IMPACT OF SOCIAL FRANCHISING ON HEALTHCARE EXPENDITURE 
WITHIN PRIVATE FACILITIES IN EMBU COUNTY KENYA 

 

 

 

STEPHEN NYAGA 

 

 

 

A RESEARCH PROJECT SUBMITTED TO THE DEPARTMENT OF 
ECONOMETRICS AND STATISTICS IN THE SCHOOL OF ECONOMICS 
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE 
AWARD OF THE DEGREE OF MASTER IN ECONOMICS 
(ECONOMETRICS) OF KENYATTA UNIVERSITY 

 

 

DECEMBER 2020

 


DECLARATION 

 

 

 

 

 


DEDICATION 

This project is dedicated to my wife Evalyne Kariuki, my son Mark Kariuki, and to my 
parents Mr. Jeremiah Nyaga and Mrs. Susan Njeri. 

 

 


ACKNOWLEDGEMENTS 

I sincerely thank my supervisor Dr. Susan Okeri for her guidance and continuance support 
during the development of this project. Her input was invaluable. I am so indebted to Dr. 
Angelica Njuguna for the Econometrics she taught me and her hint on the most appropriate 
model for this study. 

Special thanks to Dr. Angelica Njuguna, Dr. Jennifer Njaramba, and Dr. Dianah Ngui for 
their commitment during coursework and being available for consultation. I would also 
want to acknowledge Dr. Steve Makambi, Dr. George Kariuki, and Dr. Stephen Gitahi for 
their good comments on how to better this work. 

I would want to thank social franchise network mobilizers (Lydia Ngai and Purity) and 
medical providers who supported data collection for this study. 

My sincere and greatest gratitude to my parents Mr. Jeremiah Nyaga and Ms. Susan Njeri 
for putting the right foundation for my career progression and their sacrifice to support my 
studies to this far. I am also indebted to my wife Evalyne Kariuki for her sincere 
encouragement and patience all through this process. I sincerely thank my family members: 
Mary Wawira, Juliet Karimi, Cyrus Munene, Catherine Kagendo, Dennis Mutugi, Augusta 
Ngatha and Damaris Makena who encouraged me throughout my master’s program. 

Much appreciation to my close friends, Patrick Kariuki, Purity Kariuki, Steve Mwangi, 
Salome Mwangi, Jonathan Chege, Joy Wakio, Victor Murimi, Brian Mugendi, Peris 
Wachira, and Lenty Gakii for their encouragement. Thanks to my Master’s classmates 
Tony Mining and Grace Mbeke who provided encouragement during coursework. 


TABLE OF CONTENTS 
DECLARATION ........................................................................................................................... ii 
DEDICATION .............................................................................................................................. iii 
ACKNOWLEDGEMENTS ......................................................................................................... iv 
LIST OF TABLES ....................................................................................................................... vii 
LIST OF FIGURES .................................................................................................................... viii 
ACRONYMS AND ABBREVIATIONS ..................................................................................... ix 
OPERATIONAL DEFINITION OF TERMS ............................................................................. x 
ABSTRACT ................................................................................................................................... xi 
CHAPTER ONE ............................................................................................................................ 1 
INTRODUCTION.......................................................................................................................... 1 
1.1 Background of the Study ..................................................................................................... 1 
1.1.1 Kenya Health System ........................................................................................................... 3 
1.1.2 Private Healthcare Expenditure in Kenya .......................................................................... 10 
1.1.3 Social Franchising in Kenya .............................................................................................. 13 
1.2 Statement of the Problem ................................................................................................... 15 
1.3 Objectives of the Study ...................................................................................................... 17 
1.3.1 Main Objective................................................................................................................... 17 
1.3.2 Research Questions ............................................................................................................ 17 
1.3.3 Specific Objective .............................................................................................................. 18 
1.4 Significance of the Study ................................................................................................... 18 
1.5 Scope of the Study ............................................................................................................. 19 
1.6 Organization of the Study .................................................................................................. 19 
CHAPTER TWO ......................................................................................................................... 20 
LITERATURE REVIEW ........................................................................................................... 20 
2.1 Introduction ........................................................................................................................ 20 
2.2 Theoretical Literature ......................................................................................................... 20 
2.2.1 Principal-Agent Theory .................................................................................................... 20 
2.2.2 Consumer Theory ............................................................................................................. 22 
2.3 Empirical Literature ........................................................................................................... 23 
2.3.1 Impact of Cost of Health Services on Patient Affordability and Access .......................... 23 
2.3.2 Impact Social Franchising on Family Planning and Children Services ............................ 25 
2.4 Overview of the Literature ................................................................................................. 29 
CHAPTER THREE ..................................................................................................................... 31 
RESEARCH METHODOLOGY ............................................................................................... 31 
3.1 Introduction ........................................................................................................................ 31 
3.2 Research Design................................................................................................................. 31 
3.3 Theoretical Framework ...................................................................................................... 31 
3.3.1 Measuring the Impact of Social Franchising on Healthcare Expenditure ......................... 34 
3.4 Model Estimation ............................................................................................................... 36 
3.5 Definition and Measurement of Variables ......................................................................... 38 
3.6 Data Types and Sources ..................................................................................................... 39 
3.7 Target Population ............................................................................................................... 39 
3.8 Sampling Method and Sample Size ................................................................................... 40 
3.9 Data Collection .................................................................................................................. 40 
3.10 Data Analysis ..................................................................................................................... 40 
3.11 Diagnostic Tests ................................................................................................................. 41 
CHAPTER FOUR EMPIRICAL FINDINGS ........................................................................... 42 
4.1 Introduction ....................................................................................................................... 42 
4.2 Analysis of Response Rate and Descriptive Statistics. ..................................................... 42 
4.2.1 Age of the Study Participants ................................................................................. 42 
4.2.2 Education of the Study Participants ....................................................................... 43 
4.2.3 Marital Status of the Study Participants ................................................................. 43 
4.2.4 Sources of Healthcare Services .............................................................................. 44 
4.2.5 Healthcare Facility Nearest to Home ..................................................................... 45 
4.2.6 Considerations in Seeking Healthcare Services ..................................................... 45 
4.3 Propensity Score Estimate ................................................................................................ 46 
4.3.1 Specification Tests and Goodness-of-Fit ............................................................... 49 
4.4 Impact of Social Franchising on Children's Healthcare Services within Private Facilities 
in Embu County ......................................................................................................................... 51 
4.5 Impact of Social Franchising on the Expenditure of Short-term Family Planning Services 
within Private Facilities in Embu County .................................................................................. 52 
4.6 Impact of Social Franchising on the Expenditure of Long-term Family Planning Services 
within Private Facilities in Embu County .................................................................................. 53 
CHAPTER FIVE ......................................................................................................................... 55 
SUMMARY, CONCLUSION, AND POLICY IMPLICATIONS ........................................... 55 
5.1 Introduction ....................................................................................................................... 55 
5.2 Summary ........................................................................................................................... 55 
5.3 Conclusion ........................................................................................................................ 57 
5.4 Policy Recommendations ................................................................................................. 58 
5.5 Areas for Further Research ............................................................................................... 59 
REFERENCES ............................................................................................................................. 60 
APPENDIX I: INTRODUCTION LETTER ............................................................................. 65 
APPENDIX II: QUESTIONNAIRE .......................................................................................... 66 
APPENDIX III: AUTHORIZATION LETTERS ..................................................................... 57 
 LIST OF TABLES 

Table 3.1: Definition and Measurement of Variables ............................................................. 39 
Table 4.1: Age of Respondents ............................................................................................... 42 
Table 4.2: Health Services Utilization between Franchised and Non-Franchised facilities.... 44 
Table 4.3: Consideration in Seeking Healthcare Services ....................................................... 45 
Table 4.4: Logit Estimates for Individual Seeking Services in a Franchised Health Facility . 47 
Table 4.5: Specification Tests ................................................................................................. 49 
Table 4.6: Description of Estimated Propensity Score in Region of Common Support ......... 50 
Table 4.7: Blocks of Propensity scores ................................................................................... 51 
Table 4.8: Impact of Franchising on the Expenditure of Children Services ........................... 51 
Table 4.9: Impact of Franchising on the Expenditure of Short-term FP Services ................... 52 
Table 4.10: Impact of Franchising on the Expenditure of Longer-term FP services .............. 53 


 

 


LIST OF FIGURES 

Figure 1.1: Kenya Total Healthcare Expenditure. ..................................................................... 4 
Figure 1.2: Family Planning Services Uptake by Sector. .......................................................... 5 
Figure 1.3: Family Planning Implementation deficit 2017-2020. ............................................. 6 
Figure 1.4: Family Planning Budget Deficit Embu County. ..................................................... 8 
Figure 1.5: Indicators of Children Health for Embu County Compared to National Average .. 9 
Figure 1.6: Catastrophic Health Expenditure in Kenya by Wealth Quintile ........................... 11 
Figure 1.7: Sources of Inefficiency in Health Care Delivery .................................................. 12 
Figure 4.1: Education and Marital Status ................................................................................ 43 
Figure 4.2: Marital status of Study Participants ...................................................................... 43 
Figure 4.3: Health Facility Nearest to Home ........................................................................... 45 


 

 

 

 


ACRONYMS AND ABBREVIATIONS 

ATET Average Treatments Effects of the Treated 

CFW Sustainable Healthcare Foundation 

NHIF National Hospital Insurance Fund 

HPP Health Policy Project 

IUCD Intrauterine Contraceptive Device 

KEMSA Kenya Medical Supplies Authority 

LARCs Long-term Acting and Reversible Services 

MoH Ministry of Health 

MSI Marie Stopes Internationals 

MSK Marie Stopes Kenya 

NGO Nongovernmental Organization 

PMs Permanent Methods 

PSI Population Services International 

PSM Propensity Score Matching 

PSK Population Services Kenya 

SDGs Sustainable Development Goals 

SD Standard Deviation 

UHC Universal Health Coverage 

USAID United States Agency for International Development 

WHO World Health Organisation 

 


OPERATIONAL DEFINITION OF TERMS 

Franchisor: A franchisor is an entity that manages a brand and oversees the administration 
of services by healthcare providers operating under that brand. 

Franchisee: A franchisee is a private health facility that agrees to operate under a brand 
offered by a franchisor hence forming a franchise network. 

Franchisee Network: A franchise network is a collection of franchisees that have agreed 
to offer a set of services under a franchisor brand. 

Social Franchise: A social franchise is a network of private-sector health care providers 
that have agreed to offer a set of services under a common franchise brand with the aim of 
achieving social benefits. 

Short-term Family Planning Method: These are family planning methods whose period 
of service are less or equal to 1 year. 

Long-term Family Planning Method: These are FP whose period of service is more than 
1 year. 

Depo-provera: This is a short-term FP method that may prevent conception for up to three 
months. 

Intrauterine Contraceptive Device: This is a long-term FP method that may prevent 
conception for up to twelve years. 

Norplant’s Contraceptive Implants: This is a long-term FP method that may prevent 
conception for up to five years. 

 

 


ABSTRACT 

The Kenyan private health sector is one of the most developed in Sub-Saharan Africa and 
is highly critical in healthcare delivery. It is often the first point of contact of 47 percent of 
the first quintile of income earners when they fall sick and 33 percent of women seek family 
planning services in this sector. Despite private sector ability to provide greater choices for 
customers, in several parts of Kenya, the cost of health services is a great impediment to 
service utilization. The concept of social franchising attempts to increase access to services 
in the private sector through various strategies such as capacity building of healthcare 
service providers, lowering cost of treatments by provision use of vouchers or supply of 
subsidized medical supplies and demand creation. However, there is scant literature on the 
impact of social franchising on healthcare expenditure within private healthcare facilities 
in Embu County, Kenya. This study investigated how social franchising has affected the 
consumer expenditure on private healthcare services namely family planning and children 
treatment services. The family planning methods included three-month short term family 
planning method Depo-Provera and long term family planning methods IUCD and 
Norplant’s implants which are supported by social franchisors. The study used a cross-
section descriptive study design and conducted a cluster sampling in communities near 
franchised and non-franchised private health clinics in Embu County. The study used a 
total of 215 responses analyzed with STATA 14. Descriptive statistics used in this study 
include mean, median, and frequencies and inferential statistics include binary logistic 
regression with marginal effects and propensity score matching to determine the impact of 
social franchising on healthcare expenditure. The results reveal that expenditure on 
children's illnesses treatments and that of short-term family planning services were not 
statistically different between franchised and non-franchised health facilities. The study 
however found that there is a huge variance on the expenditure of long-term family 
planning services between the two classes of clinics with women in franchised health 
facilities paying on average $4.5 less due to provision of vouchers by the franchisors. The 
study recommends expansion of social franchisors network to reach bottom of economic 
pyramid and scaling up the capacity building of medical workers through training to 
improve quality of services provided. 


CHAPTER ONE 

INTRODUCTION 

1.1 Background of the Study 

Universal Health Coverage (UHC) is a priority policy agenda in the Sustainable 
Development Goals (SDG). The SDG3 “good health and well-being”, where UHC 
falls, is driven by the need for improved access to quality health services for all through 
safe, effective, quality and affordable health care (WHO, 2016). Healthcare services 
should be based on the need and not the ability to pay, however, in developing countries 
it is the ability to pay that determines access to quality health care services especially 
the services provided by the private sector (WHO, 2010). Several attempts have been 
made to improve access healthcare services in developing countries which includes 
pooling or mandatory contributions to social health insurance (National Hospital 
Insurance Fund [NHIF] for Kenyans) among formal employees and increasing national 
budget allocation on health (MoH, 2013). The WHO (2010) regards mandatory 
prepayment as the most efficient and equitable financing system for UHC. However, 
majority of people in the bottom of economic pyramid are still not covered by any 
health insurance pushing both public and private sectors to other interventions to 
improve access to quality healthcare services. 

In response to SDG 3, the Kenyan government has introduced several interventions to 
protect its people from catastrophic and impoverishing healthcare costs and to steer the 
country toward UHC. Such interventions are informed by the fact that nearly half of 
Kenyans are not covered by a health insurance such as NHIF and about 1.5 million 


Kenyans are pushed into poverty each year (impoverished) as a result of out of pocket 
payments for health care (MoH, 2017). The national government budget allocation on 
healthcare of 7 percent on average is below the Abuja Declaration of 15 percent which 
causes public health sector have inadequate resources to serve the public pushing more 
people into the private sector. To support the healthcare system social franchisors 
intervention includes capacity building private sector service providers, directly 
supplying subsidized medical supplies to reduce the cost burden of medical products 
and services and the use of vouchers as a method of demand creation especially 
amongst the poor in the society (PSK, 2017). 

The concept of social franchising adapts a commercial franchising model to achieve 
social benefits rather than financial goals (Montagu, 2002). Franchisees are then able 
to enjoy professional training and consultancy, receive subsidized medical supplies and 
equipment, and often times franchisors advertise franchise brand for the advantage of 
the franchisees (Montagu, 2002). These increase brand visibility and is intended to 
increase a wider range of services utilization within the franchise network. 

Social franchising was first introduced in the 1990s by the United States Agency for 
International Development (USAID) in the Philippines and Mexico with the aim of 
expanding markets for clinical family planning services (Montagu, 2002). According 
to PSI (2017), social franchising is currently operating in over 40 countries including 
Kenya. Franchising is geared toward service delivery, franchisees are able to enjoy 
economies of scale in purchasing and advertising as a network rather than individuals 
and hence possibly enjoy lower supply prices. It has also has been found effective in 


increasing the scope of services among franchisees and increase access to quality 
services (Montagu, 2002). However medical providers may be required to pay 
franchise fees, maintain specified levels of service quality and meet sales quotas which 
positively increases the uptake of services among franchisees. 

1.1.1 Kenya Health System 

The health system in Kenya can be classified into the public (46 percent), private sector 
(40 percent), and faith-based organizations/non-governmental organizations (14 
percent) (MOH, 2019). The private sector in Kenya can be further divided into 
franchised and non-franchised private health facilities though there is dearth of 
literature difference in terms of scope services provision, health expenditure, and 
outcomes in the society. The government budget allocation to the health sector which 
is 7.1 percent is lower than 15 percent budget allocation recommended in the Abuja 
declaration (MoH, 2017) which causes public health system have inadequate resources 
pushing people to the private health system. According to USAID (2014) due to unmet 
demand by the public sector, 37 percent of health spending occurs in private facilities, 
47 percent of Kenya uses the private facility when they fall sick and 33 percent of 
women obtain family planning services in the private sector. Also, 68 percent of 
private-sector patients use out of pocket payments (MOH, 2015). This shows private 
healthcare providers are equally important in Kenya especially in the provision of 
regular services that related to women and children. 

 

 


Figure 1.1 shows proportion of people health expenditure in Kenya by type of facility 

 

Development 
partners26%

Public sector34%

Private sector40%

Figure 1.1: Kenya Total Healthcare Expenditure. 

Source: Ministry of Health 2017 

Figure 1.1 shows that 40 percent of Kenyans health expenditure occurs in private, 34 
percent occurs in public, and 26 percent occurs in development partner’s health 
facilities. According to Wanjala (2019) due to high cost of healthcare 12.7 percent of 
Kenyans do not seek health services when they fall sick and 2.6 million (6.2 percent) 
of households risk being impoverished due to out of pocket payment for health services. 

 

 

 

 


Figure 1.2 show that family planning services uptake between private, public sector, 
and shops, mobile clinics, community health workers (CHW) and friends. 

 

74.5

39.6

63.4

62.7

78.2

23.7

21.4

57

33.6

36.4

18.2

19.4

0.6

2.9

2.8

0.6

3.5

48.3

0

20

40

60

80

Femalesterilisation

Pill

IUD

Injectables

Implants

Malecondoms

percent

Public sector

Private medical sector

shop, mobile clinic, chw, friend

Figure 1.2: Family Planning Methods by Service Provider. 

Source KNBS (2014) 

In Figure 1.2 the private sector cannot be overlooked in the provision of important 
health services such as family planning services in Kenya. While 64.3 percent of IUD 
consumers seek services in the public sector, 34.5 percent seek these services in the 
private sector out of which 36.4 percent seek injectable services and 18.2 percent seek 
implants services. Also, the majority of pills services (57 percent) and male condoms 
are provided by the private sector. 

 

 

 


Figure 1.3 shows that government will have Ksh. 2.5 Billion family planning budget 
deficit in 2020/21 fiscal year 

 

1,488

2,241

2,241

2,447

0

500

1,000

1,500

2,000

2,500

3,000

2017

2018

2019

2020

Ksh Million

Year

Family planning services fundinggap

Figure 1.3: Family Planning Implementation deficit 2017-2020. 

Source: MOH (2017). 

Despite the increased budget for family planning services, Figure 1.3 shows that the 
funding gap is widening which makes the interventions offered by the private sector 
very important. However, private sector whose optimization problem is to maximize 
profits may exclude some clients hence need for interventions within the private sector. 
Social franchising is one of such interventions whose impact is not adequately assessed 
in Kenya. 

The Embu County health system comprises of 54 percent public and 46 private health 
facilities. Data from Ministry of Health show there are 20 nurses, 4 doctors and 6 
clinical officers per 10,000 people which is below recommendation by the World 
Health Organization for the achievement of the Universal Health Coverage (MoH, 


2020). The county has a fertility rate of 3.1 children per woman, 67 percent of married 
women use contraceptives, 81 percent of the births are attended by a skilled health 
worker and 86 percent of children 12-23 months are fully vaccinated. Children illnesses 
are still a challenge with only 56 percent of women having attended more than four 
antenatal clinics, 27 percent of children under five are stunted, and malaria test 
positivity rate of 57 percent is higher than national average (KNBS, 2019). The county 
is still faced by health sector budget deficit with County Government providing only 
half of the budget needed to completely satisfy FP and children services. This means 
the private sector is very important in the provision of health services even though there 
is a dearth of literature on the impact on intervention made in the private sectors on 
heath expenditure in Embu County. 

 

 

 

 

 

 

 


Figure 1.4 below shows that Embu County has family planning deficit of 68 - 72 
million 

 

138.78

144.09

142.46

142.39

70.28

70.28

70.28

70.28

68.50

73.81

72.17

72.10

0

20

40

60

80

100

120

140

160

2017

2018

2019

2020

Million Ksh

Family planning required budget

Family planning available budget

Family planning budget deficit

Figure 1.4: Family Planning Budget Deficit Embu County. 

Source: County Government of Embu Integrated Development Plan 2018-2022 

Though Embu County have attained modern contraceptives uptake of 67 per cent, the 
uptake has reduced from 74 per cent in the past eight years (KNBS, 2019). Figure 1.4 
shows that the progress in attaining family planning by the county government is 
constrained by resources. To strengthen the uptake of maternal and children services, 
social franchisors have made interventions in Kenya and Embu County alike. The main 
interventions of franchisors in Kenya healthcare system includes capacity building 
private healthcare providers to deliver maternal and child health services at an 
affordable costs (ASFH, 2018). However, there is inadequate empirical evidence of the 
impact of social franchisors on people heath expenditure on the services being 
supported by the franchisors. 


Figure 1.5 shows various indicators on children health in Embu County which include 
percentage of children stunting, underweight and malaria positivity rates. 

 

26.8

24.5

11.1

57

26

20.4

11

41

0

10

20

30

40

50

60

Children stunting

Still birth rate

Underweightchildren

Malaria testpositivity rate

Percent (%)

Embu County

National Average

Figure 1.5: Indicators of Children Health for Embu County Compared to National 
Average 

Source: Ministry of Health Kenya 

Figure 1.5 show that Embu County needs several interventions to strengthen children 
health and improve responses in the healthcare system. The available data from the 
Ministry of Health show that the average number of children stunting is 26.8 percent 
higher than national average of 26 percent which means higher proportion of children 
in Embu County than national face impaired growth and development as a result of 
poor maternal health, nutrition and repeated infection. These are closely affected by 
inability to access healthcare services or bigger family size than the parents can afford 
to support financially. A still birth rate of 24.5 percent higher than national average 
20.4 percent reveal the county is still facing maternal health issue and malaria positivity 


rates of 57 percent higher than national average of 41 percent show that both public 
and private healthcare system is important to handle children illnesses. However, 
expenditure on health could be a challenge to services utilization especially in private 
sector. 

1.1.2 Private Healthcare Expenditure in Kenya 

Despite interventions in the health sector in Kenya to lower out of pocket healthcare 
expenditure several issues still push people to private sectors. A study by World Bank 
shows out-of-pocket expenditure as percent of total health expenditure is more than 50 
percent higher than Sub-Saharan Africa average of 29.8 percent. It is also estimated 
that 6.2 percent of households experience catastrophic health expenditure. 

 

 

 

 

 

 

 


Figure 1.6 shows catastrophic health expenditure in Kenya by wealth quintile. 
Catastrophic health expenditure is expressed as a household expenditure on health 
which is more than 40 percent or more of a household’s non-subsistence income 

 

9.9

5.6

4.2

2.7

1.2

5.7

3.2

0

4

8

12

Poorest

Second

Middle

Fourth

Richest

Rural

Urban

Consumption quintile

Residence

Percent

Figure 1.6: Catastrophic Health Expenditure in Kenya by Wealth Quintile 

Source: Kimani and Maina. (2015). Catastrophic Health Expenditures and 
Impoverishment in Kenya. 

Figure 1.6 show that the poorest and rural people are the most exposed to catastrophic 
health expenditure in Kenya yet this category is living below $1.8 Usd/day. The 
financing of healthcare budget in Kenya is contributed by 28 percent household out-of-
pocket payments and 60 percent is on curative with only 20 percent being preventative 
(MoH, 2019). The fact that public healthcare services in level one and two facilities 
which are nearly free suffers several hurdles in delivery of quality healthcare, many 
people are pushed to private facilities which adds a premium due to their optimization 
problem. 


Figure 1.7 shows push factors from public to private health facilities in Kenya 

 

77

74

52

44

43

39

29

8.7

81

62

48

0

15

30

45

60

75

90

Equipment availability

Diagnostic accuracy

Drug availability

Management of complications

Adherence to clinical guidelines

Infrastructure availability

Absence from facility

Caseload

Children drugs unavailability

All drugs unavailability

Mothers drugs unavailability

Sources of inefficiency in public healthcare delivery

Push factorsfrom public toprivate

Percentage (%)

Figure 1.7: Sources of Inefficiency in Health Care Delivery 

Source: World Bank PETS++/SDI Survey (2012) 

Figure 1.7 shows that several factors push people to private sector especially for women 
and children services. World Bank Public Expenditure Tracking Survey (PETS) and 
Service Delivery Indicators (SDI) revealed several sources of inefficiency that 
undermine the effectiveness and quality of the public health care in Kenya that are 
likely to push people to non-public health facilities. These include: drugs availability, 
equipment availability, diagnostic accuracy, drugs availability, infrastructure 


availability absence from the facility and shortage of equipment and hospital 
infrastructure. 

1.1.3 Social Franchising in Kenya 

The popularity of social franchising is growing in Kenya. Currently, there are six social 
franchising brands namely Population Services Kenya (PSK) under a brand Tunza, 
Marie Stopes Kenya (MSK) under a brand Amua, Gold Star Kenya (GSK), Kisumu 
Medical and Education Trust (KMET) under Huduma Poa brand and Sustainable 
Healthcare Foundation under CFW brand (Association of Social Franchising for 
Health, 2018). These social franchisors partner with private health facilities to provide 
selected services under respective brands, most of which include the provision of 
family planning services and integrated management of childhood illness with a core 
mandate of increasing access, quality, improve equity, and enhance the cost-
effectiveness of healthcare services (ASFH, 2018). According to Chakraborty & 
Mbondo (2016), social franchisors in Kenya are supposed to maximize family planning 
uptake, offer training to service providers, and oversees activities of franchisees. 

In Kenya, there are 6,032 private health facilities out of which 15 percent (900) 
facilities are in any of the five social franchisors but MSK and PSK form 85 percent of 
all franchised facilities in Kenya (ASFH, 2018). The intervention of social franchising 
in Kenya is to improve access to quality health services by ensuring service delivery 
points which are private health facilities adhere to specific quality standards. PSK and 
MSK support private facilities improve quality services through step wise quality 
improvement process which leads to certification. The process also include training 


service and linkages to affordable loans to help providers improve/expand their 
facilities. Social franchising is an intervention to serve the poor people in the society. 
Social franchisors uses interventions such as vouchers to reach the poorest in the 
society access quality services provided by the franchisors. Though such intervention 
are considered costly they have been found effective in increasing access to maternal 
health and children services in countries such as Pakistan and Uganda (Ali et al, 2019; 
Hastings and Sarker, 2019). 

PSK social franchising model has entered into contractual agreements with 420 private 
health providers to deliver quality and affordable target services to the customers with 
special focus on family planning and integrated management of children illness (PSK, 
2020); MSK social franchising model has entered into contractual agreements with 400 
private health providers to improve access to quality reproductive healthcare in private 
health facilities at a very affordable price (MSK, 2020); KMET is working with private 
sector to expand and promote access to affordable, quality reproductive health care to 
communities in need of such services, with an emphasis on maternal and newborn 
health and adolescent reproductive health services (KMET, 2020); Gold Star Kenya is 
also contributing to improving the management of priority health conditions including 
maternal, neonatal, child health and adolescent health, family planning, sexual and 
reproductive health services (GSK, 2020); and CFW is working with the private sector 
to improve access to essential drugs, basic healthcare, and prevention services for 
children and families through economies of scale (SHF, 2020). Despite such 
interventions by social franchisor, there is dearth of literature on the impact of social 


franchisors in Kenya especially on increasing access through shifting consumer budget 
line. 

1.2 Statement of the Problem 

Good health is considered an investment and consumption good. Investing in the health 
of women and children such as family planning and integrated management of children 
illnesses improves the health of women and children increasing their productivity and 
learning outcomes. Women who invest in family planning have the right household 
size for quality health of the family, and may indirectly promote savings as it helps to 
avert costs associated with unintended pregnancies. On the other hand children with 
good health have a better learning outcomes. A dollar spent on family planning saves 
nearly 4.5 dollars in direct healthcare costs (HPP, USAID, & MSI, 2014). Health as a 
consumption good helps consumers enjoy utility derived from good health and capacity 
to participate in the labour force or in school. However, a major deterrent to the 
consumption of healthcare services is the health expenditure incurred when purchasing 
this good and services. With more than one choices available, consumers will select 
services providers who maximize their utility at a minimum budget. In Embu County, 
the contraceptive prevalence rate is on downtrend having reduced from 74 per cent in 
2012 to 68 percent in 2018 with unmet demand of 24 percent. The county stunted 
children is 26.8 percent and underweight children is 11.1 per cent higher than national 
average of 26 percent and 11 percent respectively. Though women who access skilled 
birth attendant is above national average, still birth rate of 24.5 per cent is still higher 
than national average of 20.4 percent. Such statistics reveal the county is still facing 


challenges in access to maternal and children health services. Generally, public health 
facilities in Kenya which has the lowest cost has a challenge of frequent stock-outs 
[almost 57 percent] (WHO, 2012) pushing customers into private health facilities 
where clients pay a higher premium to receive the services. Embu County is among the 
poorest counties in Kenya with its share of Gross County Product (GCP) to Gross 
Domestic Product (GDP) having reduced from 1.5 percent to 1.3 percent between 2015 
and 2017. Its GCP per capita is the worst growing below 1.5 per cent compared to 
country average 2.8 per cent. 

There has been attempts to improve access to healthcare offered by private sector 
through public-private partnerships and social franchising model. The aim of social 
franchising in Kenya is to improve access to quality healthcare services, improve equity 
and to enhance the cost-effectiveness of healthcare service. Social franchisors also 
support private health facilities through supply of subsidized medical products to lower 
private healthcare cost and improve access. Despite the presence of social franchisors 
in Embu County such as Maries Stopes Kenya, Population Services Kenya, and 
Sustainable Healthcare Foundation, there was dearth of empirical literature on their 
impact especially on health expenditure which is a major barrier to access and 
affordability to the services provided. Studies evaluating the impact of social 
franchising had focused on quality, equity, and customer increase (Chakraborty et al., 
2016) with narrow focus on health expenditure or cost which influences demand for 
these services. Studies from other countries had shown mixed results on the impact of 
social franchising. These studies had also neglected a very important economic concept 
in their analyses that consumer expenditure is dependent on their budget. The present 


study contributed to the discussion by investigating the impact of social franchising on 
healthcare expenditure within private healthcare facilities in Kenya. The study 
employed Propensity Score matching evaluation model to estimate whether social 
franchising shifts the consumer budget line outward or inward hence influencing 
demand for such services within private facilities. 

1.3 Objectives of the Study 

1.3.1 Main Objective 

The main objective of this study was to investigate the impact of social franchising on 
healthcare expenditure within private health facilities in Embu County Kenya. 

1.3.2 Research Questions 

i. Does social franchising have a significant effect on children healthcare 
expenditure within private facilities in Embu County? 
ii. Does social franchising have a significant effect on short-term family planning 
services expenditure within private facilities in Embu County? 
iii. Does social franchising have a significant effect on long-term family planning 
expenditure within private facilities in Embu County? 


 

 


1.3.3 Specific Objective 

The specific objectives of the study include: 

i. To establish the impact of social franchising on children's healthcare services 
expenditure within private facilities in Embu County 
ii. To establish the impact of social franchising on short-term family planning 
services expenditure within private facilities in Embu County 
iii. To establish the impact of social franchising on long term family planning 
services expenditure within private facilities in Embu County. 


1.4 Significance of the Study 

Maternal and child health is important in enhancing the health of women and children. 
The purpose of this study was to establish whether social franchising affects the health 
expenditure of children services and family planning in the private sector. The findings 
of this study are very useful to policymakers as they may understand the impact social 
franchising has on the cost of services since healthcare-seeking behavior is affected by 
factors such as affordability. The private healthcare providers find this report useful in 
their decision to join a social franchise network; donors who support social franchisors 
also find this report useful to understand extent to which social franchising model has 
generated cost-effective business model in the health sector, and family planning 
consumers may find this report useful in decision making on where to seek family 
planning services. Health policy makers may also find this report useful in 
understanding the role of social franchising in health sector and their effect on health 


expenditure. The findings of this study are also helpful in contributing to the body of 
existing literature on the impact of social franchising on healthcare expenditure within 
private facilities. 

1.5 Scope of the Study 

This study investigated the impact of social franchising on healthcare expenditure 
within private facilities in Embu county Kenya. The study focused on the impact of 
social franchising on expenditure on children healthcare services and various FP 
services such as short term FP method (Depo-Provera) and longer-term FP methods 
(implants and IUCD). Data was collected from people seeking services in PSK and 
CFW franchisee for treated facilities and those who sought services in other private 
which do not belong to any social franchise network. 

1.6 Organization of the Study 

Chapter one presented the background of the study, chapter two reviews both 
theoretical and empirical literature while chapter three presents research methodology. 
Presentation and discussion of empirical results is offered in chapter four. Chapter five 
summarizes the paper, concludes it and gives policy recommendations. 

 


CHAPTER TWO 

LITERATURE REVIEW 

2.1 Introduction 

This chapter reviews theoretical literature, empirical literature, and provides an 
overview of the literature. The overview of the literature summarizes critical findings 
from the theories reviewed and empirical literature in order to draw the research gap. 

2.2 Theoretical Literature 

This section presents theories that guided the study. The study has adopted the principal 
agent theory and consumer theory as a theoretical framework. 

2.2.1 Principal-Agent Theory 

The principal-agent theory was proposed and developed by Ross (1974) and Mitnick 
(1974). The theory is normally referred to as a principal-agent theory, where principal 
refers to an individual, an institution or an economic agent, while agent refers to the 
second party working on behalf of the principal. The theory assumes there is a 
contractual relationship between the principal and the agent where the principal 
delegates some responsibilities to the agent. The agent behavior directly affects the 
principal, even though a major challenge in the relationship is information asymmetry, 
where the principal may lose control over the intentions of the agent (Volery & Hackl, 
2009). In this scenario, an agent may deviate from the agreement because they are self-
interested and engaged in opportunistic behaviors. 


There is two main application of the principal-agent theory in this study. First social 
franchisors are principal and franchisees are agents. The social franchisors may have 
an intention to increase communities’ access to quality services offered in private 
health facilities located within the communities and therefore introduce voucher 
programs to the community members, supply lower priced products to the healthcare 
providers or provide price controls to the clinics to improve affordability. However, as 
pointed out in the theory, the principle doesn’t have full control of the intentions of the 
franchisees who may engage in opportunistic behavior to maximize their profits. In the 
latter, the cost of healthcare services will be the same in both treated and untreated 
facilities despite the interventions. 

The second application of this theory is individual seeking services in private health 
facilities as principals and private facilities health providers as agents. A customer 
(principal) will procure the services from a healthcare provider (agent) with the aim of 
maximizing their satisfaction subject to their budget constraint. Under consumer theory 
consumers will chose facilities that maximize their utility with minimum budget yet 
the agent may violate consumer expectations and charge equal or above market rates 
despite the interventions provided to the social franchisors. This study applies both 
concepts to determine whether customers seeking services in franchised facilities pay 
significantly less as intended by the franchisors and receive quality services. However, 
the customer’s choice of agent (healthcare facility) has an underlying economic theory 
of consumer demand. The theory posits cost paid by the consumer as a function of their 
income and quantity demanded (Varian, 2010). 


2.2.2 Consumer Theory 

Consumer theory is an economic theory that studies how individuals make decisions 
that relate to purchasing goods and services to satisfy their needs given a budget 
constraint. It is concerned with how a rational consumer would make their consumption 
decisions to maximize their welfare. The theory assumes a utility is generated when a 
consumer purchases goods and services that satisfy their needs and wants and that they 
have a budget constraint. The definition of the utility function is a representation of the 
preferences of a consumer. The consumer utility function for consuming two units of 
goods can be represented as 𝑈(𝑍)=𝑈(𝑍1𝑍2). The theory also assumes a consumer 
has a fixed amount of money to spend (M) and each unit of good purchased has some 
costs attached to them referred to as price (P) which is expenditure on goods 𝑍1and 𝑍2. 
Therefore a consumer budget constraint for their set of choices can be presented 
mathematically as 𝑃1𝑍1+𝑃2𝑍2≤𝑀. We can therefore define consumer problem 
as 
𝑀𝑎𝑥 𝑈 (𝑍)=𝑈(𝑍1𝑍2)
𝑠𝑡
𝑃1𝑍1+𝑃2𝑍2≤𝑀
………………………………………………………….2.1 

Where 𝑈=𝑈(𝑍1𝑍2) is the utility derived by consumption of healthcare services 
𝑍1and other product and services 𝑍2, 𝑃1 is the expenditure on healthcare services; 𝑃2 is 
the consumer expenditure on other products and services and M is the individual 
income. Forming a Lagrangian function, solving an optimization process and 
transforming normal demand function into inverse demand function we find the 


healthcare expenditure as a function of healthcare services sought and individual 
income/ability to meet the cost such that 
𝑃1=𝐹(𝑀,𝑍1)………………………………………………………………………2.2 

In summary, any program that would affect the cost of family planning directly affects 
consumer welfare by shifting consumer’s budget line outward or inward. An increase 
in the price of good and services reduces available money stock shifting consumer’s 
budget line inward and reduction shifts consumer budget line inward leaving more 
money to consumer other goods and services. If the interventions of franchising are 
cost effective then consumer. 

2.3 Empirical Literature 

2.3.1 Impact of Cost of Health Services on Patient Affordability and Access 

Abiye, Tesfaye, and Hawaze (2013) studied barriers to access to essential medical 
services in Ethiopia public health sector. Data was collected from patients seeking 
medical treatment. Comparing prices of essential medical supplies between public 
facilities, pharmacies, and the private sectors, the study revealed the private sector was 
32 percent higher than the public sector, and these services are not affordable. The 
public hospitals had a regular stock-out problem. Carasso et al. (2009) study had also 
found there exists frequent drug shortages in the public sector and unaffordable prices 
deter patients from seeking services in the private sector. Programs to make private 
healthcare services affordable were significant in increasing access to services in 
several areas. 


Mhlanga and Suleman (2014) investigated “prices, availability, and affordability of 
medicines” in urban areas of Swaziland. Data was collected among private health 
facilities and public health facilities and analyzed descriptively. The study found 
medical commodities prices in the public sector were very competitive while the 
patient’s prices were very expensive in the private sector. The study concluded a lack 
of affordability of essential medical commodities is associated with their high prices 
since in Africa most healthcare seekers lack social insurance systems and therefore use 
out of pocket payment methods. Programs and policies to lower prices would help 
lower prices. It was also found high prices limit demand in the private sector where 
price markup was 31 – 53 percent above prices in the public sector. 

Ewen et al. (2017) studied “prices and availability of locally produced and imported 
medicines in Tanzania and Ethiopia” using data collected from the government 
procurement department and survey from patients. The study did not establish any price 
differences between locally produced and imported medical commodities in Tanzania. 
However, in Ethiopia, prices charged to patients were lower for imports in the public 
sector and more for imported commodities traded in the private sector. Earlier studies 
by Mackintosh and Mujinja (2008) comparing medical commodities prices between 
Tanzania, India, and Kenya found no significant patient price differences between 
locally produced and imported medicines. In countries such as Malaysia, Bangladesh 
and Vietnam prices of locally produced brands are lower compared to import ones 
(Ewen et al., 2017). These studies also found that higher prices of medical commodities 
limited their affordability and access. 


According to Collins et al. (2017) to improve access and affordability of medical 
services, there is a need to prioritize patient access and affordability in both government 
and non-government programs, enhance efficient markets where there is price 
competition, and improve the availability of information. The policy paper also points 
out that generic drugs are less expensive than patent drugs and purchasing medical 
commodities through an agency lowers the prices of medical commodities. This could 
mean, social franchising could be a better model of making family planning services 
affordable in the private sector. 

2.3.2 Impact Social Franchising on Family Planning and Children Services 

Patouillard et al. (2007) conducted a systematic review of the literature seeking to 
establish whether “working with the private for-profit sector improve utilization of 
quality health services by the poor.” Results from 52 empirical studies found working 
with the private sector to improve the utilization of healthcare services were successful 
among poor countries as it increased utilization. However, the utilization was mainly 
attributed to the use of provided vouchers among poor individuals from Zambia and 
Nicaragua. The use of vouchers reduced the price of services among franchised health 
facilities by 17 percent. The study found the use of vouchers as a strategy to lower 
prices, use of cash transfer and provider subsidies remain unexplored and more studies 
require to be conducted to determine whether the use of price strategies would improve 
access to family planning services among private health’s facilities. 

Shah, Wang, and Bishai (2011) compared family planning services provided by the 
private sector to government and NGO facilities in Ethiopia and Pakistan across cost, 


quality, and equity. Data were collected from independent private sector providers, 
NGO providers, government providers, and social franchises from private providers. 
Wilcoxon rank-sum tests were used to compare equal medians due to the problem of 
skewed data especially on the cost which was calculated in the form of currency. The 
results revealed higher clinical costs per client among franchised private health 
facilities in Ethiopia and not statistically different in Pakistan. Access to franchised 
private health facilities was lower in Ethiopia. However, the total quality of care was 
statistically significantly higher among private franchised clinics in both Ethiopia and 
Pakistan. Therefore, NGO facilities and Government facilities are the most efficient in 
serving most clients per dollar. To improve access among franchised health facilities, 
there is a need to lower prices either through vouchers, insurance or fee waiver 
programs. 

Huntington et al. (2012) studied factors influencing demand for a social franchise 
membership in Myanmar among physicians. A physician joins a franchise network for 
free and is required to adhere to price capitation specified by the franchisor on the 
various medical products. The physician enjoys signage, in-service training, receive 
medical products at highly subsidized prices and up to date information. The study 
found the impact of joining a franchise network was unclear even though there were 
increased client volumes. The surge in membership incomes was not only attributed to 
increased client volumes but also the fact that due to agency problems, franchised 
physicians are likely to charge higher prices to customers. 


Beyeler, York De La Cruz, and Montagu (2013) did a systematic review to “determine 
the impact of clinical social franchising on health services in low-and middle-income 
countries” the study covered countries such as Pakistan, Nepal and India. The use of a 
voucher system increased contraceptive use by 2.29 percent in Nepal and 23 percent in 
Pakistan. However, there was no statistically significant difference between demands 
for services in either franchised on non-franchised health facilities in Kenya. 

Azmat et al. (2014) studied the impact of social franchising on contraceptive use when 
completed by vouchers using a quasi-experimental study in rural Pakistan between 
2009 and 2010. The target population involved married women within reproductive 
age living within franchised or non-franchised health facilities. The study established 
among franchised health facilities, LARCs customers were provided with vouchers that 
reduced the prices of family planning services significantly compared to non-
franchised health facilities. The reduction of prices for LARCs customers increased 
demand by 28.5 percent while contraceptive prevalence increased by 19.6 percent. The 
study population if different from Kenya. 

Chakraborty et al. (2016) evaluated the impact of social franchising on family planning 
use in Kenya among Tunza franchised clinics. The study utilized a quasi-experimental 
study design where data was collected from Tunza franchised clinics catchment areas 
and compared to catchment areas where no franchised facility was present. The target 
population was women of reproductive age and were sampled using a systematic 
sampling technique. Binary multivariate logistic regression was used to estimate the 
marginal effects. The results showed that the presence of franchised health facilities 


did not attract new users of modern family planning methods and prices were not 
significantly different. The study, however, did not establish why there is no difference 
in the utilization of family planning services between franchised and non-franchised 
health facilities. 

Gold et al. (2017) studied “increasing access to family planning choices through public-
sector social franchising in Mali” between 2012 and 2015 among franchised and non-
franchised health facilities. The study analyzed data collected from MSI Mali Bluestar 
franchise networks that franchise health facilities to deliver affordable family planning 
services. The study found among franchised health facilities, prices were 13 times less 
compared to non-franchised facilities and the number of clients seeking LARC 
expanded four times. Therefore, lowering LARC service is likely to improve service 
utilization as rational customers seek pocket-friendly services. 

Bellows et al. (2017) studied how to improve contraceptive access for hard to reach 
populations in Uganda through a social franchising model. The study employed pricing 
strategies where prices were lowered using a voucher program. At the beginning of the 
program, only 18 percent of the women used LARCs and permanent methods (PMs) 
and unmet demand stood at 34 percent. After the implementation of the program, it was 
established lowering prices increased modern contraceptive prevalence by 8 percent. 
Therefore, lowering the cost through vouchers and social franchising is effective in 
expanding family planning access among private sector franchises. Also, lowering 
prices surged demand for LARCs and PMs. It was necessary to replicate such a study 
in Kenya to understand the extent to which social franchising has affected the pricing 


of private health facilities and therefore improved access to quality services provided 
by private health facilities in Kenya. 

Chakraborty, Montagu, Wanderi & Oduor (2019) investigated “who Serves the Poor? 
An Equity Analysis of Public and Private Providers of Family Planning and Child 
Health Services in Kenya” data was collected from social franchise, public, private for 
profit and faith based facilities. The findings of the study found that franchised health 
facilities serve the poorest than private for profit though their difference was not 
statistically significant. The impact of social franchisors was in family planning than in 
children illnesses but outperformed by public facilities. The study since social 
franchising targets services expansion to the poorest in the communities, the study did 
not employ treatment evaluation models such as propensity score matching or 
difference in difference to estimate the impact of social franchising on health 
expenditure which may have resulted to no significant differences in the mean amount 
paid by clients within private facilities. The present study used propensity score 
matching to estimate the cost differences. 

2.4 Overview of the Literature 

The study has reviewed consumer theory and principal-agent theory where the latter 
demonstrated that the principal (franchisors) directly affects agent (franchisee) 
activities and the former which demonstrates that theoretically expenditure on health 
services is a function of consumer budget for such services. The study therefore utilized 
consumer theory as a theoretical foundation of the study but bearing in mind that social 
franchisors is a principal agent concept. Consumers being rational will chose lower 


priced services which means the effect of principal is reflected on the consumer 
behavior as long as they have all the information. Assuming both theory works well, 
then health expenditure will be lower for people seeking services in franchised 
facilities. The empirical literature studies on the impact of social franchising on 
healthcare expenditure show mixed results and therefore difficult to conclude the true 
impact of social franchising in the private healthcare expenditure especially through 
the price channel. Some of the studies reviewed for instance demonstrate social 
franchising had significant effect on price or utilization of healthcare services (Azmat 
et al., 2014; Gold et al., 2017; Patouillard et al., 2007; Bellows et al., 2017), others 
show there is no significant association between the two (Huntington et al., 2012; 
Chakraborty et al., 2016), and others reveals social franchising negatively affects the 
cost of services (Shah et al., 2011). 

The study filled this empirical gap by establishing the impact of social franchising on 
private healthcare expenditure in Kenya by strategically selecting the services which 
franchisors are interested in i.e. integrated management of children illness and family 
planning. This study employed an impact evaluation model propensity score matching 
(PSM) to determine the impact of social franchising on healthcare expenditure within 
private healthcare facilities in Kenya while controlling all the potential confounders. 
Such methodology was not previously employed in the available literature on the 
subject in Kenya. 

 

 


CHAPTER THREE 

RESEARCH METHODOLOGY 

3.1 Introduction 

This chapter presents the research methodology employed to achieve the objectives of 
the study. It is organized as research design, theoretical framework, definition and 
measurement of variables, data types and sources, target population and sample size, 
sampling method, data collection, data analysis and diagnostic tests. 

3.2 Research Design 

The study used a cross-sectional research design relying on both qualitative and 
quantitative data. A cross-sectional design was used because data were collected at a 
certain point in time to describe whether social franchising has an impact on the 
expenditure of services in private facilities. 

3.3 Theoretical Framework 

This study was anchored on consumer theory where the expenditure on healthcare 
services is a function of services sought and consumer income bearing in mind that 
social franchising is a principal agent theory concept. i.e the interventions of social 
franchisors on the franchisee network is reflected on the consumer expenditure on 
health between franchised and non-franchised facilities. 

Healthcare is both an investment and a consumption good. As an investment good, 
families that have invested in reproductive health services such as family planning have 


more educated members, have reduced healthcare costs, women participate in the paid 
labour force and are therefore wealthier (WHO, 2015). The household derives utility 
from maternal and children's health from the consumption ceteris paribus. To acquire 
maternal and children health services, a household has a choice public, private for-
profit, private franchised, mission, herbalists, chemist, or shops as service providers. 
Consumers are utility maximizers and given various healthcare providers they choose 
a provider who maximizes their utility with a minimum budget. 

The utility is therefore derived by consuming goods and services, and individuals have 
a steady preference displayed in a utility function ( 𝑈{𝑍}=𝑈(𝑍1𝑍2). Where Z1 is 
utility derived by consuming healthcare services and Z2 is the utility derived by 
consumption of other services. The benefit of consuming health services is good 
maternal and children's health while the high cost of health services is considered a 
disutility because it reduces the consumption of other goods and services. Since 
consumption of maternal and children health services improves women human capital 
but lowers budget allocation to other goods and services due to economic costs 
associated with the consumption of these services, individual will chose a health facility 
who maximizes the utility subject to budget constraint such that: 
𝑀𝑎𝑥 𝑈 (𝑍)=𝑈(𝑍1𝑍2)
𝑠𝑡
𝑃1𝑍1+𝑃2𝑍2≤𝑀
………………………….3.1 

Where 𝑈=𝑈(𝑍1𝑍2) is the utility derived by consumption of health services 𝑍1and 
other product and services 𝑍2, 𝑃1 is the expenditure on health services; 𝑃2 is the 
expenditure on other products and services and M is the individual income. 


𝐿=𝑍1𝑍2−ʎ(𝑃1𝑍1+𝑃2𝑍2−𝑀).......................................................3.2 

The first order conditions for utility maximization is as follows 
𝜕𝐿
𝜕𝑍1=𝑍2−ʎ 𝑃1 = 0................................................................3.3 
𝜕𝐿
𝜕𝑍2=𝑍1−ʎ 𝑃2 = 0................................................................3.4 
𝜕𝐿
𝜕ʎ 
=𝑃1𝑍1+𝑃2𝑍2−𝑀 = 0.......................................................3.5 

From the first order conditions, healthcare demand function is derived as: 
𝑍1=
𝑀
2𝑃1...............................................................…….........3.6 

Equation 3.6 means demand for healthcare is a function of income M and the cost (𝑃1). 
The higher the cost the lower the demand for health. From this demand function, we 
can derive an inverse demand function to equate cost of healthcare as a function income 
and type services as below. 

 𝑃1=
𝑀
2𝑍1……………………………………………………………………………3.7 
𝑃1=𝐹(𝑀,𝑍1)………………………………………………………………………3.8 

Equation 3.8 simply equates the healthcare expenditure as a function of individual 
income (M) and services utilized (Z). However from empirical literature reviewed in 
this study, health expenditure does not only depend on income of customers and 
services utilized but also distance to the health facility (L), type of a facility (T) (Public, 
Private, Mission, chemist e.t.c.), personal factors such as education level (El) and 
marital status (Ms), whether the cost is paid by an insurance (Ins) or out of pocket, 
employment status of the individual (Ei), and employment status of spouse (Es). 


P=𝐹(𝑀,𝑍,𝐿,𝑇,𝐸𝑙,𝑀𝑠,𝐼𝑛𝑠,𝐸𝑖,𝐸𝑠)…………………………………………3.9 

3.3.1 Measuring the Impact of Social Franchising on Healthcare Expenditure 

The study conducted propensity score matching (PSM) to establish the average 
treatment effects of the treated (ATET) of social franchising on the expenditure of 
healthcare services. The PSM was introduced by Rosenbaum and Rubin (1983) and 
further strengthened by Heckman (1997). They define propensity score as 𝑒(𝑍𝑖) for 
subjects i (i=1…N) as simply the conditional probability (ranges from 0 to 1) of being 
assigned to a particular treatment given a vector of observed covariates 𝑍𝑖 such that 
𝑒(𝑍𝑖)=Pr(𝐹𝑖=1|𝑍𝑖)…………………………………………………………3.10 

Where 𝐹𝑖=1 for members of social franchise (treatment) and 𝐹𝑖=0 for non-franchised 
(control). 𝑍𝑖 is a vector of observed covariates for 𝑖𝑡ℎ subject. The potential outcome 
(cost of services) we define as 𝑀𝑖𝑍𝑖 for each customer i, where i=1…N and N denote 
the total sample. Therefore the treatment effect of an individual i can therefore be 
presented as 
𝜏𝑖=𝑃1𝑖−𝑃0𝑖…..…………………………………………………………..3.11 

 𝑃1𝑖 is the cost of health services sought if an individual (i) seeks services in a franchised 
clinics and 𝑃0𝑖 is the cost of health services if an individual (i) seeks services in non-
franchised clinics. 0 represents a non-franchised and 1 represents to a franchised clinic. 

The potential problem arises because only one outcome is observed for each individual 
i and the unobserved forms a counterfactual outcome hence not possible to estimate for 


individuals in the treatment group. To overcome this problem one has to calculate the 
average treatment effects of the treated defined as 
𝜏𝐴𝑇𝐸𝑇=𝐸(𝜏|𝐹=1)=𝐸[𝑃1|𝐹=1]−𝐸[𝑃0|𝐹=1]………………..3.12 

The counterfactual mean 𝐸[𝑃0|𝐹=1] is not observed for the individuals seeking 
services in franchised clinic, therefore, requires to be substituted. The easiest method 
would be to use the mean outcome of the untreated 𝐸[𝑃0|𝐹=0] however this not a 
good idea since the same factors are likely to influence the decision to seek services in 
either franchised or non-franchised clinic. This means expenditure on health services 
may differ even when social franchising had not really taken place leading to selection 
bias. We can, therefore, represent as: 

 𝐸[𝑃1|𝐹=1−𝐸[𝑃0|𝐹=0]=𝜏𝐴𝑇𝐸𝑇+𝐸[𝑃0|𝐹=1]−𝐸[𝑃(0)|𝐹=0]……..3.13 

Rearranging above we can see the selection bias on the RHS as below. 
𝐸[𝑃1|𝐹=1−𝐸[𝑃0|𝐹=0]−𝜏𝐴𝑇𝐸𝑇=𝐸[𝑃0|𝐹=1]−𝐸[𝑃0|𝐹=0]……3.14 

This means we can only estimate true parameter 𝜏𝐴𝑇𝐸𝑇 if the selection bias term on the 
RHS =0. The randomness can only be achieved through efficient matching discussed 
in model estimation. 
𝐸[𝑃0|𝐹=1]−𝐸[𝑃0|𝐹=0]=0 

Therefore average treatment effects of the treated (ATET) is presented in equation 3.14 
below 
𝐸[𝑃0|𝐹=1]−𝐸[𝑃0|𝐹=0]−𝜏𝐴𝑇𝐸𝑇=0 

𝐸[𝑃1|𝐹=1]−𝐸[𝑃0|𝐹=0]=𝜏𝐴𝑇𝐸𝑇……………………………………………3.15 


3.4 Model Estimation 

Model estimation using PSM took four step procedure (Cameron and Trivedi, 2005). 

The first stage is the selection of covariates to be included in the model should be 
related to seeking services in franchised clinics and the ability to pay the cost of services 
provided to minimize the bias. This study used covariates derived from the theoretical 
model and empirical literature. 

The second stage is the computation of the propensity scores which was done using 
binary logit where F=1 if a member of social franchise and F=0 if not a member. Hence 
the model estimated took the form of 𝑒𝑖=Pr(𝐹𝑖=1|𝑍𝑖)=𝐺(𝑍 ω) 

Where 0<G(Zω) <1 

Zω=ω0+ω1Z1+ω2Z2+ω3Z3…+ω11Z11……………3.16 

Where Zi covariates are of what determine seeking services in franchised health facilities and 
ω_i are coefficients of the covariates. Substituting in the logistic model we obtain 

 𝑒𝑖=˄(Zω)=
exp(ω0+ω1Z1+ω2Z2+ω3Z3+⋯+ω11Z11)
1+exp (ω0+ω1Z1+ω2Z2+ω3Z3…+ω11Z11)
……………...…3.17 

 Where 0<˄(Zω)<1 and equation 3.17 yielded the propensity scores used for 
matching. 

The third stage is matching individuals who seek services in franchised clinics with 
those having similar covariates and not seeking services in franchised clinics. This 
stage used the propensity scores to facilitate matching within the common support 
region hence fulfilling the overlap assumption. The study used kernel matching, nearest 


neighbor and radius matching criteria of matching. The matching ensures only similar 
individuals from control and treatment groups are compared. Individuals who seek 
services away from franchised clinics were used as counterfactuals to the 
corresponding individual in seeking services in franchised facilities such that. 

ATET=𝐸[𝑃1|𝐹=1]−𝐸[𝑃0|𝐹=0]………………………………………………..3.18 

The last stage is impact estimation which quantifies the effect of interventions. From 
equation 3.18 the impact of social franchising was estimated in equation 3.20, 3.21, 
and 3.22 for the three objectives for this study. 
𝐴𝑇𝐸𝑇=𝐸(Δ𝑃)=
1
𝑁
Σ[𝑃1𝑖−𝑃̂0𝑗(𝑖)
𝑁
𝑖=1]…………………………………….……3.19 

1. 𝐴𝑇𝐸𝑇=𝐸(Δ𝑃1)=
1
𝑁
Σ[𝑃1𝑖−𝑃̂0𝑗(𝑖)
𝑁𝑖
=1]………………………………………3.20 
2. 𝐴𝑇𝐸𝑇=𝐸(Δ𝑃2)=
1
𝑁
Σ[𝑃1𝑖−𝑃̂0𝑗(𝑖)
𝑁𝑖
=1]………………………………………3.21 
3. 𝐴𝑇𝐸𝑇=𝐸(Δ𝑃3)=
1
𝑁
Σ[𝑃1𝑖−𝑃̂0𝑗(𝑖)
𝑁𝑖
=1]………………………………………3.22 


𝑃1𝑖 is the average expenditure of health services for individuals that seek services in 
franchised clinics and 𝑃̂0𝑗(𝑖)is the average expenditure of health services for all 
comparison individuals seeking family planning or children services in non-franchised 
private facilities matched with treatment individual indexed by i. 

Equation 3.19 is the model that determines the impact of social franchising on 
healthcare expenditure within private health facilities in Kenya. The model was used 
to address the study objectives as follows: 


Model 3.20 was used to estimate the impact of social franchising on short-term family 
planning services within private health facilities in Kenya; 

Model 3.21 was used to estimate the impact of social franchising on long-term family 
planning method within private health facilities in Kenya; 

Model 3.22 was used to estimate the impact of social franchising on the expenditure of 
children services within private health facilities in Kenya. 

3.5 Definition and Measurement of Variables 

A number of variables identified from the theoretical literature and empirical literature 
affect expenditure on health services. The factors are deemed to affect seeking services 
in franchised health facilities and thus the propensity scores e_i as well. 

 


Table 3.1: Definition and Measurement of Variables 

Variables 

Measurement 

Dependent variable 

Expenditure of health services 

Expenditure on Depo Provera from the previous 
visit in Ksh. 

Expenditure on children treatment from the 
previous visit in Ksh 

Expenditure on long-term FP services (IUCD and 
Norplant’s) from the previous visit in Ksh 

Predictor variables 

Treatment status (F) 

Individuals seeking services in a social franchise 
network (F=1) and 0 for non-franchised 

Number of children 

Number of children in the households 

Household size 

Total number of people living in the household 

Education level (El) 

1-Post primary and 0 otherwise 

Marital status (Ms), 

1-married and 0-
Single/divorced/widow/separated 

Services sought (Zi) 

1-Depo Provera, 2-IUCD, 3-Norplant’s implants, 
and 4- children illnesses. 

Cost considerations when 
selecting health facility 

1=always considers cost; 0 does not 

Quality consideration 

1=always considers quality/reputation of health 
facility/provider; 0 does not 

Distance to the clinic (L) 

1-walking distance, 0-transport required. 

Job type 

1 casual/unemployed; 0 permanent/contract/well 
paying; 

Type of dwelling 

1-permanent house; 0 semi-permanent 



3.6 Data Types and Sources 

The study used primary data collected from individuals seeking services in franchised 
and non-franchised health facilities in Embu County. The researcher randomly selected 
individuals and used a researcher administered questionnaire. 

3.7 Target Population 

The study targeted population seeking services in private clinics between October and 
November 2020. The National Census done by KNBS show that there is approximately 


124,955 women of reproductive age. The study assumed all these women had equal 
chance of being selected. The prevalence of modern family planning method in Embu 
County is estimated to be 67 percent and population of 186,500 women. 

3.8 Sampling Method and Sample Size 

The study used an area sampling technique where respondents in each cluster were 
sampled randomly. The clusters included four franchised clinics and five non-
franchised clinics in the target sub-counties. The study used Yahmane's (1967) formula 
of finite sample to calculate a representative sample: 
𝑛=
𝑁
1+𝑁(𝑒)2=
186,5001+186,500(0.07)2=203 

Where n is the sample size, N is the population size and e is the level of precision. 
When the formula is applied in the equation we get a sample of 203 women of 
reproductive age who would be sampled from the target clusters. 

3.9 Data Collection 

Data collection was through questionnaire approach. The researcher administered 
surveys that was adopted due to the nature of respondents in terms of education. . 

3.10 Data Analysis 

The study used propensity score matching to estimate the ATET within private health 
facilities in Kenya on the three objectives. On the first, second and third objectives 
propensity scores were estimated by equation (3.17). The propensity scores of equation 
3.17, were used to match individuals who sought health services in franchised health 


facilities with those from non-franchised health facilities using Radius, Nearest 
Neighbor, and Kernel Method interchangeably as presented in equation 3.18. After the 
matching was complete, ATET was estimated on equation (3.19) where the Y variables 
was peoples expenditure on short-term family planning services; expenditure on long-
term family planning services and expenditure on children services. This helped 
addressing all three objectives. Following the procedure outlined under model 
estimation, equation 3.20 estimate the impact of social franchising on short-term family 
planning services; equation 3.21 impact of social franchising on long-term family 
planning; and equation 3.22 impact of social franchising on the expenditure of children 
services within private health facilities in Kenya. 

3.11 Diagnostic Tests 

The ATET was estimated using propensity score matching after balancing test has been 
carried out and common support verified using kernel density visual plots (Cameron 
and Trivedi, 2005). The balancing tests were conducted to ensure seeking services in 
franchised clinics were independent of the women's characteristics after controlling 
other covariates as estimated by the propensity score. The overlap condition was also 
tested to ensure after matching those seeking services in the franchised clinics were as 
good as random to ensure the estimators are efficient. 

 


CHAPTER FOUR EMPIRICAL FINDINGS 

4.1 Introduction 

This chapter presents the findings of the study. It’s presented as descriptive statistics, 
the impact of social franchising on children healthcare services, the impact of social 
franchising on short-term family planning methods, the impact of social franchising on 
long-term family planning methods within private health facilities in Kenya. Where 
applicable, diagnostic tests of various models used in the study are also presented. 

4.2 Analysis of Response Rate and Descriptive Statistics. 

The researcher screened 309 respondents out of which only 215 met the criteria of the 
study giving a return rate of 100 per cent hence sufficient for further analysis. 

4.2.1 Age of the Study Participants 

Table 4.1 below gives a summary of study participant’s age, number of children in each 
family and age of the youngest child in years. 

Table 4.1: Age of Respondents 

 

Mean 

Std. Dev 

Skewness 

Kurtosis 

Age of study 
participants 

Non franchised 

31.2 

7.43 

0.76 

-0.786 

Franchised 

31.8 

6.70 

0.08 

-0.701 

Number of 
children 

Non franchised 

2 

1.12 

1.01 

1.586 

Franchised 

2 

1.02 

0.89 

0.651 

Age of children 
in years 

Non franchised 

3.8 

3.65 

2.19 

5.569 

Franchised 

3.9 

3.34 

2.03 

5.325 



Source: Author’s estimation from the study data 

The study found the mean age of study participants in years from franchised and non-
franchised health facilities was 31.8 (SD: 6.7) and 31.2 (SD: 7.43) years respectively. 
There were 2 children in each family and the youngest child was 3.8 years on average. 


4.2.2 Education of the Study Participants 

Figure 4.1 below presents study participants education level 

 

31.6

13

32.1

9.3

0

10

20

30

40

No education

Primary

Secondary

Tertiary

Per cent

Figure 4.1: Education and Marital Status 

Source: Author’s plot from the study data 

The study found that 32.1 percent had secondary level of education, 28.4 percent had 
primary level and 30.2 percent had no education. 

4.2.3 Marital Status of the Study Participants 

Figure 4.2 presents marital status of study participants 

 

Married 
89.8%

Single9.3%

Widow0.9%

Figure 4.2: Marital status of Study Participants 

Source: Author’s estimation from the study data 

Results presented in figure 4.2 show that 89.8 percent of the respondents were married 
and 9.3 percent single. 


4.2.4 Sources of Healthcare Services 

Table 4.2 below shows where study participants seek healthcare services relating to 
children illness, family planning and others. 

Table 4.2: Health Services Utilization between Franchised and Non-Franchised 
facilities 

 

Franchised 

Non franchised 

 

N (%) 

N (%) 

Child illness 

35 (44.9) 

66 (48.2) 

Family Planning 

39 (50) 

56 (40.9) 

Others 

4 (5.1) 

15 (10.9) 

 Total 

n (78) 

n (137) 



Source: Author’s computation from the study data 

The study found 36.3 percent (n=78) of the respondents utilize franchised health 
facilities out of which 44.9 percent (n=35) sought children services, 50 percent (n=39) 
sought family planning services, and 5.1 percent (4) sought services for adult illness. 
Respondents who utilize private non-franchised facilities 63.7 percent (n=137) seek 
children services (48.2 percent), family planning (40.9 percent) and adult illness (10.9 
percent). This means there are more proportion of respondents seeking FP services in 
franchised facilities. 

 

 

 

 


4.2.5 Healthcare Facility Nearest to Home 

The health facilities nearest study participants home is presented in figure 4.3 below. 

 

Private56.7%

Public40.9%

Others2.3%

Figure 4.3: Health Facility Nearest to Home 

Source: Author’s computation from the study data 

The study found a majority of respondents 56.7 percent indicted a private facility is 
their nearest point of contact whenever they need healthcare services compared to 40.9 
percent who have a public health facility. 

4.2.6 Considerations in Seeking Healthcare Services 

Table 4.3 below presents factors that study participants consider when selecting health 
facility to seek services from. 

Table 4.3: Consideration in Seeking Healthcare Services 

 

Non franchised 

Franchised 

Total 

N (%) 

N (%) 

N (%) 

Consideration 
on where to 
seek FP 

Quality 

9 (6.6) 

17 (21.8) 

26 (12.1) 

Location 

42 (30.7) 

6 (7.7) 

48 (22.3) 

Cost 

31 (22.6) 

37 (47.4) 

68 (31.6) 

Relationship 

36 (26.3) 

11 (14.1) 

47 (21.9) 

Services availability 

19 (13.9) 

7 (9) 

26 (12.1) 

Total 

137 

78 

215 



Source: Author’s computation from the Study Data 


The results also indicated 31.6 percent of the respondents consider cost before seeking 
services, 22.3 percent consider the location of the facility, 21.9 percent consider a 
relationship with the provider and 12.1 percent consider quality or services availability. 
There are more respondents considering quality and cost in franchised clinic catchment 
areas and more respondents considering location wherever they make a choice of where 
to seek healthcare services. According to Montagu (2002), the main purpose of social 
franchising is to improve service quality in private facilities and ensure services are 
cost-efficient. 

4.3 Propensity Score Estimate 

The impact scores require the respondent who sought services in a franchised health 
facility are well specified to those who did not. The conditional independence 
assumption requires covariates are exogenous and non-affected by the treatment status. 
According to Heckman et al. (1998) the information on treatment and control should 
come from the same set of questionnaires for matching estimators to perform well and 
the participants should come from the same locale. Data should also cover a wide-
ranging set of covariates that influence individual’s decision. According to Lechner 
(2008), post-treatment covariates may be included in the model if they are analytically 
affected by treatment. This study conducted matching to compare individuals seeking 
services in franchised healthcare facilities to those who did not under the same 
covariates set. This was done by predicting the propensity scores 𝑒𝑖 for each respondent 
using the Logit model. To choose regressors the study did theoretical literature and 
empirical literature review. 


To predict the propensity score 𝑒𝑖 the study used a logit model presented in equation 
3.17 which was more significant and robust. However, to understand the probability of 
an individual seeking services in a franchised health facility given a set of covariates, 
the study used a Logit model with marginal effects. The initial results of a Logit model 
revealed the model was well specified as expressed in the Chi-squared and Pseudo R-
squared statistics. 

Based on the output of the Logit model, the marginal effects of the model were 
computed and the results are presented in Table 4.4. 

Table 4.4: Logit Estimates for Individual Seeking Services in a Franchised Health 
Facility 

Dependent Variable (Dummy 
variable 

Dependent Variable=1 if an individual sought 
services in a franchised facility and 0 otherwise 

Predictor Variables 

Marginal effects 

Dy/dx 

Robust 
Std. Err. 

Probability 
value 

Number of Children 

0.0495 

0.0674 

0.462 

Household Size 

-0.057 

0.0652 

0.381 

Education (Secondary 
Education) 

0.2159 

0.0689 

0.004 

Marital Status (Married) 

-0.1062 

0.1400 

0.429 

Sought Family Planning 
Services 

0.1565 

0.0784 

0.043 

Cost Consideration 

0.3577 

0.0808 

0.000 

Quality Consideration 

0.4821 

0.0961 

0.000 

Distance 

0.1793 

0.0873 

0.036 

Job Type (Employed) 

0.1230 

0.0895 

0.178 

Permanent House 

-0.0746 

0.0877 

0.395 



Source: Author’s estimation from the study data 

 

 


From the table 4.4, education level of the people, purpose of vising private health 
facility is primarily for family planning services, people perceive their selected facility 
has lower cost and is of high quality, and those who primarily want a facility closer to 
them had coefficients that were statistically significant indicating that they influenced 
seeking health services in a franchised health facility. 

However, number of children a woman have, the number of people living in the 
household, being married, and being employed had statistically insignificant 
coefficient. This reveals the variables do not explain the difference is selecting 
franchised health facility for healthcare. 

On education, its coefficient was positive and statistically significant at 1 percent level. 
On average, the probability of selecting franchised facility for people with secondary 
level of education of higher was 21.59 percent higher than that with lower level of 
education. On women primary motive of vising health facilities being family planning, 
the probability of them selecting franchised health facility was 15.65 percent higher 
than those seeking other services. On the perceived quality of services in franchised 
health facilities, the findings show that the probability of selecting a franchised health 
facility was 35.77 percent higher and those who were looking for lower cost services 
had a probability of 48.21 percent in seeking franchised health facility than those who 
were not. Finally, selecting franchised facility because it’s nearer to one home has a 
probability of 17.93 per cent. The results are support. 

 


4.3.1 Specification Tests and Goodness-of-Fit 

Table 4.5 below present’s Link test specification tests. 

Table 4.5: Specification Tests 

Logistic Regression 

Number of obs 

215 

 

 

LR chi2(2) 

58.52 

 

 

Prob > chi2 

0.000 

Log likelihood = 

-111.56885 

Pseudo R2 

0.2078 

Treatment 

Coef. 

Std. 
Err. 

z 

P>z 

[95% Conf. 
Interval] 

_hat 

1.22 

0.23 

5.3 

0.000 

0.77 

1.67 

_hatsq 

0.19 

0.13 

1.5 

0.14 

-0.06 

0.44 

_cons 

-0.15 

0.21 

-0.73 

0.47 

-0.56 

0.26 

Hosmer-Lemeshow chi2 (8) = 12.98, p 0.1124 



Source: Author’s estimation from the study data 

In Table 4.5, a p-value of 0.14 corresponding to “hatsq” implies that the null hypothesis 
that the model had been correctly specified could not be rejected at 1 percent, 5 percent 
and 10 percent levels of significance. The result from Hosmer and Lemeshow’s 
goodness-of-fit test also indicated that the model fits the date well [Hosmer-Lemeshow 
chi2 (8) = 12.98, p 0.1124]. 

 

 

 

 

 


Table 4.6 shows the description of the estimated propensity scores in the region of 
common support. 

Table 4.6: Description of Estimated Propensity Score in Region of Common 
Support 

Estimated propensity score 

 

Percentiles 

 
Smallest 

Obs 

Sum 
of 
Wgt. 

Mean 

Std. 
Dev. 

Variance 

Skewness 

Kurtosis 

1% 

.0628 

.0628 

207 

207 

.377 

.233 

.055 

.477 

2.107 

5% 

.0806 

.0628 

 

 

 

 

 

 

 

10% 

.1190 

.0628 

 

 

 

 

 

 

 

25% 

.1631 

.0628 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

50% 

.3538 

 

 

 

 

 

 

 

 

 

 

Largest 

 

 

 

 

 

 

 

75% 

.5594 

.8820 

 

 

 

 

 

 

 

90% 

.7236 

.8929 

 

 

 

 

 

 

 

95% 

.8052 

.9155 

 

 

 

 

 

 

 

99% 

.8930 

.9207 

 

 

 

 

 

 

 



Source: Author’s estimation from the study data 

After estimating the propensity score, to ensure that there was adequate common 
support, the study achieved this by eliminating treated individuals with scores outside 
the range for the individuals in the control group. Table 4.6 shows the description of 
the estimated propensity scores in the region of common support. The results revealed 
the mean propensity score for the entire sample was 0.377 implying the average 
probability of seeking services in a treated clinic for all individuals was 37.7 percent. 

 

 

 


Table 4.7 displays the inferior block of propensity scores and the number of treated and 
control in each block. 

Table 4.7: Blocks of Propensity scores 

Inferior of block of pscore 

Treatment 

 

Non-franchised 

Franchised 

Total 

0.0627688 

60 

9 

69 

0.2 

18 

18 

50 

0.4 

5 

5 

24 

0.5 

16 

16 

24 

0.6 

18 

18 

27 

0.8 

12 

12 

13 

Total 

129 

78 

207 



Source: Author’s estimation from the study data 

The final number of blocks which was six ensured the mean propensity score was not 
different for the individuals who sought services in franchised and not franchised 
clinics. The balancing property was satisfied. This means the covariates that determine 
treatment and non-treatments are similar and the propensity scores as similar as well. 

4.4 Impact of Social Franchising on Children's Healthcare Services within 
Private Facilities in Embu County 

Table 4.8 estimates the impact of social franchising on children healthcare services 
expenditure using radius, nearest neighbor, and kernel matching techniques 

Table 4.8: Impact of Franchising on the Expenditure of Children Services 

Dependent Variable: Expenditure on children services 

 

N 

ATET 

Std 
Err 

t-value 

Matching Algorithm 

Franchised 

Non-Franchised 

Nearest neighbor 

78 

64 

-86.17 

90.80 

-0.949 

Radius matching with 
a radius of 0.01 

58 

120 

-27.77 

65.23 

0.426 

Kernel method 

78 

137 

-54.68 

60.255 

-0.908 



Source: Author’s estimation from the study data 


To achieve objective one which wanted to establish the impact of social franchising on 
children healthcare services expenditure within franchised and non-franchised health 
facilities in Embu County, the study estimated Equation 3.19. The study found that the 
expenditure on children services was Ksh 86.17, Ksh 27.77 and Ksh 54.68 per visit 
lower for those seeking services in franchised facilities when compared to others in 
nearest neighbor, radius and kernel method respectively but such difference is not 
statistically significant at 1 per cent, 5 per cent and 10 per cent significance levels. The 
results are similar to a study by Chakraborty et al. (2019) who found there were no 
significant differences in the amount paid between franchised and non-franchised 
private facilities on child health services in Kenya. 

4.5 Impact of Social Franchising on the Expenditure of Short-term Family 
Planning Services within Private Facilities in Embu County 

In order to estimate the second objective, equation 3.20 was estimated and results 
presented in Table 4.9. 

Table 4.9: Impact of Franchising on the Expenditure of Short-term FP Services 

Dependent Variable: Expenditure on Depo-Provera 

 

N 

ATET 

Std Err 

t-value 

Matching Algorithm 

Franchised 

Non-Franchised 

Nearest neighbor 
with Replacement 

78 

24 

-22.5 

15.67 

-1.436 

Kernel matching 

78 

137 

-19.6 

16.086 

-1.218 



Source: Author’s estimation from the study data 

The study wanted to establish whether being members of a social franchise network 
lowers the expenditure on short term family planning services. The results from Nearest 
neighbor and Kernel matching indicate that the mean expenditure of these services are 
Ksh 22.5 and Ksh 19.6 lower in franchised facilities respectively but such difference is 


not statistically different from non-franchised facilities at 1 percent, 5 percent and 10 
percent significance levels. These results are similar to the study by Chakraborty et al. 
(2019) and Azman et al. (2011) who found that franchising did not have a significant 
effect on the expenditure of these services. 

4.6 Impact of Social Franchising on the Expenditure of Long-term Family 
Planning Services within Private Facilities in Embu County 

In order to estimate the third objective, equation 3.21 was estimated and results 
presented in Table 4.10. 

Table 4.10: Impact of Franchising on the Expenditure of Longer-term FP services 

Dependent Variable: Expenditure on Longer-term FP services 

 

N 

ATET 

Std 
Err 

t-value 

Matching Algorithm 

Franchised 

Non-Franchised 

Nearest neighbor 
with Replacement 

78 

59 

-459.95 

85.603 

-5.373 

Kernel method 

78 

137 

-462.59 

70.812 

-6.533 



Source: Author’s estimation from the study data 

The study wanted to establish whether being members of a social franchise network 
lowers the expenditure on long-term family planning services. A computed t-value of 
5.573 and 6.533 corresponding to nearest neighbor and kernel method in matching 
estimation (Table 4.10) implies that the null hypothesis is rejected at 5% levels of 
significance. The study, therefore, concludes individuals seeking longer-term family 
planning services pay Ksh 459.95 – Ksh 462.59 less than those seeking services from 
non-franchised facilities. This big variation is attributed to zero expenditure reported 
by some women. Those who reported zero expenditure reported to have been issued 
with vouchers that were used to access the family planning services. 


These results are similar to Azmat et al. (2014) study that investigated the impact of 
social franchising on contraceptive use in rural Pakistan and found the use of vouchers 
reduced the cost of LARCs significantly in franchised health facilities. Patouillard et 
al. (2007), Bellows et al. (2017) study also found the use of vouchers reduced the price 
of services among franchised health facilities making them more affordable. Gold et 
al. (2017) study among MSI Mali Bluestar franchise networks also found that 
franchised health facilities deliver affordable family planning services increasing 
demand for LARCs. The results are however different from the ones by Shah et al. 
(2011) in Ethiopia and Pakistan who revealed higher clinical costs per client among 
franchised private health facilities in Ethiopia and not statistically different in Pakistan. 
Similar to previous studies (Ewen et al., 2017; Mackintosh et al., 2008; Ewen et al., 
2017) higher prices of medical commodities limits affordability and access. To improve 
access and affordability of medical services, there is a need to prioritize patient access 
and affordability in health facilities, enhance efficient markets where there is price 
competition, and improve the availability of information. 


CHAPTER FIVE 

SUMMARY, CONCLUSION, AND POLICY IMPLICATIONS 

5.1 Introduction 

This chapter presents a summary of the study findings, conclusion and implications of 
the study findings. The chapter also presents the areas of further research. 

5.2 Summary 

The main purpose of this study was to establish the impact of social franchising private 
healthcare expenditure in Embu County. The specific objectives were to establish the 
impact of social franchising on children health services, the impact of social franchising 
on short-term family planning services, and the impact of social franchising on long-
term family planning services. Social franchising meant a respondent has sought 
services in a franchised health facility such as Tunza (PSK) and Sustainable Healthcare 
Foundation under (CFW) which are the available social franchisors in Embu County. 
Non-franchised meant a respondent sought services in a clinic that is not a member of 
the two social franchise networks. 

The study was motivated by the fact healthcare is an important component of SDGs 
and government Big4 agendas in Kenya and the fact the expenditure of healthcare 
especially children services has an effect on access and therefore a direct effect on the 
children's mortalities. On the other hand, private health facilities are the first point of 
contact among many Kenyans and therefore often time provides the first response. In 
Embu County, the FP prevalence has reduced by 7 percent in the past three years, the 


government has a deficit on FP provision (50 percent) and the presence of social 
franchisors should increase prevalence. 

The available studies on the effect of social franchising on healthcare have focused on 
quality, equity, and customer increase. This study has introduced the concept of the 
expenditure on private healthcare on the discussion of the impact of social franchising. 
The study introduced the concept of expenditure due to the theoretical foundation, price 
influences demand for goods and services. Therefore, the key contribution of this study 
is the evaluation of the way social franchising affects the expenditure of private 
healthcare which previous researchers have not considered in their analysis in Kenya. 
The other key strength of this study is the ability to employ impact evaluation model 
PSM, unlike previous researchers who have primarily focused on binary choice models 
especially Logit, Chi-square test of association, ANOVA, while others relied on 
descriptive statistics. The use of PSM ensured comparison is only between similar units 
and therefore taken care of non-randomization. The method is superior as it eliminates 
selection bias and makes selection into the treatment group as good as random. 

The first objective was to establish the impact of social franchising on children 
healthcare services within private health services in Embu County. The study found 
among the significant factors in the choice of where to seek children healthcare services 
includes the education level of the guardian, perceived quality of services, cost and 
distance of the facility. The study used PSM to estimate the ATET of the expenditure 
of children's healthcare services. The ATET estimation revealed respondents seeking 
services in franchised facilities paid a similar amount with those seeking services in 


non-franchised health facilities. However, the changes by the franchised facility were 
slightly less. 

The second objective was to establish the impact of social franchising on short-term 
family planning services within private health services. The study used PSM to estimate 
the ATET the expenditure of short-term FP services. The ATET estimation revealed 
the expenditure on these services in franchised facilities were not significantly different 
from those seeking them in non-franchised health facilities. 

The third objective was to establish the impact of social franchising on long-term 
family planning services within private health services. The study used PSM to estimate 
the ATET of the expenditure of longer-term FP services. The ATET estimation 
revealed the expenditure of these services in franchised facilities were significantly 
lower than those seeking them in non-franchised health facilities. This means the social 
franchisor's main impact is in the long-term family planning services. These services 
were subsidized among franchised facilities to a point where some clients paid zero 
cost. This shifts consumer’s budget line outward increasing demand for other services 
thus improving household welfare. 

5.3 Conclusion 

The findings of the study indicated the choice of health facility is pegged on perceived 
quality of services provided, the type of services sought with women whose primary 
motive was to seek family planning services twice likely visit franchised health 
facilities, cost consideration with such individuals having higher probability of seeking 
services in franchised health facilities and distance with those residing franchised 


facilities having higher probaility of seeking services in these facilities. These results 
imply private facilities need to consider quality and pricing to attract customers. The 
fact that these attributes were singled out by those seeking services in franchised 
facilities means a franchised health facilities are perceived to be of higher quality and 
of lower cost by customers. 

The findings revealed individuals seeking services in franchised health facilities paid 
less on longer-term family planning services relative to those seeking services in non-
franchised health facilities. However, the children's treatment services and short-term 
services were not significantly different in terms of costs even though they were lower. 
This means social franchising is benefiting its franchisee by providing quality services 
that are cost-effective. 

5.4 Policy Recommendations 

In light of the study findings, its evident private healthcare consumers demand quality 
services which are affordable. This means service providers need to ensure services 
provided are of high quality and delivered at affordable costs. 

The study recommends that private healthcare providers should consider joining a 
franchise network as the findings of the study found there are benefits which includes 
people perceive services to be of high quality. The findings also shown that consumer’s 
budget line is pushed outward leaving consumers with more money to consume for 
other critical services. 


5.5 Areas for Further Research 

This study suggests the methodology be replicated in other counties and future 
researchers may consider one franchisor at a time in their analyses. There is a need to 
also conduct a study on the supply side and acquire qualitative responses on the 
experiences of providers in the franchised network. 

 

 

 

 

 


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APPENDIX I: INTRODUCTION LETTER 

 Hello Sir/Madam, my name is Stephen Kariuki Nyaga, a student from the 
school of economics, Kenyatta University pursuing a Master's degree in Economics. 
As part of the Kenyatta University requirement am required to conduct a study that 
adds value to the policy framework. I am contacting you to request your participation 
in a research study that I am conducting for my project and you have been randomly 
selected to participate in this research as a person living near a private clinic. The 
purpose of this study is to understand the cost of healthcare services you seek in private 
clinics. These services include family planning services and children services. 

Procedures: If you choose to participate, we shall ask you a few questions about the 
cost of the services you received in this clinic and your preference for this clinic. The 
survey will take approximately 5-10 minutes. 

Risks and benefits: There are no direct benefits to participating in this study. It’s hoped 
the information you give will help policymakers, researchers and scholars understand 
the cost of services charged in these clinics and therefore come up with policies that 
would increase access to services offered in private health facilities. We anticipate very 
minimal risks associated with participating in this study. If any of the questions asked 
to make you feel uncomfortable, please feel free to decline to answer or end the 
interview at any time. 

Confidentiality: The information you provide is utterly confidential. We are not asking 
any information that may directly identify you nor the clinics in which you attend. All 
information gathered in this study will be generalized. 

Voluntary participation: Lastly, please note that this research is voluntary. You may 
accept to respond or refuse to respond without any negative consequences. 

I am therefore humbly requesting a few minutes of your time for this short survey. May 
I start this short survey? 

Respondent signature ___________________________ 

Date ___________________________ 

Thanks for your cooperation. 

 


APPENDIX II: QUESTIONNAIRE 

 

1. Age in years 
 <18 
 18-24 
 25-34 
 35-44 
 >45 
2. How many children do you have _____________ 
3. How old is the youngest child _______________ 
4. How old is the oldest Child ________________ 
5. What is your highest education level? 
 No education 
 Primary 
 Secondary 
 Above secondary 
6. What is your marital status? 
 Married 
 Single 
 Separated/widowed 
7. Which services do you seek in a private clinic? 
 Child related services 
 Family planning services 
 Adult illness 
 Others specify ___________ 
8. Where do you normally seek your family planning services? 
 Private (Tunza, Amua, Huduma Poa, CFW) 
 Private (non-franchised) 
 Others specify _________________________________________________ 


 

 

 


 

9. Which health facility is nearest your Home? 
 Private 
 Public 
 Others___________________________________ 
10. How many minutes would you take to walk to the nearest private 
facility____________ 
11. What do you consider when choosing where to seek family planning services? 
 Quality of services 
 Location 
 Pocket friendly 
 Know the owner used to a certain health facility 
 Others ________________________________________ 
12. Have you ever gone to private facility for Family Planning? 
 Yes 
 No 
13. When your child was sick where did you seek services and how much did you pay 
for full visit 
 Private (Franchised: Tunza, Amua, Huduma Poa, CFW) 
 Other private_______________________ 
 Public _____________________________ 
 Mission _____________________________ 
14. When your child (below 5 years) was sick where did you seek services and how 
much did you pay for full visit 
 Private (Tunza, Amua, Huduma Poa, CFW) ___________________________ 
 Other private_______________________ 



 Public _____________________________ 
 Mission _____________________________ 
15. Which family planning methods do use and how much do you pay for their costs 
each visit 


Family 
Planning 
method 

Where do you 
get the services 

How much did 
you pay 
(Provider 1 

How much did 
you pay 
(Provider 2 

How much did 
you pay 
(Provider 3 

Depo-
provera 

1 

2 

3 

 

 

 

IUCD 

1 

2 

3 

 

 

 

Norplant’s 
implants 

1 

2 

3 

 

 

 

Pills 

1 

2 

3 

 

 

 

Herbal 

1 

2 

3 

 

 

 

Others 

1 

2 

3 

 

 

 



 

 

 


 

16. Have you ever used vouchers for family planning services? 
 Yes 
 No 
17. If yes to 15, 
i. For which method _____________________________________ 
ii. How much did the voucher pay ___________________________ 
iii. How much did you pay __________________________________ 
iv. Which clinic did you use the voucher _______________________ 
18. What type of job do you do? 
 Casual/farmer 
 Contract 
 Permanent 
 Unemployed 
 Business 
19. Type of house (Observation) 
 Mud 
 Timber 
 Thatches 
 Bricks 
20. What type of job do you do? 
 Casual 
 Contract 
 Permanent 
 Housewife/unemployed 
21. How much do you earn monthly 
 <5,000 
 5,000-10,000 
 10,000-20,000 
 >20,000 
22. How much do you spouse earn monthly 
 <5,000 
 5,000-10,000 
 10,000-20,000 
 >20,000 


 

THAT’S ALL FOR NOW AND THANK YOU FOR YOUR TIME 

 


APPENDIX III: AUTHORIZATION LETTERS