Overall |
N |
Mean |
Std. Dev |
OOP (Out-of-pocket) |
2,836 |
10,904.5 |
58,936.1 |
Pre-pay_exp (Total consumption gross of all health care payments) |
2,836 |
160,517.9 |
297,918.1 |
Post-payment_exp (Total consumption net of all health care payments) |
2,836 |
149,613.4 |
286,327.5 |
HIC (Health Insurance Contribution) |
191 |
11,988.4 |
8,915.67 |
hhsize (Household size) |
2,836 |
1.0 |
.2 |
wt_wave1 (Household weights) |
2,836 |
6,105.4 |
3,739.5 |
Urban |
|||
OOP (Out-of-pocket) |
920 |
12,569.5 |
74,534.1 |
Pre-pay_exp (Total consumption gross of all health care payments) |
920 |
205,621.4 |
316,494.2 |
Post-payment_exp (Total consumption net of all health care payments) |
920 |
193,052.0 |
301,392.9 |
HIC (Health Insurance Contribution) |
135 |
2,434.0 |
10,336.2 |
hhsize (Household size) |
920 |
1.0 |
.15 |
Rural |
|||
OOP (Out-of-pocket) |
1,934 |
9,938.5 |
47,573.7 |
Pre-pay_exp (Total consumption gross of all health care payments) |
1,934 |
134,347.2 |
283,326.6 |
Post-payment_exp (Total consumption net of all health care payments) |
1,934 |
124,408.7 |
274,083.6 |
HIC (Health Insurance Contribution) |
56 |
2,140.7 |
7,974.7 |
hhsize (Household size) |
1,934 |
1.0 |
.17 |
Source: Authors Computation from GHS-Panel, 2010/2011
The results for Gini coefficient of the out-of-pocket payment and the health insurance contributions alongside their respective Kakwani progressivity index for Nigeria and the six geopolitical zones are presented in tables 4 and 5 respectively. Table 4 showed that in the 2010/2011 period overall the estimates of the Gini index of the prepayment income 0.55 was statistically significant. This implies that the prepayment income was concentrated with the wealthy. This finding was indicative of the high level of income inequality that exist in the nation’s distribution of income. The result was similar to that obtained for Nigeria [10]. In the 2012/2013 period the gini coefficient for the country was 0.58. This suggested a worsening of income inequality in the country especially when compared with the gini estimates for the 2010/ 2011 period. The gini estimate for the country in 2015/2016 period declined marginally to 0.55.
The estimates from the zones for the 2010/ 2011 period revealed that the South-South had the highest Gini estimate of 0.72 and was followed by the South- East with an index of 0.52. The North-Central zone had the lowest value of 0.41. Intuitively, these results indicated that the South- South and South-East regions have the bulk of their income concentrated among the upper-half of the income distribution. In the 2012/2013 year the estimates from the zones tended to indicate that the South-South and South East have the worst unequal distribution of income with a gini index of 0.68 and 0.65 respectively. These findings were similar to those obtained from the 2010/ 2011 data set. The North East and the North West zones had the least values of 0.41 and 0.42. In the 2015/2016 period the South-South had the highest gini index of 0.72 and was followed by the South-West 0.55. The North-East zone had the lowest value 0.41. Intuitively, these results indicated that the South- South and South-West regions had the most unequal distribution of income with the largest share of their income concentrated with the better-off. The overall estimates of the Kakwani Progressivity Index (KPI) for the out-of-pocket health care payments (OOP) in Table 4, suggest that overall the OOP was regressive for the three periods. The negative significant value of the KPI for the out-of-pocket payment fluctuated between -0.04 in 2010/2011 to -0.12 in 2012/2013 and thereafter to -0.09 in 2015/2016. This is an indication that between the period 2010 and 2015, the proportion of consumption expenditure spent as OOP for health care was higher for individuals on lower income quintiles than those on higher income quintiles. The regressive KPI however improved slightly by 25 per cent in 2015/ 2016.
Overall |
N |
Mean |
Std. Dev |
OOP (Out-of-pocket) |
3,999 |
10,013.3 |
28,849.0 |
Pre-pay_exp (Total consumption gross of all health care payments) |
3,999 |
61,387.6 |
104,339.4 |
Post-payment_exp (Total consumption net of all health care payments) |
3,999 |
51,374.3 |
97,164.7 |
HIC (Health Insurance Contribution) |
345 |
9,380.3 |
8,631.5 |
hhsize (Household size) |
3,999 |
1.1 |
1.0 |
wt_wave2 (Household weights) |
3,999 |
7,055.2 |
4,818.9 |
Urban |
|||
OOP (Out-of-pocket) |
1,278 |
10,398.99 |
31,019.86 |
Pre-pay_exp (Total consumption gross of all health care payments) |
1,278 |
77,114.4 |
126,519.8 |
Post-payment_exp (Total consumption net of all health care payments) |
1,278 |
66,715.4 |
119,098.4 |
HIC (Health Insurance Contribution) |
181 |
2,585.4 |
9,240.7 |
hhsize (Household size) |
1,278 |
1.1 |
.4 |
Rural |
|||
OOP (Out-of-pocket) |
2,721 |
9,781.9 |
27,462.7 |
Pre-pay_exp (Total consumption gross of all health care payments) |
2,721 |
51,954.1 |
87,053.7 |
Post-payment_exp (Total consumption net of all health care payments) |
2,721 |
42,172.2 |
79,813.7 |
HIC (Health Insurance Contribution) |
164 |
2,185.7 |
8,241.0 |
hhsize (Household size) |
2,721 |
1.1 |
.4 |
Source: Authors Computation from GHS-Panel, 2012/2013. |
Table 2: Descriptive Statistics 2012/2013
Table 3: Descriptive Statistics for 2015/2016
Overall |
N |
Mean |
Std. Dev |
OOP (Out-of-pocket) |
4,051 |
10,262.4 |
31086.1 |
Pre-pay_exp (Total consumption gross of all health care payments) |
4,051 |
50,855.1 |
73583.4 |
Post-payment_exp (Total consumption net of all health care payments) |
4,051 |
40,592.7 |
64872.3 |
HIC (Health Insurance Contribution) |
416 |
9,865.1 |
8335.6 |
hhsize (Household size) |
4,051 |
1.1 |
.3 |
wt_wave3 (Household weights) |
4,051 |
6,670.3 |
4,398.7 |
Urban |
|||
OOP (Out-of-pocket) |
1,305 |
10,975.6 |
28,289.7 |
Pre-pay_exp (Total consumption gross of all health care payments) |
1,305 |
59,830.1 |
77,163.24 |
Post-payment_exp (Total consumption net of all health care payments) |
1,305 |
48,854.54 |
71,036.43 |
HIC (Health Insurance Contribution) |
320 |
2,690.2 |
7,476.2 |
hhsize (Household size) |
1,305 |
1.1 |
.3 |
Rural |
|||
OOP (Out-of-pocket) |
2,746 |
9836.7 |
32,634.32 |
Pre-pay_exp (Total consumption gross of all health care payments) |
2,746 |
45,497.8 |
70,822.3 |
Post-payment_exp (Total consumption net of all health care payments) |
2,746 |
35,661.0 |
60,360.8 |
HIC (Health Insurance Contribution) |
94 |
2,170.0 |
8803.143 |
hhsize (Household size) |
2,746 |
1.1 |
.2 |
Source: Authors Computation from GHS-Panel, 2015/2016
The findings from the North central zone revealed that in 2010/2011 the OOP was a progressive health care financing source having a positive and significant KPI of 0.56. However, in 2012/2013 and 2015/2016 the KPI estimates (-0.16 and -0.13) were negative and significant, indicating that the OOP was regressive. The KPI of out-of-pocket finance in the North East zone for the period 2010/2011, 2012/2013 and 2015/2016 were negative and statistically significantly (-0.12, -0.05 and -0.15 respectively). The findings showed that the OOP was regressive for the three periods. This result suggested that the poor bore the burden of direct health care payments. The KPI for out-of-pocket payment in the North West zone experienced some oscillatory movements. In 2010/2011 and 2015/2016 the estimates were significantly negative (-0.27 and -0.08). In 2012/2013 the estimate of the KPI although positive was not significantly different from zero suggesting that the OOP was a proportional financing source.
The findings from the South East zone for the period of the study reveal that the estimates of the OOP for the first and third periods were positive and significant KPI (0.19 and 0.06) suggesting that it was a progressive health care financing source. In 2012/2013, the KPI estimate for the OOP -0.07 was negative but not significant indicating that it was a proportional financing source. The findings from the South-West zones indicated that for the period of the study that KPI for the out-of-pocket payment for the period of 2010-2015 was significantly negative (-0.45, -0.26 and -0.11) indicating that the OOP was generally
Table 4: The Trend of Changes in the Gini coefficient, Concentration Index and Kakwani Progressivity index for out of pocket payment.
Out-of-Pocket Payment (OOP) |
2010/2011 |
2012/2013 |
2015/2016 |
Gini index /robust standard error |
0.546 (0.006) |
0.578(0.003) |
0.5489(0.003) |
Concentration index / robust standard error |
0.5111(0.021) |
0.454(0.008) |
0.452(0.008) |
KPI/ standard error |
-0.035*(0.02) |
-0.123(0.007) |
-0.097**(0.007) |
North Central |
|||
Gini index /robust standard error |
0.406(0.011) |
0.525(0.115) |
0.443(0.006) |
Concentration index / robust standard error |
0.965(0.104) |
0.361(0.018) |
0.312(0.013) |
KPI/robust standard error |
0.559**(0.095) |
-0.165**(0.019) |
-0.132**(0.013) |
North East |
|||
Gini index /robust standard error |
0.478(0.015) |
0.409(0.005) |
0.435(0.0071) |
Concentration index / robust standard error |
0.349(0.024) |
0.358(0.021) |
0.286(0.026) |
KPI/robust standard error |
-0.129**(0.029) |
-0.051**(0.019) |
-0.149**(0.022) |
North West |
|||
Gini index /robust standard error |
0.448(0.014) |
0.415(0.004) |
0.457(0.004) |
Concentration index / robust standard error |
0.178(0.133) |
0.428(0.014) |
0.380(0.016) |
KPI/robust standard error |
-0.27**(0.02) |
0.013(0.014) |
-0.0765**(0.015) |
South East |
|||
Gini index /robust standard error |
0.525(0.008) |
0.645(0.007) |
0.533(0.007) |
Concentration index / robust standard error |
0.714(0.035) |
0.567(0.017) |
0.596(0.018) |
KPI/robust standard error |
0.189**(0.034) |
-0.078 (0.164) |
0.064**(0.015) |
South-South |
|||
Gini index /robust standard error |
0.722(0.024) |
0.687(0.011) |
0.715(0.009) |
Concentration index / robust standard error |
0.53(0.062) |
0.575(0.022) |
0.527(0.022) |
KPI/robust standard error |
0.192**(0.061) |
-0.112**(0.0214) |
-0.189**(0.021) |
South West |
|||
Gini index /robust standard error |
0.639(0.019) |
0.598(0.009) |
0.549 (0.005) |
Concentration index / robust standard error |
0.183(0.023) |
0.33(0.015) |
0.43(0.015) |
KPI/robust standard error |
-0.456**(0.029) |
-0.268(0.016) |
-0.119**(0.014) |
Source: Author’s computation
Note: *** significant at 1%; **significant at 5%; *significant at 10% Standard errors are reported in parenthesis. KPI: Kakwani Progressivity Index
a regressive form of health care financing. The estimates of the South -South zone for the period of the study revealed that overall the out of pocket payment was regressive in nature with a negative but significant KPI of (-0.19, -0.11 and -0.19). The KPI estimate of the health insurance contribution (HIC) -0.16 in Table 5, revealed that in 2010/2011, the HIC was negative and statistically significant indicating that it was a regressive financing source. In 2012/2013, the KPI estimate of the HIC -0.03 was not significantly different from zero. This suggested that the HIC was marginally proportional. This finding suggests that the burden of payment was evenly distributed between the poor and non-poor. However, in 2015/2016 the KPI of the HIC -0.18 was negative and significant indicating that the HIC was a regressive funding source. The regressivity of the HIC in 2015/2016 was worse than that of the OOP at -0.09. The findings suggest that the burden of financing health care using the HIC was not evenly distributed across the population. Individuals on lower income levels spent a greater share of their consumption expenditure on health care when financing health using the health insurance contributions than when paying for health care out-of-pocket. The health insurance contribution in the North Central zone for the period of 2010/2011 had a significant negative KPI (-0.36). In 2012/2013 and 2015/2016 the HIC was positive and significant 0.86 and 0.93 respectively. This suggest that the HIC was a progressive means of health care financing with contributions being an increasing share of consumption expenditure for persons on higher income levels. The results could also imply that the poor do not have access to health insurance but pay for health care out-of-pocket. The findings from the health insurance contributions of the North East zone indicated that the KPI 0.51 was significantly positive for the 2010/ 2011 period but experienced a change in trend in 2012/2013 and 2015/2016, having significantly negative KPI (-0.26 and -0.68). The estimates of the health insurance contributions suggested that in the North East health it was a regressive financing source. In the North West Zone the estimates of the KPI for the health insurance contributions (0.19, -0.27 and -0.09) was not significantly different from zero for the three periods. This finding tends to indicate that the health insurance contribution was a proportional financing source. The findings from the South East Zone indicated that the health insurance contribution was a progressive means of health care financing in the first period and proportional in the second period with KPI (0.46 and 0.08) respectively. In 2015/2016 the KPI estimate for the HIC -0.36 was negative and statistically significant indicating that the HIC was a regressive source of health care finance. Findings from the South West zone in 2010/ 2011 and 2012/2013 revealed that the KPI estimates of the HIC (0.32 and 0.65) were not significantly different from zero indicating that it was a proportional financing source. In 2015/2016 the statistically significant estimate of the KPI -0.39 confirmed that it was a regressive. The non-significant estimates in the South -South zone of the HIC (-0.35 and -0.14) in 2010/2011 and 2012 and 2013, periods confirmed that it was a proportional financing mechanism. The HIC was regressive with a significantly negative KPI of -0.39 in 2010/2011.
Results of the sensitivity analysis
The results of the disaggregated analysis for progressivity across income quintiles are presented in Table 6. The disaggregated results obtained using the Multiple Comparison Estimation Technique are shown as overall estimates and not across geo-political zones given that the estimation technique did not provide results for the zones. Overall the results of the dominance test show that for the three-year period except in 2012/ 2013 year for the health insurance contributions, the ordinates of the concentration curve of out-of-pocket payments and the health insurance contributions dominated those of the Lorenz curve of equivalent consumption expenditure at all income quintiles. This is an indication that for the study period across these income levels individuals on lower income quintiles spends a greater proportion of their consumption expenditure spent as OOP and HIC than individual’s higher income quintiles. The results provide empirical evidence for existing vertical inequity in the Nigerian health care financing system.
Discussion
The estimates of the Gini coefficient for the period of the study confirmed that high levels of income inequality exist in the country. It was observed that in the zones the South-South and South East had the worst unequal distribution of income which could be exacerbated with the regressivity of the health care financing sources. For the three periods of the study, the out-of-pocket payment was a regressive health care financing source.
Table 5: The Trend of Changes in the Gini coefficient, Concentration Index and Kakwani Progressivity index for the Health Insurance Contributions 2010-2015.
Health Insurance Contributions |
2010 |
2012 |
2015 |
Overall |
|||
Gini index /robust standard error |
0.471(0.021) |
0.5718 (0.043) |
0.529(0.028) |
Concentration index / robust standard error |
0.311(0.074) |
0.534(0.535) |
0.344(0.062) |
KPI/ standard error |
-0.16**(0.034) |
-0.037(0.075) |
0.185**(0.065) |
North Central |
|||
Gini index /robust standard error |
0.367(0.0325) |
0.611(0.076) |
0.421(0.049) |
Concentration index / robust standard error |
0.002(0.2) |
1.468(0.438) |
1.351(0.348) |
KPI/robust standard error |
-0.364(0.209) |
0.858**(0.389) |
0.9296**(0.302) |
North East |
|||
Gini index /robust standard error |
0.481(0.023) |
0.314(0.013) |
0.729(0.136) |
Concentration index / robust standard error |
0.988(0.118) |
0.049(0.023) |
0.062(0.045) |
KPI/robust standard error |
0.507**(0.113) |
0.265(0.03) |
-0.668**(0.153) |
North West |
|||
Gini index /robust standard error |
0.306(0.797) |
0.288(0.019) |
0.274(0.013) |
Concentration index / robust standard error |
0.506(0.509) |
-0.002(0.189) |
0.185(0.092) |
KPI/robust standard error |
0.2(0.431) |
-0.289(0.201) |
-0.089(0.094) |
South East |
|||
Gini index /robust standard error |
0.627(0.082) |
0.761(0.105) |
0.410(0.03) |
Concentration Index / robust standard error |
0.17(0.1280 |
0.875(0.27) |
0.042(0.029) |
KPI/robust standard error |
0.457**(0.162) |
0.078(0.24) |
-0.368**(0.042) |
South South |
|||
Gini index /robust standard error |
0.517(0.031) |
0.706(0.114) |
0.847(0.109) |
Concentration index / robust standard error |
0.123(0.202) |
0.564(0.175) |
0.599(0.231) |
KPI/robust standard error |
-0.39**(0.198) |
-0.142(0.122) |
-0.248(0.284) |
South West |
|||
Gini index /robust standard error |
0.528(0.04) |
0.537(0.061) |
0.423(0.032) |
Concentration index / robust standard error |
0.205(0.19) |
-0.107(0.16) |
0.024(0.023) |
KPI/robust standard error |
-0.323**(0.203) |
-0.645(0.186) |
-0.399**(0.039) |
Source: Author’s computation
Note: *** significant at 1%; **significant at 5%; *significant at 10% Standard errors are reported in parenthesis. KPI: Kakwani Progressivity Index
This result was in tandem with those obtained from other studies [8, 21-23]. The results indicated that health care financing in Nigeria through the use of out-of-pocket health care payment and the health insurance contributions resulted in the poor spending more of their income on health care than the non-poor. This produced inequity in health care financing system and a widened of the income gap between the poor and the non-poor. Regressive health care payments imply that the poor do have the needed resources to access health care even when enrolled in the National health Insurance Scheme (NHIS) and have to make increased direct payments to cover their treatment cost resulting in the further impoverishment of already poor households. Although the NHIS was established to protect these households from inequities associated with out-of-pocket payments individuals on lower income levels spend more on health insurance contributions than their counterparts on higher income levels. These findings were confirmed for the lower income earners in the population using the disaggregated analysis indicating that the poor do not have access to universal health care coverage in Nigeria.
Table 6: Dominance test result
Out-of-Pocket Payments |
||||||
2010-2011 |
2012-2013 |
20115-2016 |
||||
Quintile |
Cumulative share of eqoop |
Dominance test |
Cumulative share of eqoop |
Dominance test |
Cumulative share of eqoop |
Dominance test |
q20 |
4.95%** (0.04) |
CC |
4.39%** (0.02) |
CC |
4.90%** (0.02) |
CC |
q40 |
13.93%** (0.13) |
CC |
14.06% **(0.06) |
CC |
15.07%** (0.06) |
CC |
q60 |
27.01% **(0.25) |
CC |
28.73%** (0.13) |
CC |
29.10% **(0.12) |
CC |
q80 |
46.11% ** (0.43) |
CC |
49.41%** (0.24) |
CC |
49.15%** (0.21) |
CC |
Health Insurance Contributions |
||||||
2010-2011 |
2012-2013 |
2015-2016 |
||||
Quintile |
Cumulative share of eqnhic |
Dominance test |
Cumulative share of eqnhic |
Dominance test |
Cumulative share of eqnhic |
Dominance test |
q20 |
11.21%** (3.13) |
CC |
5.03%**(0.96) |
CC |
8.04%** (1.01) |
CC |
q40 |
25.86%** (4.93) |
CC |
17.78%** (2.74) |
CC |
21.42%** (2.19) |
CC |
q60 |
59.38%** (5.92) |
CC |
32.14%**(4.52) |
CC |
35.38%** (3.25) |
CC |
q80 |
73.58%** (5.98) |
CC |
46.06% (6.16) |
LC |
61.21%** (4.84) |
CC |
Source: Author’s Computation. Percentage estimates of health care payments reported. eqoop: equivalent out-of-pocket payment. eqnhic: equivalent national health insurance contribution. CC: concentration curve dominance. LC Lorenz curve dominance. 5 % level of significance is applied at all quintile points. Standard errors are in parenthesis.
The results for the six geopolitical zones suggested that the out-of-pocket payment for health care was most regressive in the South-South and South-West zones. The regressivity of the out-of-pocket payment in the South-West zone could be attributed to the regressivity of the social health insurance contribution within the zone. Health insurance contribution was generally a proportional financing arrangement although it was regressive in the North- East. The regressivity of the health insurance contributions in the North East could be attributed to the greater disease burden borne by the poor arising from civil unrest within the zone. This necessitated their greater need for health care leading to their increased health insurance payment. Proportionality of the health insurance contributions across the zones could have occurred because of the flat rate co-payments of 10 per cent paid at the point of service by those enrolled in the health insurance scheme for medical care received irrespective of income levels. This proportionality could also have occurred because of the scheme’s mode of operation. Membership of the scheme only covers the formal sector that comprises only three per cent of the population while those in the informal sector are not covered. Furthermore, membership of the scheme is voluntary in nature and this greatly limits the pool of funds available for risk pooling and cross-subsidization of financial resources from the healthy to the sick and from the wealthy to the poor. The implication of this finding is that across the six geopolitical zones of the country, the NHIS has not been effective in protecting its members from the impoverishing effect of out-of-pocket payments. This could result in households neglecting the use of conventional health care, worsening of health outcomes, declining labour productivity and increasing mortality which are inimical to economic development.
Conclusion
The current health care financing arrangement in Nigeria reveals that the out-of-pocket payment for health which is the major health care financing source in the country results in the poor spending a greater share of their income on direct payments for health care. Although, the National Health Insurance Scheme was established to ensure universal coverage for the population and protect poor households from the impoverishment associated with direct payment for health care it has not been able to achieve this goal. The empirical evidence has shown that overall the social health insurance financing was regressive. For Universal coverage to be achieved in Nigeria, the National Health insurance Scheme (NHIS) must be expanded to cover the informal sector which makes up over 65 per cent of the working population. This will ensure increased pooling and cross-subsidization of financial resources which will tend to reduce the regressive effect of out-of-pocket payments. The membership of the scheme must be made mandatory for all formal sector workers. The flat co-insurance paid at the point of service implies that in real terms individual on lower income levels pay more for health than those on higher income levels resulting in regressive health financing mechanism. This is inimical to the survival of already poor households who may be forced into forgoing the consumption of health care due to the double burden of disease and the lack of financial resources to access much needed health care services.
Limitation
This study covered only the issue of equity in health care financing. Other grey areas that were not explored included equity in health care utilization and the benefit incidence of public health care funding. Health care funding arrangements examined in this study were mainly the national health insurance contributions and out-of-pocket health care payments. There are however other methods of financing that are not considered in this study such as tax revenue and the private health insurance premium. This is because the General Household Survey data does not provide data for these funding sources.
Author Contributions
Conceptualization of study – OO
Estimation/ Interpretation- CSO
Ethical Approval and Consent to Participate
This form of study does not require any form of ethical approval it is not a clinical study.
Conflicting Interest
None declared.
Funding Sources
None.
References:
References
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