Workplace based Insurance against Malaria Experimental ... · Workplace based Insurance against Malaria Experimental Evidence from Nigeria Pieter Serneels, University of East Anglia

Workplace based Insurance against Malaria

Experimental Evidence from Nigeria

Pieter Serneels, University of East AngliaOladele Akogun, Modibbo Adama University of Technology

Andrew Dillon, Michigan State UniversityJed Friedman, The World Bank

1

6th Development Economics Workshop Tilburg & Wageningen Universities 12-13 March 2015

Motivation� Ill health can have substantial negative effects on agricultural worker income, labour supply and productivity� HIV/AIDS reduces labour supply in Kenya, Botswana (Fox et

al. 2004; Thirumurti et al., 2006;Habyarimana et al 2010)

� Schistomosiasis reduces labor supply in Mali (Audibert and Etard, 1998)

� Pollution reduces worker output in US (Zivin 2010)

� Malaria treatment increases earnings with 11-14% in Nigeria, due to increased labor supply and productivity (Dillon et al., 2015)

Motivation

Motivation� Ill health can have substantial negative effects on agricultural worker income, labour supply and productivity

� But demand for health care is often failing

� Supply side failures: poor health care provision

� Demand side failures: still poorly understood - credit constraints, perceived returns to health care (information, trust, understanding)

� We offer a very specific insurance giving workers access to malaria testing and treatment at the workplace

1. Analyse willingness to pay and its correlates and clinic use and its correlates

2. Impact: Do access and use to this type of insurance impact worker income, labour supply and productivity?

Motivation

� Aim of the paper: Increase understanding of take up and use of health insurance in rural settings

� Literature suggests low take up of insurance

� poor quality of the services offered (at health facilities)

� lack of information about the insurance and its modalities

� ill-designed contracts (De Bock & Ugarte Ontiveros 2014)

� trust (Dercon, Gunning, Zeitlin 2011)

� understanding (Platteau, Ugarte Ontiveros 2013)

=> Offer a specific service that addresses these

� Little knowledge of the economic impact of health insurance

=> Study impact on worker income, ls and productivity

5

Motivation� Focus on malaria

� One of top three diseases worldwide, particularly in sub-Sahara Africa

� Estimated 210 million infections and ~1 million deaths per year

� 51% of households in Nigeria reported at least one episode of malaria (NLSS 2003/4) – in endemic settings, “malaria” often becomes a general term for illness/fever

� Of special relevance for agricultural growth in low income countries as investments in favourable agro-ecological areas or irrigation may increase mosquito breeding

� Both the biological and economic impacts can be severe

� Malaria is characterized by recurring fever, headache, muscle pain, and weakness/fatigue; severe cases can result in encephalopathy and death (especially for young children)

� Malaria typically reoccurs unless treated

� In past 4 years, dramatic global gains in malaria-related morbidity and mortality: ACTs, almost 100% effective

6

The malaria parasite lifecycle

Motivation� Investigate promise of a workplace based approach

� Part of search for sustainable provision of malaria care, where costs are shared by government, workers, and employers

8

Framework for analysis

� Workers derive utility from income and disutility from effort; they maximize:

� = � �� , ��; ��

� A worker will take insurance if it increases his utility:

�� = � ��

, ��; �� > ��

� = � �� , ��

�; ��

� willingness to pay for insurance will be a function of changes in income and effort, cost of use, preferences

�� = � �� − ��

� , �� − ��

� , �; ��

� We can derive a labor response function

�� = � � ℎ� , �, �� , ��

where R is the piece rate, A is ability

Study setting

� One large sugarcane plantation in rural Nigeria –5,700 hectares

� Employs ~1000 sugarcane cutters who work throughout the season, and are organized into 10 work groups, managed by a supervisor and headmen

� Workers are paid (piece rate) 2.04 naira per “measured rod” of sugarcane they cut

Study setting




Study setting




Study setting




� The plantation records for each worker the daily amount cut, days worked, and earnings – average daily wage: 1428 Naira (~ US$9.5)

Study setting





Study setting





Field Experimental Method

� We offer each worker one chance to get access to malaria testing and treatment two times during six weeks in the harvest period.

� Only covers worker himself, not family or friends� Only covers malaria: treated after test� Allows two tests during six weeks period� No refund or rebate

� We elicit each worker’s willingness to pay using Becker-De Groot-Maarschak (BDM) method� Incentive compatible: “worker puts his money where his mouth is”

� No bargaining� Price paid is determined exogenously through a draw� Careful explanation and double checking of understanding (extensive training, use 6 page script)

� Performs well (Berry, Fisher, Gutierrez, 2014)


� Extensive pilot� Develop script for our service and context� Pre-pilot (January 2013)� Translate and back translate in Hausa by registered translators

� First pilot: 3 rounds with 24 workers to revise script and price levels (Sep 2013)

� Second pilot: 5 rounds with 40 workers to fine tune price levels and logistics of game (Jan 2014)

� Final price levels� Capture the (hypothetical) distribution of wtp observed during the pilot

� Is deliberately nontransparent to avoid information spillovers

� 12 price levels with a nontrivial step of 190 (50, 240, 430, 620, 810, 1000, 1190, 1380, 1570, 1760, 1950, 2140)

Method� Enumerators trained to follow detailed script

1. Explanation & demonstration - small group session:

1. Explain service, contract, game

2. Practice round with mosquito coil

3. Test understanding of game and re-explain

4. Emphasize worker has to buy service if he draws a price lower or equal than his bid

2. Decision – individual session:

1. Explain service and contract again

2. Elicit wtp

3. Several checks and opportunities to revise wtp

4. Draw a price from a bag

5. Buy service if price drawn ≤ bid

6. Ask whether worker has enough money in his account

7. Worker signs a paper where he agrees that funds can be taken from earnings from work at the plantation

8. Ask worker to keep price and outcome confidential for 6 weeks

Method� Trained enumerators followed a detailed script

1. Small group session:

1. Explain service, wtp, game,




2. Individual session:

1. Explain service

2. ask wtp

3. several checks and opportunities to revise wtp




7. Worker signs a paper where he agrees that funds can be taken from earnings from work at the plantation

8. Workers is asked to keep price and outcome confidential for 6 weeks

Method� Enumerators trained to follow detailed script

1. Explanation & demonstration - small worker group session:

1. Explain service, contract, game,




2. Decision – worker individual session:

1. Explain service and contract again

2. Elicit wtp

3. Several checks and opportunities to revise wtp




7. Worker signs a paper where he agrees that the funds can be taken from his earnings at the plantation

8. Worker is asked to keep price and outcome confidential for 6 weeks


� If worker gains access:� He receives a card entitling him to 2 visits to our mobile health facility

� Worker chooses time of visit� During a visit the worker is tested and, if positive, treated with ACT

� Implementation is overseen by a committee consisting of 2 worker representatives, a harvest manager, the head nurse, and a senior researcher

� No workers refused to participate� Small worker survey on work, health, malaria� Successful implementation in at times demanding conditions

� Random draw of 12 price levels was successful


� If worker gains access:� He receives a card entitling him to 2 visits to our mobile health facility

� Worker chooses time of visit� During a visit the worker is tested and, if positive, treated with ACT

� Implementation is overseen by a committee consisting of 2 worker representatives, a harvest manager, the head nurse, and a senior researcher

� No workers refused to participate� Small worker survey on work, health, malaria� Successful implementation in at times demanding conditions

� Random draw of 12 price levels was successful

0.0

2.0

4.0

6.0

8.1

Fra

ction

50 240 430 620 810 1000 1190 1380 1570 1760 1950 2140Drawn Price Y

Econometric Analysis

1. Demand and correlates

�� = � + � !� + �"#� + �$�� + �%�� + �&'(� + �)*� + �+�� + ��

2. Use of the service

�� = � + �,�� + � !� + �"#� + �$�� + �%�� + �&'(� + �)*� + �+�� + ��

*� = � + �� + ��

I=InformationE=experience and exposureT=TrustR=Risk preferences

SH=Self assessed healthD=DemographicsW=Wealth

3. Impact of having access to the insurance on income, labor supply and productivity?

4. Impact of clinic use (when gained access) on income, labor supply and productivity?

25

�� = � + � �-� + �".� + �� = � + � �� + �".� + ��

�� = � + � �/� + �".� + �� = � + � ��'�� + �"�#'�#*0#12�#� + ��

DemandWillingness to pay

0.1

.2.3

.4F

raction

100350 7001000 1500 2000 2500 3000 3500 4000 4500 5000Max WTP for Malaria Health Service?

Mean 350 Naira

Standard Deviation 405 Naira

Frequencies

0-99 15%

100-199 20%

200-299 23%

300-499 12%

500-999 20%

1000-5000 10%

Demand

-.2

0.2

.4.6

.8ta

ke

up

0 500 1000 1500 2000price drawn

Linear prediction lpoly smooth: takeup

0.2

.4.6

.81

Pro

babili

ty >

max_w

tp_health

200 500 1000 1500 2000 2500 3000 3500 4000 4500 5000willingness to pay

Demand

� Demand is nonlinear: workers are more responsive to a change in price at low levels of price. Especially price reductions below 700 Naira will lead to increased demand

-.2

0.2

.4.6

.8ta

ke

up

0 500 1000 1500 2000price drawn

Linear prediction lpoly smooth: takeup

� Demand is price sensitive: ε=-0.25

Correlates of willingness to pay

29

wtp

Education 0

Wealth 0

Experience with and exposure to malaria care +

Information: knowledge malaria treatment +

Information: expected cost of treatment +

Trust +

Risk preferences 0

Time preferences 0

Self assessed health +

Demographics 0

30

(2) (4) (5) (9) (10) (11) (13)

VARIABLES lwtpp1 lwtpp1 lwtpp1 lwtpp1 lwtpp1 lwtpp1 lwtpp1

age2 -0.003 -0.003 -0.002 -0.002 -0.002 -0.004

(0.005) (0.005) (0.005) (0.005) (0.005) (0.006)

firmexpy 0.024** 0.023** 0.018** 0.020** 0.020** 0.017*

(0.008) (0.008) (0.007) (0.007) (0.007) (0.008)

yos 0.000 -0.003 -0.000 -0.002 -0.002 0.001

(0.018) (0.018) (0.018) (0.017) (0.017) (0.018)

hhasset_m1D000 0.012 0.010 0.008 0.008 0.008 0.007

(0.007) (0.008) (0.008) (0.008) (0.008) (0.008)

selfrepmallm 0.279** 0.258** 0.256** 0.267**

(0.109) (0.110) (0.113) (0.112)

evertested 0.273 0.261

(0.153) (0.157)

lastyear -0.005 0.022 -0.002

(0.088) (0.098) (0.088)

everprescribedaftest 0.363** 0.359*

(0.158) (0.161)

hh_members_diagnosed 0.031

(0.028)

hhsize2 0.012

(0.013)

Constant 5.344*** 5.188*** 5.234*** 5.129*** 5.166*** 5.161*** 5.148***

(0.350) (0.171) (0.377) (0.378) (0.385) (0.381) (0.383)

Observations 825 971 825 825 825 825 825

R-squared 0.014 0.005 0.018 0.040 0.041 0.041 0.043

31

(7) (6) (8) (9) (10) (11) (13) (14) (17)

VARIABLES lwtpp1 lwtpp1 lwtpp1 lwtpp1 lwtpp1 lwtpp1 lwtpp1 lwtpp1 lwtpp1

age2 -0.002 -0.002 -0.004 -0.006 -0.004 -0.003 -0.003 -0.002 -0.002

(0.005) (0.005) (0.006) (0.005) (0.006) (0.005) (0.005) (0.005) (0.005)

firmexpy 0.022** 0.022** 0.021** 0.019** 0.021** 0.023** 0.023** 0.022** 0.021**

(0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.007)

yos -0.004 -0.005 -0.001 -0.006 -0.001 -0.003 -0.004 -0.006 -0.005

(0.019) (0.019) (0.018) (0.018) (0.018) (0.018) (0.017) (0.019) (0.019)

hhasset_m1D000 0.008 0.008 0.007 0.008 0.007 0.010 0.009 0.008 0.008

(0.008) (0.009) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008) (0.008)

knowmaltotpos 0.029

(0.017)

knowmalsymp -0.053 -0.060* -0.054

(0.031) (0.031) (0.031)

knowmaltreatpos 0.108* 0.102* 0.106*

(0.053) (0.054) (0.054)

knowmalprev 0.033 0.026 0.032

(0.063) (0.066) (0.064)

lnexpected_cost 0.118**

(0.047)

lnexpected_cost_treat 0.120**

(0.046)

lnexpected_cost_test 0.046

(0.047)

time_min_facility 0.000

(0.001)

selfassesmalinfo 0.019* 0.015

(0.010) (0.012)

knowhi 0.050

(0.185)

Constant 5.075*** 5.164*** 4.499*** 4.729*** 5.011*** 5.224*** 5.169*** 5.148*** 5.174***

(0.338) (0.347) (0.586) (0.409) (0.614) (0.377) (0.377) (0.343) (0.355)

Observations 825 825 798 784 797 822 825 825 825

R-squared 0.020 0.026 0.022 0.022 0.016 0.018 0.020 0.027 0.026

32

(1) (7) (11)

VARIABLES lwtpp1 lwtpp1 lwtpp1

age2 -0.002 -0.001 -0.002

(0.005) (0.005) (0.005)

firmexpy 0.017** 0.016** 0.018**

(0.006) (0.007) (0.007)

yos 0.004 0.005 -0.002

(0.018) (0.019) (0.019)

hhasset_m1D000 0.006 0.006 0.007

(0.008) (0.008) (0.008)

lastyear -0.019 -0.016 0.013

(0.093) (0.091) (0.091)

selfrepmallm 0.272** 0.274** 0.302**

(0.106) (0.106) (0.107)

evertested 0.257 0.251 0.282

(0.156) (0.159) (0.161)

lntrust 0.187*** 0.184***

(0.052) (0.050)

crra -0.016

(0.032)

twoweeki 0.120

(0.370)

perceivedhealth2d2 -0.221*

(0.110)

perceivedhealth2d3 -0.006

(0.087)

Constant 4.111*** 4.125*** 5.226***

(0.452) (0.468) (0.381)

Observations 811 811 823

R-squared 0.057 0.057 0.049

Use of care when gained accessHow often is the service used, once gained access?

� Workers do not all use their full entitlement

01

02

03

04

0P

erc

en

t

0 .5 1 1.5 2useservice

Parasite

count

First visit Second visit

# of

workers

Percentage of

total

# of

workers

Percentage

of total

Malaria negative rate =

1st

visit: 76.7%

2nd

visit: 66.3%

0 14 7.1 7 7.1

1 62 31.5 27 27.6

2 75 38.1 31 31.6

Malaria positive rate =

1st

visit: 23.3%

2nd

visit: 33.7%

3 25 12.7 19 19.4

4 11 5.6 6 6.1

5+ 10 5.0 8 8.2

197 workers assessed 98 workers assessed

Correlates of use of care

34

Use

Education +

Wealth 0

Experience with and exposure to malaria care +

Information: knowledge malaria treatment 0

Information: expected cost of treatment -

Trust +

Risk preferences -?

Time preferences 0

Self assessed health +

Demographics 0

Overview correlates

35

wtp Use

Education 0 +

Wealth 0 0

Experience with and exposure to malaria care + +

Information: knowledge malaria treatment + 0

Information: expected cost of treatment + -

Trust + +

Risk preferences 0 -?

Time preferences 0 0

Self assessed health + +

Demographics 0 0

Causal effects on labour outcomes

ITT: impact of access to insurance on labour outcomes

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

VARIABLES lny lny ls ls lnw lnw

OLS IV OLS IV OLS IV

havecare 0.034 0.067** 0.176 -0.366 0.032 0.075***

(0.026) (0.031) (0.282) (0.548) (0.023) (0.024)

Observations 969 969 969 969 969 969

� Having gained access by level of wtp

38

0.2

.4.6

.81

lpoly

sm

ooth

: havecare

0 1000 2000 3000 4000 5000lpoly smoothing grid

� Having gained access by level of wtp

39

0.2

.4.6

.81

lpoly

sm

ooth

: havecare

0 1000 2000 3000 4000 5000lpoly smoothing grid

Table: Willingness to pay

Low 0-199 35%

Low-medium 200-499 35%

Medium- High 500-999 20%

High 1000-5000 10%

� ITT by level of wtp

40

Low wtp Low-med wtp Med-high wtp High wtp

lny lny lny lny

OLS -0.002 0.015 0.063 0.003

(0.066) (0.065) (0.067) (0.053)

IV 0.231** -0.034 0.078 0.023

(0.096) (0.056) (0.061) (0.146)

ls ls ls ls

OLS -1.817 -0.522 0.876 2.161

(1.543) (1.156) (1.351) (1.778)

IV 0.296 -1.092 0.851 5.089*

(1.738) (0.788) (1.195) (2.877)

w w w w

OLS 0.054 0.021 0.034 -0.008

(0.049) (0.059) (0.043) (0.042)

IV 0.236** -0.007 0.047 -0.079

(0.093) (0.049) (0.041) (0.093)

Effect of clinic use on labour outcomes

Effect of clinic use on labour outcomes

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

VARIABLES lntotalamount lntotalamount work_days work_days lnseasonwage lnseasonwage useservice

OLS IV OLS IV OLS IV 1st

Stage

Use 0.105*** 0.270* 2.331*** 8.149*** 0.047 0.083

(0.032) (0.160) (0.683) (3.138) (0.028) (0.108)

Age -0.006* -0.005* -0.099 -0.093* -0.004 -0.004 -0.003

(0.003) (0.003) (0.059) (0.050) (0.002) (0.002) (0.007)

experience 0.002 -0.001 -0.146 -0.251*** 0.006 0.006 0.014*

(0.003) (0.006) (0.088) (0.083) (0.004) (0.005) (0.008)

yos -0.018 -0.024 -0.349 -0.574 -0.013 -0.014 0.041**

(0.016) (0.021) (0.382) (0.497) (0.008) (0.011) (0.019)

lntrust 0.198***

(0.073)

Ever tested 0.288**

(0.114)

Constant 11.287*** 11.150*** 50.104*** 45.452*** 7.422*** 7.388*** -0.265

(0.250) (0.181) (6.215) (3.166) (0.122) (0.137) (0.478)

Observations 189 186 189 186 189 186 194

R-squared 0.048 0.055 0.042 0.031 0.114

Conclusion

� Average wtp for a very specific insurance that provides access to malaria testing and treatment is modest but has considerable variation.

� Demand is price sensitive, especially at lower price levels

� Correlates of demand: experience and exposure, information, trust

� Correlates of use: experience and exposure, information, trust

� Access to insurance has strong causal effects on income and productivity, but not ls, in particular for workers with low willingness to pay

� Use of clinic has strong causal effects on income, through labour supply

� workplace based health insurance may provide useful

� As complement for malaria control and eradication

� As a way to provide health insurance

Coda

� Thanks for listening!

Appendix

Knowledge about malaria

back

47

.1.2

.3.4

.5.6

Fra

ctio

n

0 1 2 3 4knowing the symptoms of malaria

.1.2

.3.4

.5.6

Fra

ctio

n

0 1 2 3 4knowing the prevention of malaria

.1.2

.3.4

.5.6

Fra

ctio

n

0 1 2 3 4knowing the treatment of malaria

(a) Prevention (b) symptoms (c) treatment

Knowledge about malaria

48

0.2

.4.6

.81

Pro

bab

ility

<=

kn

ow

mals

ym

p

0 1 2 3 4knowmalsymp

0.2

.4.6

.81

Pro

bab

ility

<=

kn

ow

maltre

atp

os

0 1 2 3 4knowmaltreatpos

0.2

.4.6

.81

Pro

bab

ility

<=

kn

ow

malp

rev

0 1 2 3 4knowmalprev

back

Trust

49

0.0

02

.004

.006

.008

.01

De

nsity

0 200 400 600 800 1000trust

back

Risk preferences

50

1,800

Naira

Lottery A

1,800

Naira

6000

RWF

Lottery C

5,400

Naira-200

Naira

Lottery F

3,400

Naira1000

Naira

5000

Naira

200

Naira

Lottery E

4,200

Naira

600

Naira

Lottery D

2,600

Naira

Lottery B

1,400

Naira

back

Risk preferences

51

low

payoff

high

payoff

expected

payoff

Implied range crra

fraction of

subjects

Lottery A 1800 1800 1800 2.24 < r 39%

Lottery B 1400 2600 2000 0.75 - 2.24 17%

Lottery C 1000 3400 2200 0.44 - 0.75 10%

Lottery D 600 4200 2400 0.28 - 0.44 3%

Lottery E 200 5000 2600 0.00 - 0.28 10%

Lottery F -200 5400 2600 r < 0.00 22%

back

Workplace based Insurance against Malaria Experimental ... · Workplace based Insurance against Malaria Experimental Evidence from Nigeria Pieter Serneels, University of East Anglia

Documents