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

I The majority of people in developing countries are self-employed

I Typically means that they can set their own work hours.

I While this has many advantages, it also has the fundamental

disadvantage of potentially generating vulnerability to self-control

issues.

I 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 theywould have planned


Introduction

I Recent work shows that workers with self-control problems demand

external constraints to help them work as hard as they would like

I Indian data processors (Kaur, Kremer and Mullainathan, 2010, 2013)I Berkeley undergraduates (Augenblick, Niederle and Sprenger, 2013)

I But such external commitment devices are not typically present

outside formal work arrangements or a laboratory setting.

I How do individuals free to set their own hours in low-skill, repetitive

occupations motivate themselves to work hard day after day?


This Paper

I Makes use of a unique dataset collected from daily passenger-level

logbooks among 257 Kenyan bicycle taxi drivers

I context: physically demanding to to be carrying passengers in the hotsun - especially since many respondents self-report bad health

I Like previous taxi cab papers, the log includes the pickup time, dropo�

time, and fare for each passenger

I But also includes other key variables

I Most importantly, whether the respondent had a particular �need� thatday and, if so, the monetary amount required to meet the need.

I Shocks


Preview of Results

1. Cash needs vary substantially across days

I Very sensitive to �shocks�I Some of these are unexpected (illness, funerals)I Some are expected (ROSCA payments or school fees due)

I Yet people put these o� until the last dayI Suggests that people are present-biased in e�ort

I Importantly, the needs are uncorrelated with the (realized) wage rate

2. Labor supply is sensitive to the needs

I At day level, people work more on days when they have greater needsI Using passenger-level data, people are more likely to quit just after

reaching the need


Preview of Results

3. Random cash payout

I Provided random cash payments on randomly selected days

I After work had started

I No e�ect on labor supply → on its own, completely consistent withneoclassical labor supply model

I But combined with other results, suggests that people set targets overlabor income speci�cally

I Any explanation for overall results must reconcile this

I Many alternatives would predict quitting (subsistence constraints, purehyperbolic discounting, limited attention, etc).

I We conjecture that people are either (1) loss averse around a targetpoint; (2) keeping to a personal rule of meeting targets

I Can't be easily undone


Preview of Results

4. Welfare costs

I Implications of behavior

I Negative wage elasticity → people make less total income for the samehours

I Will work some very long hour days → may harm health if very longhour days depletes health capital

I Some e�ect on income, and some speculative evidence of e�ect onhealth


Related Literature

I Measuring income targets

I hard to measure - only other direct evidence comes from a labexperiment by Abeler et al. (2011)

I Taxi cab literature

I NYC cabs (Camerer et al. 1997; Farber 2005, 2008; Crawford andMeng 2011; Doran 2012), Singapore cabs (Chou 2002), Swiss bikemessengers (Fehr and Goette 2007), US fruit packers (Chang andGross 2012)

I Main di�erence is direct need approachesI Can incorporate need with some of these earlier approaches

I Intensive and extensive margins (Oettinger 1999; Goldberg 2012; Giné

et al. 2009)


Related Literature

I Self-control at work

I Augenblick et al. (2013) in lab, Kaur et al. (2013) with Indian dataentry workers

I as Kaur et al. (2013) note, self-control at work di�erent than overconsumption or income

I Results for one very speci�c population, but is useful to consider since

so many people in developing countries are self employed

I High e�ort costs, low ability to save, etc. might be similar in othersettings


Outline

1. Conceptual Framework

2. Sample and Data

3. Results

4. Alternative Hypotheses

5. Welfare Consequences

6. Discussion & Conclusion


Conceptual Framework

I Conceptual framework is meant to just be for motivation

I Two parts

I Workers choose daily targetsI Given those targets, workers make labor supply decisions

I Why do workers have daily income targets?

I That targets are daily is likely driven by �narrow bracketing�I Workers focus on daily targets rather than optimizing subject to a

lifetime budget constraint as in MaCurdy (1981)


Targets

I Several possible interpretations of targets

I Target may be a reference point

I Camerer et al. (1997), Köszegi and Rabin (2006) Crawford and Meng(2011), etc.

I Workers loss averse around this point � so marginal utility of additionalincome decreases discontinuously

I Alternatively, once set, people use their target as a personal rule or

internal commitment device

I i.e. Ainslie 1992; Bénabou and Tirole 2004I If people know that they won't reach their goal, might as well not even

tryI Imperfect recall about whether they made the goal on previous daysI Want to keep to rule to avoid setting a precedent


Dealing with needs

I Some needs are unexpected, others are foreseeable

I School fees have �xed due date in advanceI So do ROSCA payments

I If workers face no other constraints, should save up to deal with these

over some time period

I If, however, they are present-biased in e�ort, 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

I In addition, we would expect heterogeneity by certain baseline

characteristics

I E�ort costsI Ability to save


Outline


2. Sample and Data

3. Results





Study Design Overview

I Project took place from September to December 2009 in Busia

District, Western Kenya

I Sample of 257 bicycle-taxi drivers, locally called �boda-bodas�


Sampling Frame

I August 2009: Census of all bicycle-taxi drivers in 14 market centers

scattered around the district

I 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)

I Left with 303 respondents

I Successfully enrolled 257 (84%) into the �nal sampleI Remainder had moved, quit bike taxiing, or didn't consent to the heavy

data collection requirements


Data

I Participants enrolled on a rolling basis from September to November

2009

I Stayed in the study for up to three months

I Background Survey

I demographics, SES, health, time and risk preferences, loss aversion

I Daily diaries Daily Diary

I respondents self-reported their labor supply, income, health statusI crucially, �rst question about special need for the day

I Weekly survey Weekly Survey

I day-by-day 1-week recall survey to the respondentI transfers to spouses, relatives or friends, savings, labor supply in other

jobs, more details on health shocks


Data

I Median and mean number of log days �lled per respondent: 47 days

(min: 7, max: 90); total of 10,835 person-days in main speci�cations

I Accompanying 1-week recall survey for 72% of the days

I not 100% because some weeks enumerators were not able to �nd therespondent (e.g., if the respondent was away)

I Full data available for an average of 34 (mostly consecutive) days per

study participants


Baseline Characteristics

(1) (2)Mean Std. Dev.

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

Number of observations 244




Panel B. Financial AccessParticipates in ROSCA 0.75 0.43If yes, number of ROSCAs 1.06 0.84If yes, ROSCA contributions in last year (in Ksh) 5972.35 7880.52Owns Bank Account 0.32 0.47Received gift/loan in past 3 months 0.24 0.43If yes, amount 0.29 0.45Gave gift/loan in past 3 months 2204.22 2348.50If yes, amount 1195.88 1877.05If 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

Number of observations 244




Panel C. HealthHealth Problems Index (scale 1-5)1 1.97 0.68Average Score on Activities of Daily Living (scale 1-5)2 1.51 0.39Overall, how would you rate your health (scale 1-5)?3 2.59 0.73Missed work due to illness in past month 0.39 0.49If yes, number of days missed 2.20 1.80Early Bird: Starts work before 8am on median work day 0.51 0.50

Number of observations 244Notes: Exchange rate was roughly 70 Ksh to US $1 during the study period. 1The 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 A1.2Average score across all activities shown in Table A1. Codes are the same as above.3Codes: 1-excellent, 2-good, 3-OK, 4-poor, 5-very poor.4The 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 than between 7days from today and 9 days from today, "More Patient in Future than in Present" is a dummy equal to 1 if the respondent is more patient in 7 days than she is




Panel D. Preferences for Risk, Time, and Loss AversionAmount invested (out of 100 Ksh) in Risky Asset4 56.19 26.02Time-consistent5 0.32 0.47Present-biased 0.13 0.34More Patient in Future than in Present 0.27 0.44Maximal Discount Rate in Present and in Future 0.29 0.45More loss averse: Refuses the 50-50 gamble (win 30 or lose 10) 0.28 0.45More loss averse: Refuses the 50-50 gamble (win 120 or lose 50) 0.58 0.50

Number of observations 244Notes: Exchange rate was roughly 70 Ksh to US $1 during the study period. 4The 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 SupplyWorked today 0.74 0.44 12466 257If yes, total income (Ksh) 144.70 93.72 9192 257If yes, total hours 8.95 2.77 8583 256Received income from some other source 0.24 0.43 8159 248If yes, amount earned (Ksh) 79.59 485.39 1971 217If 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 257If yes, amount (Ksh) 203.74 340.44 10560 257

C. Cash payouts Respondent Sick 0.18 0.38 12461 257Somebody in household sick 0.10 0.29 12466 257School fees due 0.02 0.13 9732 256Funeral 0.05 0.21 9783 256Had to make repairs to bike 0.21 0.41 9658 255If yes, amount spent on repairs (Ksh) 77.63 92.65 2012 253Made a ROSCA contribution 0.15 0.36 10674 257Received 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 FlowsSomebody asked for money 0.02 0.14 9765 256If yes, respondent gave money 1.00 0.07 201 109Got money from somebody 0.02 0.14 9779 256Got money from spouse 0.01 0.10 9726 256Gave money to spouse 0.12 0.32 9721 256Made withdrawal from home savings 0.04 0.20 8469 249Made withdrawal from bank savings 0.01 0.09 3141 77Received lump sum payment from regular cus 0.01 0.11 9751 256

E. Individual-level variablesEver rented bike 0.18 0.38 13417 255Ever got lump sum payment from regular cus 0.27 0.45 13443 256


Outline


2. Sample and Data

3. Results

a. Main Resultsb. Heterogeneityc. Lotteryd. Robustness





Determinants of Needs

Nit = βwit + Suitγ

u + Seitγ

e +Ditθ + µi + εit (1)

I Nit is the daily need,

I Suit are unexpected shocks (such as sickness or funeral expenses)

I Seit are expected events which require cash (such as ROSCA payments

or school fees coming due).

I We also include a measure of the realized wage rate (wit).

I We follow Camerer et al. (1997) and construct a realized wage that isexogenous to the individual by taking the average wage of all of theother respondents in that market center.

I Restrict to observations where we can construct this

I Include day of the week (Dit) dummies in the speci�cation, since these

are predictable determinants of the wage.

I Worker �xed e�ects, 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

WageRealized log local wage rate1 -0.034 0.040 -0.045 0.137

(0.020)* (0.022)* (0.023)* (0.027)***Cash NeedsSchool 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 WeekMonday 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 10835Number of IDs 257 257 257 257Mean of Dep. Var. 0.177 0.876 0.202 0.737Std. Dev. of Dep. Var 0.331 0.329 0.347 0.440Note: other day dummies omitted for space.

Notes: Standard errors are in parentheses, clustered at the individual level. Regressions include individual fixed effects, and controls for the week and the day of the week. ***, **, * indicates significance at 1, 5 and 10%. All monetary values in 1,000 Ksh.1The wage rate is the realized average wage across all the other respondents in the same market on that


When do people deal with needs?

I Some of the needs are unexpected (sickness, funerals)

I But some should be fully anticipated

I School fees due on a standard scheduleI ROSCA meeting schedule is �xed

I People could deal with these needs over several days

I 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 6692Number of IDs 253 252 253 252 243 242Mean 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

I To be transparent, �rst look cross-sectionally, then at day level, and

then �nally within-day


Results: Cross-Sectional (Hours)

7.5

88.

59

9.5

# of h

ours

wor

ked

0 100 200 300 400

Cash need for the day (Ksh)

Mean Quadratic fit

100

120

140

160

180

Dai

ly In

com

e

0 100 200 300 400


Notes: Each circle corresponds to an average across at least50 man-days. Size of circle indicates number of man-days.


Results: Cross-Sectional (Income)

7.5

88.

59

9.5

# of h

ours

wor

ked

0 100 200 300 400


Mean Quadratic fit

100

120

140

160

180

Dai

ly In

com

e

0 100 200 300 400


Notes: Each circle corresponds to an average across at least50 man-days. Size of circle indicates number of man-days.


Across days

I Across day speci�cation

Lit = αNit + βwit + Sitγ +Ditθ + µi + εit (2)

I where Lit is a measure of labor supply

I 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)***

Observations (individual-days) 12301 10434 12301 10434Number of IDs 256 256 256 256ID fixed effects Yes Yes Yes YesR-squared 0.20 0.16 0.12 0.10Mean of Dep. Var. 0.741 0.776 0.107 0.112Std. 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 7613Number of IDs 256 256 256 256 256 256 256 256ID fixed effects Yes Yes Yes Yes Yes Yes Yes YesR-squared 0.03 0.05 0.04 0.05 0.03 0.03 0.02 0.03Mean of Dep. Var. 0.145 0.144 4.417 4.420 8.953 8.936 0.285 0.283Std. 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

γbDibt + δHipt + ψH2ipt + ηNit + µi + ηt + εipt (3)

I qipt is a dummy for quitting after passenger p on date t

I Hpt is hours worked up to that passenger

I Nit is the goal amount

I Db is a dummy for being in income bin b (of width 20 Ksh)

I Minimum fare usually 10 Ksh, not very sensitive to bin width

I Includes individual and day �xed e�ects, and errors are clustered at the

individual level


Results: Hazard

0.0

4.0

8.1

2.1

6.2

Pr(

quitt

ing)

-200 -160 -120 -80 -40 0 40 80 120 160Ksh from need

Coefficient95% CI

All Cash Need Amounts

By Goal


Parametrization

qipt = α + γ1Oipt + β1Dipt + θ1Dipt ∗Oipt + δHipt + ψH2ipt

+ηNit + µi + ηt + εipt (4)

I Dipt is the distance from the target

I Oipt is a dummy for being over the target

I Also control for hours Hipt and H2ipt


Results: Regression

Dependent Variable = Quit After Passenger

(1) (2) (3) (4) (5) (6) (7) (8)

Coefficient on:Cumulative

Hours WorkedCumulative

hours worked2 Distance

from needOver 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

I Need to check for composition e�ects since need amounts vary over

days

I Looks similar for a given need amount

I Also qualitatively similar when including respondent-day �xed e�ects

I By de�nition, controls for the needI Much less power however


Needs, Income Expectations, and Hours Expectations

I As discussed in Köszegi and Rabin (2006) and Crawford and Meng

(2011), target is likely determined in part by income expectations

I Appears also to have a need component

I To examine both together, we integrate our approach with Crawford

and Meng (2011)

I Proxy income expectations with average amount earned by respondenton that day of the week

I 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 36070Number of bodas 257 257 257 257R-squared 0.15 0.16 0.15 0.16Mean of dependent variable 0.09 0.09 0.09 0.09



I Expectations appear to matter as well

I Why then do we observe such a sharp break at the need?

I 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 33879Number of bodas 257 257 256 256R-squared 0.04 0.04 0.03 0.03Mean 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

0.0

5.1

.15

.2.2

5P

r(qu

ittin

g)

-200 -160 -120 -80 -40 0 40 80 120 160Ksh from Proxied Target

Coefficient95% 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(

quitt

ing)

-5 -4 -3 -2 -1 0 1 2 3 4Hours from Proxied Target

Coefficient95% CI

Proxying target with history of realized hours on that week day


Outline


2. Sample and Data

3. Results






Results: Heterogeneity

I Basic framework suggests heterogeneity based on several

characteristics

I Loss aversion

I Would you take gamble if you had a 50% chance of winning 30 Kshand a 50% chance at losing 10 Ksh?

I Present-bias

I More patient in future than in present in laboratory-type timepreference questions



I Savings di�culties

I If you needed 1,000 Ksh, how would you come up with money?I Look at people who can use savings for at least part of this

I Marginal e�ort costs

I No direct measureI 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

I No evidence that needs vary by these characteristicsNeeds by Baseline Characteristics


Results: HeterogeneityFigure 3. Hazard Regressions by Subgroups

-.1

0.1

.2.3

Pr(

quitt

ing)

-200 -160 -120 -80 -40 0 40 80 120 160Ksh from need

Can use savings to get 1k 95% CICan't use savings to get 1k 95% CI

Hazard by using savings to get 1k

0.1

.2.3

.4P

r(qu

ittin

g)

-200 -160 -120 -80 -40 0 40 80 120 160Ksh from need

Starts work before 8 on average 95% CIStarts work after 8 on average 95% CI

Hazard by starting work before 8am on average

-.1

0.1

.2.3

.4P

r(qu

ittin

g)

-200 -160 -120 -80 -40 0 40 80 120 160Ksh from need

Time consistent 95% CIPresent biased 95% CI

Hazard by Time Consistency

0.1

.2.3

.4P

r(qu

ittin

g)

-200 -160 -120 -80 -40 0 40 80 120 160Ksh from need

More loss averse 95% CILess loss averse 95% CI

Hazard by loss aversion level




(1) (2) (3) (4) (5) (6) (7) (8)

Coefficient on:

Cumulative Hours Worked

Cumulative


from needOver 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


from needOver need


Mean of dep. var.

N # IDs

Panel B. Heterogeneity Analysis based on Model PredictionsPresent 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.


Outline


2. Sample and Data

3. Results






Lottery

I We created exogenous variation in non-labor income by holding

unannounced lotteries on random days

I Respondents informed about lottery on same day

I Lottery = pick a prize card from a bag, at market center

I Odds:

I 50% chance � 20 Ksh (�control� � prize compensated for opportunitycost of time)

I 25% chance � 200 KshI 12.5% chance � 250 KshI 12.5% chance � 300 Ksh


Lottery

I Sizeable compared to median daily income from bicycle-taxi driving of

around 150 Ksh

I 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)

Observations (individual-days) 10704 7926 7487 7540

Number of IDs 256 256 256 256R-squared 0.24 0.21 0.04 0.01Mean 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

I Lottery results suggest that, once set, targets are not undone by

unexpected cash payouts

I If this heuristic really is how people solve a self-control issue, maybe

this isn't so surprising

I Would be very easily undoneI For example, a day with a ROSCA payment might be a day people quit

early


Outline


2. Sample and Data

3. Results






Robustness - Experimenter E�ects

I Experimenter e�ects: did asking about needs make the need more

salient?

I Ideally have sample not asked about needs during same time periodI We don't have that, but did collect similar logs in 2006-2008 in Dupas

and Robinson (2013)I If asking about needs made them salient, expect greater variance in

days worked in this sampleI But, �nd comparable (and if anything, larger) within-worker variance in

hours worked across days in that earlier sample: 2.74 compared to 2.16in the sample considered in the present paper.


Robustness - Persistence

I Another approach: how persistent are e�ects?

I If having to record a daily cash need made otherwise perfectlyoptimizing people income targeting, we'd expect this to be strongest atthe beginning of sample period

I Eventually people should go back to normal since there is an incomeloss to this kind of targeting

I Hazard gives same pattern of results, with the same magnitude, at thebeginning and end of the data collection period.


Robustness - Timing of needs

I Possible endogenous timing of needs: at some level, workers can

choose when to deal with some of these needs. Is this an issue?

I First, even if chosen, the needs would still mean something, from thehazard � more about interpreting the mechanism

I Second, some needs are not anticipated, yet behavior is the same forthose (sickness, funerals)

I However, there is strong evidence of procrastination

I Fewer needs on Sundays, more on MondaysI Putting o� expenses until the last possible momentI (the timing though does suggest the reported needs match reality)

I Needs are not everything � earning expectations matter too


Outline


2. Sample and Data

3. Results





Alternative Hypotheses

I They are other possible explanation

I Labor supply under subsistence constraints (i.e. Barzel and MacDonald1973; Basu and Van 1999; Jayachandran 2006; Bhalotra 2007; Halliday2012)

I Limited attentionI Risk sharingI Others?

I 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)

I Unlikely since only 15% have other regular income and 20% haveseasonal income

I Nevertheless, can also examine heterogeneity by these factors-.

10

.1.2

.3.4

Pr(

quitt

ing)

-200 -160 -120 -80 -40 0 40 80 120 160Ksh from need

Has other regular income 95% CIDoes 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

I People with savings problems do drop o� more quicklyI 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 e�ort costs might be so extreme that people quit

immediately after

I Would likely have to interact with present-bias or savings di�cultiessince otherwise better in the long run to work more when wage is highand less overall


Limited attention

I Another alternative is that people have a limited attention constraint

I Forget to deal with these things until the last possible moment

I Possible, but people do report wanting to save for these sorts of needs

I Also not consistent with lottery payments


Risk sharing

I Workers work in same area, may know who has need

I Perhaps they compete less hard if they know somebody else needs thecash

I Seems unlikely

I If health capital is depleted, this is dominated by a transfer systemI Income and needs both must be observableI Needs are really common: 88% of days. Not much scope for insurance.

I Check this by including average needs of others in the same market

I i.e. control for days in which everybody has a needI Get same results


Outline


2. Sample and Data

3. Results





Welfare Implications

I What e�ect does any of this have on outcomes?

I Two main things we can look at

I Income. Basic implication is the worker will work more hours for thesame level of income than he would have if he substitutedintertemporally.

I Health. If the health costs of e�ort are convex, such that working verylong days can deplete health capital, people induced to work more byshocks may be in worse endline health.


E�ect on Income

I Perfectly optimizing worker should work more on days in which wage is

high

I Workers can observe this much better than we can in the data

I We construct a lower bound - what would impact on income be if

people worked same hours every day

I Construct average hours and multiple by daily wage rateI Lost income could be much larger if workers had a positive elasticity


CDF of Income gain from constant daily hours

This graph shows the cumulative distribution function, across all 256 individuals in our sample, of the variable "potential % income gain", which was constructed as follows. For each individual, we divided their total hours worked over the study period by the number of days worked and call this the "fixed hours target". We then compute how much they would have earned on each day worked, if they had worked exactly as many hours as the fixed hours target, at the average wage rate observed that day among other individuals working in the same market. We then sum up the total earnings under that fixed hours target rule, and compare it to the total actually earned.

0.1

.2.3

.4.5

.6.7

.8.9

1%

at o

r bel

ow

-.1 -.05 0 .05 .1 .15 .2 .25 .3 .35 .4 .45 .5Potential % Income Gain

Potential Percentage Income Gain to Constant Fixed Daily Hours


E�ect on Income

I Lower bound: median = 2.5%, mean = 5.0%,

I Very large for some individualsI Interestingly, negative for only about 10% of the sample.

I Is this big or small?

I Doesn't look like a life-changing amountI But, it is consistent with other studies

I Camerer et al (1997) also �nd about 5-10% increaseI Kaur et al. (2013): 6% e�ect on productivity for those who choose a

positive target

I Another benchmark: de Mel et al. (2008) cash drops to Sri Lankan�rms

I 4.6-5.3% monthly return to capital, not reinvestedI Baseline pro�ts, inventories of 3,840 LKR and 26,530 LKRI Implies a 14-16% increase in capital stock to increase pro�ts by 5%


E�ect on Health

I Does the variance in hours induced by targeting have health

consequences?

I Ideally want random variation in targets across individuals - we don't

have that

I Instead, just look cross-sectionally

I No endline measure of health

I Instead, look at whether worker was sick in the last week of the log

I Examine how this measure varies with the total number of hours

worked, and the standard deviation of hours worked

I Would like to instrument this with shocks, but �rst stage is too weak


E�ect on Health

(1) (2) (3) (4)

Total hours workedLog (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 MeasuresHealth 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 252R-squared 0.03 0.06 0.02 0.05Mean of Dep. Var. 0.25 0.25 0.15 0.15Std. 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

I All of this is cross-sectional and ultimately only suggestive

I But, some evidence that the e�ects on income and health are not

completely negligible


Outline


2. Sample and Data

3. Results





Wrapping up

I People have time-varying cash needs, and tend to quit just after

reaching their need

I Suggests a form of income targeting

I Work up to the needI Behavior also determined by earnings expectations (as in previous work)

I Yet random cash payouts do not undue this behavior

I Once set for the day, targets appear to stickI Targets therefore are over labor incomeI People appear to be loss averse over these target amounts, or see them

as an internal commitment device

I Heterogeneity: people with savings problems, and people with high

e�ort costs, are more likely to quit after reaching target

I Some welfare e�ects on income and potentially health


Discussion

I Why targets?

I As discussed in Camerer et al. (1997), could be a way to solveself-control problems

I present-biased workers may be tempted to quit too early

I Especially relevant in a physical demanding job like bike taxiing

I setting a target before work may help prevent succumbing to thattemptation

I But why not just choose a very high target then?

I Must be real to a�ect behavior

I Why income and not hours?

I Prevents taking too many breaksI Potentially more veri�able to others, for instance spouse?


Policy Implications

I Possibly policy implications

I Commitment devices to control e�ort (i.e. Kaur et al. 2013)I Modern work arrangements (i.e. Kaur et al. 2013)I Some role for things that force people to smooth needs: ROSCA

payments, micro loans paid back in regular installments, etc.

I Quite high ROSCA participation in our sampleI 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 moneyI Augenblick et al. (2012) show that UCB undergrads are more

present-biased in e�ort than money.


Policy Implications

I Possibly policy implications

I Some evidence suggesting that micro entrepreneurs in developingcountries more similar (in terms of attitudes, cognitive ability,motivation, etc.) to wage workers than large business owners (i.e. deMel, McKenzie and Woodru� 2010). These results suggest anotherchannel by which wage employment may be bene�cial.

I 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

I 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 Sunday25-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-NovWednesday

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 Sunday25-Nov 26-Nov 27-Nov

HEPRO Diary Monday Tuesday Thursday23-Nov 24-Nov

Wednesday28-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 = clinic3 = doctor4 = traditional healer5 = pharmacy6 = 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 apply1 = 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-DecMonday 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


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

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Hazard by Goal Amount

-.2-.

10

.1.2

.3P

r(qu

ittin

g)

-200-160-120 -80 -40 0 40 80 120 160Ksh from need

Coefficient

95% CI

Cash need is at most 50 Ksh

-.2

-.1

0.1

.2.3

Pr(

quitt

ing)

-200-160-120 -80 -40 0 40 80 120 160Ksh from need

Coefficient

95% CI

Cash need is at most 100 Ksh-.

2-.

10

.1.2

.3P

r(qu

ittin

g)

-200-160-120 -80 -40 0 40 80 120 160Ksh from need

Coefficient

95% CI

Cash need is between 100 and 200 Ksh

-.2

-.1

0.1

.2.3

Pr(

quitt

ing)

-200-160-120 -80 -40 0 40 80 120 160Ksh from need

Coefficient

95% CI

Cash need is greater than 200 Ksh

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Needs and baseline characteristics

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Coef Obs

Mean (Std. Dev.)

when Ind. Var = 0

Coef Obs


0

Coef Obs


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

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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 252Mean of dependent variable 10.01 3.99Standard deviation of dependent variable 0.94 1.05Notes: Standard errors in parentheses. *, **, and *** indicate significance at 10%, 5%, and 1% respectively.

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05.02.2013 - Jonathan Robinson

Career

Transcript of 05.02.2013 - Jonathan Robinson