The Social Value of Health Insurance: Results from Ghana

The Social Value of Health Insurance -
Results from Ghana
Silvia Garcia-Mandico (World Bank), Arndt Reichert (World Bank) and
Christoph Strupat (DIE)

© German Development Institute / Deutsches Institut für Entwicklungspolitik (DIE)
Introduction
• Large and unpredictable medical expenditures expose uninsured
households to substantial financial risk.
• In poorer economies limited wealth and access to financial
instruments make it difficult to smooth consumption over health
shocks.
• Households may sell assets, agricultural land or take their children
out of school and put them to work (child labor).
• We examine the impact of health insurance coverage on main
coping strategies including loans, remittances, child labor and
reduction of consumption and physical capital investments.
• So this paper aims to assess the broader social value of public
health insurance.
2

Introduction
• Few studies have examined the extent to which health insurance
impacts child labor (Landmann and Frölich 2015; Liu 2016).
• Rigorous evidence on the impacts of a country-wide health
insurance on welfare-decreasing coping mechanisms such as
child labor is currently missing for a lower income country
(Dammert et al. 2018).
• Ghana is of particular interest because it had the first country-
wide NHIS in Sub-Saharan Africa and served as a example for
many countries of the continent that subsequently introduced HI
schemes at scale.
3

• The law on the National Health Insurance Scheme (NHIS) passed
Ghanaian parliament in 2003 and was implemented at the district
level until the end of 2007.
• The aim of the scheme was to provide health care services to a
broad part of the population (basic outpatient, inpatient and dental
health services)
• The membership in the NHIS is voluntary for all adults (age 18-
69) that work in the informal sector.
• Income-related premiums vary between 7.5$ and 50$ per year.
Poor, children and the elderly were exempted from premium
payments.
• In a typical two-parent family with three children, the entire family
would have been covered for 15$ per year.
NHIS in Ghana

• The district authorities were responsible for the introduction of the
scheme
• They found District Mutual Health Insurance Schemes (DMHIS)
• The ability to set up DMHIS varied across local district
assemblies, in part as it depended on already existing health
insurance schemes.
• Existing government-sponsored HI schemes were automatically
converted to DMHIS (Aggyepong and Adjei 2008)
• As a result, districts that had a better healthcare provision system
were able to more easily obtain licenses for DMHIS than districts
that needed their healthcare organizations rebuilt from scratch.
NHIS in Ghana

NHIS in Ghana
0
10
20
30
40
50
60
70
80
90
100
110
Jan03
Mrz03
Mai03
Jul03
Sep03
Nov03
Jan04
Mrz04
Mai04
Jul04
Sep04
Nov04
Jan05
Mrz05
Mai05
Jul05
Sep05
Nov05
Jan06
Mrz06
Mai06
Jul06
Sep06
Nov06
Jan07
Mrz07
Mai07
Jul07
Sep07
Nov07
Numberofdistricts

NHIS in Ghana

0
5
10
15
20
25
30
35
40
2005 2006 2007
Percentage of population
enrolled with the NHIS
Enrollment rates of the NHIS
NHIS in Ghana
The numbers, however, are lower bounds of the real coverage rate
of the NHIS, as households were frequently allowed to enroll under
the NHIS after the occurrence of a health shock (Gajate-Garrido,
2013).

Incidence of child labor:
9
Child labor in Ghana
24.3 percent of children aged 5-14
years are engaged in child labor

Definition of child labor related to ILO conventions C138 (1973) and
C182 (1999) (CL=Child labor) (age 5 – 14)
• This definition only includes economic activities of children;
Household activities such as helping their parents with e.g. cooking,
fetching water, firewood etc are not considered as child labor.
Definition of child labor

• The main identification problem when analyzing the effects of a
national policy is finding a suitable control group.
• We use the fact that the staggered implementation of the NHIS
overlapped with the roll-out of the 5th round of the Ghanaian
Living Standards Survey (October 2005 to September 2006).
• We are able to observe, within the same district, sub-districts
interviewed right before and after the NHIS introduction.
• Timing of interview of households was external to the
timing of the NHIS adoption.
 Thus, within the same district, whether a household was
observed before or after NHIS implementation is as good as
random.
Identification strategy

• We exploit this variation using a regression discontinuity
design, where the running variable is months to the NHIS
implementation at the district level.
• The staggered implementation of the scheme allows us to control
for time fixed effects in addition to district fixed effects.
• During the period of the survey, 53 districts adopted the NHIS.
Out of these, 52 districts had at least one sub-district surveyed
before and one after the adoption of the NHIS.
• We only keep households in such districts, resulting in a final
sample of 2,235 households.
• Optimal bandwidth analysis show that we should use 6 months
before and 6 months after the introduction of the NHIS.
Identification strategy

Balance table
13

Descriptive results

Results – OOP payments
17
• We find a reduction of OOP payments by 21% for all and 35%
among households with higher sickness intensity.
• The drop in OOP payments is driven by the large savings in
medicine purchases, 66% reduction among households higher
sickness intensity.

Results – Consumption
18
• NHIS have may reduced the need to cut non-food consumption
as a coping strategy to health shocks.
• For households with high sickness intensity, however,
we find a significant increase in food expenditures with NHIS.

Results – Loans and remittances for health
19
• No effect on loan uptake; Substantial effect on amount of loan?
• Households receive 4% less remittances which corresponds to
25% of the reduction in the OOP payments.

Results – Assets and land
20

Results – Child outcomes
21

22
• School enrollment increased by 5% and class attendance
raised by 2% due to the implementation of the NHIS.
• No effect on homework.
• Child labor incidence is reduced by 8 percentage points
among households with higher sickness intensity; This
corresponds to a relative decrease of 22%.
• No effect on earnings => in-kind compensation (agric sector)
• Weekly hours on household chores are reduced by 2% for all
and 3% among households with higher sickness intensity.
 Households with higher sickness intensity do not take children
out of school and engage in child labor after the implementation
of the NHIS.

23

Conclusion
24
 The introduction of the NHIS induced savings in healthcare
expenditures, particularly from expenditures in medicines.
 Decreased OOP payments have translated into greater non-
food consumption and a drop in remittances received.
 Our results suggest that these effects were larger for households
that experienced lengthier and more incapacitating health
problems.
 For these households, in addition, we find a drop in child labor
and an increase in class attendance.
 This is indicative that households experiencing severe health
shocks refrained from reducing their investments in human
capital of children.

Conclusion
25
• Child labor is in the long run arguably one of the most welfare-
decreasing coping strategies.
• It leads to high expenses in human capital formation,
cognitive development and later life economic activities.
• Public health insurance schemes seem to be an effective
instrument in breaking this poverty trap.
 Avoidance of this risk coping measure is an important part of the
social value of public health insurance.
 Policymakers should, therefore, consider this value in their cost-
benefit analysis when implementing public health insurance
schemes.

Appendix
27

Health shock
28
• To gain intuition on whether NHIS has the strongest impact on
households experiencing health shocks, we split the sample by
a measure of health shock.
• Following Liu (2016), we define our health shock measure as the
ratio of the weighted sum of the days that every adult in the
household has reported being ill over the last two weeks
before the interview. => Household level measure
• Among households reporting an illness or injury, the median of
this sickness intensity measure is 10 percent. 10 percent of
the days in the last 14 days were lost to illness.
 Low intensity sickness households, therefore, are households in
which less than 10 percent of the days in the last two weeks
were lost to illness.

Health shock
29
• As a comparison to our main measure, we construct a second health
shock variable by taking the ratio of the weighted sum of the days that
every adult in the household reported having their activities of daily
living (ADL) limited by an illness or injury.

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