58443
W O R L D B A N K W O R K I N G PA P E R N O . 2 0 9
A F R I C A H U M A N D E V E L O P M E N T S E R I E S
Reducing Geographical Imbalances
of Health Workers in
Sub-Saharan Africa
A Labor Market Perspective on What Works,
What Does Not, and Why
Christophe Lemiere
Christopher H. Herbst
Negda Jahanshahi
Ellen Smith
Agnes Soucat
THE WORLD BANK
W O R L D B A N K W O R K I N G P A P E R N O. 2 0 9
Reducing Geographical
Imbalances of Health Workers
in Sub-Saharan Africa
A Labor Market Perspective on What Works,
What Does Not, and Why
Christophe Lemiere, Christopher H. Herbst, Negda Jahanshahi,
Ellen Smith, and Agnes Soucat
Copyright � 2011
The International Bank for Reconstruction and Development / The World Bank
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Washington DC 20433
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ISBN: 978-0-8213-8599-9
eISBN: 978-0-8213-8600-2
ISSN:1726-5878DOI: 10.1596/978-0-8213-8599-9
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Contents
List of Abbreviations .....................................................................................................................v
Introduction ..................................................................................................................................... 1
Chapter 1. What Is Wrong with the Current Policy Perspective on Urban-Rural
Imbalances? .............................................................................................................................. 3
Chapter 2. Urban-Rural Imbalances: Extent and Consequences ........................................... 5
National level imbalances ....................................................................................................... 5
Urban-rural imbalances by country, profession, and gender ............................................ 6
Effect of urban-rural health worker imbalances on achieving health MDGs,
reducing poverty, and improving health system efficiency ............................................... 9
Chapter 3. Explaining Urban-Rural Imbalances from a Labor Market Perspective:
Theory and Evidence ............................................................................................................ 11
Urban-rural differences in demand for labor ..................................................................... 12
Urban-rural differences in the supply of labor .................................................................. 12
Urban-rural differences in compensation ........................................................................... 14
Chapter 4. Policy Options for Addressing Urban-Rural Imbalances:
Theory and Evidence ............................................................................................................ 16
Increasing health worker demand ....................................................................................... 17
Reducing the opportunity cost of rural employment: incentive policies ....................... 17
Transferring urban health workers to rural areas through compulsory
policies (bonding) ................................................................................................................... 22
Increasing the overall supply of health workers by scaling up HRH education .......... 23
Improving the rural orientation of the HRH education system ...................................... 23
Creating alternative skill mixes in rural clinics .................................................................. 24
A racting health workers from abroad (immigration policies) ...................................... 26
Chapter 5. Conclusion: A Roadmap for Policymaking .......................................................... 29
Appendix A. Countries Reviewed ............................................................................................. 33
Appendix B. The Lorenz Curve, the Concentration Index, and the Gini Coefficient ...... 34
The Lorenz curve .................................................................................................................... 33
The Concentration index ....................................................................................................... 34
The Gini coefficient................................................................................................................. 34
Appendix C. Applying Labor Economics to Health Care ..................................................... 37
Market-clearing equilibrium, unemployment, and labor shortages............................... 38
Appendix D. Health Labor Market Analysis........................................................................... 43
References....................................................................................................................................... 45
iii
iv World Bank Study
Boxes
Box 2.1 Overview of methods to apply to the measurement of geographical
imbalances of HRH .................................................................................................................. 6
Box 4.1 Using discrete choice experiments to elicit health workers' preferences
regarding rural jobs ................................................................................................................ 18
Box 4.2 Using incentives to recruit rural doctors in Mali and Zambia .................................. 20
Figures
Figure 1.1 Policymaking and health labor markets..................................................................... 3
Figure 2.1 Doctor, nurse, midwife per 1,000 population ratio in Sub-Saharan Africa ........... 6
Figure 2.2 Concentration indices for doctors and nurses .......................................................... 7
Figure 2.3 Distribution of health workers per capita by cadre in all districts of Tanzania ... 8
Figure 2.4 Male : female ratios among health workers in rural
and urban areas of Zambia ..................................................................................................... 9
Figure 3.1 Urban employment and rural shortage situation ................................................... 12
Figure 3.2 Reasons Ethiopian healthcare workers prefer working in urban areas .............. 14
Figure 3.3 Compensation for doctors and nurses across regions in Ethiopia ....................... 15
Figure 4.1 Effect of various incentives on probability of doctors and nurses
accepting a post in a rural area ............................................................................................. 17
Figure 4.2 Reasons for migration in Cameroon, South Africa, Uganda, and Zimbabwe .... 27
Figure 4.3 Growth trend of Cuban brigade doctors present in Ghana .................................. 27
Figure 5.1 Policy roadmap for addressing urban-rural health workers imbalances............ 29
Figure B.1 Sample Lorenz curve .................................................................................................. 34
Figure B.2 Lorenz curve for under-five mortality rate in India and Mali .............................. 36
Figure C.1 Market-clearing equilibrium ..................................................................................... 39
Figure C.2 Unemployment ........................................................................................................... 40
Figure C.3 Labor shortage............................................................................................................. 40
Figure C.4 Urban employment and rural shortage situation .................................................. 41
Figure C.5 Urban and rural HRH markets with improved information ............................... 41
Figure D.1 Health labor market analysis: a country example (for doctors) .......................... 43
Tables
Table 2.1 Number of doctors and nurses per 1,000 people in rural and urban
regions of Sub-Saharan Africa ................................................................................................ 7
Table 2.2 Poverty indicators by region in Mozambique in 2000.............................................. 10
Table 4.1 Policy options for reducing urban-rural imbalances in HRH ................................. 16
Table 4.2 Examples of incentive programs implemented in Sub-Saharan Africa ................. 19
Table 4.3 Compulsory programs implemented in selected
Sub-Saharan Africa countries ............................................................................................... 22
Table 4.4 Skill mix programs in selected countries in Sub-Saharan Africa ............................ 25
Table 5.1 Policy options for reducing urban-rural gaps in HRH ............................................ 31
List of Abbreviations
DRC Democratic Republic of the Congo
HRH Health human resources
MDG Millennium Development Goals (United Nations)
MGI Medecine General Integral
SNNPR Southern Nations, Nationalities, and Peoples' Region (Ethiopia)
SSA Sub-Saharan Africa
UN United Nations
USAID United States Agency for International Development
WB World Bank
WHO World Health Organization
ZHWRS Zambian Health Workers Retention Scheme
v
Introduction
T he human resources crisis in the health sector has been gathering a ention on the
global stage. To date, however, most of this a ention has focused on shortages of health
human resources (HRH) at the national level. At least as important are problems at the sub-
national level. Massive geographic and skill mix imbalances are reflected in the perilous
undersupply of HRH in most rural areas. Virtually all Sub-Saharan African countries suffer
from significant geographic imbalances.
Very li le substantive information or documentation exists on the problem. Even less
is known about the lessons from policies aimed at addressing urban-rural human resource
imbalances, let alone experiences of Sub-Saharan Africa countries, with such policies.
There also appears to be a disconnect between the objectives and efforts of policymakers
on the one hand and the functioning of national health labor markets and labor market
behavior on the other hand. This disconnect hinders policy effectiveness and the efficient
utilization of resources intended to narrow urban-rural inequities. In Sub-Saharan Africa,
government policies, o en limited to the management of public sector vacancies, appear to
be elaborated, prescribed, and implemented independently of labor market considerations.
Partly as a result, they are unable to effectively address urban-rural imbalances, which are
an outcome of labor market dynamics.
This report discusses and analyzes labor market dynamics and outcomes (including
unemployment, worker shortages, and urban-rural imbalances of categories of health
workers) from a labor economics perspective. It then uses insights from this perspective as
a basis for elaborating policy options that incorporate the underlying labor market forces.
The goal of the study is to address undesirable outcomes (including urban-rural HRH
imbalances) more effectively. The study draws on an extensive inventory of policy options
relevant to urban-rural labor force imbalance in Sub-Saharan Africa and the experiences
with these imbalances to date. Given the limited documentation available on this topic
through formal channels, the review relies heavily on "gray literature" from policymakers
in Sub-Saharan Africa and their development partners, especially the World Bank and the
World Health Organization (WHO). The report is divided into five main sections. The first
section focuses on economic policies related to HRH objectives. It argues that policymaking
has ignored health labor market dynamics. The second section provides data showing
the extent of urban-rural imbalances and describes how these imbalances affect health
system outcomes. The third section uses a health labor market framework to explain these
imbalances. The fourth section outlines policy options relevant to Sub-Saharan Africa for
addressing market distortions and affecting labor market outcomes. It also reviews evidence
on the policies, strategies, and programs designed to address geographic imbalances in
Sub-Saharan Africa, highlighting what has been done, what has worked, and what has not.
The last section provides a roadmap for policymakers.
1
CHAPTER 1
What Is Wrong with
the Current Policy Perspective
on Urban-Rural Imbalances?
P olicymakers set health policy objectives (such as, for example, achieving the Millennium
Development Goals for health). These health policy objectives are measured and
monitored using health policy indicators.
Reaching the Millennium Development Goals requires that adequate numbers of
health workers be in place to serve the population in urban and rural areas. Two indicators
are generally used to monitor urban-rural health worker imbalances. The main indicator is
a regional health worker density indicator (the number of health workers per person). The
vacancy rate of rural health worker positions is a second policy outcome indicator. Even if
an adequate number of rural positions are funded, it is likely that some of them will remain
vacant.
Both indicators depend on the dynamics of the health labor market. Within these
markets, the supply of human resources for health (HRH) (that is, the number of health
workers willing to work at various compensation levels) equals the demand for HRH
(that is, the number of employers able and willing to recruit health workers at various
compensation levels). When labor market outcomes need to be adjusted, the government
uses policy instruments to influence the supply of or demand for labor, changing the market
outcome (figure 1.1).
Figure 1.1 Policymaking and health labor markets
Health Labor Market
COMPONENTS
Government
HRH Policy Health Labor Market Health Policy Health Policy
HRH supply OUTCOMES INDICATORS OBJECTIVE
(Levels of hired HRH
Available HRH demand & average HRH (HRH density in rural areas (Health care system
INSTRUMENTS compensation) & vacancy rates in rural areas) utilization and quality)
Other economic
Private health sectors
sector:
households
and private health Foreign
care facilities countries
Source: Authors
3
4 World Bank Working Paper
To date most policymakers have mistakenly assumed that they have complete control
over labor market demand and supply and hence on labor market outcomes. Based on
this assumption, they have relied on the following policies to address urban-rural health
worker imbalances:
Allocating more budget funds in order to create additional health worker positions,
especially in rural areas (that is, increasing HRH demand)
Increasing capacity for training more health workers (that is, increasing HRH
supply)
Forcing health workers to work in rural areas (a practice known as bonding)
Providing financial incentives for health workers accepting work in rural areas
Most of these policies have failed to deliver sustained results, because they have assumed
that health workers have only two alternatives: working for the government or being
unemployed. Although governments in most Sub-Saharan Africa countries probably did
have monopsony power until the 1980s, other employers now compete for health workers
who can (legally or illegally) work in the private sector, move to other economic sectors, or
emigrate. Failing to take account of the fact that governments are no longer the sole source
of demand for health workers leads to flawed policies.
Considering health labor market behavior helps policymakers identify (and measure)
not only constraints but also opportunities for be er policymaking. Health labor markets
can also help identify key success (or failure) factors for some policies.
CHAPTER 2
Urban-Rural Imbalances:
Extent and Consequences
H ealth Worker imbalances and regional inequities in distribution can be measured and
are evident at the national and particularly the sub-national level. Several methods
have been applied to capture the uneven distribution of health works. One popular method
is the ratio of health workers to population, o en used to measure HRH imbalances. Other
methods to analyze and compare imbalances of health workers is through economic tools
such as the Concentration index, alongside the Gini index and Lorenzo Curve (see Box 2.1
and more detailed description in Annex B).
National level imbalances
Although the majority of countries have health worker ratios below the 2006 WHO
benchmark, seven countries have a combined density of physicians, nurses and midwives
that is above the 2006 benchmark of 2.28 (see figure 2.1). These countries are Botswana (3.05),
Gabon (5.45), Mauritius (4.79), Namibia (3.36), Sao Tome and Principe (2.70), Seychelles (9.44)
and South Africa (4.85). Interestingly, a lot more countries reach the pre-2006 benchmark of
0.4 professionals per 1000 population. This would suggest that many countries in SSA are
actually not as badly off in terms of health worker availability as o en suggested. However,
examining HRH to population ratios only at the national level can produce a misleading
Figure 2.1: Doctor, nurse, midwife per 1,000 population ratio in Sub-Saharan Africa
y
10
Density per 10,000 Population
Physician, Nurse & Midwife
0 2 4 6 8
Seychelles (2005)
Gabon (2005)
South Africa (2005)
Mauritius(2005)
Namibia (2005)
Botswana (2005)
Sao Tome and Principe (2008)
Zambia (2005)
Cape Verde (2008)
Nigeria (2008)
Swaziland (2009)
Kenya (2007)
Angola (2005)
Sudan (2007)
Mauritania (2009)
Ghana (2008)
Congo (2008)
Zimbabwe (2008)
Benin (2008)
DRC (2009)
Equatorial Guinea (2005)
Eritrea (2008)
Uganda (2007)
Burkina Faso (2008)
Comoros (2008)
Rwanda (2007)
Guinea (2005)
Guinea-Bissau (2009)
Lesotho (2005)
Gambia (2008)
Madagascar (2005)
Cameroon (2009)
Mali (2008)
Cote d'Ivoire (2008)
Senegal (2008)
Mozambique (2007)
Togo (2008)
Tanzania (2007)
Central African Republic (2009)
Ethiopia (2008)
Liberia (2009)
Malawi (2008)
Chad (2009)
Burundi (2005)
Somalia (2006)
Sierra Leone (2009)
Niger (2009)
Source: WHO/Global Atlas
5
6 World Bank Working Paper
Box 2.1: Overview of methods to apply to the measurement of geographical imbalances of HRH
HRH to pop ratio
One common HRH benchmark to measure HRH imbalances was proposed by the WHO following
several studies carried out with the Joint Learning Initiative (WHO 2006). This benchmark indicates
that there should be at least 2.28 health workers (doctors, nurses, and midwives) per 1,000 people to
achieve the MDGs. This followed earlier pre-2006 WHO benchmarks of 0.1 doctors, 0.2 nurses, and
0.1 midwives per 1,000 people and about 0.4 health professionals per 1,000 people. Many countries
have developed their own benchmarks, usually based on a standard skill mix for each type of health
care facility. In Benin, for example, a district hospital is supposed have three doctors and a primary
health care center is supposed to have one. Standards like these are very rigid and do not take into
account the local burden of disease (especially HIV/AIDS). More sophisticated benchmarks have
been designed, using clinical guidelines (see, for instance, Kurowski and others 2004). Yet, those also
do not take into account the labor market determinants that may infl uence the final HRH outcome
The Lorenz curve
The Lorenz curve is a graphical representation of the proportionality of a distribution (the cumulative
percentage of the values). To build the Lorenz curve, all the elements of a distribution must be ordered
from the most important to the least important. Then, each element is plotted according to their
cumulative percentage of X and Y, X being the cumulative percentage of elements and Y being their
cumulative importance. For instance, out of a distribution of 10 elements (N), the first element would
represent 10% of X and whatever percentage of Y it represents (this percentage must be the highest
in the distribution). The second element would cumulatively represent 20% of X (its 10% plus the 10%
of the first element) and its percentage of Y plus the percentage of Y of the first element.
The Gini coefficient
The Gini coefficient was developed to measure the degree of concentration (inequality) of a variable
in a distribution of its elements. It compares the Lorenz curve of a ranked empirical distribution with
the line of perfect equality. This line assumes that each element has the same contribution to the
total summation of the values of a variable. The Gini coefficient ranges between 0, where there is no
concentration (perfect equality), and 1 where there is total concentration (perfect inequality).
The Concentration index
The concentration index summarizes the information presented by the Lorenz curve. The distance
between the Lorenz curve (the observed distribution of the indicator) and the "ideal distribution line"
represents the degree of inequality. The number measuring the area between the two lines thus
summarizes the degree to which the distribution is equitable.
The concentration index formula calculates twice the area between the Lorenz curve and the line of
inequality (Wagstaff 2008). It ranges in value from �1 to 1. By convention the concentration index is negative
when the Lorenz curve is above the ideal distribution line (representing an undesirable indicator variable)
and positive when the Lorenz curve is below the ideal distribution line (representing a desirable indicator).
picture. Countries that have acceptable national level HRH to population ratios may be
experiencing health worker shortages or surplus in certain geographical areas.
Urban-rural imbalances by country, profession, and gender
A disaggregated analysis of HRH to population ratios o en exposes significant diff erences
between urban and rural areas. In Kampala for example, by far the largest urban area in
Uganda, the number of doctors is 4.5 times the minimum benchmark--and 45 times that of
rural Kamuli district. In Sudan, the doctor to population ratio in urban areas is 24 times that of
rural areas, and the nurse to population ratio is 20 times higher than in rural areas (table 2.1).
And stark and yet diverse inequities in the distribution of health workers in several
countries in SSA can also be illustrated using a concentration index. Figure 2.2 reveals the
Reducing Geographical Imbalances of Health Workers in Sub-Saharan Africa 7
Table 2.1 Number of doctors and nurses per 1,000 people in rural and urban regions of
Sub-Saharan Africa
Country Doctors Nurses
rural urban rural urban
region region region region
Chad 0.222 0.549 ND'jamena 0.885 0.685
Congo, 0.175 1.449 4.762 10.101
Dem. Rep.
Ethiopia 0.167 Amhara 0.769 Addis 0.909 Amhara 0.752 Addis
Guinea 0.714 Moyenne 3.774 Conakry 0.952 Moyenne 3.333 Conakry
Guinee Guinnee
Mali 0.196 Koulikouro 1.852 Bamako 0.909 0.806 Bamako
Mauritania 0.303 Gorgol 1.031 Nouadhibou 2.632 Gorgol 0.794 Nouadhibou
Mozambique 0.154 2.564
Niger 0.056 Tillabery 1.429 Niaey 0.556 Tillabery 0.833 Niamey
Rwanda 0.034 Gikongoro 0.588 Kigali 0.541 Gikongoro 0.617 Kigali
Senegal 0.069 Kolda 2.326 Dakar 0.800 Dakar
Sudan 0.143 South 3.497 Khartoum 0.615 South 12.594 North
Kenya 0.006 Turkana 2.000 Nairobi
Uganda 0.100 Kamuli 4.545 Kampala
Source: Author's calculations based on country status reports.
Figure 2.2: Concentration indices for doctors and nurses
0.600
Doctors
0.500 Nurses
Concentration Indexes
0.400
0.300
0.200
0.100
0.000
l
e
ad
C
n
r
ia
i
a
ga
al
ge
qu
ni
ny
DR
an
M
Ch
ne
Be
Ni
Ke
bi
rit
o
Se
am
au
ng
oz
M
Co
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Source: Authors
8 World Bank Working Paper
degree of geographical imbalance of doctors and of nurses across seven SSA countries.
The closer the concentration index is to zero, the more equal the distribution of a given
resource (doctors or nurses in this context). Conversely, the closer a concentration index
is to 1, the less equal is the distribution of a given resource. As the concentration indices
reveal, in the majority of countries, the distribution of doctors is more and fairly equally
skewed towards urban areas, whereas the distribution of nurses is much more variant and
diverse.
Figure 2.3: Distribution of health workers per capita by cadre in all districts of Tanzania
Source: Munga and Maestad 2009.
The disproportionate allocation of doctors in urban areas can be generalized by
speaking in terms of highly- and lowly-trained (or qualified) health workers. Health
workers with more years of formal education, such as doctors, are heavily concentrated
in urban areas and especially sparse in rural areas, while cadres with fewer years of
education, such as nurses or auxiliary nurses, have a higher concentration (relative to
population) in rural areas. An interesting example (see figure 2.3) is provided by the
Tanzania case, where the density of health workers, by cadres, has been analyzed using
concentration curves1).
As shown in the figure, about 80% of the population in Tanzania (mostly in rural
areas) is served by only 20% of the doctors (named "medical officers" in Tanzania). Rural
areas have a higher proportion of mid-level cadres such as Clinical Officers2 (see the green
line in graph) than urban areas. The Figure also shows that there is no cadre of which the
Reducing Geographical Imbalances of Health Workers in Sub-Saharan Africa 9
Figure 2.4 Male : female ratios among health workers in rural and urban areas of
Zambia
Rural areas Urban areas
Source: Herbst 2007
disadvantaged districts have a larger share of the health workers than is suggested by their
relative population levels (i.e. all concentration curves in Fig. 2.4 fall below the diagonal).
Furthermore, rural areas tend to have a higher proportion of health workers without
formal training. These health workers include community health workers, health extension
workers, and traditional health workers, and are predominantly found largely in poorer,
rural areas. For instance, Lesotho, where more than 75 percent of the population is rural,
has 8,600 healthcare workers. Only 44 percent of them have a formal education. The other
workers--4,800--are community health workers, most of whom work in rural areas. Sierra
Leone is home to only 3,736 conventional workers but also to 10,723 traditional birth
a endants, who are mostly found in more districts.
Urban and rural healthcare workers also tend to have different gender profiles, with
females more heavily concentrated in urban areas than in rural areas. For instance, of all
healthcare workers in Zambia, 64.7 percent are female. Nonetheless, all cadres except nurses
are majority male in rural areas. All cadres have a large female contingent in urban areas.
This gender imbalance further exacerbates the discussed deficits of nurses and midwives in
rural areas, since these cadres are largely made up of females.
Effect of urban-rural health worker imbalances on achieving health MDGs,
reducing poverty, and improving health system efficiency
One reason why rural ratios below benchmarks are especially troublesome is that they
signal a double burden on the poorest of the poor, many of whom live in rural areas and
face greater public health needs than people in urban areas (table 2.2).
The most severe public health problems are o en found in rural and remote areas,
where mortality levels are higher. Yet, the lack of health workers in these areas greatly
decreases the likelihood that the sick obtain treatment or that the healthy prevent infection.
Greater exposure to health risks and inadequate access to healthcare in rural areas reduce
the likelihood of achieving the MDGs, which depend on the expansion of coverage of key,
high-impact interventions. It is difficult for rural areas without adequate access to the
providers of these interventions to achieve the MDGs. A case-in-point is maternal mortality.
Strong evidence points to a high coverage rate of skilled birth a endants as a critical input
for reducing maternal mortality rates (Anand and Baernighausen 2004). The lack of specific
professionals, such as midwives, in rural areas is strongly associated with the failure to
achieve the maternal mortality MDG.
10 World Bank Working Paper
Table 2.2 Poverty indicators by region in Mozambique in 2000
Region Prevalence of poverty Depth of poverty Doctor to population ratio
Rural 71.25 29.92 0.154
Urban 62.01 26.67 2.564
North 66.28 26.62 -
Center 73.81 32.71 -
South (including Maputo city) 65.8 6.8 -
South (excluding Maputo city) 71.67 30.17 -
National 69.37 29.26 -
Source: Minist�rio do Plano e Finan�as 2000.
A frequent contention is that higher-level professionals (for example, doctors) are not
in demand in rural areas. And that the urban-rural imbalance of higher-level professionals
may actually reflect this apparent lack of demand for them. Community and traditional
health providers may be more trusted and respected than formal health workers in some
remote areas and, thus, considered a more appropriate solution. Furthering this argument
is the apparently different utilization pa ern of higher-level professional health workers
by rural populations. When doctors are made more accessible in rural areas, however, the
utilization pa ern is not significantly different from that of the non-poor (Ouendo 2005).
Many demographic and health surveys find that the poor have lower overall utilization
rates of health workers, but when they do seek out health services, they choose the same
combination of services and health professionals as do the non-poor. Thus, the apparent
lower utilization of higher-level health workers may reflect the poor's limited access to
higher-level and qualified health workers rather than different preferences (for Tanzania,
see Leonard, Mliga, and Haile Mariam 2003; Klemick 2007).
Urban-rural imbalances in health workers hinder the development of primary health
care services, reducing the efficiency of the health system. Using urban referral centers
rather than rural centers dramatically increases the costs of health care services for the poor
for two main reasons. First, rural people incur costs in traveling to an urban area.1 Second,
when they reach their destination, they pay more for health care services, because urban
health care services are usually more expensive than rural services. Sanders and others
(1998) show that for the same medical case (that is, acute malaria fever), the length of stay
and number of lab tests may be 10 times as great in an urban area than in a rural one.
Notes
1
This cost includes both the direct cost for transportation and the opportunity cost of taking a half-
day or a day off.
2
A cadre that commonly has a skill set somewhere between that of doctors and that of nurses.
CHAPTER 3
Explaining Urban-Rural
Imbalances from a Labor Market
Perspective: Theory and Evidence
L abor economics theory provides an insightful framework to be er understand the
reasons behind this unequal distribution in HRH (see Appendix C for a detailed
description). It offers two different types of explanations, which are basically related to
the two key features of a labor market. These explanations are not exclusive and a given
country can experience both.
(i) A first explanation is the reduced demand for HRH in rural areas. Such demand does not
reflect needs-based demand for HRH, but the demand expressed by employers to hire health
workers or by individuals willing to buy health services. It is important to note that there are
always two kinds of employers: public ones (government-run health care facilities) and private
ones (private health care facilities and patients paying user fees). Yet, demand for HRH in rural
areas is usually not adequately funded, which contributes to the rural-urban imbalances of
health workers.
(ii) A second explanation centers on reduced supply for rural areas. Indeed, even if there is
enough funding and a significant rural demand for HRH, health workers may not be sufficient
in number and or have some preferences and characteristics that make them reluctant to work in
rural areas. Health worker numbers, preferences and characteristics lead therefore to a limited
number of health workers in rural areas.
In many instances in Sub-Saharan Africa, the urban market is in unemployment or at best
at a market-clearing equilibrium (although occasionally also in a shortage situation); whilst
rural areas are o en in shortage situations. As a result, the urban and rural markets together
are usually in one of three different situations: urban unemployment equals rural shortage,
urban unemployment is greater than rural shortage, or urban unemployment is less than rural
shortage. Vujicic and Zurn (2006) find that Malawi experienced high vacancy rates and high
underemployment of health workers, even in urban markets, although HRH needs are far
from being met. Data from health facilities in Zambia in 2006 reveal health worker shortages
throughout the country, albeit one that is much worse in rural than urban areas (table 3.1). The
Republic of Congo suffers from twin urban unemployment and rural health worker shortages.
A 2008 assessment of health worker distribution and urban-rural vacancy rates there finds that
"a severely unequal distribution of human resources between urban and rural se ings caused
by a variety of problems has resulted in 302 rural clinics closing and an over-supply of workers
in urban facilities" (Crigler, Boniface, and Shannon 2008). Rates of underemployment are high
in urban areas in other countries as well. In C�te d'Ivoire, for instance, about 35 percent of
doctors were underemployed while vacancy rates in rural areas remained significant (Loukou
and others 2006). Comparable rates have been found in Mali and Madagascar.
Figure 3.1 below illustrates how urban employment versus rural labor shortages can be
explained by the economics of demand and supply side behavior of actors within the rural and
11
12 World Bank Working Paper
Figure 3.1 Urban employment and rural shortage situation
Source: Authors.
urban health labor markets. The differences in demand and supply, as well as compensation in
rural versus urban areas, are discussed in the remainder of this chapter.
Urban-rural differences in demand for labor
Evidence shows that demand for HRH is lower in rural areas than in urban ones. This means
that, all other things equal, at any given compensation level, rural employers will employ
fewer healthcare workers than their urban counterparts. One of the reasons for this situation
is, that because of low employer income and limited progress in fiscal decentralization in
many Sub-Saharan Africa countries, rural health facilities are struggling to receive adequate
funding. In Benin, for example, less than 40 percent of regional credits allocated for rural
health centers actually reached them; the balance remained at regional levels (World Bank
2003). Disproportionate allocation of public funds to district health authorities has also
been observed in Zambia. This fiscal centralization, coupled with the abolishment of user
fees in several countries, has limited the income of local health facilities and decreased their
ability to hire healthcare workers. Such limitations on rural health facilities' budgets means
that the demand curve, representing the number of HRH that employers want and can
afford, is farther to the le in rural markets than in urban markets. HRH funding shortages
in rural areas are probably significant factors explaining urban-rural imbalances.
Urban-rural differences in the supply of labor
The evidence suggests that the supply of labor is larger in urban areas than in rural areas,
although not necessarily for all professionals. Some health workers are willing to work
in rural areas given sufficient compensation. Others (for example, doctors, people with
children, and people originally from urban areas) are less willing to relocate to rural areas,
even with a comprehensive incentive package. The motivation for working in rural areas is
thus different across subgroups of health workers. Graphically, this implies that the rural
Reducing Geographical Imbalances of Health Workers in Sub-Saharan Africa 13
supply curve may be slightly to the le of the urban supply curve, especially for health
workers that are less sensitive to compensation levels.
There is some evidence that lower-level professionals are more amenable to working
in rural areas than higher-level professionals. A study in Ghana finds that some lower-level
professionals appreciate the exposure to a wide range of pathologies that comes with rural
service. The reasons cited for preferring rural areas include the fact that they are given
duties above their skill level; bond more tightly with staff, facilitate on-the-job learning skills
(such as surgery); have more opportunities to manage teams, allowing them to develop
management and leadership skills; and have higher social recognition in villages (they are
sometimes called doctor) and receive gi s (Lievens and others 2007).
Evidence also suggests that younger health workers may be more willing to work
in rural areas than older workers. A study in Ethiopia tracking the graduating nursing
class of 2004 finds that 34 percent of graduating nurses were willing to accept a rural
placement in 2004; by 2007, this proportion had declined to 18 percent (Serneels et al.
2005). In Niger, young doctors cite lack of opportunities for postgraduate training as
a key reason for not accepting rural positions (Souleymane et al. 2005). In contrast,
older doctors cite weak remuneration as their main reason for not considering rural
positions. Older doctors face a higher opportunity cost for moving to rural areas, as
the reputation they have built in the city o en allows them to run a private practice.
Anecdotal evidence from Benin and Niger indicates that doctors can more than double
their salary by accepting private clients a er hours. Given that moonlighting can make
up a significant proportion of doctors' incomes, moving to a rural area may indeed
represent a considerable income decrease.
Evidence also points to a gender difference in willingness to work in rural areas, with
men more willing to do so than women. Dussault and others (2006) suggest that female
doctors are likely to live near their husband's place of employment. In the Republic of
Congo, as in many Sub-Saharan Africa countries, married couples are required by law to
live together; providers assigned to rural areas, therefore, o en marry and move to the city
to be with their spouses (Crigler, Boniface, and Shannon 2008). There is anecdotal evidence
that once a woman is assigned to a rural job, she tries to get married quickly in order to
move back to the capital.
For nonnative women without family or friends in the region, locating to a rural area
without support or protections can also create safety concerns. A 2008 HRH assessment
of the Republic of Congo finds that "rural se ings are also considered too dangerous for
unaccompanied women, as they cannot ride buses by themselves or feel comfortable leaving
their homes to work in the villages at night if needed. As more than 61 percent of health
workers are women, this further complicates staffing rural regions" (Crigler, Boniface,
and Shannon 2008). The fact that many female healthcare professionals come from more
educated, elite, or urban backgrounds also makes them less likely to accept positions in
rural or remote areas.
Evidence shows that health workers with rural or poor backgrounds are o en more
willing to work in rural areas than those from urban or wealthier backgrounds. Data
from Ethiopia indicates that health workers from rural areas or less well-off backgrounds
are more motivated and willing to work in rural areas (Serneels et al. 2008). Some of the
willingness of lower level or alternative professionals may stem from the fact that a greater
proportion of such people have rural backgrounds.2 Some health workers may be willing
to work in rural areas for altruistic reasons. Among both nursing and medical students
in Ethiopia, the most frequently cited reason for seeking a rural placement is "to provide
healthcare where it is needed most" (this reason was cited more o en by nursing students
than by medical students; Serneels et al. 2005)--see figure 3.2. At the same time a follow-up
cohort study three years later finds that altruism diminishes once workers gain experience
and begin working as healthcare professionals (Serneels et al. 2008).
14 World Bank Working Paper
Figure 3.2 Reasons Ethiopian healthcare workers prefer working in urban areas
Source: Serneels et al. 2005
Urban-rural differences in compensation
There is strong evidence that urban areas offer higher monetary and non monetary
compensation than rural areas. A study on Ethiopia illustrates this and finds that monetary
compensation levels for doctors and nurses in the capital city (Addis) and rural regions
(Tigray and SNNPR) are significantly different (Jack 2008), with compensation much higher
in urban areas (figure 3.1).
Various studies, including the World Health Report 2006 (WHO 2006), also document
the lack of career-related incentives, such as professional development and training
opportunities, in rural areas. Lower rural net incentives also reflect the higher costs
associated with working in rural areas. Inadequate management, lack of supplies, and
Figure 3.3 Compensation for doctors and nurses across regions in Ethiopia
Doctors Nurses
Source: Jack 2008
Reducing Geographical Imbalances of Health Workers in Sub-Saharan Africa 15
heavy workloads are only some of the factors disproportionately evident in rural facilities.
A study of the Republic of Congo finds that "this [urban-rural] imbalance has many
causes, not the least [of which] is the difficult working conditions providers encounter in
rural areas, such as lack of infrastructure, roads, electricity, water, and housing" (Crigler,
Boniface, and Shannon 2008). Opportunities for education and jobs for spouses are also
limited, increasing the costs of living in a rural area. Workers who transfer to rural areas
also may have to rent a house in the city for their children who remain there.
Notes
1
This low demand will depend on the employment arrangement in place in rural areas. This
phenomenon will, of course, be present in decentralized systems, where rural health care facilities
can directly recruit and pay some of their staff but have difficulties doing so because of limited (or
delayed) budget and revenues. Conversely, in countries where recruitment and payment of salaries
are still fully centralized, there is no public rural employer as such.
2
A large body of evidence indicates that professionals from rural area are more likely than
professionals from urban areas to se le in remote areas (see Rolfe and others 1995; Dunbabin and
Levi 2003; Vries and Reid 2003). Easterbrook and others (1999) find that Canadian physicians who
were raised in rural communities were 2.3 times more likely than those from non-rural communities
to choose to practice in a rural community immediately a er graduation.
CHAPTER 4
Policy Options for Addressing
Urban-Rural Imbalances:
Theory and Evidence
A number of policies and interventions are available to policy makers to impact labor
market dynamics and reduce geographical imbalances of health workers (table 4.1).
This chapter focuses on the supply side policy options to address rural/urban imbalances
and includes a review of the experience of countries with policies and their impact on the
rural/urban distribution of health workers in countries in SSA. For each policy category
except increasing demand for health workers in rural areas-- a detailed discussion of
which is beyond the scope of this study--it reviews theoretical insights and the evidence
of success and failure, including key factors for explaining possible impacts. The chapter
shows that many SSA have a empted to correct the rural urban imbalance - some with
success-, by directly influencing key labor market dynamics.
Table 4.1 Policy options for reducing urban-rural imbalances in HRH
Policy category Policy options
Increasing health worker demand in Policy 1: Increase funding available to health centers so that they
rural areas can hire more health workers (fiscal decentralization)
Reducing opportunity costs associated Policy 2: Increase net compensation in rural areas (with a package
with rural jobs (through incentives) consisting of both monetary and nonmonetary incentives)
Transferring urban health workers to Policy 3: Require graduates to complete a placement in rural
rural areas through compulsory policies areas ("bonding")
Increasing the overall supply of health Policy 4: Scale up education (by increasing the number of health
workers by scaling up education graduates per institution or creating more institutions)
Improving rural orientation of existing Policy 5: Establish local schools for doctors and nurses in order to
education system (creating a "rural attract students from rural areas
pipeline") Policy 6: Change curricula to better train students regarding
clinical situations often experienced in rural areas
Policy 7: Implement a preferential admission policy in health
schools in order to select more health workers with rural
backgrounds ("rural pipeline")
Creating alternative skill mixes for rural Policy 8: Create alternative groups of professionals, such as
areas midlevel professionals
Attracting health workers from abroad Policy 9: Provide incentives or facilitate immigration for specific
(through immigration policies) professions
Source: Authors.
16
Reducing Geographical Imbalances of Health Workers in Sub-Saharan Africa 17
Increasing health worker demand
Although demand side policy options are not discussed in detail here, they do warrant a brief
mention. A first approach to strengthening the demand for health workers in rural areas is to
increase the funding going to rural districts and facilities. This is increasingly done throughout
SSA as more and more countries adopt and implement fiscal decentralization. Depending on the
model adopted, funding for rural health workers wages is transferred to local governments in the
form of block grants �as in Ethiopia and temporarily in Zambia- or earmarked transfers �as in
Benin, Uganda and Mali -. In other countries funding for wages is directly transferred to facilities
as in Rwanda. When the funds transferred are calculated in function of needs and poverty �needs
based or equity formula- this contributes further to increasing demand for rural workers. (see
Vujicic et al). A further and more extensive discussion of country experience with increasing
demand in rural areas and information on some of the key successes and failures with doing so is
beyond the scope of this study and will have to be addressed in future research.
Reducing the opportunity cost of rural employment: incentive policies
Increasing monetary and nonmonetary incentives is one of the most important policy options
available to facilities or districts facing a shortage of HRH because they are unable to a ract
health workers from urban areas. Direct financial incentives include salaries, bonuses, hardship
allowances, and any other monetary benefits. Indirect financial incentives include loan
repayment schemes; scholarships; allowances for childcare, housing, and children's schooling;
health insurance; benefits; travel subsidies; and the right to moonlight or maintain a private
practice. Nonfinancial incentives fall into three main categories: career-related incentives, such
as professional development opportunities, training, and job security; incentives to improve
the working environment, such as improved management, flexibility, availability of supplies
and equipment, and reduced workloads; and family and lifestyle incentives, such as increased
vacation time, provision of housing, and spousal employment.
In many environments, changing the wage rate (direct financial incentive) can be
difficult, because wages may be set by the central ministry of health, frozen by budget
constraints, or slow to respond to market forces. If wages cannot be changed, rural clinics
must compensate by improving nonwage benefits, such as be er working conditions,
Figure 4.1 Effect of various incentives on probability of doctors and nurses accepting
a post in a rural area
Doctors Nurses
Source: Jack 2008
18 World Bank Working Paper
Box 4.1 Using discrete choice experiments to elicit health workers' preferences regarding
rural jobs
Stated preference methodologies include conjoint analysis, contingent valuation, and other
techniques for assessing the utility of alternatives for individuals. These increasingly popular
methodologies have been used in several countries, including Ethiopia, Indonesia, Malawi, and
Niger, to elicit health workers' preferences regarding rural jobs
In a discrete choice experiment, a sample of health workers is asked to choose between simple
job descriptions, usually arranged in about 15 pairs (box table). Collected observations are then
analyzed using econometric models. Figure 4.1 was produced through such a process.
Box table. Sample discrete choice set
Source: Mangham 2007
The effects of such incentive policies vary significantly across professions (figure 4.1). Doubling pay
increases the probability that a doctor accepts a rural job from 7 percent to 57 percent; doubling
the pay of nurses increases the probability of accepting a rural job from 4 percent to 27 percent.
Provisions of basic housing, reduced payback time, and (for nurses) improved supervision also
have positive, albeit smaller, effects on the likelihood of choosing a rural post. These analytical
results were obtained from a discrete choice experiment (box 4.1).
A discrete choice experiment is more powerful than a normal questionnaire for two reasons. First,
people are not good at assessing their own preferences. They can rank them (to some extent)
along an ordinal scale, but they have difficulties assigning cardinal values to these preferences.
For instance, given the choice among jobs located in the capital, 100 kilometers from the capital,
and 200 kilometers from the capital, an individual could rank the choices ordinally; however, a mere
ranking does not reveal the utility associated with each distance.
Second, even if individuals can estimate their utility on a cardinal scale, they will still have difficulties
assessing the tradeoffs between alternatives. A discrete choice experiment replicates actual choices
and does not assume any ability of individuals to precisely estimate their preferences. It is currently
the most reliable method for measuring tradeoffs and eliciting preferences.
Source: Authors
Table 4.2 Examples of incentive programs implemented in Sub-Saharan Africa
Type of Health workers Quality of
Country Type of monetary Incentives nonmonetary incentive targeted Impact evidence/source
Ghana Additional monthly allowance of 20�30 percent of base salary paid to None All health workers Unknown Low
health workers working in one of 55 deprived areas (Dolea et al. 2008)
Mali Doctors setting up and maintaining a rural private practice receive Private doctors receive technical Young and Strong Medium
Reducing Geographical Imbalances of Health Workers in Sub-Saharan Africa
financial aid for purchasing housing and basic medical supplies, support for preparing their unemployed doctors (Coulibaly et al.
retain 50 percent of user fees, and receive a base salary from local business plans and strong clinical 2007)
communities of $200 a month mentoring
Mauritania Additional monthly allowance equal to about half the base salary None All health workers Unknown Low
(Sy and Hamed
2006)
Niger Additional monthly allowance of $40�$160 (7�30 percent of base None Doctors who Weak High
salary) are already civil (Souleymane and
servants others 2005, 2008)
Senegal Additional monthly allowance of $370 for doctors (equivalent to Motorcycles given to nurses in All doctors and Medium Low [[AQ: source
75 percent of base salary) and $290 for nurses (equivalent to 100 some rural provinces nurses for Senegal info?]]
percent of base salary)
South Africa Additional monthly allowance of 8�22 percent of base salary None Doctors and nurses Medium Medium
(Vujicic and
Lindelow
forthcoming)
Tanzania Since 2005, all health workers willing to spend at least one year in a None All health workers Unknown Low
rural area may apply for a Mkapa fellowship (Merkle and
Prytherch 2007)
Zambia Under the Zambian Health Workers Retention Scheme (ZHWRS), Preferred access to postgraduate Doctors Strong Medium
program participants receive additional monthly allowance of $250� education (if a doctor spends at (Koot and
$300 (equal to about half of base salary); end-of-contract bonus of least three years in a rural area) Martineau 2005)
$2,000�$2,500, paid upon satisfactory completion; allowance for and clinical mentoring during
purchasing basic medical supplies and equipment; $3,000 bonus for installation of young doctors in
renovating a house; $1,500 annual subsidy for each child attending rural areas
school; and preferred access to home and car loans
19
Source: Authors.
20 World Bank Working Paper
training opportunities, or lifestyle factors. Although the value of these a ributes is more
difficult to quantify than wage a ributes, they are important levers in shaping workers'
satisfaction (Vujicic and Lindelow forthcoming).
The incentive package must be carefully selected for each country, profession, profile,
and circumstance. In Ghana, a focus group discussion reveals that a policy requiring a limited
period of rural service together with rural incentives and a firm commitment to promotion
a er rural service would be a strong incentive to some health workers (Lievens and others
2007). A study in Ethiopia (Jack 2007) finds that proportionate wage bonuses for rural
service would probably increase rural labor supply (the effects appeared to be much larger
for doctors than for nurses). For both doctors and nurses, superior housing and equipment
significantly increase the probability that a worker accepts a rural job (figure 4.1).
Country experiences
Most Sub-Saharan Africa countries that have implemented incentive policies have used
both monetary and nonmonetary incentives. Incentive policies aiming at reducing the
opportunity cost associated with rural employment are the most frequently used, with
almost all Sub-Saharan Africa having tested at least one such type of incentive policy. The
range of experiences is wide, ranging from very simple incentive schemes (in Niger or
Mauritania) to comprehensive packages (in Zambia); see table 4.2.
Experience with incentives--which increasingly mix monetary and nonmonetary
incentives--has generally been mixed, with successes occurring in Mali, South Africa, and
Box 4.2 Using incentives to recruit rural doctors in Mali and Zambia
Rural private practices in Mali
Mali implemented a very innovative strategy to support young doctors in setting up rural private
practices (Coulibaly and others 2007). Madagascar also recently implemented a similar program,
about which little information is available. With the support of an NGO (Sant�-Sud) and the Bamako
medical faculty, young doctors interested in working in rural areas are identified before graduation.
Those that are interested receive help in preparing a business plan that is in line with the incomes of
the targeted population. Once they start their business, these doctors benefit from regular mentoring
by seasoned doctors. They receive a small, fixed amount of money ($200 a month) and, sometimes,
a house from the community; most of their revenues come from user fees, half of which they are
allowed to keep. Very active rural doctors can earn about $1,000 a month, not much less than they
would earn in urban areas. Since 2000, more than 100 doctors have been attracted to and retained
in rural areas thanks to this program.
The Zambian Health Workers Retention Scheme
To address the issue of uneven distribution of doctors throughout the country, Zambia developed
a new policy in 2003 based on a comprehensive incentive package. The package, known as the
Zambian Health Workers Retention Scheme (ZHWRS), includes both monetary and nonmonetary
incentives. Monetary incentives included a monthly allowance of $250�$300 (equivalent to roughly
50 percent of base salary); an end-of-contract bonus of $2,000�$2,500, paid upon satisfactory
completion; an allowance for purchasing basic medical supplies and equipment; a $3,000 bonus for
renovating a house; a $1,500 annual subsidy for each child attending school; and preferred access
to home and car loans. Nonmonetary incentives consisted of preferred access to postgraduate
education (if a doctor stayed at least three years in a rural area) and clinical mentoring during
installation. Even though some of these incentives were not fully implemented, the ZHWRS
attracted more than 50 doctors in rural areas within the first 14 months after implementation (Koot
and Martineau 2005). The success of the program has prompted some hospital doctors to complain
about the medical "brain drain" from urban to rural areas. The program, which was heavily subsidized
by USAID, may not be sustainable, however.
Source: Authors
Reducing Geographical Imbalances of Health Workers in Sub-Saharan Africa 21
Zambia (box 4.2).1 Niger's financial incentive program for doctors, for example, where one
of the few truly prospective evaluations of an incentive program was carried out, had no
significant impact. The distribution of doctors remaining highly skewed to the benefit of
the capital city, with the percentage of all doctors working in the capital remaining virtually
constant before and a er implementation of an incentive scheme (34 percent doctors in 2005,
before the program was implemented, and 35 percent in 2008, a er it was implemented)
(Souleymane et al. 2005, 2008).
Key success and failure factors
The lack of success has generally been a ributed to the level of monetary and nonmonetary
incentive, which was insufficient to outweigh the opportunity costs associated with se ling
in rural areas. At a minimum, monetary incentives should offset the loss of private practice
revenues (which are more difficult to generate in rural areas); the added cost of children's
education (as children usually live in the capital city, which entails transportation and lodging
costs); and the loss of the spouse's salary. Few programs analyzed these opportunity costs
before se ing the level of financial incentive, thus greatly increasing the chances of failure.
Younger doctors, who are usually unmarried, have no children, and lack enough
experience to a ract private clients, may face lower opportunity costs. They also highly value
opportunities for learning, including clinical mentoring and preferred access to postgraduate
training. Such nonmonetary incentives have not been included in most incentive packages.
More research is needed to evaluate the outcomes of incentive programs on rural
shortages. The best policies may well be those that successfully combine monetary and
nonmonetary incentives. Most incentive programs have targeted only doctors; li le is
known about their effect on other types of health workers. In situations where health
workers are unresponsive to both monetary and nonmonetary incentives, other solutions
(such as compulsory placement) may have to be considered.
Where incentives succeeded, their success was generally a ributed to the fact that the
incentive packages comprehensively addressed the needs and opportunity costs of doctors.
In Mali, the needs and constraints of the targeted group of doctors were well researched
and understood. In Zambia, the incentive package was not only financially a ractive, it also
took into account all the various opportunity costs faced by rural staff (technical support,
children's education, housing, transportation, and so forth) and included nonmonetary
incentives for expanding learning opportunities for young doctors. In both cases,
preparatory analysis was conducted to evaluate the specific types of incentives needed to
motivate specific subgroups of health workers before the programs were put into place.
Neither country used sophisticated techniques for eliciting health workers' preferences.
Instead, they adjusted their policy through a long process of trial and error. Other countries
could avoid this costly and lengthy process by using state-of-the-art techniques, such as
discrete choice experiment.
Success is also related to some contextual factors, notably the dynamics between urban
and rural labor markets. A racting health workers to rural areas is easier when there is
unemployment in urban labor markets--this factor may explain part of the success of
the Malian experience. Because of fiscal constraints, the Malian government is able to
hire only a small proportion of new medical graduates. As a consequence, there is a large
pool of unemployed doctors in the capital city, Bamako. These underemployed doctors
were explicitly targeted by the incentive program; many of them reacted favorably to the
proposal to set up a private practice in a rural area.
Another contextual factor is the degree of decentralization in a given country. The Malian
experience could not have been successful without the high degree of decentralization
already achieved in the country. Local communities in Mali have substantial experience
managing and outsourcing local services. Contracting private doctors was, thus, not a
major challenge for these communities.
22 World Bank Working Paper
Another possible issue relates to civil service regulation of compensation. In many Sub-
Saharan African countries, governments are still the main employer of health workers, whose
remuneration is usually defined by national and multisectorial salary scales. Ministries of
finance are leery of rural incentives for health workers, fearing the risk of spillover effects. In
Niger, for instance, as soon as the government agreed to implement incentives for doctors,
other health workers asked for (and obtained) similar advantages. Spillover can also
extend beyond the health sector, especially to education, as teachers share health workers'
reluctance to work in rural areas. Several countries are trying to overcome this constraint by
creating health worker categories outside the civil service system (delinkage). Evidence on
this policy is mixed, with successes in Rwanda and failures in Benin and Zambia.
Transferring urban health workers to rural areas
through compulsory policies (bonding)
Health workers can be moved from urban areas to rural areas through compulsory
placement policy, also known as bonding. Creating a period of obligatory rural service for
graduating health workers is one way of bonding. This option falls outside of labor market
theory, as labor is no longer a commodity that health workers are free to offer or not offer
according to the compensation level.
Country experiences
Many Sub-Saharan Africa countries have implemented compulsory placement programs
(table 4.3). Although such policies may temporarily reduce short-term shortages, they have
had li le or no impact on long-term rural retention. Anecdotal evidence suggests that these
programs are also difficult to enforce.2
Key success and failure factors
Interactions between policies and incentives may backfire on bonding policies. In the early
years of Thailand's compulsory placement program, young doctors used the financial
benefits received from working in rural areas to pay the fines associated with not completing
their obligatory rural service time (Wibulpolprasert and others 2003a). The unintended--
and paradoxical--consequence of this rural policy package was that a significant number
of doctors le remote rural areas thanks to rural financial incentives. Another fundamental
factor in the failure of bonding policies is the fact that they do not compensate for the
financial and nonfinancial opportunity costs borne by doctors serving in rural areas.
Sub-Saharan African countries that have this kind of policy have not been able to
enforce it consistently. Most "displaced" health workers returned illegally to the capital
city, sometimes a er bribing ministry of health officials. Such a policy cannot be easily
implemented if there is no shortage of health workers in the urban market. Conversely,
when a health worker surplus is experienced in urban markets, the opportunity cost of
"displaced" health workers is significantly lower.
Table 4.3 Compulsory programs implemented in selected Sub-Saharan Africa countries
Country Requirement
Ethiopia Compulsory periods of work in rural areas as a condition for obtaining medical degree
Ghana Compulsory two-year service in rural areas for new doctors entering civil service
Niger Compulsory three-year service in rural areas for new doctors entering civil service
Zambia Compulsory one-year placement of young doctors in rural areas
Zimbabwe Compulsory program for entering civil service
Source: Authors.
Reducing Geographical Imbalances of Health Workers in Sub-Saharan Africa 23
Failure can also be a ributed to the lack of support of key stakeholder groups,
associations, and health workers themselves. Doctors in Zimbabwe, for example, went on
strike in response to compulsory placement policies requiring medical students to work
two years in rural stations (Mutizwa-Mangiza 1998).
Increasing the overall supply of health workers by scaling up HRH education
Increasing the overall supply of health workers does not target urban-rural imbalances as such,
and is thus not discussed in detail here. It may however increase the numbers of health workers
working in rural areas. Indeed, increasing the overall supply of health workers may address the
problem that even if there is sufficient rural demand, and a potential willingness of some health
workers to work in rural areas, the number of available health workers to spread into rural areas
is simply too low. In theory, the more health workers there are, the larger the proportion willing
to work in rural areas. To scale up the number of health workers, the capacity of the institutions
that produce them must be scaled up as well. First assessments of current institutional and
organization capacity must take place. To increase cost effectiveness relevant existing resources
should be maximized. This includes faculty, material and infrastructural resources and systems
that are currently producing well-qualified health workers. However, it is important to
examine innovative approaches and lessons learned from other health systems to help improve
national health worker production capacity. Finally, many countries may find that significant
investment in physical, information and communication infrastructures as well as in knowledge
management systems is necessary and cost-effective in the long run. Governments might also
find that building partnerships across, institutions, sectors and borders is required to maximize
the use of scarce resources and take advantage of learning from relevant experiences. (GHWA,
2008). On the whole thus, increasing the overall supply of health workers does not address rural-
urban imbalances as such, but may increase the number of health workers that are available
and willing to work in rural areas. It may thus be a policy option at least worth exploring as a
requirement before designing other policies for addressing urban-rural imbalances.
Improving the rural orientation of the HRH education system
Rural pipeline policies combine several features to create a sustainable rural health
workforce. Two education-related policy options address geographic imbalances. The first
involves targeting potential students with the profile most likely to be amenable to rural
postings (especially those with rural backgrounds)--by, for example, admi ing many more
rural students to existing institutions or establishing new institutions in rural areas. The
second involves changing the health education curriculum so that it is more relevant to rural
healthcare and exposes students to rural environments as a part of their formal education.
Several countries have designed curricula that are more rural-friendly. Such curricula
include compulsory internships in rural areas, more training on community or rural
medicine, and general surgery. Equatorial Guinea launched a postgraduate program--
Medecine General Integral (MGI)--in 2000, in cooperation with Cuba. Students receive in-
depth training in all necessary fields to face the most commonly found situations in rural
areas (pediatrics, internal medicine, general surgery, gynecology-obstetrics).
Rural pipeline policies can be made up of a combination of the following elements:
Admissions policies giving preference to or allo ing a specific number of slots to
applicants from rural regions
Creation of regional rural medical and nursing schools
Development of curricula with strong emphases on family or community medicine
Compulsory rural internships
Financial aid and scholarships for rural students
Mentoring by experienced rural doctors for new health workers in rural areas.
24 World Bank Working Paper
Country experiences
A few Sub-Saharan African countries have implemented rural pipeline programs. Benin
created a regional medical school in 2001 that trains only general practitioners. Niger
established two nursing schools in rural areas in 2006 and Mali xx schools since xxx. Senegal
created a regional branch of the medical school in Dakar in 2008. Ethiopia has established
nursing schools not just in the capital Addis Ababa, but also in the south and west of the
country. South Africa experienced good results from a scholarship program that required
rural students to return to their districts a er graduating.
Almost all empirical studies of such programs are from non-African programs (in
Australia, Japan, Norway, Thailand, and the United States) where evidence of a strong
impact is clear. Overall, rural pipeline programs in these countries have been very effective
in a racting and retaining doctors in rural areas (see Murray and others 2006 and Rolfe
and others 1995 for Australia; Rabinowitz 1999 and 2001 for the United States; Hsueh and
others 2004 for Norway and Japan). Except in Thailand, none of these programs included
a mechanism for obligating or creating special incentives to ensure that rural students
returned to rural areas to work. Instead, most of the rural graduates chose to se le in
rural areas, underscoring the potential power of the rural pipeline tool. South Africa has
tested a rural scholarship program in the Mosvold district where scholarships are given
to rural students on the condition that they return to their district a er graduation. Rural
students receiving the scholarship were three to eight times more likely to practice in rural
areas a er graduation. Such cross-national studies provide evidence that people with
rural backgrounds are, indeed, not only more willing to work in rural areas but also more
likely to do so voluntarily. Policies to recruit and train students from rural backgrounds are
potentially a low-cost and sustainable part of the solution to the urban-rural gap in HRH.
Key success factors
Results from the most in-depth study of a rural pipeline program (Rabinowitz and others
2001) find that a preferred admissions policy and a revised curriculum are the most relevant
factors in a racting rural students and influencing their decision to later se le in rural areas
of Pennsylvania, in the United States. Rural internships and financial aid had li le impact
on student decisions.
Creating alternative skill mixes in rural clinics
Highly trained health workers with many years of formal education, such as doctors, are
the most difficult to recruit and retain in rural health clinics. Consequently, rural clinics
and ministries of health are now training lower level professionals to take on some roles of
doctors and nurses. Because they are recruited locally, such professionals are more likely
to stay (Dovlo 1994). Such task shi ing does not increase the number of health workers in
rural areas; rather, it increases the capacity to perform health interventions, which lies at
the heart of the health worker shortage. Shi ing some tasks to lower or midlevel health
workers, such as auxiliary professionals, nurses, or community health workers, through in-
service training is increasingly hailed as a potential way to increase the number of personnel
qualified to carry out key health interventions in rural areas.
In principle, delegating additional tasks to lower level health workers is a feasible
response to rural skills shortage. It can also serve as a key motivator by giving lower level
workers more responsibility and scope for professional development, which may increase
productivity and quality. Medical care knowledge and technology have evolved rapidly
in recent years, allowing health workers without full medical training to perform some
diagnoses and treatments that would have needed medical skills a few years ago.
There is much divergence across countries regarding which tasks it is acceptable for
health workers of different levels to carry out. It is imperative that strategies focus on
Reducing Geographical Imbalances of Health Workers in Sub-Saharan Africa 25
Table 4.4 Skill mix programs in selected countries in Sub-Saharan Africa
Training and
Targeted supervision
Country Task shifted professionals arrangements Reference
Botswana Management of antiretroviral Nurses In-service training Miles and others
therapy (2007)
Cameroon Prevention of maternal- Rural birth Ongoing Wanyu and
to-child HIV transmission attendants supervision by others(2007)
services, including nurses
counseling, testing, and
administration of nevirapine
Ethiopia Counseling and testing Community health 6-week in-service Mengistu (2008)
workers training
Ghana Management of childhood Community health 11-day in-service WHO (2006)
diseases nurses training
Kenya Management of obstetrical Nurses and In-service training Thairu and
complications clinical officers Schmidt (2003)
Malawi General surgery, including Clinical officers 3 years Chilopora and
Caesarian sections (district- others (2007)
level)
Antiretroviral therapy initiation Nurses and 3-week intensive Philips (2008)
community health training
workers
Mozambique General surgery, including Medical assistants 3 years Vaz (1999);
Caesarian sections (district- Pereira and
level) others (1996)
Evaluation of patient eligibility Basic level nurses Standardized Gimbel-Sherr
for highly active antiretroviral training on staging and others
therapy HIV�positive (2007)
patients using
CD4 counts and
WHO criteria
Rwanda HIV/AIDS services, including Nurses Ivers (2008)
prescription of antiretroviral
therapy
HIV/AIDS services, including Community health
diagnosis of opportunistic workers
infections associated with
HIV/AIDS
Tanzania General surgery, including Assistant Medical Mbaruku and
Caesarian sections (district- Officers Bergstr�m
level) (1995)
Source: Authors.
26 World Bank Working Paper
matching the skills of workers to the local profile of health needs. Although the objective
of training lower and midlevel professionals to deliver health care at the community level
is to eventually delegate to them work normally reserved for high-level professionals, all
professionals should continue to provide care they are uniquely equipped to provide.
Country experiences
Several types of skills-expansion policies have been observed in Sub-Saharan Africa (table
4.4). These activities focus on increasing the skill sets of lower and midlevel professionals
as well as higher-level professionals, such as general practitioners.
The experience with shi ing higher-skill tasks to lower-level professionals in rural
areas has generally been successful. Alternative skill mixes can deliver positive results
when they focus on a specific procedure, such as obstetrics or antiretroviral therapy. In
Mozambique, the lack of doctors combined with the urgent need for emergency surgical
care and skills for maternal health necessitated a reorientation of the training of health staff.
A comparison of 1,000 consecutive Caesarean sections conducted by medical assistants,
with the same number conducted by obstetricians and gynecologists, showed no differences
in quality (Pereira and others 1996). In Malawi a 2007 study of more than 2,000 emergency
obstetric operations performed by clinical officers found that postoperative outcomes were
comparable to those performed by medical officers (Philips, Zachariah, and Venis 2008).
Key success and failure factors
Adequate training, monitoring, and support are key success factors of any alternative skill
mix policy. Although concerns have been raised about the safety of services provided by
lower and midlevel health workers, most of the evidence shows that, with appropriate
training and support, such health workers can respond effectively to most emergency
problems in general surgery and obstetrics.
There is li le empirical evidence of precisely how much support, monitoring, and
supervision are required in alternative skill mix schemes. Some observers argue that the
new task distribution must be institutionalized in order for the programs to be sustained or
expanded. If, for example, nurses are given the task of managing antiretroviral therapy, as
in Botswana, their new position should be clearly defined so that there is no ambiguity, and
their performance should be closely monitored (Miles and others 2007).
Some observers argue that an alternative skill mix initiative that permits treatment
where people otherwise would get none at all should be viewed positively (Philips,
Zachariah, and Venis 2008). The overall, but limited, picture from studies to date indicates
that alternative skill mix is a viable option to provide support to the overstretched healthcare
systems in many African countries; although, it cannot complete solve the region's human
resources problem.
Another factor in determining the success of alternative skill mix initiatives is the
choice of which professionals take on which tasks. Two considerations are crucial: the
professional must be able to take on the additional workload, and the professional should
be considered appropriate to taking on the task in view of the local community's features
and potential clients. The first consideration demands that the current workload and
potential for its increase be analyzed before deciding if an individual is able to take on the
new task. In Lesotho, a focus group study found that many people with HIV/AIDS had had
unsatisfactory experiences with nursing staff, which affected their willingness to receive
treatment (Pa erson and others 2007). The participants in the study acknowledged that these
experiences had occurred largely because the nurses were overworked and underpaid. The
participants stated that they would be happy with nurses taking on additional tasks, such
as prescribing antiretroviral treatment, as long as they received adequate clinical training
and anti-stigma training.
Reducing Geographical Imbalances of Health Workers in Sub-Saharan Africa 27
The clients' perceived view of the nurses' a itudes underscores the second consideration:
the importance of taking account of local social factors when assigning roles to specific
workers. In some communities, women may prefer to receive treatment from women.
Goodman and others (2006) raise the idea that, for simple public health interventions,
tasks may even be shi ed to nonmedical professionals. Their study finds that a program
that trained shopkeepers in Kenya to treat childhood fevers was both cost-effective and
sustainable.
These findings are consistent with those of other studies on the cost-effectiveness of
alternative skill mix for the delivery of health services in both high-income and resource-
constrained countries. Much of that evidence is from high-income countries; however, more
work needs to be done to develop a clearer picture of these experiences in Sub-Saharan
Africa. More studies are needed on the performance of lower-level professionals recently
trained in higher-level interventions.
Attracting health workers from abroad (immigration policies)
Country-level shortages can be eliminated by encouraging the inflow of foreign health
workers. Sometimes, this can be a political arrangement or exchange. When giving them
a choice, however, workers tend to go where working conditions are be er. Although
income is an important motivation for migration, it is not the only one (figure 4.2).
Other incentives include working conditions, career and training opportunities, and
management quality. Political instability, war, and the threat of violence are also strong
drivers of migration in many Sub-Saharan African countries. The reasons behind their
migration serve as basis for creating contracts and incentive packages aimed at a racting
health workers from abroad.
Several countries in SSA are already a racting immigrant health workers, formally
and informally. Within Africa, migration is already common in some countries, particularly
migration to Southern Africa (i.e., migration of Ethiopian and Cameroonian doctors to South
Africa and Botswana). Furthermore, many West African countries have contracts with Cuba
Figure 4.2 Reasons for migration in Cameroon, South Africa, Uganda, and Zimbabwe
28 World Bank Working Paper
Figure 4.3 Growth trend of Cuban brigade doctors present in Ghana
for organizing extended stays of Cuban health workers in rural areas. Figure 4.3 shows
that the number of Cuban doctors presently employed in Ghana has grown steadily since
1982, and today hovers around the 200 mark. Very li le is known on the impact of these
contracts (Dussault and others 2006); however, Ghana emphasizes the commitment and
dedication, as well as low cost, of Cuban health workers. Other countries hire immigrant
health workers on a case-by-case basis.
An advantage that immigration policies have over education is their time frame.
Immigration polices allow a much quicker response to national shortages than education
policies, at least for most categories of health workers.
Notes
1
In Mali, more than 100 doctors agreed to start private practices in rural areas (Coulibaly and others
2007). Under the South African Rural Allowance and Scarce Skills Allowance, 28�35 percent of rural
health workers who received the 8�22 percent salary bonus believed it affected their career plans
for the following year (Vujicic and Lindelow forthcoming). The Zambian Health Workers Retention
Scheme a racted and retained more than 50 doctors in rural areas in less than two years (Koot and
Martineau 2005).
2
Zambia's compulsory one-year placement of young doctors in rural areas was not strictly enforced;
when it was, many doctors resigned rather than work in rural areas (Koot and Martineau 2005). One
exception to enforcement failure can be found outside the African region: Thailand was successful in
enforcing a comprehensive package of benefits and incentives for rural health workers as well as a
rural pipeline program (Wibulpolprasert and others 2003b).
3
In Nepal, a nationwide health worker surplus was intentionally created to overwhelm health
labor markets. The underlying assumption was that such a policy would entail high unemployment
in urban areas, which would create a natural incentive for health workers to move in rural areas.
Evidence on results is scarce and, in most cases, disappointing.
CHAPTER 5
Conclusion: A Roadmap for
Policymaking
T he effectiveness of a given policy option depends strongly on the country context and
the way in which the policy is designed. A roadmap for policymakers can nevertheless
be proposed that may help them avoid major and costly blunders (figure 5.1).
The first step is to evaluate and address any imbalance caused by insufficient resources
(referred to here as a funding shortage). It is useless to try to a ract more health workers
to rural areas if adequate budgetary allocations and financial and management processes
are not already in place with which to remunerate them. This step, which would increase
Figure 5.1 Policy roadmap for addressing urban-rural health workers imbalances
SHORT-TERM LONG-TERM
OPTIONS OPTIONS
STEP 1
YES 1. Strengthening
Is there a funding shortage
rural demand
in rural areas ?
(fiscal decentralization)
NO
2. Incentives
(monetary and/or
non monetary)
STEP 2
YES
Is there a HW surplus
3. Bonding
on the urban labor market ?
NO 4. Pre-service
training
scaling up
5. Local medical
and
nursing schools
8. Alternative
skill-mix
6. Curriculum
changes
toward rural needs
9. Migration
from abroad
7. Preferential
admission policy
for rural candidates
(rural pipeline)
Source: Authors
29
30 World Bank Working Paper
rural demand for health workers, must be prioritized. This can be done through increased
resources from governments as well as from users. Fiscal decentralization helps increase
public resources for peripheral providers whether channeled directly to providers and
combined with providers' autonomy or as subsidies to demand side financing, including
health insurance or cash transfers. Private resources can also be used more efficiently,
for example, by pooling user payments into rural health insurance mechanisms that can
contribute to increased utilization and, thus, resources for rural providers.
The second step is to address health workers' willingness to work in rural areas.
Choosing the right policy mix strongly depends on the labor market situation. Incentive
and bonding policies are likely to be more effective if there is a surplus of health workers
on the urban market and a pool of unemployed or underemployed health workers to draw
from in urban areas. Consequently, an analysis of labor supply, rural and urban, is highly
recommended before selecting the mix of policy options. (Appendix D provides a brief
presentation of such an analysis.)
If health workers are available and underemployed and incentives or bonding policies
emerge as promising options, policymakers must carefully estimate the opportunity costs
associated with rural jobs. Many countries carry out feasibility studies in a very traditional
way (that is, by administering a simple questionnaire). Stated preferences techniques
(especially discrete choice experiment) are not more expensive and provide much more
insight than traditional surveys. They should be carried out systematically before designing
the package to a ract health workers to rural areas.
If there is no health worker surplus on the urban labor market, incentives and bonding
is unlikely to work. In this case, education and alternative skill mix policies are probably
the best solutions. Rural pipelines are o en needed to serve particularly remote and poor
areas. Evidence from industrial countries suggests that this set of policies can have a strong
impact. However, when alternative skill mix is implemented through in-service training,
these policies are longer-term options. Creating midlevel professionals, such as medical
or clinical officers, for example, requires waiting three or four years before the first class
graduates. As health workers will live in the system for many years, this also has long-term
consequences on the type of health system that is developed.
Most policy options can be combined, especially to take into account the fact that
some are short-term and others longer-term solutions. Niger started to prepare its policy in
2004�05, when it decided to scale up the production of surgeons. Given that the first class
would not graduate until 2008, two additional (short-term) policies were implemented: an
incentive scheme for doctors and fast-track (one year) training in district surgery for a group
of general practitioners. In Ethiopia, the recent scaling up efforts supports a combination of
short-term solutions. The rapid training of 30,000 health extension workers trained in one
year is associated with the increase in production of medical officers trained in four years
as well as the creation of new medical schools in rural areas.
Policy options can be evaluated on the basis of two criteria: ease of implementation and
potential impact (table 5.1).
Because country-specific conditions o en dictate how policies work in practice, learning
by doing is the best approach. Learning from experience requires the regular evaluation of
the impact of implemented policies. Evidence on policy impact remains very limited in
Sub-Saharan Africa. Enhancing data collection and analysis is a key priority for improving
the geographical distribution of health workers within the region's countries.
Reducing Geographical Imbalances of Health Workers in Sub-Saharan Africa 31
Table 5.1 Policy options for reducing urban-rural gaps in HRH
Policy category Ease of implementation Potential impact
Increasing demand for health Requires an adequate fiscal Strong if rural shortage is caused
workers in rural areas space and, possibly, some fiscal largely by funding shortage
decentralization
Reducing opportunity costs May require some changes in Limited evidence; can have
associated with rural jobs civil service system (including significant impact if opportunity
(incentive) delinkage); otherwise quickest costs and preferences
policy to implement associated with rural jobs are
correctly measured and there is
health worker surplus in urban
markets.
Transferring urban health Stakeholders' resistance is No success story in Africa,
workers to rural areas through usually weak, because young probably because of weak
compulsory policies health workers are targeted capacity for enforcement.
Requires high governance level
to ensure enforcement
Easier to implement if there is
health worker surplus in urban
markets
Increasing the overall supply Adequate fiscal space needed, Evidence lacking; impact on
of health workers by scaling up as well as detailed assessment rural shortage possible if funding
education of needs for HRH education shortage is large and addressed
capacity at same time. Because of cohort
effects, impact appears slowly.
Improving the rural orientation Stakeholders may view policy as Evidence on success limited,
of the existing education system a "two-tier" system (urban health but potential impact is probably
("rural pipeline") worker professionals versus rural strong, given experiences in
health worker professionals); industrial countries.
otherwise easy to implement
Creating alternative skill mixes Stakeholders may view policy as Evidence on impact compelling;
for rural areas "two-tier" system can emerge quickly if alternative
skill mix is created through
in-service training.
Attracting health workers from Usually the last-resort policy No evidence available; impact
abroad (immigration policies) probably small.
Source: Authors.
APPENDIX A
Countries Reviewed
Forty-six Sub-Saharan Africa countries were analyzed.
Some quantitative Only anecdotal
data on rural evidence on rural No data on rural
incentives found incentives found incentives found Not reviewed
Benin Angola Botswana Burkina Faso
Ethiopia Equatorial Guinea Burundi Cameroon
Ghana Lesotho Cape Verde Chad
C�te d'Ivoire Namibia Djibouti Comoros
Madagascar Swaziland Eritrea Congo, Dem. Rep. of
Malawi Zimbabwe Gambia Congo, Rep. of
Mali Guinea Bissau Gabon
Mauritania S�o Tome and Principe Guinea
Mozambique Somalia Kenya
Niger Uganda Liberia
Rwanda Mauritius
Senegal Nigeria
South Africa Seychelles
Tanzania Sierra Leone
Zambia Sudan
Togo
Source: Authors.
33
APPENDIX B
The Lorenz Curve,
the Concentration Index,
and the Gini Coefficient
E conomists use a variety of indicators to measure the concentration or equitable
distribution of resources, including the Gini coefficient, the concentration index, and
the Lorenz curve. All three measures are used to measure equity, concentration, and the
adequacy of targeted policy interventions.
The Lorenz curve
The Lorenz curve, generally used by economists to assess the equity of income distribution,
is a cumulative frequency curve associated with the distribution of a specific variable (for
Figure B.1 Sample Lorenz curve
last 25%
has 70% of
national
income
Cumulative % of income
n
u tio
rib
st third 25%
l di has 25% of
ua national
eq income
second 25%
has 10% of
national
income
first 25%
has 5% of
national
income
Cumulative % of population
Source: Authors
34
Reducing Geographical Imbalances of Health Workers in Sub-Saharan Africa 35
example, income). It shows the proportion of the distribution of a variable associated with the
bo om X percent of values over which it is distributed (for example, population) (figure B.1).
The equal distribution is represented by a diagonal line. On this line, 20 percent of
the population receives 20 percent of income, 40 percent of the population receives 40
percent of the income, and so forth. This line represents the most equitable distribution of
income across population. The more the Lorenz curve deviates from this line, the greater
the inequity.
The Lorenz curve can be below or above the diagonal, depending on the variable.
When the variable is valuable to the population--as, for example, in the case of income,
expenditure on health or education, access to health services or to water--the curve is
below the diagonal line. When the variable is detrimental to the population, as in the case
of accidents, injuries, or deaths, the curve falls above the diagonal line.
The Concentration index
The concentration index summarizes the information presented by the Lorenz curve. The
distance between the Lorenz curve (the observed distribution of the indicator) and the "ideal
distribution line" represents the degree of inequality. The number measuring the area between
the two lines thus summarizes the degree to which the distribution is equitable.
The concentration index formula calculates twice the area between the Lorenz curve
and the line of inequality. It ranges in value from �1 to 1. By convention, the concentration
index is negative when the Lorenz curve is above the ideal distribution line (representing
an undesirable indicator variable) and positive when the Lorenz curve is below the ideal
distribution line (representing a desirable indicator).
The Gini coefficient
The Gini coefficient is an index that ranges from 0 to 1. It can be calculated in various ways.
A simple formula was elaborated by Brown (1994):
n
G =|1 - (Xk - Xk -1 )(Yk - Yk -1 )|
k =1
The first step in calculating the Gini coefficient for HRH is to sort the geographic units
by the health variable (for example, infant mortality rate) from the worst to the best situation
(highest to lowest rate). The rates are then transformed into continuous variables and the
cumulative proportion calculated for both variables. A figure showing the cumulative
proportion for the health variable (Y-axis) and the cumulative proportion of the population
(X-axis) is then prepared.
The Gini coefficient reflects the level of equity of the distribution of a variable.
Comparing Gini coefficients of different distributions (say, over time) o en provides useful
interpretation.
To estimate the Gini coefficient and graph the Lorenz curve, it is first necessary to sort
the health variable (infant mortality rate in India and Mali in figure B.2) from the worst
situation (highest rate) to the best situation (lowest rate). The following steps are then taken
for each country (or region within a country):
Estimate the under-five mortality and live birth rates
Calculate the percentage of the under-five mortality rate and the percentage of
total live births observed in each population group ranked by wealth
Calculate the cumulative percentage of each of the two variables for each group
Calculate the Gini coefficient using the formula
Graph the curve, using the X-axis for cumulative shares of live births by population
groups and the Y-axis for the cumulative shares of under-five mortality rate
36 World Bank Working Paper
Figure B.2 Lorenz curve for under-five mortality rate in India and Mali
Source: Wagstaff 2008
If the under-five mortality rate were completely independent of wealth, the lowest-
earning 20 percent of the population of India would account for 20 percent of India's
under-five mortality rate. In this case, the Lorenz curve for India would fall along the equal
distribution line.
The concentration index approach can be used to measure the unequal distribution of
health workers in urban versus rural areas. As the variable of interest is the distribution of
healthcare workers, the number of healthcare workers is the indicator variable. Because of
data availability, and because doctors and nurses are usually the most educated and skilled
health professionals, the analysis was limited to doctors and nurses (analyzed separately).
The regions of the country are ranked on a rural-urban scale, as approximated by the
population density (population divided by area of the region); the lower the population
density, the less urban the region.
The steps for estimating a Gini-like coefficient for the distribution of doctors across
regions are as follows. First, the regions of the country are sorted on a rural-urban scale,
as approximated by the population density (population divided by area of the region).
Second, the following steps are taken for each region of the country:
Estimate the population
Estimate the percentage of all doctors and the percentage of the population
observed in each region (ranked by population density)
Estimate the cumulative percentage of each of the two variables for each region
Use the Brown formula to estimate the Gini coefficient
Graph the Lorenz curve, using the X-axis for cumulative population and the Y-axis
for the cumulative number of doctors
APPENDIX C
Applying Labor Economics to
Health Care
T his appendix elaborates on the underlying analytical framework and the formalization
of the dynamics behind labor market outcomes in three labor market situations. It then
focuses on urban versus rural labor market forces and dynamics.
What is presented here is a generic and basic model built on a set of simplifying
assumptions. In practice, country- and case-specific labor market characteristics need to be
taken into account in determining the menu of policy options. For example, if, for certain
categories of health workers, the public sector is the sole employer, this should be taken into
account, especially in any reservation wage analysis (that is, analysis of workers' next best
alternatives). In practice, there are cases in which it is difficult to get simple general results
from purely theoretical models. In such cases, conclusions regarding the menu of policy
options must rely on empirical analysis. The main objective here is not to provide policy
recipes but to illustrate the interconnectedness between government measures and labor
market outcomes (that is, who works where and under what conditions).
Economists view the functioning of labor markets as similar to any other market, in
that the dynamics of supply and demand determine price (the wage, remuneration, or
compensation rate) and quantity (the number of people employed or the number of hours
worked). The framework used in this appendix is a basic classical labor market model
suitable for policy design and analysis. In these models, labor markets are assumed to clear,
either through a simplifying assumption that markets always converge to a situation in
which the quantity of labor supplied equals the quantity of labor demanded, or, given
enough time, through a phased process of adjustments in remuneration, which presumes
that wages are free to change. In simple terms, this means that markets tend to move
toward wages or compensation levels that balance the quantities of labor supplied and the
quantities demanded, such that the market will eventually be cleared of all labor surpluses
(excess supply) and shortages (excess demand).
In principle, when there is excess demand for doctors in rural areas (which creates a
shortage), doctors' wages or remuneration packages in rural areas should increase. This
increase results in doctors already practicing in rural areas working more in the short run
and new doctors entering the rural labor market in the longer run. In the short run, the
adjustment mechanism clears the shortage, establishing a new equilibrium in the rural
labor market. A similar mechanism operates when there is a labor market surplus. In this
case, the wage (or the value of the remuneration package) declines to end the excess supply.
As a result of the decline, doctors in rural areas work fewer hours or days of work in the
short run and some of them exit the rural labor market in the longer run.
This model assumes freely operating labor markets without imperfections or rigidities.
Without these assumptions, "sticky" wages might be observed and markets will not
necessarily clear. Some economists a ribute what appear to be labor market imbalances to
factors such as government policy, labor unions, and the like. Although not all economists
adhere to the classical labor market model, many economists consider wage flexibility as
37
38 World Bank Working Paper
a plausible assumption for policy design and analysis, arguing that wages are not sticky
forever.
In the health sector, the labor market is the economic space in which health workers sell
their skilled labor and employers buy skilled labor. This market is made up of the supply
of health workers (the number of health workers willing to work at various compensation
levels) and the demand for health workers (the number of workers employers are willing
and able to hire at various compensation levels). The interplay between supply and demand
should determine the compensation level within a labor market.
Compensation is a multidimensional concept that takes into account many monetary and
nonmonetary factors. Monetary compensation may be direct or indirect. Direct financial or
monetary compensation is paid as a wage or salary, a financial bonus (for good performance
or as an incentive to serve in a rural area), or as part of a supplementary income scheme.
Indirect financial compensation offers goods and services that would otherwise have to be
paid for. Examples include scholarships and loan repayments, childcare, housing, cars and
motorcycles, health insurance, and education. Nonfinancial compensation refers to benefits
that could not be paid for but are nonetheless considered valuable. Such compensation can
be related to career (on-the-job training, experience, professional development opportunities,
job security, career advising, networking); the working environment (availability of supplies,
equipment, technology, be er infrastructure, reduced workload, a flexible schedule); or
lifestyle and family life (vacation time, opportunities for spousal employment). Keeping in
mind these many forms of compensation is vital when reviewing policy options.
Compensation refers to net compensation--the difference between the gross (monetary
plus nonmonetary) compensation and the costs associated with a particular job that are
borne by the employed individual. For example, doctors in rural areas may bear additional
monetary and nonmonetary costs--such as having to pay for children's private schooling
(because rural public schools are of a lower quality than urban schools) or having to pay
both their own rural housing and urban housing for children who remain in the city to
study; forgone opportunities for professional development; and forgone amenities available
only in urban areas and proximity to family in the city. Net compensation compares costs
and benefits, creating a more accurate picture of the choices facing health workers.
Economists argue that the supply side of the health labor market is determined by
the number of health workers available (the health labor force) and their willingness to
work at various compensation levels. Because many workers are willing to work at a high
compensation levels and few are willing to work at low compensation levels, the market
supply curve is always upward sloping.
The demand for health labor is determined by the number and the size of employers
and their willingness to hire health workers at various compensation levels. Because
employers hire only a few workers at a high compensation level but many workers at a
low compensation level, the demand curve is always downward sloping (demand in the
economic sense is distinct from demand in the social welfare sense, where it refers to the
number or healthcare workers needed to address all health care concerns). Demand is
determined by the desire, ability, and willingness of employers to pay for providers of
health-related services. Healthcare that is desired but cannot be paid for is not considered
part of demand and does not affect the health labor market.
Market-clearing equilibrium, unemployment, and labor shortages
The classical labor market model assumes that all markets ultimately clear. Key assumptions
underlying this model are that compensation levels are allowed to fluctuate (for example,
they are not fixed by the government or trade unions); that people are free to work or not;
and that accurate information is available to both employers and workers about labor market
conditions. Failure to meet these conditions must be taken into account in cra ing policy.
Reducing Geographical Imbalances of Health Workers in Sub-Saharan Africa 39
Figure C.1 Market-clearing equilibrium
Source: Authors
At the market-clearing equilibrium, the quantity of labor supplied equals the quantity
of labor demanded. In other words, employers are willing to employ LaborMC units of labor,
and employees are willing to offer LaborMC units of labor at CompMC level of compensation
(figure C.1).
The situation in which the quantity of labor supplied exceeds the quantity of labor
demanded is referred to as unemployment. If the compensation level were higher, the
quantity of labor supplied would exceed the quantity of labor demanded, as more people
would be willing to work for a high compensation level but employers would be willing to
hire fewer employers.
A situation in which more labor is demanded than is supplied is called a labor
shortage. When the compensation level is set below the market-clearing level--say, at,
CompLS--employers will want to hire workers, but only be willing to work for such a low
compensation (figure C.3).
When the assumptions of the classical labor market model are met, the unemployment
and labor shortage situations will move to the market-clearing situation over the long
run. When compensation is higher than the market-clearing compensation level, some
unemployed workers in the labor market will begin to offer their labor for a lower
compensation level, which is beneficial to employers; in this way, the compensation level
will be driven downwards toward CompMC. When the compensation level is lower than the
market-clearing compensation level, employers will offer a higher compensation in order
to a ract workers unwilling to work for CompLS. People previously unwilling to work
at CompLS or people entering the labor market will increase the supply of labor until the
market-clearing situation is reached.
The economics of urban versus rural labor markets
Non-market-clearing labor markets can persist in the long run when the classical labor
market model assumptions are not met. Market distortions take different shapes. Trade
unions, minimum wage regulation, government pay policies, multinational corporations'
40 World Bank Working Paper
Figure C.2 Unemployment
Source: Authors
Figure C.3 Labor shortage
Source: Authors
Reducing Geographical Imbalances of Health Workers in Sub-Saharan Africa 41
Figure C.4 Urban employment and rural shortage situation
Source: Authors
pay policies, and labor codes may keep compensation levels higher than CompMC. Both
unemployment and labor shortages are undesirable. Unemployed workers represent a
major inefficiency in a country's labor market. Shortages in rural areas mean that clients do
not receive the health services they are willing to pay for.
In most instances in Sub-Saharan Africa, the urban market is in unemployment or at
best at a market-clearing equilibrium (although, occasionally, in a shortage situation); rural
areas are o en in shortage situations. As a result, the urban and rural markets together are
Figure C.5 Urban and rural HRH markets with improved information
Source: Authors
42 World Bank Working Paper
usually in one of three different situations: urban unemployment equals rural shortage
(figure C.4), urban unemployment is greater than rural shortage, or urban unemployment
is less than rural shortage.
Market distortions of all kinds can cause markets to fail to clear. An urban surplus of
health workers may not equilibrate in the long run with a rural deficit of health workers
because of market imperfections, such as lack of information. In practice, most urban
workers remain uninformed about compensation and vacancies in rural areas, making them
less likely to move there. A more effective system to communicate information regarding
vacancies would increase the rural supply of labor. Increased information translates into
an outward shi in the rural supply curve, along with an inward shi in the urban supply
curve, which would increase the rural labor force and employment while decreasing urban
unemployment (figure C.5).
APPENDIX D
Health Labor Market Analysis
V ujicic and Zurn (2006) provide a useful framework for analyzing the supply of labor.
The main idea is that HRH density can be seen as the outcome of a complex process
that begins with the education of high school students and ends in the employment of
health workers (figure D.1). Many possible leakages prevent a country with an adequate
education system from obtaining the needed density of health workers.
Until recently, most HRH country analyses focused on only two boxes in figure D.1:
HRH education capacity and the number of health workers employed in the public sector.
For be er understanding of the HRH situation of a given country, leakage sources--
particularly migration trends, underemployed workers, and private sector workers--have
to be measured and, if possible, addressed. Exploring these categories of health workers is
especially difficult, but some rigorous analyses have been conducted (see C�te d'Ivoire for
underemployment and private sector; see Ghana for migration trends).
Figure D.1 Health labor market analysis: a country example (for doctors)
Source: Vujicic and Zurn 2006
43
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Reducing Geographical Imbalances of Health Workers in Sub-Saharan Africa
is part of the World Bank Working Paper series. These papers are published to
communicate the results of the Bank's ongoing research and to stimulate public
discussion.
This paper discusses and analyzes labor market dynamics in Sub-Saharan Africa
and outcomes (including unemployment, worker shortages, and urban-rural
imbalances of categories of health workers) from a labor economics perspective.
This analysis reviews the experience of many Sub-Saharan African countries
and is the basis for elaborating policy options that incorporate the underlying
labor market forces to address undesirable outcomes such as the urban-rural
imbalance more effectively. The conclusions are relevant to researchers, policy
analysts, and policy makers with an interest in understanding and improving the
allocation of human resources for health in the developing world.
This working paper was produced as part of the World Bank's Africa Region
Health Systems for Outcomes (HSO) Program. The Program, funded by the
World Bank, the Government of Norway, the Government of the United
Kingdom and the Global Alliance for Vaccines and Immunization (GAVI),
focuses on strengthening health systems in Africa to reach the poor and achieve
tangible results related to Health, Nutrition and Population. The main pillars
and focus of the program center on knowledge and capacity building related to
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