DEMOGRAPHIC

AND

HEALTH

RESEARCH

DHS WORKING PAPERS

2010 No. 76

Amanda Pomeroy

Marge Koblinsky

Soumya Alva

October 2010

This document was produced for review by the United States Agency for 

International Development.

Private Delivery Care 

in Developing Countries: 

Trends and Determinants
The DHS Working Papers series is a prepublication series of papers reporting on research in progress that 

is based on Demographic and Health Surveys (DHS) data. This research is carried out with support 

provided by the United States Agency for International Development (USAID) through the MEASURE 

DHS project (#GPO-C-00-08-00008-00). The views expressed are those of the authors and do not 

necessarily reflect the views of USAID or the United States Government. 

MEASURE DHS assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. Additional information about the MEASURE DHS project can 

be obtained by contacting MEASURE DHS, ICF Macro, 11785 Beltsville Drive, Suite 300, Calverton, 

MD 20705 (telephone: 301-572-0200; fax: 301-572-0999; e-mail: reports@measuredhs.com; internet: 

www.measuredhs.com).
Private Delivery Care in Developing Countries: Trends and Determinants 

Amanda Pomeroy1

Marge Koblinsky1

Soumya Alva2

ICF Macro 

Calverton, Maryland, USA 

October 2010 

Corresponding author: Amanda Pomeroy, Program Manager/Technical Advisor, JSI Center for Health Information 

and Monitoring and Evaluation (CHIME), John Snow, Inc., 1300 N. 17th Street, 9th Floor, Arlington, VA 22209; 

amanda_pomeroy@jsi.com 

1

 John Snow, Inc. 

2

 ICF Macro 
ACKNOWLEDGEMENT 

The authors wish to thank Mary Ellen Stanton and the U.S. Agency for International 

Development (USAID), through its Analysis, Information, and Management (AIM) Activity, for 

their financial support of this work. We would also like to thank the Eckhard Kleinau for 

reviewing this paper, and the AIM teaming partners, ICF Macro, John Snow, Inc., RTI 

International, and Insight Systems, Corp. for their support during this process. 
ABSTRACT

Over the past two decades, multilateral organizations have encouraged increased 

engagement with private health care providers in developing countries. As these efforts progress, 

there are concerns that private delivery care may have adverse effects on maternal health. 

Currently available data do not allow for an in-depth study of the direct effect of privatization on 

maternal health. However, as a first step, we can use Demographic and Health Surveys (DHS) 

data to examine a) trends in growth of delivery care provided by private facilities, and b) 

determinants of private sector use within the health care system. To construct trends, this 

study uses DHS from 16 sub-Saharan African, Asian, and Latin American countries, selecting 

those countries with one DHS in phase 4 (1997–2003) and one in phase 5 (2003–present). 

For a subset of eight countries, we examine determinants of a mother‟s choice to deliver 

in a health facility and then, among women delivering in a facility, their decision to use a private 

provider. Determinants of use are grouped by socioeconomic characteristics, economic and 

physical access and by actual/perceived need. 

Results show a significant trend toward privatization of delivery care over the 13 years 

covered in the study but there is considerable variation in the characteristics driving this 

increased use across countries. In three African countries, socio-demographic characteristics are 

associated with use of private delivery care, while in Bolivia and four Asian countries, economic 

indicators are more relevant. In the former this may suggest complementarity to public facilities 

(e.g. private delivery services cover populations that may not be reached by public services), 

while in the latter it may mean competition. These results warn against making generalizations 

on the effects of privatization on maternal health use. 

1

INTRODUCTION

Over the past two decades, multilateral organizations have encouraged increased 

engagement with private health care providers in low- and middle-income countries (Ferrinho, 

Bugalho, & Van Lerberghe 2001, Zwi et al. 2001). As these efforts progress, there are concerns 

that private delivery care may have adverse effects on maternal health. 

The most vocal critics of privatization have stated that private providers do not have the 

same incentive to provide services with public health benefits and may be more likely to provide 

low- quality treatment while overprescribing diagnostics, procedures, and pharmaceuticals 

(Hanson et al. 2008, Marriott 2009). It is not clear, however, that the private sector functions the 

same way in every health system (Brugha & Pritze-Aliassime 2003, Hanson & Berman 1998, 

Parkhurst et al. 2005, Shaikh & Hatcher 2005). In some cases, the private sector may cater to 

subgroups of patients for whom the public sector underprovides, acting as a complement (Brugha 

& Pritze-Aliassime 2003). In other countries, public and private health facilities may act as 

substitutes for each other, and patients can choose between them for care based on quality and 

cost (Hanson & Berman, 1998). In the case where it complements public services, the private 

sector can contribute to greater coverage of maternal care. In the case where it substitutes, the 

direction of the effect on care is less clear. 

Currently available data do not allow for an in-depth study of the direct effect of 

privatization on maternal health across countries. As a first step, however we can use 

Demographic and Health Surveys (DHS) data to examine a) trends in growth of delivery care 

provided by private facilities, and b) determinants of private sector use within the health 

care system. We expect that in health systems where the public sector functions at all socioeconomic levels, the private sector competes for clients based on perceived quality. In health 

systems where the public sector fails to provide for all subgroups of the population, the private 

sector may substitute for the public sector. We test this hypothesis on a subset of our 16 countries 

by modeling two related care-seeking decision points: a mother‟s choice to deliver in a health 

facility, and then among women delivering in a facility, her decision to use a private provider. 
2

THEORETICAL FRAMEWORK AND RESEARCH QUESTIONS 

As figure 1 shows, from the woman‟s perspective, two key sets of factors should 

influence her decision on where to give birth: 

1. Her individual determinants, such as socio-demographic characteristics, economic 

and physical access based on household wealth and proximity to birth facilities, and 

actual/perceived need for health care based on risks associated with childbirth and the 

use of antenatal care (ANC) and other health care services 

2. The structure of the health system in her country, including availability of public and 

private providers, financing mechanisms for the demand and supply side, the supply 

and location of the health workforce as well as their decisions on care provision, 

health information available to the public, and government policies influencing 

private/public sector behavior as well as patient choice 

Figure 1: Factors Affecting a Woman’s Choice of Birth Facility 

Health system determinants: 

Service availability, financing mechanisms, 

workforce behavior, public health information, 

government policies on private sector behavior

Place 

of 

birth

Home

NGO 

facility

Public 

facility

Private 

facility

Health 

facility

Individual determinants:

Socio-demographic characteristics, 

economic and physical access, 

perceived/actual need
3

As data on system-level determinants at the individual or even community level over 

time are not now available, our analysis is drawn from the Demographic and Health Surveys, 

which provide nationally representative individual-level survey data on the individual 

determinants of choice of facility for birth. With this in mind, we address the following 

questions: 

 Has private sector delivery care increased over the last decade? 

 What role does the private sector play in overall growth of facility delivery care? 

 Who is using private sector delivery care? 
4

LITERATURE REVIEW 

Use of Facility Births 

A vast body of literature has examined facility use for childbirth in low- and middleincome countries. As a follow-up to a review by Thaddeus and Maine (1994), a comprehensive 

literature review by Gabrysch and Campbell (2009) noted that across studies, socio-demographic 

factors such as higher maternal age (Bell et al. 2003, Magadi et al. 2007) and education of the 

mother and her husband (Thaddeus & Maine 1994, Elo 1992, Raghupathy 1996) increase use of 

birth facilities among women. 

Perceived benefit of/need for facility delivery care, as indicated by facility use for the 

previous delivery and antenatal care (ANC) use for the index pregnancy, are also significantly 

related to delivery in a facility (Stephenson et al. 2006, Mishra & Retherford 2006). However, 

these indicators may be picking up unmeasured factors such as availability and ready access of 

services and familiarity/comfort of mother with health services (Bell et al. 2003, Stephenson et 

al. 2006). Facility use is also higher among first and low-order births (Bell et al. 2003, 

Stephenson et al. 2006). Self-reported obstetric complications are also relevant although data 

availability limits their inclusion (Hotchkiss et al. 2003, Anwar et al. 2008). Perceived quality of 

care is judged to be essential in influencing facility use in qualitative studies, but it is not easily 

measured in household surveys and hence lacking for most countries (Amooti-Kaguna & 

Nuwaha 2000, Hodnett 2000). 

Economic and physical accessibility are key factors that contribute to choice of facility. 

Households with a greater ability to pay are more likely to access delivery services outside the 

home (Thaddeus & Maine 1994, Mayhew et al. 2008, Say & Raine 2007). Physical access is 

often difficult to determine. Where data are available, greater distance to health facilities does 

decrease facility use (Yanagisawa et al. 2006, Gage et al. 2006, Chowdhury et al. 2006, Rahman 

et al. 2007). Where data are not available, proxies such as lack of transport and/or poor roads in 

conjunction with distance can be used (Gage & Calixte 2006). Rural residence also captures 

some of aspects of physical accessibility and is often negatively related to facility use, though 

this measure also picks up other unobservable household characteristics (Say & Raine 2007, Bell 

et al. 2003, Mekonnen & Mekonnen 2003). 
5

Privatization of Birth Facilities 

Mothers who go to a facility for delivery care make a choice on the type of facility to 

attend. In many countries, public facilities are the most common option, but for various reasons a 

woman may choose to seek a private facility. Literature on facility choice has found a wide range 

of determinants, and across countries the same determinants have been found to have opposing 

affects, hindering consensus on what influences mothers to seek private care. 

For the purposes of this study we assume that facility type does not play a significant role 

in the mother‟s initial decision to go to a facility or stay home for delivery. We also assume that 

she chooses the type of facility after making the decision to go to any facility. Sociodemographic factors are a key determinant of the choice of a private birth facility. Higher 

education is often significant in facility choice, though whether it predicts public or private 

facility use varies by setting (Osubor et al. 2005, Thind et al. 2008, Berman & Rose 1996 –- 

positive effect; Do et al. 2009 – negative effect). Other relevant factors are ethnicity and 

caste/tribe status, both of which are negatively associated with use of private facilities in India 

and Nairobi (Bazant et al. 2009; Thind et al. 2008). 

A woman‟s real or perceived need for care is also influential. Women who attend more 

ANC visits are more likely to use a private facility for delivery (Thind et al. 2008). More than 

half (54.3 percent) of those who went to a private hospital had received five or more ANC visits 

compared with 28.8 percent in a public hospital in Jordan (Obermeyer & Potter 1991). An ANC 

visit at a public rather than a private facility is also associated with public facility delivery 

(Bazant et al. 2009). Perceived obstetric complications can act as a catalyst for private facility 

use due to the general perception that they provide better quality of care (Amooti-Kaguna & 

Nuwaha 2000, World Bank 2005, Ferrinho, Bugalho, & Van Lerberghe 2001, Hodnett 2000). 

However, research on this issue is contradictory. For instance, having perceived suffering with 

an obstetric complication actually encourages use of public facilities instead of private facilities 

in Nairobi (Bazant et al. 2009). 

Regarding economic and physical accessibility indicators, a higher standard of living is 

associated with use of private facilities, as is urban residence (Thind, et al. 2008, Obermeyer & 

Potter 1991, Berman & Rose 1996).
6

DATA AND METHODS 

Sixteen countries with multiple rounds of data were examined for trends in delivery care 

over time as well as trends in privatization of delivery care. Data from two time points were 

used, with four to seven years separating the two rounds of data. The year for the first time point 

was chosen from the fourth round of DHS survey collection (1997–2003) while the second time 

point was chosen from the fifth phase (2003–present). The details of the surveys chosen are 

listed in table 1. 

Table 1: Details of Demographic and Health Surveys

Country Year N (Women) N (Children) Country Year N (Women) N (Children)

Asia

Africa Bangladesh 1999 10,544 6,832

Ethiopia 2000 15,367 10,873 Bangladesh 2007 10,996 6,150

Ethiopia 2005 14,070 9,861 Cambodia 2000 15,351 8,834

Kenya 2003 8,195 5,949 Cambodia 2005 16,823 8,290

Kenya 2008 8,444 6,079 India 1998 89,199 33,026

Malawi 2000 13,220 11,926 India 2005 124,385 51,555

Malawi 2004 11,698 10,914 Indonesia 2002 29,483 16,206

Mali 2001 12,849 13,097 Indonesia 2007 32,895 18,645

Mali 2006 14,583 14,238 Nepal 2001 8,726 6,931

Rwanda 2000 10,421 7,922 Nepal 2006 10,793 5,783

Rwanda 2005 11,321 8,649 Philippines 2003 13,633 7,145

Tanzania 1999 4,029 3,215 Philippines 2008 13,594 6,572

Tanzania 2004 10,329 8,564 Latin America

Uganda 2000 7,246 7,113 Bolivia 2003 17,654 10,448

Uganda 2006 8,531 8,369 Bolivia 2008 16,939 8,605

Zambia 2001 7,658 6,877 Haiti 2000 10,159 6,685

Zambia 2007 7,146 6,401 Haiti 2006 10,757 6,015

A subset of eight countries (Mali, Zambia, Rwanda, Bangladesh, the Philippines, 

Indonesia, Nepal, and Bolivia) were analyzed in depth on the drivers of facility usage, in 

particular private facility usage. They were chosen to represent the three regions listed above, 

and the upper and lower ends of the privatization trend as described later in this paper (see figure 

2). For each country, both years of data were pooled to increase statistical power and to allow for 

a limited examination of trend over time. 
7

In the pooled analysis, we estimate two related probit equations with a Heckman 

selection model (Heckman 1979, Dubin & Rivers 1989) to determine a) who is more likely to 

deliver in a facility than at home, and b) conditional on choosing a facility, who is more likely to 

use a private facility than a public facility. This model is meant to correct for the fact that we can 

only observe a woman‟s choice of a public or private facility if she chooses to go to a facility for 

birth. This self-selection means that if the equations are estimated separately, the results for 

drivers of choice between public and private facilities may be biased (for a more in-depth 

discussion of the Heckman selection model, see Heckman 1979, Dubin & Rivers 1989). All 

regressions included the built-in survey data corrections available in Stata 10 (Stata Corp., 

College Station TX 2009). 

Our outcome variables are constructed from the DHS question “Where did you give birth 

to (child)?” Respondents‟ answers are broken down by various facility and home options, which 

are then grouped by DHS. These data are collected for births in the last five years, with the 

exception of India, where they are for the last three years. This process produces two outcome 

variables, one that identifies home births versus facility births and another that identifies public 

or private facility births among those who go to a facility. 

Facilities of nongovernmental organizations (NGOs) are excluded in this analysis. In 

most of our countries, few if any births occurred in the NGO sector. In the remaining countries 

where NGO births were non-negligible, there were difficulties defining precisely what facilities 

were a part of the NGO sector.

Our key variables of interest were chosen from the categories of socio-demographics 

(mother‟s age, education of mother and father, household size1

), perceived/actual need (birth 

order, previous child death, mean ANC visits for mother, delivery complications), and economic 

and physical access (perceived distance to health facility, residence, wealth index, unmet need 

for family planning as a proxy for access to care) as those that had the strongest theoretical 

relationships with choice of facility. 

Because data were pooled over two separate years, we include a dummy indicating 

whether the observation was recorded in the first or second year of data. With this we can 

observe if a woman is more likely to deliver in a private facility in the second year versus the 

first year, controlling for other factors. 

 

1 Ethnicity and religion may also be significant, but are not included because they were not measured 

consistently across our sample of countries and years.
8

RESULTS 

Table 2 shows simple weighted tabulations for all 16 countries and all years to describe 

the share of births that took place in a facility and the share of births delivered in each type of 

facility. 

Table 2: Facility Births by Facility Type

Country

% of all births in the last 

5 years in a facility

Type of Facility (%)*

Government Private NGO

Africa 

Ethiopia (2000) 5.0 4.7 0.1 0.2

Ethiopia (2005) 5.3 4.8 0.3 0.2

Kenya (2003) 40.1 26.1 8.2 5.8

Kenya (2008) 42.6 32.3 5.9 4.4

Malawi (2000) 55.4 40.2 2.0 13.1

Malawi (2004) 57.2 41.9 1.7 13.7

Mali (2001) 37.8 36.9 0.9 0.0

Mali (2006) 45.1 42.7 2.4 0.0

Rwanda (2000) 26.5 24.2 2.3 0.0

Rwanda (2005) 28.2 26.9 1.3 0.0

Tanzania (1999) 43.5 36.9 6.6 0.0

Tanzania (2004) 47.1 37.7 6.4 3.0

Uganda (2000) 36.6 22.2 14.4 0.0

Uganda (2006) 41.1 29.1 12.0 0.0

Zambia (2001) 43.6 34.5 3.3 5.8

Zambia (2007) 47.7 42.6 1.2 3.9

Asia 

Bangladesh (1999) 7.9 5.2 2.3 0.4

Bangladesh (2007) 14.6 7.1 6.6 1.0

Cambodia (2000) 9.9 8.2 1.7 0.0

Cambodia (2005) 21.5 16.8 4.7 0.0

India (1998) 33.6 16.2 16.6 0.7

India (2005) 40.8 19.0 21.4 0.4

Indonesia (2002) 39.7 9.1 30.6 0.0

Indonesia (2007) 46.1 9.6 36.5 0.0

Nepal (2001) 9.1 7.0 1.1 1.0

Nepal (2006) 17.7 13.1 3.7 1.0

Philippines (2003) 37.9 24.2 13.7 0.0

Philippines (2008) 44.2 26.5 17.7 0.0

Latin America 

Bolivia (2003) 57.1 48.2 7.9 1.0

Bolivia (2008) 67.5 56.8 8.8 1.9

Haiti (2000) 23.2 12.7 4.1 6.4

Haiti (2006) 24.7 14.6 7.3 2.8

*Type of facility (%) may not add up exactly to the % of all births that take place in a facility due to rounding. 
9

The share of facility births handled by private facilities increased in 10 of the 16 

countries over the two time points. Figure 2 shows trends over time for each country, with each 

facility type displayed as a percent of all births in the country. 

Figure 2: Place of Birth 

0

10

20

30

40

50

60

70

80

90

100

Ethiopia

Kenya

Malawi

Mali

Rwanda

Tanzania

Uganda

Zambia

Bangladesh

Cambodia

India

Indonesia

Nepal

Philippines

Bolivia

Haiti

Private Government NGO

In every country, use of any facility for birth increased between the first and last time 

points. In Asian countries such as Bangladesh, Indonesia, India, and the Philippines, that increase 

seems to come almost entirely from a growth in private sector care, a positive contribution to 

increasing maternal care. As seen in figure 2, the private sector delivers more than 10 percent of 

all births in Uganda, the Philippines, India, and Indonesia, while in Bolivia, Tanzania, Haiti, 

Bangladesh and Kenya it delivers between 5 and 10 percent of all births. In six of the eight 

African countries however, use of private facilities decreases between the two time points. 

Zambia has seen the biggest percentage drop in private facility births, but from a very low 

starting point of 3.3 percent of all births in 2001. 

First Bar: First Year Second Bar: Second Year

 Africa Asia LAC

% of all births (bar height indicates total facility births)
10

The following tables contain the results of the in-depth analysis in eight countries. Table 

3 shows the results of the selection model, modeling a woman‟s choice of birth in a facility 

rather than at home. Table 4 displays the results for the outcome model of a woman‟s choice to 

go to a private facility over a public facility, which was jointly estimated with the selection 

model. 
Table 3: Selection Model – Probit Results of the Choice to Go to a Delivery Facility vs. Home Delivery, Births in the Last 5 Years

Africa Asia LAC

Characteristic Categories Mali Rwanda Zambia Bangladesh Indonesia Nepal Philippines Bolivia

N 24,840 15,288 11,082 12,107 24,709 12,107 9,287 16,990

Time effect (year) year 1=0

year 2=1

0.08 0.08* 0.26** 0.41** 0.06 0.21** 0.18** 0.13**

Perceived/Actual Need

Multiparity 1st child=0

2 or higher=1

-0.19** -0.75** -0.24** -0.42** -0.27** -0.60** -0.35** -0.25**

Previous child to mother 

died

no=0 

yes=1

0.07 -0.01 0.13 0.35** 0.20 0.15 0.14 -0.19*

Mother mean ANC visits: 

1-3 visits

no visits=0 1.02** 0.67** 0.94** 0.58** 0.10 0.43** 0.10 0.79**

Mother mean ANC visits: 

4 or more visits

1.42** 1.11** 1.20** 1.22** 0.59** 0.94** 0.50** 1.32**

Complication: 

Prolonged labor

no=0 

>12 hr labor=1

0.52** 0.02 0.26**

Complication: 

Convulsions

no=0 

convulsions=1

0.53** -0.05 0.31

Economic and Physical 

Access

Unmet need for family 

planning

no unmet 

need=0

0.11** -0.03 -0.06 -0.17** -0.12* 0.03 -0.12** -0.19**

Distance to health facility a 

barrier to seeking care

not a barrier 

to care=0

-0.34** -0.14** -0.32** 0.10 -0.21** -0.15* -0.21** -0.12**

Wealth status: 

Middle 3 wealth quintiles

bottom wealth 

quintile=0

0.06 0.07 0.10* 0.08 0.50** 0.31** 0.51** 0.54**

Wealth status: 

Top wealth quintile

0.34** 0.59** 0.86** 0.58** 1.10** 0.84** 1.08** 1.61**

Cont’d..

11
Table 3: Cont’d

Africa Asia LAC

Characteristic Categories Mali Rwanda Zambia Bangladesh Indonesia Nepal Philippines Bolivia

Socio-Demographic 

Characteristics

Region of residence rural=0

urban=1

0.76** 0.48** 0.95** 0.32** 0.62** 0.50** 0.36** 0.55**

Aged 20-34 years <20 years=0 -0.05 0.15* -0.01 0.28** 0.20** 0.21** 0.24** -0.10

Aged 35 and over 0.04 0.14* -0.13 0.34** 0.37** 0.46** 0.45** -0.03

5 to 8 household members less than 5 

members=0

0.02 -0.02 -0.03 -0.09 0.00 -0.14** -0.09* -0.08*

More than 8 members 0.04 0.07 0.06 -0.06 0.02 -0.11 -0.15** 0.07

Primary education: Mother less than 

primary=0

0.23** 0.18** 0.29** 0.04 0.04 0.16* 0.06 0.11

Secondary education: 

Mother

0.37** 0.76** 0.68** 0.31** 0.36* 0.39** 0.31 0.52**

Tertiary education: Mother -0.05 1.26** 1.24** 0.68** 0.66** 0.71** 0.72** 1.14**

Primary education: Husband less than 

primary=0

0.26** 0.18** 0.00 -0.01 -0.03 0.05 0.42* -0.04

Secondary education: 

Husband

0.30** 0.42** 0.25** 0.15* 0.13 0.01 0.63** 0.03

Tertiary education: Husband 0.57** 0.58** 0.55** 0.32** 0.29* 0.30** 0.88** 0.20

Constant -1.23** -1.43** -1.95** -2.60** -1.71** -2.05** -2.26** -1.25**

F Statistic 6.45 11.59 7.24 27.30 4.70 1.92 4.97 25.28

Significance denoted by stars: ** at 99%, * at 95% confidence level.

12
Table 4: Outcome model – Probit Results of the Choice to Go to a Private vs. Public Delivery Facility, Births in the Last 5 Years

Africa Asia LAC

Characteristic Categories Mali Rwanda Zambia Bangladesh Indonesia Nepal Philippines Bolivia

N 9,637 4,516 4,428 1,482 10,528 1,482 3,729 10,931

Time effect (year) year 1=0

year 2=1

0.46** -0.22** -0.56** 0.56** 0.09 0.35** 0.13** -0.03

Perceived/Actual Need

Multiparity 1st child=0

2 or higher=1

-0.02 -0.25 0.26* -0.32** 0.03 -0.02 -0.05 -0.11*

Previous child to mother 

died

no=0 

yes=1

-0.03 -0.28 -0.75* 0.02 -0.11 -0.15 -0.33 -0.41*

Mother mean ANC visits: 

1-3 visits

no visits=0 0.09 -0.16 -1.07 0.75** -0.28 0.25 0.01 -0.09

Mother mean ANC visits: 

4 or more visits

0.34 -0.09 -0.48 1.30** 0.01 0.29 0.05 0.06

Delivery complication: 

Prolonged labor

no=0 

>12 hr labor=1

0.19 -0.17** -0.07

Delivery complication: 

Convulsions

no=0 

convulsions=1

0.28 -0.14 0.32

Economic and Physical Access

Unmet need for family 

planning

no unmet 

need=0

-0.08 -0.02 -0.14 0.00 -0.09 -0.04 0.08 0.00

Wealth Status: 

Middle 3 wealth quintiles

bottom wealth 

quintile=0

-0.34** -0.12 0.47 0.02 0.37* 0.03 0.35* 0.53**

Wealth Status: 

Top wealth quintile

-0.05 0.11 1.85** 0.59** 0.76** 0.03 0.87** 1.31**

Cont’d..

13
Table 4: Cont’d

Africa Asia LAC

Characteristic Categories Mali Rwanda Zambia Bangladesh Indonesia Nepal Philippines Bolivia

Socio-Demographic Characteristics

Region of residence rural=0 

urban=1

-0.30 0.33** 0.37 0.10 0.21 -0.21 0.23** 0.18*

Aged 20-34 years <20 years=0 0.27* 0.04 0.27 0.24* 0.04 0.09 0.10 0.16*

Aged 35 and over 0.27* -0.07 0.61** 0.43* -0.07 -0.22 0.13 0.13

Primary education: Mother less than 

primary=0

-0.01 0.02 0.94** 0.11 0.24 0.16 -0.24 0.18

Secondary education: 

Mother

0.24* 0.42** 1.07* 0.37* 0.38 0.45** -0.20 0.41*

Tertiary education: Mother 0.96** 0.80** 1.43** 0.62** 0.39 0.44 0.01 0.51**

Primary education: Husband less than 

primary=0

-0.05 0.10 -0.37 -0.06 0.10 -0.01 0.15 0.55

Secondary education: 

Husband

-0.02 0.11 -0.64 0.01 0.02 -0.09 0.16 0.59

Tertiary education: Husband 0.51** 0.67** -0.72 0.16 -0.13 0.12 0.37 0.69

Constant -2.28** -1.89** -3.21** -3.25** -0.41 -2.02* -1.21 -3.02**

F Statistic 6.45 11.59 7.24 27.30 4.70 4.97 1.92 25.28

Significance denoted by stars: ** at 99%, * at 95% confidence level.

Note: Ns derived from post-estimation commands.

14
15

In table 3, we find generally homogenous determinants of facility usage across countries. 

Use trends upward over time even after inclusion of controls. Across nearly every country, 

women are more likely to choose to deliver at a facility instead of home if they are having a first 

birth; have had more ANC visits; live in urban areas, have greater wealth, and higher education; 

and do not report distance to a health facility as a barrier to health care. Husband‟s education is 

also positively related to facility use in some countries, as is maternal age and previous death of a 

child, though in fewer countries, and largely in Asia. In about half of our countries, if the woman 

reported an unmet need for family planning, she is less likely to go to a facility for birth. We 

include this measure because we believe it is an indirect measure of lack of access to health 

services, not because of any theoretical link between perceived need for family planning and 

delivery care. In two of the three countries with data on delivery complications, prolonged labor 

are both positively related to facility use. In Bangladesh, convulsions (a sign of eclampsia) were 

also positively associated with use. 

In the outcome model determining private facility use (table 4), private sector use 

significantly changes over time, though use both increases and decreases depending on the 

country. We find no other universal determinants of private delivery care across all eight 

countries. 

Within regions, however, some trends appear. In Africa, private sector use was related 

less to perceived/actual need or economic indicators and more to socio-demographic

characteristics. Only in Zambia was perceived/actual need significant, namely multiparity (more 

likely) and previous death of a child (less likely). Zambia was also the only African country to 

see a greater likelihood of private facility use if the mother was from the top wealth quintile. 

Surprisingly, middle wealth status actually decreased use in Mali, while top wealth status had no 

effect. In all three countries, mother‟s education was associated with increased use of private 

facilities. In Mali and Rwanda, the role of husband‟s tertiary education was also relevant. 

Increased maternal age was also associated with greater use of private facilities in two of the 

three African countries (Mali, Zambia), as was urban residence (Rwanda, Zambia). 

In Asia and Bolivia, the only qualifying LAC country, perceived/actual need variables 

were more statistically significant for use of a private facility for birth as compared to countries 

in Africa, but among these variables no distinct patterns emerged. Primiparity increases the 
16

likelihood of using a private facility in Bangladesh and Bolivia. We would expect that mothers 

with a previous child death would want to go to a private facility due to perceived risk. This was 

not the case however. In Bolivia, a previous child death decreased the likelihood of private 

facility use, and was not found to be significant in any of the Asian countries. 

The only labor complication that had an effect was prolonged labor in Indonesia, which 

was associated with a lower likelihood of private sector use. Greater mean ANC visits was 

positive in Bangladesh but did not seem to have an effect in any other country. Unmet need did 

not have a significant effect on the choice of facility in any country. 

Wealth status appears to influence private facility use more in Asia and Bolivia than in 

Africa. Household wealth had a positive effect on private delivery care in all five countries 

except Nepal. Socio-demographics however played a relatively smaller role as compared to 

Africa. Maternal education positively influenced use of private facilities in three of the five 

Asian /LAC countries, while in two of the countries maternal age (Bangladesh & Bolivia) and 

urban residence (Philippines & Bolivia) were associated with greater use. Husband‟s education 

was not a factor. 
17

DISCUSSION 

This analysis provides further evidence of a trend of privatization in delivery care. It also 

sheds some light on what type of women choose private facilities over public facilities for birth, 

although no consistent results were found across the three regions. While we cannot clearly 

explain from our data how the public-private relationship differs by region or by country, it does 

beg for further analysis to better understand why it appears that in Africa, socio-demographic 

factors drive the decision to go to a private facility, while in Asia and possibly LAC, economic 

status matters more. 

One source of variation may come from the wide array of facilities that are categorized as 

belonging to the private sector. These facilities range from large modern hospitals to simple onebed facilities. Because the private sector often does not face the same regulations as the public 

sector, private providers are also of widely varying quality. (See Das and Hammer 2005 for an 

example of such variations among public and private providers in India).)

Some staff employed by the public sector also work in the private sector, increasing the 

ambiguity of who exactly is providing delivery care (Ferrinho, Bugalho, & Van Lerberghe 

2001). Governance of this is lacking, with some countries (many in Africa) having no specific 

regulations regarding the practice of working across sectors, while others (including our Asian 

countries) such practice is formally or informally recognized, as long as it occurs outside the 

main employment in the public sector (Prata et al. 2005). 

Given that these market distortions occur in all the countries examined here, they would 

not fully explain why mothers seem to choose private delivery care for different reasons across

regions. Results from Africa loosely suggest that the private sector may act as a complement to 

the public sector, appealing to certain socio-demographic populations that may not be able to 

access the public system. Anecdotally, Prata et al. (2005) notes that government spending for 

curative health care may target the wealthy and urban residents in many sub-Saharan African 

countries. Therefore the rural poor may have to choose between locally available private health 

care providers or no care at all. 

On the other hand, our findings in Asia and Bolivia seem to suggest a more competitive 

relationship between the public and private sectors, given that wealth was one of the most 
18

significant predictors. There is some evidence that private providers are competing with the 

public sector based on perceived quality of care and by targeting wealthier patients in developing 

countries, particularly as GDP per capita rises (Brugha & Pritze-Aliassime 2003, Das & Hammer 

2004). Indeed, the average GDP per capita (US$) for the four Asian countries and Bolivia in

2007 was $ 1145, as compared to $613 for the three African countries (Zambia, the richest of the 

African countries, was the only one of that group where the positive role of household wealth 

was statistically significant).

The differences in how the private sector operates have a direct impact on how to 

interpret the impact of privatization on maternal delivery care. In theory, a more competitive 

market can benefit women by keeping overall prices down, and by pushing providers to see more 

patients. However, as has been noted widely in the literature, the market for medical care is 

imperfect and nontransparent, and as such the effect of this model of care on maternal health is 

ambiguous. In some situations, overprovision of care and overcharging may increase in the 

private sector in order for them to maximize their income (Brugha & Pritze-Aliassime 2003, 

Ferrinho, Bugalho, & Van Lerberghe 2001). There has been a significant body of work on 

overprovision of cesarean section in the developing world and its link to privatization of delivery 

care (e.g. Potter et al. 2001, Roberts et al. 2000). Few studies used randomized controlled trials, 

however, and as such important outside influences such as payment systems and case mix may 

not have been properly accounted for in the results. 

Across all countries examined in this paper, there is an increase in use of facilities for 

birth, and in several countries a key factor driving this increase is use of private facilities. 

Definitions of private facilities and the drivers in their use vary by region and perhaps even 

within countries. In some cases, private delivery services play an important role in covering 

populations not covered by the public sector. In others, privatization may increase the gap in 

delivery care provided to the rich and poor. While more in-depth work is needed to truly 

understand the behavior of the private sector in these countries, these results warn against 

making generalizations on the effects of privatization on maternal health use. 
19

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