharing
Workers work in same area, may know who has need
Perhaps they compete less hard if they know somebody else needs the
cash
Seems unlikely
If health capital is depleted, this is dominated by a transfer system
Income and needs both must be observable
Needs are really common: 88% of days. Not much scope for insurance.
Check this by including average needs of others in the same market
i.e. control for days in which everybody has a need
Get same results

Welfare Implications
What eect does any of this have on outcomes?
Two main things we can look at
Income. Basic implication is the worker will work more hours for the
same level of income than he would have if he substituted
intertemporally.
Health. If the health costs of eort are convex, such that working very
long days can deplete health capital, people induced to work more by
shocks may be in worse endline health.

Eect on Income
Perfectly optimizing worker should work more on days in which wage is
high
Workers can observe this much better than we can in the data
We construct a lower bound - what would impact on income be if
people worked same hours every day
Construct average hours and multiple by daily wage rate
Lost income could be much larger if workers had a positive elasticity

CDF of Income gain from constant daily hours
his graph shows the cumulative distribution function, across all 256 individuals in our sample, of the variable potential %
0.1.2.3.4.5.6.7.8.91
%atorbelow
-.1 -.05 0 .05 .1 .15 .2 .25 .3 .35 .4 .45 .5
Potential % Income Gain
Potential Percentage Income Gain to Constant Fixed Daily Hours

Eect on Income
Lower bound: median = 2.5%, mean = 5.0%,
Very large for some individuals
Interestingly, negative for only about 10% of the sample.
Is this big or small?
Doesn't look like a life-changing amount
But, it is consistent with other studies
Camerer et al (1997) also nd about 5-10% increase
Kaur et al. (2013): 6% eect on productivity for those who choose a
positive target
Another benchmark: de Mel et al. (2008) cash drops to Sri Lankan
rms
4.6-5.3% monthly return to capital, not reinvested
Baseline prots, inventories of 3,840 LKR and 26,530 LKR
Implies a 14-16% increase in capital stock to increase prots by 5%

Eect on Health
Does the variance in hours induced by targeting have health
consequences?
Ideally want random variation in targets across individuals - we don't
have that
Instead, just look cross-sectionally
No endline measure of health
Instead, look at whether worker was sick in the last week of the log
Examine how this measure varies with the total number of hours
worked, and the standard deviation of hours worked
Would like to instrument this with shocks, but rst stage is too weak

Eect on Health
(1) (2) (3) (4)
Total hours worked
Log (total hours over sample period) 0.03 0.03 0.01 0.01
(0.03) (0.03) (0.03) (0.02)
Log (std. deviation daily hours over sample period) 0.18 0.14 0.12 0.08
(0.083)** (0.084)* (0.069)* (0.07)
Baseline Health Measures
Health is worse than average at baseline 0.06 0.12
(0.06) (0.049)**
Missed work at least once due to sickness 0.09 0.02
in month prior to baseline (0.06) (0.05)
Observations (one per individual) 252 252 252 252
R-squared 0.03 0.06 0.02 0.05
Mean of Dep. Var. 0.25 0.25 0.15 0.15
Std. Dev. of Dep. Var 0.43 0.43 0.36 0.36
Missed work due to sickness
in last week of logs
Notes: Column 1 presents means of the independent variables (standard deviations in parentheses). Columns 2 and
3 report regression coefficients of the given dependent variable on the full set of independent variables (standard
errors are in parentheses, clustered at the individual level). Regressions include controls for the total number of days
covered in the logbooks. ***, **, * indicates significance at 1, 5 and 10%.
Sick in last week of logs
Total shocks

Overall impact
All of this is cross-sectional and ultimately only suggestive
But, some evidence that the eects on income and health are not
completely negligible

Wrapping up
People have time-varying cash needs, and tend to quit just after
reaching their need
Suggests a form of income targeting
Work up to the need
Behavior also determined by earnings expectations (as in previous work)
Yet random cash payouts do not undue this behavior
Once set for the day, targets appear to stick
Targets therefore are over labor income
People appear to be loss averse over these target amounts, or see them
as an internal commitment device
Heterogeneity: people with savings problems, and people with high
eort costs, are more likely to quit after reaching target
Some welfare eects on income and potentially health

Discussion
Why targets?
As discussed in Camerer et al. (1997), could be a way to solve
self-control problems
present-biased workers may be tempted to quit too early
Especially relevant in a physical demanding job like bike taxiing
setting a target before work may help prevent succumbing to that
temptation
But why not just choose a very high target then?
Must be real to aect behavior
Why income and not hours?
Prevents taking too many breaks
Potentially more veriable to others, for instance spouse?

Policy Implications
Possibly policy implications
Commitment devices to control eort (i.e. Kaur et al. 2013)
Modern work arrangements (i.e. Kaur et al. 2013)
Some role for things that force people to smooth needs: ROSCA
payments, micro loans paid back in regular installments, etc.
Quite high ROSCA participation in our sample
Similar to Bauer et al. (2012) who argue that women take out loans to
be forced to pay them back because they are present-biased in money
Augenblick et al. (2012) show that UCB undergrads are more
present-biased in eort than money.

Policy Implications
Possibly policy implications
Some evidence suggesting that micro entrepreneurs in developing
countries more similar (in terms of attitudes, cognitive ability,
motivation, etc.) to wage workers than large business owners (i.e. de
Mel, McKenzie and Woodru 2010). These results suggest another
channel by which wage employment may be benecial.
Though our evidence is from one small part of the world, these issues
may be relevant for many in the developing world, since many people
work in demanding jobs in which they can set their own hours

Conclusion
Thank you!

APPENDIX SLIDES

Daily Log (page 1)
2
88 88 88 88 88 88 88
A2.
What is the amount that you
need?
A3.
Did you work as a boda boda
today? (if yes, skip to A5)
Kama LA ruka hadi A5
A4.
If yes, at what time did you
start?
Ukishajibu RUKA HADI A6
A5.
Why didn't you work as a
boda today?
A6.
Time trip
started
Time trip
ended
Price
paid
(Ksh)
Time trip
started
Time trip
ended
Price
paid
(Ksh)
Time trip
started
Time trip
ended
Price
paid
(Ksh)
Time trip
started
Time trip
ended
Price
paid
(Ksh)
Time trip
started
Time trip
ended
Price
paid
(Ksh)
Time trip
started
Time
trip
ended
Price
paid
(Ksh)
Time trip
started
Time trip
ended
Price
paid
(Ksh)
1.
Passenger 1 : : /= : : /= : : /= : : /= : : /= : : /= : : /=
2.
Passenger 2 : : /= : : /= : : /= : : /= : : /= : : /= : : /=
3.
Passenger 3 : : /= : : /= : : /= : : /= : : /= : : /= : : /=
4.
Passenger 4 : : /= : : /= : : /= : : /= : : /= : : /= : : /=
5.
Passenger 5 : : /= : : /= : : /= : : /= : : /= : : /= : : /=
6.
Passenger 6 : : /= : : /= : : /= : : /= : : /= : : /= : : /=
7.
Passenger 7 : : /= : : /= : : /= : : /= : : /= : : /= : : /=
8.
Passenger 8 : : /= : : /= : : /= : : /= : : /= : : /= : : /=
9.
Passenger 9 : : /= : : /= : : /= : : /= : : /= : : /= : : /=
10.
Passenger 10 : : /= : : /= : : /= : : /= : : /= : : /= : : /=
11.
Passenger 11 : : /= : : /= : : /= : : /= : : /= : : /= : : /=
12.
Passenger 12 : : /= : : /= : : /= : : /= : : /= : : /= : : /=
13.
Passenger 13 : : /= : : /= : : /= : : /= : : /= : : /= : : /=
14.
Passenger 14 : : /= : : /= : : /= : : /= : : /= : : /= : : /=
1 2 3
4 5 6
7 _________________
|_____|_____|:|_____|_____|
/= /= /= /=
|_____|_____|:|_____|_____|
1 2 3
4 5 6
7 _________________
1 2 3
4 5 6
7 _________________
1 2 3
4 5 6
7 _________________
1 2 3
4 5 6
7 _________________
1 2 3
4 5 6
7 _________________
Friday Saturday Sunday
25-Nov 26-Nov 27-Nov
1 = Yes 2 = No 1 = Yes 2 = No1 = Yes 2 = No1 = Yes 2 = No 1 = Yes 2 = No
1 2 3
4 5 6
7 8 9
/=
10_____________10_____________ 10_____________ 10_____________ 10_____________ 10_____________ 10_____________
Please tell us about all events related to your boda boda work you have had today
|_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____|
A1. Is there something in particular
that you need money for today?
1 = repair bike, 2 = pay for medical
expenses, 3 = pay housing bill, 4 =
pay back loan, 5 = pay school
expenses, 6 = contribute to burial, 7
= contribute to ROSCA, 8 = buy
food, 9=make up for recent big
expense, 10 = Other (describe), 88
= nothing special
Passengers
1 2 3
4 5 6
7 _________________
HEPRO Diary Monday Tuesday
Please circle your answer, where applicable
1 2 3
4 5 6
7 8 9
Thursday
1 2 3
4 5 6
7 8 9
1 2 3
4 5 6
7 8 9
1 2 3
4 5 6
7 8 9
23-Nov 24-Nov
Wednesday
28-Nov
1 2 3
4 5 6
7 8 9
1 2 3
4 5 6
7 8 9
/= /=
1 = Yes 2 = No 1 = Yes 2 = No
Date 29-Nov
1 = was sick, 2 = something in HH
was sick, 3 = had to do some other
work, 4 = had to travel, 5 = went to
church, 6 = no customers today, 7 =
other (describe)
SKIP TO B1 after circling an
answer
|_____|_____|:|_____|_____|

Daily Log (page 2)
3
Friday Saturday Sunday
25-Nov 26-Nov 27-Nov
HEPRO Diary Monday Tuesday Thursday
23-Nov 24-Nov
Wednesday
28-NovDate 29-Nov
15.
Passenger 15 : : /= : : /= : : /= : : /= : : /= : : /= : : /=
16.
Passenger 16 : : /= : : /= : : /= : : /= : : /= : : /= : : /=
17.
Passenger 17 : : /= : : /= : : /= : : /= : : /= : : /= : : /=
18.
Passenger 18 : : /= : : /= : : /= : : /= : : /= : : /= : : /=
A7. When did you stop boda work?
A8.
In total, how much money did you
earn in fares today?
B1.
Did it rain today?
If NO, skip to C1.
B2a
If yes, at what time did it
start?
B2b When did the rain stop?
C1. Were you feeling sick today?
If NO, skip to D1.
C2a. If yes, did you seek care and
if so where? (circle all that
apply)
88 = Did not seek care (→C3a)
1 = hospital 2 = clinic
3 = doctor
4 = traditional healer
5 = pharmacy
6 = other (describe)
C2b At what time did you seek
care?
C3a. Did you take any medicine?
C3b If yes, at what time did you
take the medicine?
C4. Total Ksh spent on care /
medicines for yourself today?
D1.
Is anyone in the household
sick today? If NO, end.
D2.
If yes, which family member
is sick? Circle all that apply
1 = spouse, 2 = child, 3 = other
(describe)
D3.
Total spent on medical care /
medicines for family member
(Please include money you gave to
your spouse to buy drugs)
|_____|_____|:|_____|_____|
1 = Yes 2 = No 1 = Yes 2 = No 1 = Yes 2 = No 1 = Yes 2 = No
|_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____|
|_____|_____|:|_____|_____|
|_____|_____|:|_____|_____|
88 1
2 3
4 5
6 ____________
1 = Yes 2 = No 1 = Yes 2 = No 1 = Yes 2 = No 1 = Yes 2 = No 1 = Yes 2 = No
|_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____|
|_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____|
1 = Yes 2 = No 1 = Yes 2 = No
1 = Yes 2 = No 1 = Yes 2 = No
|_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____|
/=/= /=
1 = Yes 2 = No 1 = Yes 2 = No 1 = Yes 2 = No 1 = Yes 2 = No
1 = Yes 2 = No 1 = Yes 2 = No
|_____|_____|:|_____|_____| |_____|_____|:|_____|_____|
|_____|_____|:|_____|_____| |_____|_____|:|_____|_____|
Please tell us about tyour health today. Circle your answer, where applicable
/= /=/= /= /=/=
Please tell us about the weather today. Circle your answer, where applicable
|_____|_____|:|_____|_____| |_____|_____|:|_____|_____| |_____|_____|:|_____|_____|
1 = Yes 2 = No 1 = Yes 2 = No 1 = Yes 2 = No
|_____|_____|:|_____|_____|
1 = Yes 2 = No
88 1
2 3
4 5
6 ____________
88 1
2 3
4 5
6 _____________
88 1
2 3
4 5
6 ____________
88 1
2 3
4 5
6 _____________
88 1
2 3
4 5
6 ____________
88 1
2 3
4 5
6 _____________
/=
/= /=/=
/= /=
/= /= /= /=
1 2
3 _________________
1 2
3 _________________
1 2
3 _________________
Please tell us briefly about any health issues your household is experiencing today. Circle your answer, where applicable.
1 2
3
_________________
1 2
3
_________________
1 2
3
_________________
1 2
3 _________________
/= /=
1 = Yes 2 = No 1 = Yes 2 = No 1 = Yes 2 = No 1 = Yes 2 = No 1 = Yes 2 = No
Back

Weekly Log
FO COMMENTS: _______________________________________________________________________________________________________________________________________
12-Dec 13-Dec
Monday Tuesday Wednesday Thursday FridayHEPRO Diary
Tarehe [Date]
Saturday Sunday
7-Dec 8-Dec 9-Dec 10-Dec 11-Dec
B1a.
B2. 1 = Ndio 2 = La 1 = Ndio 2 = La 1 = Ndio 2 = La 1 = Ndio 2 = La 1 = Ndio 2 = La 1 = Ndio 2 = La 1 = Ndio 2 = La
B2a.
B3a.
B3b.
B4a.
B5a.
B6a.
B7a.
B8a.
B9a.
B10a.
B11a.
B12a.
B13a.
C1.
1 = Ndio 2 = La 1 = Ndio 2 = La 1 = Ndio 2 = La 1 = Ndio 2 = La 1 = Ndio 2 = La 1 = Ndio 2 = La 1 = Ndio 2 = La
If it was a loan, what did you offer as collateral?
FO: write-in answer. If no collateral, mark with a dash
If YES, how much did you pay? If goods / services, list
approximate cash value
Did you receive money, goods or services as a gift or loan
from a family member / friend?
FO: do not include transfers to spouse.
/=
/=
/= /=
/= /=
If YES, how much did you contribute?
If YES, how much did you pay?
/=/= /=
If YES, how much did you receive? If goods / services, list
approximate cash value
Did you make a withdrawal from the bank?
Did you contribute to a burial / matanga?
If YES, how much did you contribute?
/= /=
Did you receive a payment from a ROSCA?
If YES, how much did you receive?
Did you purchase a durable good?
If YES, how much did you spend?
Did you receive a payment from a regular customer?
If YES, how much did you receive?
Did you repay a loan?
If YES, what amount did you repay?
Did you withraw funds from your home savings?
If YES, how much did you withdraw?
Did you give money, goods or services to a friend or
family member without being asked (as a gift)?
/=
/= /=
/= /=
/= /=
/= /=
/=
/= /=
/= /=
/=
/= /=
/=
/=
Did you receive any money from your spouse?
Did a family member / friend ask you for money, goods or
services as a gift or loan?
FO: do not include transfers to spouse.
If YES, how much did you withdraw?
Did you take a loan from the bank?
If YES, what was the amount of the loan?
Did you make a contribution to a ROSCA?
/=
/=
/= /= /= /= /= /=
/=
/=
/= /=
/=
/= /= /=
/= /=
/=
Please tell us about any intra-household transfers. FO: Ask each set of questions (e.g., C1 C1a) for each day of the week, then proceed to the next set of questions (e.g., C2 C2a)
If YES, how much did you give?
If goods / services, list approximate cash value
/=
/=/= /=
/=
/=
/= /=
/=
/=
/= /=
/= /=
/=
/= /=
/=
/=
/=
/= /= /=
/=
/= /= /= /=
/= /= /= /=
/= /= /=
/=
/= /= /=
/= /= /=
Back

Labor supply by baseline characteristics
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Coef Obs
Mean (Std.
Dev.)
when Ind.
Var = 0
Coef Obs
Mean (Std.
Dev.) when
Ind. Var =
0
Coef Obs
Mean (Std.
Dev.) when
Ind. Var =
0
Symptoms are worse than average -0.06 12237 0.89 -0.66 11645 0.89 -7.19 12237 0.89
(0.02)*** [251] (0.31) (0.30)** [251] (0.31) (7.65) [251] (0.31)
Missed work in month prior to -0.05 12133 0.87 -0.49 11546 0.87 -12.10 12133 0.87
baseline (0.02)** [249] (0.33) (0.29)* [249] (0.34) (7.35) [249] (0.33)
Loss averse -0.07 11870 0.89 -0.85 11286 0.89 -15.41 11870 0.89
(0.03)*** [245] (0.31) (0.32)*** [245] (0.31) (7.26)** [245] (0.31)
Present-biased 0.00 11866 0.89 -0.31 11285 0.89 14.61 11866 0.89
(0.03) [243] (0.31) (0.39) [243] (0.31) (9.54) [243] (0.31)
Can get 1,000 Ksh from savings -0.02 11832 0.88 -0.20 11251 0.88 49.81 11832 0.88
(0.05) [243] (0.32) (0.54) [243] (0.32) (20.99)** [243] (0.32)
In ROSCA -0.05 12172 0.88 -0.63 11582 0.88 10.83 12172 0.88
(0.02)** [249] (0.33) (0.34)* [249] (0.33) (6.97) [249] (0.33)
Has bank account -0.03 12166 0.89 -0.42 11576 0.89 7.06 12166 0.89
(0.02) [249] (0.31) (0.32) [249] (0.31) (9.30) [249] (0.31)
Rents bike 0.14 8844 0.88 2.56 8478 0.88 97.86 8844 0.88
(0.02)*** [262] (0.32) (0.43)*** [262] (0.32) (27.51)*** [262] (0.32)
Has seasonal income -0.02 12017 0.88 -0.47 11439 0.88 5.79 12017 0.88
(0.03) [247] (0.32) (0.34) [247] (0.32) (8.64) [247] (0.32)
Has other regular income -0.08 12211 0.89 -1.35 11624 0.89 -16.40 12211 0.89
(0.03)** [250] (0.32) (0.34)*** [250] (0.32) (7.41)** [250] (0.32)
Worked Today If yes, total hours If yes, total income
back

Hazard by Goal Amount
-.2-.10.1.2.3
Pr(quitting)
-200-160-120 -80 -40 0 40 80 120 160
Ksh from need
Coefficient
95% CI
Cash need is at most 50 Ksh
-.2-.10.1.2.3
Pr(quitting)
-200-160-120 -80 -40 0 40 80 120 160
Ksh from need
Coefficient
95% CI
Cash need is at most 100 Ksh-.2-.10.1.2.3
Pr(quitting)
-200-160-120 -80 -40 0 40 80 120 160
Ksh from need
Coefficient
95% CI
Cash need is between 100 and 200 Ksh
-.2-.10.1.2.3
Pr(quitting)
-200-160-120 -80 -40 0 40 80 120 160
Ksh from need
Coefficient
95% CI
Cash need is greater than 200 Ksh
Back

Needs and baseline characteristics
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Coef Obs
Mean (Std.
Dev.)
when Ind.
Var = 0
Coef Obs
Mean (Std.
Dev.) when
Ind. Var =
0
Coef Obs
Mean (Std.
Dev.) when
Ind. Var =
0
Symptoms are worse than average -0.06 12237 0.89 -0.66 11645 0.89 -7.19 12237 0.89
(0.02)*** [251] (0.31) (0.30)** [251] (0.31) (7.65) [251] (0.31)
Missed work in month prior to -0.05 12133 0.87 -0.49 11546 0.87 -12.10 12133 0.87
baseline (0.02)** [249] (0.33) (0.29)* [249] (0.34) (7.35) [249] (0.33)
Loss averse -0.07 11870 0.89 -0.85 11286 0.89 -15.41 11870 0.89
(0.03)*** [245] (0.31) (0.32)*** [245] (0.31) (7.26)** [245] (0.31)
Present-biased 0.00 11866 0.89 -0.31 11285 0.89 14.61 11866 0.89
(0.03) [243] (0.31) (0.39) [243] (0.31) (9.54) [243] (0.31)
Can get 1,000 Ksh from savings -0.02 11832 0.88 -0.20 11251 0.88 49.81 11832 0.88
(0.05) [243] (0.32) (0.54) [243] (0.32) (20.99)** [243] (0.32)
In ROSCA -0.05 12172 0.88 -0.63 11582 0.88 10.83 12172 0.88
(0.02)** [249] (0.33) (0.34)* [249] (0.33) (6.97) [249] (0.33)
Has bank account -0.03 12166 0.89 -0.42 11576 0.89 7.06 12166 0.89
(0.02) [249] (0.31) (0.32) [249] (0.31) (9.30) [249] (0.31)
Rents bike 0.14 8844 0.88 2.56 8478 0.88 97.86 8844 0.88
(0.02)*** [262] (0.32) (0.43)*** [262] (0.32) (27.51)*** [262] (0.32)
Has seasonal income -0.02 12017 0.88 -0.47 11439 0.88 5.79 12017 0.88
(0.03) [247] (0.32) (0.34) [247] (0.32) (8.64) [247] (0.32)
Has other regular income -0.08 12211 0.89 -1.35 11624 0.89 -16.40 12211 0.89
(0.03)** [250] (0.32) (0.34)*** [250] (0.32) (7.41)** [250] (0.32)
Worked Today If yes, total hours If yes, total income
back

Hours and Shocks over sample period
Table A7. Relationship between hours and shocks over entire sample period
(1) (2)
Log (Sum of Total Hours)
Log (Standard Deviation of Total
Hour Across Days)
Total number of times school fees due 0.10 0.01
(0.055)* (0.02)
Total number of times ROSCA contributions due -0.01 0.00
(0.01) (0.00)
Total number of times asked for money by somebody 0.03 0.05
(0.06) (0.021)**
Total number of times had to attend funeral -0.01 0.02
(0.04) (0.01)
Total number of times household member sick 0.01 0.00
(0.01) (0.00)
Total number of time respondent sick 0.00 0.01
(0.02) (0.01)
Observations 253 252
Mean of dependent variable 10.01 3.99
Standard deviation of dependent variable 0.94 1.05
Notes: Standard errors in parentheses. *, **, and *** indicate significance at 10%, 5%, and 1% respectively.
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05.02.2013 - Jonathan Robinson

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