HEALTH POLICY AND PLANNING; 17(Suppl 1): 5–11
© Oxford University Press 2002
Equitable financing, out-of-pocket payments and the role of
health care reform in Colombia
RAMON A CASTANO,1 JOSE J ARBELAEZ,2 URSULA B GIEDION3 AND LUIS G MORALES4
Colombia, 2Johns Hopkins University, Baltimore, USA*, 3Bitran Asociados, Santiago, Chile*
4
and Abt Associates, Bethesda, MD, USA*
* Not representing these institutions at the time the research project was undertaken.
1FEDESARROLLO,
For a health care system to be considered equitable in its financing, the financial burden of contributions has
to be progressive or at least proportional. Out-of-pocket financing takes a larger proportion of poor than of
non-poor households’ income. To remedy this regressive burden, among other goals, Colombia launched a
health care reform based on social insurance as a means to reduce health care financing through out-ofpocket payments, and to reduce financial barriers to access. This paper analyzes the evolution of regressivity in out-of-pocket financing from 1984 to 1997, in order to detect if the 1993 health care reform had an
impact on such regressivity.
The Kakwani index of progressivity was estimated using three national household surveys. Kakwani indices
showed a constant trend towards more regressivity (–0.126 in 1984, to –0.3498 in 1997) when using income
to build the index, but a trend towards progressivity (–0.0092 in 1984, to 0.0026 in 1997) when using expenses.
Our findings suggest that there was a progressive impact of the reform on out-of-pocket financing when
household expenses are used to build the Kakwani index; however, due to issues of comparability between
surveys, the findings are not conclusive.
Key words: out-of-pocket, equity, financing, Colombia, Kakwani
Introduction
Within the wave of health care reforms that took place in
many countries during the past decade, Colombia launched
its own health care reform process in 1993, creating a system
based on social insurance, as opposed to the previously segmented model (Londoño and Frenk 1997). In the previous
model, three strictly separate systems coexisted: one (social
insurance) for the formal employees, another (private insurance) for those who could afford to pay for it, and the
remaining population were covered by the publicly-owned
network of supply-subsidized providers. Although employees in the formal sector had access to employment-based
insurance before the reform, the poor, the informal employees and the family members of the formal sector employees
did not. These are the groups that the reform included in
its new health insurance coverage. More specifically, one
interesting innovation of the reform was the creation of a
health insurance system for the poor, aimed at reducing
financial barriers to access to health care services. In this
insurance scheme, the poor are selected based on a proxymeans test, and their insurance premiums are paid for with
resources from the government and part of the payroll taxes
contributed by the formal employees for their own insurance. Insurance coverage, however, is far from universal: by
the year 2000, roughly half of the population was covered,
either by insurance for the formal sector or insurance for
the poor.
One of the boons of an insurance-based health care system is
the spreading of risks among large population groups, which
leads to the reduction of out-of-pocket (OOP) payments
which a household faces when one of its members falls sick.
The negative impact of OOP payments on usage of health
care services has been demonstrated by Manning et al.
(1987), who concluded that co-insurance decreases overall
utilization. Such impact is significant on the utilization of
outpatient services but not inpatient services. However, given
the information asymmetry between provider and patient,
reductions in utilization can affect unnecessary but also
medically necessary services.
Restrictions to access as a consequence of inability to pay are
best reduced if there is a larger proportion of poor covered
by a pre-paid system of health insurance and if the benefit
package of such a system is more comprehensive. This is
consistent with the principle of fairness of financial risk
protection, which ‘requires the highest possible degree of
separation between contributions and utilization’ (WHO
2000). Put another way, the requirements of an equitable
health care system demand a financing scheme in which
contributions vary according to ability to pay, and a delivery
scheme in which access to services varies with need and
irrespective of ability to pay.
A strong test on how equitable the financing of a health care
system is rests on how the financial burden of OOP expenses
6
Ramon A Castano et al.
is distributed among households (Daniels et al. 2000). If such
financial burden is heavier for the poor (i.e. they pay a larger
share of their income as OOP expenses when compared with
the rich), it is said that the financing scheme is regressive; on
the contrary, if such burden is heavier for the rich, the financing scheme is said to be progressive. However, given that
health care systems use four more alternative sources of
financing (i.e. general taxation, payroll taxes for social insurance, private insurance and, in poorer countries, donor contributions) in different combinations, the overall fairness of
financial contribution will depend on how each of the sources
affects poor households’ income in comparison with the nonpoor, and on the weight corresponding to each of the five
alternative sources of financing (Wagstaff and van Doorslaer
2000).
Evidence from other countries shows that OOP expenses are
a very regressive source of financing. Rassel et al. (1994)
showed that in the United States, low-income families spent,
on average, 8.5% of their income on health, while highincome families spent only 1%. In a comprehensive analysis
of 13 industrialized countries, Wagstaff et al. (1999) show
that direct payments behave regressively (i.e. they take a
larger proportion of poor households’ income) in all of
them. Evidence from developing countries is not as readily
available as that from industrialized economies; however,
Pannarunothai (1997) shows that in Thailand, underprivileged families spent about 5–6% of their incomes as OOP
payments, whereas other groups spent 1–2%.
In the Colombian health care system, the share of system-wide
financing that is raised through OOP payments is still large,
though estimations are contradictory. Evidence from 1993
suggests that at least 40% of the health system revenues were
provided by households as OOP payments (Harvard 1996),
while other evidence suggests that in 1995 and 1996 the figure
was 59% (Vargas 1997). A more recent study undertaken by
the National Department of Planning within the National
Health Accounts project shows unofficial preliminary
evidence that the share paid by households as direct payments
to providers has decreased from around 40% in 1993 to
around 25% in 1997 (Departamento Nacional de Planeacion
2000). Although this evidence is still subject to review, it is
consistent with Harvard’s estimations for 1993, and is also
consistent with the fact that public funds allocated to health
care have had substantial growth during the first 5 years
following the health care reform; the growth in funds raised
from other sources has caused the relative weight of OOP
financing to decrease but, in absolute terms, the total amount
of OOP financing has increased between 1993 and 1997.
If we assume that at least one-quarter to one-third of the
Colombian health care system is financed through households’
cash payments, we would expect a heavy impact of the regressivity of this source on the overall fairness of financial contribution. However, Ramirez et al. (2000) and Sanchez and
Nuñez (1999) found that the increase in insurance coverage for
the poor has contributed to progressive income redistribution.
Although the overall regressivity or progressivity of the
financing scheme will also depend on the ultimate incidence
of such a scheme on household incomes, it is interesting to
assess how the regressive burden of OOP payments has
evolved as a result of the 1993 reform. If the increase in insurance coverage has benefited the poor, it could be possible to
show a decrease in the share of their income that goes to
direct payments to providers.
In previous studies (Castano et al. 2001) we showed that
Colombian households had decreased cash outlays for medications, but the highest difference was seen in the top income
quintile. This study, however, did not assess health carerelated OOP expenses as a proportion of income, and its
analysis was restricted to expenses on medications. Beyond
this previous research, the aim of this study is to determine
the evolution of regressivity of OOP financing (including
medications and other items) in Colombia between 1985 and
2000. Given that no research has been done so far on this
topic, this study contributes to a better understanding of the
impact of the Colombian health care reform on the equity
concerns that, among others, inspired its design.
Methodology
In order to track the evolution of regressivity of the OOP
burden of financing, we used three nation-wide crosssectional surveys carried out in Colombia between 1985 and
1997 by the National Department of Statistics (DANE).
These three surveys cover a period from 9 years before to 4
years after the 1993 health care reform. This timeframe is
long enough to undertake relevant comparisons that would
show changes before and after the reform. The surveys we
used are:
1 Income and expenses survey (1984–85); hereinafter
Inexp8485.
2 Income and expenses survey (1994–95); hereinafter
Inexp9495.
The original purpose of these two surveys was to determine
the basket of goods to set the baseline for the estimation
of the Consumer Price Index. These surveys collected comprehensive information about the items purchased by
urban households during a 1-week period, as well as
additional information on expenses for longer periods of
time.
3 Quality of life survey (1997); hereinafter QOL97.
This survey was an application of the Living Standards
Measurement Survey (LSMS) methodology implemented
by the World Bank.
Table 1 summarizes some characteristics of the surveys we
used and shows the sample sizes and dates of performance
for each of them.
Statistical methods
To estimate the regressiveness of OOP payments, we used
the Kakwani Index of progressivity (Kakwani 1977), which is
defined as the difference between the Gini coefficient for
income distribution (Gpre) and the concentration index for
the distribution of OOP payments (Cpay). Kakwani’s index of
progressivity, K, is thus defined as:
Equitable financing and health care reform in Colombia
7
Table 1. Summary of characteristics of the three surveys we used for the analysis
Survey
Date of
application
Sample size
(households)
Urban/rural
coverage
Purpose
Reference
Inexp8485
March 1984 to
February 1985
March 1994 to
February 1995
25 August to
15 November 1997
26 117
Only urban
DANE (1998a)
28 022
Only urban
9 121
Urban and
rural
Set the baseline
for CPI
Set a new baseline
for CPI
Measuring quality
of life (LSMS
methodology)
Inexp9495
QOL97
DANE (1998a)
DANE (1998b)
CPI = consumer price index; LSMS = Living Standards Measurement Survey.
K = Cpay – Gpre
which is twice the area between the concentration curve for
OOP payments and the Lorenz curve. If OOP expenses are
a progressive source of financing, the concentration curve will
lie below the Lorenz curve, and K will be positive. On the
contrary, if OOP expenses are a regressive source of financing, K will be negative because the concentration curve for
OOP expenses will lie above the Lorenz curve. If OOP
payments are perfectly correlated with income, K will be
zero and the financing source will be proportional. One
advantage of the Kakwani index over the crude concentration index is that the former controls for the distribution
of income, which is the most relevant variable when it comes
to defining how regressive a financing scheme is. For instance,
if OOP payments show a pro-poor pattern, its concentration
index will be favourable and the concentration curve will lie
below the diagonal; but if income shows a more concentrated
pattern than that of OOP payments, the Kakwani index
will take this finding into account and will unveil the truly
regressive pattern of such apparently progressive distribution
of OOP payments.
In order to control for the effect of potential differences in
items included in the surveys, we estimated Kakwani indices
with and without over-the-counter (OTC) drugs for
Inexp8485, Inexp9495 and QOL97, because information
about this variable, although appearing in these three
surveys, was less comprehensive in QOL97. In the same vein,
given differences in information about income and expenses,
we undertook separate estimates of the Kakwani index (one
using income and the other using expenses). To check if
hospital-related OOP expenses had an important impact on
the indices, we also estimated them with and without hospital
expenses.
Given the methodological differences found in the three
surveys, we analyzed them in two different ways to achieve
the highest possible degree of comparability, i.e. using information on cash-income on the one hand and expenses on the
other hand; given that both Inexp8485 and Inexp9495 are
restricted to urban households, we excluded non-urban
households from QOL97. Regarding information about
income in the three surveys, we also found differences in
items included, levels of aggregation-disaggregation and
reference periods. We then built a common cash-revenue
variable that summarizes information from all the household
members on the following cash items that were consistently
found in the three surveys and were readily comparable:
•
•
•
•
•
•
salaries from main job;
interests from investments;
rents (from rented real estate or other assets);
retirement pension;
cash transfers (from relatives, friends or institutions);
cash income from independent work (only for selfemployed individuals).
Items such as income from additional sources (second
employment or independent extra-time work) or occasional
earnings (lotteries, sales of assets, etc.) were not constantly
found in all the surveys; accordingly, they were not included
in the estimation of household cash revenues.
Results
Table 2 shows a descriptive analysis of the data; money values
are in Colombian pesos and are not adjusted for inflation. It
is clear from this table that OOP expenses as a proportion of
income are higher than as a proportion of expenses. Table 3
shows the unadjusted distribution of the shares of OOP of
household expenses for the three surveys.
Variables
We estimated OOP expenses for the three surveys, and then
compared them with either cash-income or total household
expenses for the Kakwani indices. Total household expenditures included only those items purchased outside the household, and did not include consumption of items produced
within the household or items received as ‘in-kind’ salary.
Kakwani index
The Gini coefficients and concentration indices were
estimated for the observations grouped by income (or
expenses) deciles. Figure 1 shows the evolution of the
Kakwani index and its components, using cash-income data
only, including hospital payments but excluding cash
8
Ramon A Castano et al.
Table 2. Descriptive statistics of the three surveys used for the analysis
Survey and variables
Inexp8485
Income (cash revenue)
OOP expenses (including OTC drugs and hospital outlays)
OOP expenses as a proportion of income
Total expenses
OOP expenses as a proportion of total expenses
Inexp9495
Income (cash revenue)
OOP expenses (including OTC drugs and hospital outlays)
OOP expenses as a proportion of income
Total expenses
OOP expenses as a proportion of total expenses
QOL97 (only urban households)
Income (cash revenue)
OOP expenses (including OTC drugs and hospital outlays)
OOP expenses as a proportion of income
Total expenses
OOP expenses as a proportion of total expenses
Mean
SD
Median
n
47 328
2 488
0.074
75 989
0.033
88 316
5 578
0.309
91 441
0.046
30 667
855
0.026
49 877
0.017
25 713
26 117
24 820
26 039
26 039
396 894
17 266
0.052
719 941
0.025
454 305
44 632
0.127
838 151
0.039
283 875
5 680
0.019
523 930
0.01
28 022
28 022
27 647
28 022
28 022
637 679
54 317
0.17
874 991
0.062
990 950
134 235
1.86
1 046 477
0.092
373 333
16 600
0.028
604 307
0.027
5 366
5 366
5 321
5 366
5 366
OOP = out-of-pocket; OTC = over-the-counter.
payments for medications. There is a trend towards a more
negative Kakwani index, from –0.1257 in 1984 to –0.3498 in
1997, which means that OOP payments are turning more
regressive. Such changes can be split into changes in the Gini
coefficient, which has increased, and the concentration index,
which has decreased. The inclusion or exclusion of hospital
expenses and OOP expenses for medications showed no
relevant differences in our results, and the corresponding
indices showed an almost identical graphical appearance,
therefore, they are not presented here. We also estimated the
indices using expenses instead of cash-income. Figure 2
shows the evolution of the Kakwani index and its components when using household expenses for the estimation of
the Gini coefficient; the corresponding values are –0.0092 for
1984 and 0.0026 for 1997.
Table 3. Unadjusted distribution of the shares of household
payments (out-of-pocket), of total household expenditures
OOP payments as % of household expenditure
1
2
3
4
5
6
7
8
9
10
Inexp8485
Inexp9495
QOL97
3.20
3.25
3.18
3.26
3.27
3.34
3.45
3.39
3.54
2.98
2.90
2.66
2.55
2.48
2.37
2.37
2.51
2.54
2.54
2.05
5.89
5.46
5.99
6.18
6.10
6.19
5.79
7.16
6.07
6.44
Both the Figures and Tables show that when we use income
as the comparison variable, the Kakwani index shows a regressive impact and a trend towards a more regressive impact.
However, when we use expenses as the comparison variable,
the index moves around proportionality (neither regressive
nor progressive), increasing towards a slightly positive value.
Discussion
Our study suggests that the burden of OOP financing for
health care has evolved between a regressive and a progressive pattern. When Kakwani indices are estimated using
household cash income, the burden appears increasingly
regressive, but when total expenses are used, the burden
becomes regressive in INEXP9495 and then slightly progressive in QOL97.
The lack of a clear evolution towards a less regressive burden
can be plausibly explained by arguing that OOP payments
increased for all households, but their rates of increase were
larger for households in the middle deciles of expenses and
lower for the better off and the worse off, at least for urban
households. We could argue that an increase in insurance
coverage should lead to a substitution of prepayment for
OOP payments. Such substitution would have been possible
in Colombia thanks to the substantial growth in public funds
to provide insurance for the poor and the increase in earmarked taxes from the formal employees. However, as noted
before, the National Department of Planning’s figures for
inflation-adjusted aggregated OOP payments have grown;
this means that households have increased the amount of
money they disburse as direct payments to providers.
Moreover, if we look at the evolution of the concentration
index of OOP payments compared with income, it can be
clearly seen that it has decreased towards zero, i.e. the burden
for the rich has become lower.
Equitable financing and health care reform in Colombia
0.6
0.6
0.5
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
9
0.1
0
0
Ð0.1
Ð0.3
QOL97
Inexp9495
Inexp8485
Ð0.4
QOL97
Ð0.2
Inexp9495
Inexp8485
Ð0.1
Gini coef.
Conc. Ind
Kakwani Ind
Gini coef.
Conc. Ind
Kakwani Ind
Figure 1. Evolution of the Kakwani index and its components
(Gini coefficient and concentration index), from 1985 to 1997,
including hospital out-of-pocket expenses but excluding cash
payments for over-the-counter drugs, and comparing with household
income
It could also be argued that had the reform not taken place,
the growth of OOP payments would have been larger. It
could also be argued that total households’ OOP expenses
increased as a result of higher prices, higher quantities
consumed, or both. However, our data do not allow for splitting the effect of either component of total consumption,
neither for the estimation of the potential growth of OOP
financing, had the reform not taken place.
Incomplete progress in coverage by the new health insurance
system for the poor could also explain the finding regarding
the slight effect of the reform on regressivity of OOP financing. As of 2001, at least 40% of the eligible poor were still not
covered by the new insurance scheme (Superintendencia de
Salud 2001), and among those covered, the benefit package
they received was limited to primary-level (low complexity)
care and some catastrophic coverage, plus a comprehensive
coverage for all care related to maternal and child health.
This package leaves uncovered almost all interventions of
medium and high complexity and most inpatient care, which
means that poor households facing these events have to pay
high proportions of their income as user fees, no matter that
these services are heavily subsidized through the supply side.
The slight difference between Inexp8485 and Inexp9495
regarding regressivity would suggest that the trend towards
Figure 2. Evolution of the Kakwani index and its components
(Gini coefficient and concentration index), from 1985 to 1997,
including hospital out-of-pocket expenses and cash payments for
over-the-counter drugs, and comparing with household total
expenses
worsening regressivity was taking place before the reform. It
is important to highlight that by 1995 the reform was poorly
implemented and small changes in insurance status had
hardly taken place, for which we cannot attribute any effect
to the reform (Superintendencia de Salud 1997).
Regarding information on income, except for an atypical fall
in Inexp9495, the Gini coefficients for 1984 and 1997 show
a strongly growing trend that could be explained by the
exclusive use of cash-income information. In other words, if
the poor increased the share of their income that was paid ‘in
kind’ or if they increased auto-consumption or bartering, and
at the same time the rich increased their cash revenues, it is
likely that the Gini coefficient would have increased in such
a dramatic way. However, with the data used, we cannot conclusively say that this type of situation is causing the observed
increase in the Gini coefficient. The fall in the Gini coefficient
in Inexp9495 also affects the concentration index, which
suggests that there could have been a sampling problem that
systematically affected the proportion of either poor or rich
people in the survey sample. However, the consistent trend
of regressivity still gives us room for comparing them with the
other surveys.
The estimation of household income has many limitations
when it depends on respondents’ reported income; one way
to circumvent this problem is estimating expenses as a proxy
of income, but, as has been suggested by Murray et al. (2002),
expenses cannot be taken for granted as a good proxy of
income when it comes to assessing equity in health care
10
Ramon A Castano et al.
financing, because equity in health care financing has to be
analyzed by estimating the burden of OOP expenses on any
disposable income beyond the minimum to survive. Following Murray et al. we should have deduced expenditures
related to food from our expenses variable in order to make
a better estimation of expenses. However, we decided to use
the more inclusive version of expenses so as to make this
study more comparable with previous studies; nonetheless,
we agree that future studies should start to adopt Murray et
al.’s suggestion. We consider, however, that the findings
obtained when we use expenses are more realistic than those
obtained using income, because of the inconsistencies of the
reported income variable.
The similar figures we obtained for the shares of household
expenses represented as OOP in Inexp8485 and Inexp9495
suggest that the strikingly different findings in QOL97 are due
to some methodological issues related to either sampling or the
questionnaire itself, although we cannot rule out the presence
of error. Another interesting analysis would have been the
comparison of the Kakwani indexes among the insured households and the uninsured. However, given that the household is
not the coverage unit, it is not possible to carry out this
analysis; indeed, 47% of the urban households in QOL97 had
more than one but less than all members covered.
One important limitation of our study relates to the comparability of the three surveys we used. As is commonly seen
when comparing general purpose surveys, we found that the
ones we used were not strictly comparable because: (1) they
did not ask the same questions about all the items; (2) they
did not use the same reference period for OOP payments, i.e.
one asked for the last 30 days (QOL97) and the other two for
the past month (Inexp8485, Inexp9495); (3) beyond the wellknown under-reporting problem, information about income
does not always take into account the fact that many lowincome rural households derive self-consumption from their
crops, and urban and rural households receive ‘in kind’
payments. These factors weaken our ability to reach robust
conclusions about the evolution of the OOP burden. Two
more important caveats have to be considered. On the one
hand, Inexp surveys are restricted to urban households,
unlike QOL97, which covers both urban and rural households. We controlled for this weakness by excluding nonurban households from QOL97, which gives an incomplete
picture of the overall evolution of regressivity that cannot be
completed with the available data. On the other hand, Inexp
surveys cover 1 year of observations by spreading sample
households through the 52 weeks of the year (1 week per
household) in order to avoid errors due to seasonality in
consumption, while QOL97 was undertaken during the
second half of the year. Although these comparability issues
could have affected our results, our effort to combine the
surveys in two different ways (using income and expenses
separately) could have lessened the potential effect of
wording or structuring of the questionnaires.
The pitfalls we faced in comparing surveys that had been
originally designed for other purposes are not uncommon.
Berk and Schur (1998) underscored the difficulties inherent
to the use of health surveys for health policy-making in their
comment on the problem of measuring access to health care.
The same argument they make for access can be made for
OOP expenses: the ‘lack of agreement about standards for
measuring [OOP expenses] from an operational point of view
and a lack of consensus about the conceptual definition of
[OOP expenses]’. The authors point to differences in wording
that, no matter how slight, can affect survey responses. They
also mention the conditioning effects found in long surveys
vis a vis shorter ones, the former being more prone to underreporting as compared with the latter.
Our analysis confirms Berk and Schur’s concerns, and we
strongly urge for the implementation of national health
surveys with a standardized methodology that allows for
better comparisons and the estimation of time trends, or at
least better data on health use and spending in general
purpose household surveys. Survey analysis for health policy
has had to appeal to general purpose household surveys with
the limitations shown by this work; this, consequently, limits
the scope of evidence-based health policy.
Conclusion
The findings of this study suggest that the expected impact of
pre-payment on households’ direct payments to providers
has not taken place to the extent policy-makers assumed, at
least in urban settings. When using household income for the
estimation of the Kakwani index, there has been a trend
towards a more regressive burden, and when using household
expenses, the regressive pattern is slightly reversed to progressivity; unfortunately, we cannot say anything about the
evolution of regressivity in non-urban households. Finally, to
collect relevant and comparable information for this and
other kinds of analyses, we recommend the design of a
national household health survey to be applied on a regular
basis, or at least the design of a standard set of questions to
be included in surveys done for other purposes. Matters of
comparability between surveys do not allow us to reach to a
conclusion on the evolution of equity in the financing of
health care in Colombia.
References
Berk ML, Schur CL. 1998. Measuring access to care: improving
information for policymakers. Health Affairs 17: 180–90.
Castano RA, Arbelaez JJ, Giedion U, Morales LG. 2001. Evolucion
de la equidad en el sistema de salud Colombiano. Santiago de
Chile: CEPAL.
Daniels N, Bryant J, Castano RA, Dantes OG, Khan KS, Pannarunothai S. 2000. Benchmarks of fairness for health care
reform: a policy tool for developing countries. Bulletin of the
World Health Organization 78: 740–50.
Departamento Administrativo Nacional de Estadistica (DANE).
1998a. Resumen metodologico encuesta nacional de ingresos y
gastos. Bogota, Colombia: DANE.
Departamento Administrativo Nacional de Estadistica (DANE).
1998b. Encuesta nacional de calidad de vida; informe final.
Bogota, Colombia: DANE.
Departamento Administrativo Nacional de Estadistica (DANE).
1998c. Resumen metodologico encuesta nacional de hogares.
Bogota, Colombia: DANE.
Departamento Nacional de Planeación (DNP). 1994. Ecuesta de caracterizacion socioeconomica: informe final. Bogota, Colombia:
DNP.
Equitable financing and health care reform in Colombia
Departamento Nacional de Planeación (DNP). 2000. Proyecto
Cuentas Nacionales de Salud. Unpublished data. Bogota,
Colombia: DNP.
Harvard University School of Public Health. 1996. Report on
Colombia health sector reform and proposed master
implementation plan. Final report. Boston, MA: Harvard
University.
Kakwani N. 1977. Measurement of tax progressivity: an international comparison. The Economics Journal 87: 71–80.
Londoño JL, Frenk J. 1997. Structured pluralism: towards an innovative model for health system reform in Latin America. Health
Policy 41: 1–36.
Manning WG, Newhouse JP, Duan N et al. 1987. Health insurance
and the demand for medical dare: evidence from a randomized
experiment. American Economic Review 77: 251–77.
Murray CJL, Knaul F, Musgrove P, Xu K, Kawabata K. 2002.
Fairness in financial contribution: conceptual framework. In:
Murray CJL, Frenk J (eds). Health system performance:
concepts, measurements and determinants. Geneva: World
Health Organization (in press).
Pannarunothai S, Mills A. 1997. The poor pay more: health-related
inequality in Thailand. Social Science and Medicine 44: 1781–90.
Ramírez C, Delgado CL, Baldión E. 2000. Coyuntura económica e
indicadores sociales. Boletín #26, Sistema de Indicadores
Sociodemográficos para Colombia. Bogota, Colombia: Departamento Nacional de Planeación.
Rassel E, Bernstein J, Tang K. 1994. The impact of health care
financing on family budgets. International Journal of Health
Services 24: 691–714.
Sanchez F, Núñez J. 1999. La ley 100/93 sí llegó a las personas de
menores ingresos. Rev VíaSalud 9: 6–12.
Superintendencia de Salud. 1997. Estadisticas de afiliacion a la
seguridad social en Colombia. Bogota: Imprenta Nacional.
Superintendencia de Salud. 2001. Estadisticas de afiliacion a la
seguridad social en Colombia. Draft paper.
Vargas JE. 1997. Descentralización de los servicios de salud en
Colombia. Planeación y Desarrollo 28: 143–53.
Wagstaff A, van Doorslaer E, can der Burgh H et al. 1999. Equity in
the finance of health care: some further international comparisons. Journal of Health Economics 18: 263–90.
Wagstaff A, van Doorslaer E. 2000. Equity in health care finance and
delivery. In: Culyer AJ, Newhouse JP (eds). Handbook of
health economics, Volume 1. Dordrecht: Elsevier publishers,
pp. 1803–62.
WHO. 2000. The world health report 2000. Health systems: improving performance. Geneva: World Health Organization.
11
Acknowledgements
We are indebted to Luis Angel Rodriguez from the National Department of Planning in Colombia, for his support in the procurement of
the databases. We also acknowledge the WHO/TDR support team,
specifically Erik Blas, Norman Hearst, Bo Eriksson and Johannes
Sommerfeld for their valuable contributions in the writing of this
manuscript. However, they are exempt from any responsibility for
the contents of this paper.
Biographies
Ramon Castano is a physician, currently enrolled as a Research
Student at the London School of Hygiene and Tropical Medicine.*
His topics of interest include the impact of health care reform on
equity, and the improvement of production processes in health care
providers.
Jose Arbelaez is a physician, and is currently enrolled in a Ph.D. programme in epidemiology. He has worked in Colombia at the
Ministry of Health and at the Instituto de Seguros Sociales, the
largest health insurer.*
Ursula Giedion is an economist and health policy analyst and
researcher. She has carried out research on the financing of health
care and the transformation of subsidies from the supply-side to the
demand-side. She has also worked for the Colombian Ministry of
Health, and currently works as a consultant for Bitran & Asociados
in Chile.*
Luis Morales is a physician, and has worked at the Ministry of Health
in Colombia. He is currently enrolled with Abt Associates as Senior
advisor for the Programme for the implementation of health care
reform in the Dominican Republic.* He has also undertaken
research on equity issues and the organization of public health care
providers.*
* Not representing these institutions at the time the research project
was undertaken.
Correspondence: Dr Ramon A Castano-Yepes, Health Policy Unit,
London School of Hygiene and Tropical Medicine, Keppel Street,
London, WC1E 7HT, UK.
Email: Ramon.Castano-Yepes@lshtm .ac.uk