Dualism of the Health System for Sustainable Health System Financing in Benin: Collaboration or Competition?
Abstract
1. Introduction
2. Health Pyramid and Healthcare Supply in Benin
3. Literature Review
3.1. Competition Effects
3.2. Collaboration Benefits
3.3. Co-Opetition in Healthcare
4. Conceptual Framework
5. Methodology
5.1. Sampling
5.2. Empirical Strategy
5.2.1. Investigating the Degree of Collaboration and Competition Between Public and Private Health Centres in Benin
5.2.2. Investigating the Influence of Competition on the Degree of Collaboration Between the Two Health Systems in Benin
6. Results
6.1. Descriptive Statistics
6.2. Main Findings and Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Dependent Variable: | |||
---|---|---|---|
Collaboration 2019 | Collaboration 2020 | Collaboration 2021 | |
Competition | −5.599 | −10.693 *** | −6.842 * |
(6.542) | (4.055) | (3.753) | |
Competition×Laboratory | 0.390 | 0.046 | −1.235 |
(3.491) | (2.246) | (1.970) | |
Years of experience | 0.220 | 0.141 | −0.003 |
(0.234) | (0.185) | (0.140) | |
Number of Generalist | 1.768 | 3.171 *** | 0.932 |
(1.314) | (0.928) | (0.713) | |
Number of Specialist | −1.336 | 0.597 * | −0.034 |
(1.076) | (0.361) | (0.395) | |
Number of Midwife | 2.304 * | 0.933 | 0.802 |
(1.199) | (0.715) | (0.591) | |
Number of Laboratory technicians | 0.177 | −2.585 | −0.006 |
(2.846) | (2.151) | (1.740) | |
Number of Imagery technicians | −5.912 * | −3.105 ** | −2.515 * |
(3.502) | (1.428) | (1.408) | |
Number of Beds | −0.674 ** | 0.056 | 0.259 * |
(0.311) | (0.172) | (0.148) | |
Years of Experience dyad | −0.004 | −0.043 | −0.103 * |
(0.071) | (0.061) | (0.053) | |
Number of Generalist dyad | −0.228 | −0.234 ** | −0.154 |
(0.361) | (0.102) | (0.104) | |
Number of Specialists dyad | 0.628 | −0.374 | −0.084 |
(0.966) | (0.319) | (0.329) | |
Bed dyad | 0.095 | 0.039 | −0.054 |
(0.156) | (0.099) | (0.093) | |
Sage dyad | −0.375 | 0.440 * | 0.502 ** |
(0.442) | (0.241) | (0.230) | |
Laboratory dyad | −0.278 | −1.045 * | −1.126 ** |
(0.790) | (0.592) | (0.530) | |
Imagery dyad | 0.550 | −0.688 | −1.733 |
(1.807) | (1.066) | (1.066) | |
factor(ZoneSani)Allada-Toffo-Z’e | 8.905 ** | 8.236 *** | 5.404 ** |
(3.510) | (2.421) | (2.114) | |
factor(ZoneSani)Ouidah-Kpomass’e-Tori-Bossito | −1.401 | 10.340 *** | 7.778 *** |
(4.809) | (3.020) | (2.283) | |
logSigma | 2.288 *** | 2.374 *** | 2.214 *** |
(0.189) | (0.105) | (0.104) | |
Constant | −20.660 *** | −17.228 *** | −10.673 *** |
(7.650) | (4.927) | (3.802) | |
Observations | 884 | 884 | 884 |
Log Likelihood | −147.263 | −360.847 | −363.677 |
Akaike Inf. Crit. | 334.526 | 761.694 | 767.354 |
Dependent Variable: | |||
---|---|---|---|
Collaboration 2019 | Collaboration 2020 | Collaboration 2021 | |
Competition | −5.671 | −10.064 *** | −6.756 * |
(6.057) | (3.862) | (3.551) | |
Competition×Imagery | 1.866 | −2.117 | −4.571 |
(10.584) | (4.928) | (4.941) | |
Years of Experience | 0.223 | 0.133 | −0.004 |
(0.232) | (0.183) | (0.139) | |
Number of Generalist | 1.773 | 3.177 *** | 0.905 |
(1.307) | (0.930) | (0.717) | |
Number of Specialist | −1.330 | 0.586 | −0.078 |
(1.074) | (0.363) | (0.399) | |
Number of Midwife | 2.329 * | 0.900 | 0.800 |
(1.217) | (0.712) | (0.583) | |
Number of Laboratory technicians | 0.432 | −2.537 ** | −0.816 |
(1.537) | (1.285) | (1.026) | |
Number of Imagery technicians | −7.326 | −1.468 | 0.869 |
(8.932) | (4.039) | (3.838) | |
Number on Beds | −0.677 ** | 0.061 | 0.253 * |
(0.310) | (0.171) | (0.148) | |
Experience dyad | −0.003 | −0.043 | −0.102 * |
(0.071) | (0.061) | (0.053) | |
Number of Generalist dyad | −0.227 | −0.234 ** | −0.152 |
(0.360) | (0.102) | (0.105) | |
Number of Specialist dyad | 0.630 | −0.381 | −0.083 |
(0.965) | (0.319) | (0.328) | |
Bed dyad | 0.094 | 0.042 | −0.054 |
(0.156) | (0.099) | (0.093) | |
Sage dyad | −0.374 | 0.438 * | 0.497 ** |
(0.441) | (0.241) | (0.231) | |
Laboratory dyad | −0.280 | −1.043 * | −1.133 ** |
(0.790) | (0.593) | (0.530) | |
Imagery dyad | 0.542 | −0.706 | −1.687 |
(1.806) | (1.068) | (1.069) | |
factor(ZoneSani)Allada-Toffo-Z’e | 8.892 ** | 8.251 *** | 5.480 *** |
(3.505) | (2.423) | (2.118) | |
factor(ZoneSani)Ouidah-Kpomass’e-Tori-Bossito | −1.464 | 10.325 *** | 7.850 *** |
(4.806) | (3.011) | (2.276) | |
logSigma | 2.287 *** | 2.375 *** | 2.212 *** |
(0.189) | (0.105) | (0.104) | |
Constant | −20.641 *** | −17.595 *** | −10.692 *** |
(7.462) | (4.942) | (3.760) | |
Observations | 884 | 884 | 884 |
Log Likelihood | −147.254 | −360.755 | −363.443 |
Akaike Inf. Crit. | 334.507 | 761.509 | 766.887 |
Bayesian Inf. Crit. | 429.739 | 856.741 | 862.118 |
1 | Though our model and that of Mascia et al. (2012) were not estimated in log form, our comparison is still valid. This is because the mean of the collaboration variable in our study ranged between 0.11 and 0.44, which is lower than Mascia et al. (2012)’s mean that ranged between 0.72 and 0.73. It is important to note that zero-inflated Poisson regression estimation is not consistent when logging the dependent variable for two reasons. First, the dependent variable is a count variable, not a continuous variable. Second, the dependent variable contains excess zeros. |
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Health Administrative Area | Private | Public | Total |
---|---|---|---|
Abomey-Calavi-So-ava | 92 × 55/212 = 24 | 23 × 55/212 = 6 | 30 |
Ouidah-Kpomass’e-Tori-Bossito | 36 × 55/212 = 4 | 14 × 55/212 = 9 | 13 |
Allada-Toffo-Z’e | 35 × 55/212 = 3 | 12 × 55/212 = 9 | 12 |
Total | 31 | 24 | 55 |
Adjusted for 10% non-responses | 34 | 26 | 60 |
N | Mean | St. Dev. | Min | Max | |
---|---|---|---|---|---|
2019 | |||||
Collaboration | 884 | 0.111 | 0.811 | 0.000 | 11 |
Competition | 884 | 0.823 | 0.215 | 0.048 | 1 |
2020 | |||||
Collaboration | 884 | 0.442 | 2.211 | 0.000 | 36 |
Competition | 884 | 0.807 | 0.234 | 0.06 | 1 |
2021 | |||||
Collaboration | 884 | 0.365 | 0.872 | 0.000 | 27 |
Competition | 884 | 0.822 | 0.220 | 0.046 | 1 |
Private | Public | |||
---|---|---|---|---|
Mean | St. Dev. | Mean | St. Dev | |
Number of experiences years | 8.528 | 7.300 | 38.636 | 15.716 |
Number of specialists | 1.806 | 2.974 | 1.045 | 2.984 |
Number of Generalist | 2.028 | 1.748 | 0.727 | 1.518 |
Number of Sage femme | 3.611 | 2.101 | 7.591 | 10.536 |
Number of Technical lab | 1.333 | 1.195 | 1.727 | 3.654 |
Number of Technical Imag | 0.417 | 0.649 | 0.636 | 1.177 |
Number of bed | 10.472 | 7.378 | 17.091 | 24.464 |
Number of Ambulances | 0.114 | 0.404 | 0.409 | 0.734 |
Nbr. Observation | 36 | 24 |
Dependent Variable: | |||
---|---|---|---|
Collaboration 2019 | Collaboration 2020 | Collaboration 2021 | |
Competition | −4.869 *** | −0.283 | −2.875 *** |
(1.457) | (0.524) | (0.876) | |
Number of experiences years | −0.075 ** | 0.072 | 0.047 |
(0.038) | (0.045) | (0.037) | |
Number of Generalists | −0.188 | 0.459 *** | −0.364 |
(0.250) | (0.171) | (0.227) | |
Number of Specialist | −0.150 | −0.089 | 0.262 * |
(0.334) | (0.086) | (0.147) | |
Number of Midwife | 0.971 ** | −0.229 | −0.095 |
(0.405) | (0.163) | (0.171) | |
Number of Laboratory technician | −0.525 * | −0.267 | 0.354 |
(0.282) | (0.206) | (0.350) | |
Number of Imagery technician | −2.596 ** | −0.150 | −0.355 |
(1.215) | (0.212) | (0.318) | |
Number of Bed | −0.228 *** | 0.066 ** | −0.0004 |
(0.084) | (0.028) | (0.037) | |
Number of experiences years dyad | 0.006 | 0.002 | 0.024 |
(0.019) | (0.008) | (0.016) | |
Number of Generalist dyad | −0.080 | −0.030 * | −0.005 |
(0.092) | (0.017) | (0.027) | |
Number of Specialist dyad | 0.453 * | −0.022 | 0.003 |
(0.271) | (0.051) | (0.064) | |
Number of Bed dyad | 0.035 | −0.014 | 0.005 |
(0.035) | (0.014) | (0.027) | |
Number of Midwife dyad | −0.038 | 0.029 | 0.019 |
(0.090) | (0.029) | (0.073) | |
Number of Imagery technician dyad | −0.141 | 0.025 | −0.172 |
(0.139) | (0.072) | (0.122) | |
Number of Imagery technician dyad | −1.176 *** | 0.044 | 0.008 |
(0.419) | (0.118) | (0.283) | |
factor (ZoneSani)Allada-Toffo-Z’e | 2.542 *** | −0.480 | 1.931 *** |
(0.936) | (0.387) | (0.654) | |
factor (ZoneSani)Ouidah-Kpomass’e-Tori-Bossito | −4.402 *** | 0.901 | 0.987 |
(1.575) | (0.799) | (0.720) | |
Constant | 5.810 *** | 0.344 | 1.830 ** |
(1.294) | (0.637) | (0.833) | |
Observations | 884 | 884 | 884 |
Log Likelihood | −128.802 | −349.219 | −356.325 |
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Share and Cite
Alakonon, C.B.; Gbeto, J.R.A.; Bassongui, N.; Alinsato, A.S. Dualism of the Health System for Sustainable Health System Financing in Benin: Collaboration or Competition? Economies 2025, 13, 220. https://doi.org/10.3390/economies13080220
Alakonon CB, Gbeto JRA, Bassongui N, Alinsato AS. Dualism of the Health System for Sustainable Health System Financing in Benin: Collaboration or Competition? Economies. 2025; 13(8):220. https://doi.org/10.3390/economies13080220
Chicago/Turabian StyleAlakonon, Calixe Bidossessi, Josette Rosine Aniwuvi Gbeto, Nassibou Bassongui, and Alastaire Sèna Alinsato. 2025. "Dualism of the Health System for Sustainable Health System Financing in Benin: Collaboration or Competition?" Economies 13, no. 8: 220. https://doi.org/10.3390/economies13080220
APA StyleAlakonon, C. B., Gbeto, J. R. A., Bassongui, N., & Alinsato, A. S. (2025). Dualism of the Health System for Sustainable Health System Financing in Benin: Collaboration or Competition? Economies, 13(8), 220. https://doi.org/10.3390/economies13080220