Intervening Role of the Poverty Level on Public Health Care and Tax Revenues Linkage: Evidence from the East Africa Community Members Countries

Universal Health Coverage (UHC) has dominated government and development partners’ boardrooms in the last two decades and will continue to do so in the foreseeable future. Debate is ongoing as to whether governments allocate additional public funds to health care as a result of expanded tax revenues or whether public health financing (PHF) can be harnessed to spur growth in tax revenues. Evidence from prior studies has varied, mainly stemming from differences in models adopted, choice and measurement of variables, as well as contextual disparities. The study was premised on a peacock-wiseman theory of public spending and the ability to pay theory of taxation. We explored the intervening effect of the Poverty Level (PL) on the PHF-tax revenue relationship. The study employed fiscal allocation and spending proportion to total government spending to measure public healthcare financing; poverty index to operationalize poverty level and total tax revenues as proxied by the sum of VAT, PAYE, and corporation tax as an indicator of tax revenues across three East Africa Community (EAC) Member countries. A research hypothesis was tested on a population of three EACs. Preliminary descriptive tests were conducted on mean, skewness kurtosis, and correlation. For hypotheses tests, simple linear regression was run to test PHF-tax revenue linkage; hierarchical multiple regression was used to test for intervention. Secondary data for all countries was available for all years under study. Findings were as follows: there was a statistically significant link between PHF and Tax revenues for EAC, Kenya, and Tanzania; there was the intervening effect of PL on the relationship between PHF and Tax revenues in Kenya only. Health, budgetary, and finance sectors and other agencies in the government can use the findings to guide healthcare allocations, particularly when setting goals to expand tax revenues.

Introduction

The passing of the United Nations General Assembly resolution on UHC has not been sufficient to translate into meaningful results on public healthcare financing among many member countries. Africa has an annual financing gap for healthcare amounting to USD 66 billion, thus limiting the continent’s progress towards attaining sustainable development goals (SDG) and UHC (Ngepah & Ndzignat, 2024). Partly, this has been attributed to the unavailability of funds, especially among low- and medium-income countries, to finance the provision of healthcare (Belgibayevaet al., 2022). Many arguments have been advanced, including lack of fiscal space and limited opportunities for domestic funding, hence limiting the government’s ability to allocate adequate financing to healthcare. The desire to have everyone obtain health care services without suffering financial hardship is critical to realizing many government objectives (Mueller & Morgan, 2017; OECD, 2017). How this financing can be optimized, whether from government, public, or private financing, is still an area of policy review among many stakeholders.

The size and nature of healthcare financing affect the quality of healthcare received in terms of the extent of services and timeliness of the intervention. This can significantly affect the economic well-being of citizens both directly in terms of their ability to generate taxable income and indirectly through economic hardships and impoverishment. Some scholars and practitioners consider public health expenditure as an input that can be harnessed to influence growth (Liping & Zhang, 2018), while others conceive it as an outcome or an exogenous factor (Kazemianet al., 2017; Stepovic, 2022). Behera and Dash (2017) and Hu and Mendoza (2013) suggest that a well-funded healthcare system leads to a healthy, productive population, hence reducing the poverty levels of the citizenry. This productive population contributes to positive macro-economic indicators like GDP and avails income to households, hence alleviating poverty, expanding the tax base, and improving overall total government tax revenues. Therefore, the linkage between a healthy nation, macroeconomic indicators, and the effects of poverty level becomes core in understanding variations in tax revenues across economies.

In Africa and other low-income nations, the average health spending was only $41 per person compared to $2,937 in high-income countries (WHO, 2022). Even after committing to allocate 15% of the budget to health in the Abuja Declaration 2001, EAC member countries have not honored the commitment (Khanet al., 2019). This notwithstanding, global economies have persistently faced declining or stagnating tax revenues. Economies, especially in developing countries, are entangled in a vicious cycle of poverty whereby budgets fail to cater to healthcare sufficiently. This leaves households exposed to catastrophic out-of-pocket (OOP) payments, thus rendering a large proportion of the population unproductive and unable to contribute to a country’s GDP (OECD, 2017). Public spending on health is not directly associated with national income. In this case, higher per capita income does not automatically translate to higher priority to health in public spending (Lelnikova & Wilinski, 2022). Public health coverage is regarded as a boost to the economy since it leads to improved health among workers, decreased time off, and a slower mortality rate due to illness. This contributes to the productivity levels of the citizens. The more productive the labor is, the larger the expansion of the GDP, ceteris paribus, which translates to generating more revenues through taxes (Thiagarajan, 2022).

Whereas in EAC, many families are considered large, the World Bank indicates that poverty levels are lower for small families and higher for large families, with pointedly higher in developing than developed economies. Rising costs in healthcare contribute significantly to individual and family impoverishment and national deficits and reduce the ability to spend in other basic areas like housing, education, and economic development. The poverty level acts as a critical barrier to accessing quality healthcare. In developing countries, the proportion of households financing healthcare through out-of-pocket payment is 40%. There is a growing trend of households being driven to the risk of catastrophic health expenditures and thus being impoverished. About 808 million citizens were exposed to catastrophic health expenditures, of which 80% were from Africa and Asia (Gordon & Wei, 2016; Hooda, 2015).

The capacity of a nation to raise satisfactory revenues to fund government outlay is dictated by the collection systems and structure, the capacity of the economy to generate taxable revenues, winning macroeconomic conditions, the stage of advancement, and the economy structure (Cantarero & Lago-Peñas, 2010). In developing nations, tax revenues have not adequately funded healthcare expansion amid the increasing population. This is associated with challenges encountered during tax collection and the narrow tax base. The role of healthcare financing is found in its ability to expand the taxable capacity through the expansion of productivity among the taxable population. The number of EA citizens steered to poverty due to health care spending and those limited in accessing health care keeps growing (Karamagiet al., 2023). Kenya, Uganda, and Tanzania are at the same level of UHC implementation and were selected for this study. Tax simulation models conclude that although increased taxes on consumption or general income may expand health coverage, consumption taxes would impoverish less fortunate families’ nutritional and healthcare status (Khan & Mahumud, 2015). EAC member countries collect about 15 percent of GDP in taxes compared to the average of 20% for Sub-Saharan Africa (SSA). The low tax collection has been attributed to the low tax base and inadequate allocations (Takuma & Iyke, 2017). The study sought to expound on the role of public health finance as a catalyst to spur domestic income through enhancing labor productivity.

Research Problem

Many East Africa Countries, being Low- and medium-income Countries (LMIC), have not achieved any significant growth in the tax-to-GDP ratios in the last 15 years, signaling low growth in tax revenues. East Africa countries have experienced fluctuations in tax revenues over the years, punctuated by a decline in tax revenues, with the decline being associated with the lack of potential taxpayers to contribute to the tax basket (Kabiaet al., 2019). Public healthcare financing has been projected as an influencer of taxes. The three countries have experienced a similar challenge in financing their healthcare budgets, exposing their citizens to catastrophic healthcare costs. This further pre-exposes taxpayers to the extra burden, causing them to be impoverished further and unable to generate tax revenues for their countries (Moore, 2014). The direct relationship between public health financing and its effects on tax revenues is an emerging area of development finance, with researchers documenting different results for developed and emergent countries (Kimet al., 2017; Luet al., 2016).

The conflicting nature of empirical findings arises from variations in construct operationalization and the nature of country healthcare policies and structures. Evidence has also been presented that tax increases or increased productivity are largely pegged into other factors, such as political regimes and external markets (Takumah & Iyke, 2017). Although some studies have found a direct association between tax revenue and GDP (Stepovic, 2022; Yuet al., 2017), the literature lacks the link between healthcare financing and tax revenues, with many studies inclined to healthcare financing and broad economic growth. As to whether we allocate more resources to healthcare to stimulate growth and expand tax revenues or whether expanded healthcare financing is an output of growth in tax revenue is a matter of abstract debate. Private and donor funds and public resources within the EA countries finance health care. The basket of funds is not uniform among the member countries, but a salient denominator is that except Rwanda, all other EAC countries have not met the Abuja declaration (WHO, 2022). Despite the growing economies of the EAC countries, policymakers have not prioritized the expansion of public funds allocated to health care. With healthcare allocation for Kenya, Uganda, and Tanzania being in the same range, it warrants this analysis to compare how the three countries’ tax revenues vary. Although health financing has been widely studied, most empirical studies have looked at the progressivity of healthcare financing in different countries.

Studies done by Hu and Mendoza (2013) on public healthcare expenditures and economic development and that of Takumah and Iyke (2017), among others, point to contradicting results on the association between public health expenditure and tax revenues. Despite having incomplete data, Chenet al. (2017) reported that poor healthcare funding in Sub-Saharan Africa (SSA) was attributed to poor tax revenues. Methodological gaps also emanate from differences in the periods analyzed and the incompleteness of data sets. In studies that sought data from citizens concerning their incomes and spending on healthcare, most cited inconsistency, incompleteness, or wrong undocumented information from respondents (Tamakoshi & Shigeyuki, 2014; Yuet al., 2017).

From the analysis of Rwanda, Uganda, and Tanzania, the findings of Kabiaet al. (2019) indicated that EAC countries, like many SSA, have slugged in achieving direct benefits of expanding health financing to stimulate economic growth. The study focused on tax collection channels, while the present study focuses on actual tax revenue collected. Munge and Harvey-Briggs (2014), Akaziliet al. (2012), looked at the progressivity of health financing in Kenya, Ghana, and Uganda by analyzing the Kakwani index. Barasaet al. (2017) found a direct correlation between healthcare and development, but East Africa member countries have not been studied on how that contribution is made through the expansion of tax revenues. From the literature reviewed, no known study looked at public healthcare financing’s influence on tax revenues in EAC. Most studies have been limited to the direct effects. In contrast, this study sought to test the intervening effect of the poverty level in the public health financing – tax revenues linkage in the EAC context to eliminate the probable bias of overestimating the single variable effect.

Research Objective

The present study aimed to determine the effect of poverty level on the relationship between public healthcare financing and tax revenues in East Africa community member countries.

Theoretical Underpinning

Peacock and Wiseman’s Theory founded the relationship between public healthcare spending and tax n’s theory. Peacock and Wiseman (1967) viewed the citizens as beneficiaries of public goods who detested paying taxes. Guided by this theory, it can be presumed that when an economy grows, tax revenues will increase, thereby allowing public expenditure to grow commensurate with GDP. In normal times, public expenditure would gradually indicate an upward trend despite diverging expectations between desirable levels of public expenditure and taxation. In times of social disturbance, the rising trend in public expenditure would be distressed. The government would be compelled to grow tax revenues to finance the upsurge in public expenditures. In conceptualizing the study constructs, the theory guides in predictions of public financing of healthcare as an input of national public spending, hence its relationship and role as a catalyst to spur growth and expand tax revenues. The theory helps explain how government spending on healthcare leads to a healthy nation and general population productivity, reducing poverty level, improving GDP, reducing borrowing (reduction in interest rate and unemployment, and vice versa. In such situations, a government may experience better revenues generated from taxation depending on the weight it puts on public healthcare budgetary allocations. The subject of this study is what can be done to stimulate this growth of tax revenues and the role of public financing for healthcare in that growth.

The ability to pay theory of taxation is centered on contribution in relation to ability as measured by income and wealth, which are appreciated under the assurance of the state’s (benefit) and with respect to capacities. The ability-to-pay hypothesis has been supported by numerous economists, such as Khanet al. (2019). The fundamentals of this theory have their significance as it considers the distributional perspective dependent on the standard of fairness and equity. As per this theory, taxes for essential services such as healthcare should be directly proportional to the individual’s ability to pay tax. Individuals’ payment of taxes is considered an opportunity cost since the taxpayers forego alternative expenses to pay tax. For the most part, salary has been taken as an essential marker of capacity to pay; however, less significantly, wealth has additionally been utilized. This notwithstanding, poverty has been projected as a key reason as to why those who need fundamental services, such as healthcare, cannot pay.

A major limitation of this theory is how to determine the ability to pay. It is unclear if it should be pegged on property held, income, or expenditure. Critics argue that if various taxes should be imposed on incomes, property, and expenditure to spread the tax burden and achieve equity, such a decision places a heavy tax on the rich, hence discouraging saving and enterprise. The assessment of an individual’s ability to pay tax depends on interpersonal comparisons of sacrifice to establish a tax burden for different classes of people that is infeasible. Despite contradictory arguments among modern economists on measuring utility and comparing different individuals, the theory assumes that utilitarian concepts significantly influence public policies.

Empirical Literature

Chenet al. (2017) analyzed healthcare financing progressivity with respect to income using the Kakwani index for overall taxes, health insurance premiums, and OOP as the four healthcare financing channels. Two survey rounds were conducted in 2008 with 13,619 individuals and 2013 with 12,973 individuals. Interviews captured household socio-economic and healthcare payments. Results indicated a retrogressive system, implying that low-income households have a higher healthcare financing burden since the Kakwani Index was negative. Indices for general taxation were found to be negative. While the study analyzed private and public healthcare funding, the present study focuses on the proportion allocated from the government budget and how it affects tax revenues. The analysis did two rounds of surveys spanning five years. It is difficult to conclude that the respondents in the first survey may not be the same as those in the second survey; hence, the findings are those of two distinct studies. The study captured household socio-economic and healthcare payments by interviews. The tool may be prone to user or social desirability bias. At the same time, the investigator’s communication style can impact the willingness to share experiences or standpoints, particularly on personal matters like health. The current study relies on secondary data to minimize bias, and the analysis is based on panel data from forty years in three countries.

An investigation that focused on OOP spending influence on health, income, and other well-being dimensions by using survey data from 2007 and Kenya Household Health Expenditures (n = 8414) was done by Kimani (2014). Findings indicated that 12% of those utilizing health care suffered catastrophic expenditures, and 4% were extremely affected by medical expenses, with the poor being most affected. The econometric investigation uncovered that OOP spending was an obstacle to access to health care and was mostly proportional to catastrophic expenditure and poverty. This finding was similar to that of Gordon and Wei (2016), where inaccessibility of healthcare services in India was found to lead to huge health expenditures that impoverished households by draining their health and wealth. Financing healthcare by low-income households is inhibited by affordability as they are forced into huge debts and sell assets to settle medical bills; hence, they become unproductive and more impoverished. While the study probed how OOP impacted the economic well-being of households, the current study sought to test further how PHF and the intervening effect of poverty level constructs affect tax revenues. The study, however, focused on OOP and analyzed healthcare spending from a household standpoint, unlike the current study that seeks to analyze government expenditure on health from a macro point of view. The context of the current study is broader, and an analysis of three countries across East Africa was undertaken.

Akaziliet al. (2012) study on the progressivity of health financing in Ghana sourced data from a 2005/2006 national household survey using a sample of 8,687 households, with 36,488 individuals, which is 0.17% of Ghana’s population. The Ministry of Finance provided tax revenue data triangulated with estimates in the GLSS-5 data. Individual funding source Kakwani Index was computed by weighting its proportion to the total healthcare funding. A Kakwani index of 0.075 indicates income tax progressivity; hence, the rich bear the corporate tax burden. In addition, the national health insurance levy scored an index of 0.026, showing progressivity. The study utilized two-year data, which would not be a reliable or sufficient indication of progressivity since the changes could be short-lived and, hence, inappropriate for long-term forecasting due to variations in the political or economic environment. The analysis examined the general progressivity of healthcare financing stemming from the progressivity of tax allocations. At the same time, the current study examined the impact of public healthcare financing on tax revenue and compared three countries.

Conceptual Framework

The conceptual model depicts a relationship between public healthcare financing and tax revenues for the East Africa member countries. A possible intervention on the link between public healthcare financing and tax revenues was represented in the study model. The conceptual model showing the schematic linkages between the study variables is illustrated in Fig. 1.

Fig. 1. The conceptual model.

Research Hypothesis

H0: The intervening effect of poverty level on the relationship between public healthcare financing and tax revenues for the East Africa member countries is not significant.

Method

The study population was all the eight East Africa community member countries while the target population was restricted to three East Africa member countries: Kenya, Uganda and Tanzania. These countries comprise the largest three economies in the region and have the oldest governments, having gained independence almost simultaneously. The target population was ideal because the researcher had a clear idea of the attributes of interest and intended to select a sample representative of known characteristics. Still, the three countries’ dataset was considered complete and thus selected for this study. Rwanda and Burundi were characterized with incomplete or missing data, while the Democratic Republic of Congo, the Federal Republic of Somalia, and South Sudan are relatively unstable and their data set incomplete for the period under review; hence, they were excluded from the study. Kenya, Uganda, and Tanzania have significant similarities in their public health financing structure and tax regime.

The secondary data was sourced from the research departments of the National Treasury, State Departments of Planning/National Bureau of Statistics, the Revenue Authorities, and the Central Banks of the three countries using a set of data collection matrices for the period 1982 to 2022.

Measurements

Specifically, tax revenues were proxied by the sum of revenues realized annually (sum of VAT + PAYE + Corporation Tax). Public healthcare financing was proxied by the total sum of expenditure towards health (Recurrent and Capital spending on Health). The poverty level was measured based on the poverty index.

Data Analysis

Regressions analysis was utilized to test the association of public healthcare financing and tax revenues. Correlation analysis was conducted to establish relationships between the study variables, to reveal direction as well as the magnitude of the relationships.

Model

The (1) describes the model adopted in this study.

(1) $$\begin{array}{r}{R}_t={\omega }_0+{\omega }_1{H}_t+{\omega }_{2}Nt+{\omega }_3{H}_t{N}_t\dots +\epsilon \end{array}$$

where:

$H_t$ = Public healthcare financing score,

$R_t$ = Tax revenues score,

$N_t$ = Poverty level,

${\omega }_0$ = Regression constant,

${\omega }_1$ to ${\omega }_3$ = Regression coefficients,

ɛ is the random error term that accounts for variability unexplained by linear effects.

The correlation coefficient was determined and the tests of significance done using t-test to establish existence of intervention effect poverty level on the relationship between public healthcare financing and tax revenues. A relationship existed if the coefficients were found to be statistically significant.

Findings and Discussions

Descriptive statistics for the study variables were as shown in Table I.

Table I. Descriptive Statistics

Statistics	PHF	PovLevel	TaxRev
Mean	4.518	49.239	7.833
Std.Dev	1.319	15.529	2. 2.852
Variance	1.741	241.158	8.133
Range	5.96	65	9.089
Min	1.64	21.2	3.89
Max	7.6	86.2	12.98
Skewness	0.403	0.187	0.592
Kurtosis	2.449	2.162	1.862

Results shown in Table I indicated mean and standard deviation values for PHF (4.51;1.3), poverty level index (49.23;15.5), and tax revenues (7.83;2.85) for East African countries. This was an indication that the variables deviated from large data. Tax revenues averaged 7.8, with the lowest being reported at 3.9 while the highest was 12.9; this was an indication that in some years, the countries collect high taxes while in some other years, the tax revenue was so low. The poverty level index was also high at some point (86.2) while the average was 49, and 12.2 was reported on the lower side, an indication that over the years, extreme cases of poverty prevailed in the three countries. PHF reported maximum values of 7.6 while the average for EAC was 4.5 and the minimum was 1.6, an indication that public healthcare financing was taken with more weight in some countries over different regimes than in others. All the study variables reported positive skewness, implying that the long tail was on the right, and positive kurtosis, implying that they were symmetrical and within range.

Pearson Moment Correlation was utilized to test the presence of relationships. Results of correlation analysis between public healthcare financing (PHF), poverty level (PovLev), and tax revenues (TaxRev) were as follows: PHF–TaxRev reported moderate inverse but significant values −0.316^*; PovLel-TaxRev-reported strong inverse and significant values −0.7495^*; PHF-PovLel reported weak and non-significant values 0.1363. The results of the regression analysis are indicated in Table II.

Table II. Regression Results for Public Healthcare Financing and Tax Revenues

Variable	Coefficient	Std. error	Z	Prob.
C	5.685909	2.285628	2.49	0.013
PHF	−0.4751954	0.0610472	−7.78	<0.001
R-squared	0.100
Wald chi	60.590
Prob.	<0.001
Dependent variable: Tax revenue
Predictors: (Constant), PHF
Number of groups: 3
Observations: 126
Observation per group:42
Panel variable ID (strongly balanced)
Time variable: 1982–2022

The model to test the first hierarchy is given in (2a–b):

(2a) $$\begin{array}{r}{R}_t={\beta }_0+{\beta }_1\left\{H_t\right\}+{\epsilon }_t\end{array}$$

(2b) $$\begin{array}{r}{R}_t={\omega }_0+{\omega }_1{H}_t+{\omega }_{2}Nt+{\omega }_3{H}_t{N}_t\end{array}$$

As indicated by Table II, regression results indicate that the F-statistics were significant at the 95% level of the test (P < 0.05), implying that PHF was a significant predictor of tax revenues in EAC. In addition, the overall model was significant (p < 0.001). Thus, t-tests on the regression coefficients would be statistically feasible. However, the results also indicated low values of R-squared of 0.10. In this case, where the model has independent variables that are statistically significant but have a low R-squared value, it is an indication of correlation presence but has little explanation of variability in tax revenues. The model is framed as: ${\mathit{R}}_{\mathit{t}}$ = 5.685909 − 0.4751954 ${\mathit{H}}_{\mathit{t}}$

Findings resonate with those of Khanet al. (2019), who indicated a linear positive association between tax returns and healthcare financing. On the same note, Munge and Harvey-Briggs’s (2014) findings demonstrated that the overall healthcare financing system and OOP exhibited a regressive trend while the other payments were proportional. The findings are found to contradict those of Hellowell (2022) and Chenet al. (2017), who projected that a retrogressive system implies that households with low income have a higher healthcare financing burden since the Kakwani Index was negative, suggesting that poor households barely contributed to tax basket. Results are in harmony with the predictions of Peacock-Wiseman’s theory of public spending, which point out the fact that government expenditure is primarily dependent on government tax revenues and that generally tolerable tax burden is always steady except on occasions of uncommon disturbance. Findings are also congruent with Ability to Pay theory projections that postulate the distribution of taxes for essential services such as healthcare to be directly proportional to the individual’s tax-paying ability.

The second hierarchy of mediation was performed on public healthcare financing and poverty level index, and the results are indicated in Table III.

Table III. Regression Results for Public Healthcare Financing and Poverty Level

Variable	Coefficient	Std. error	Z	Prob.
C	46.0766	12.1438	3.79	0.000
PHF	0.699925	0. 72335	0.97	0.333
R-squared	0.1186
Wald chi	0.940
Prob.	0.3332
Dependent variable: Poverty level
Predictors: (Constant), PHF
Number of groups: 3
Observations: 126
Observation per group:42
Panel variable ID (strongly balanced)
Time variable: 1982–2022

The outcomes in Table III reveal that the F-statistics were not significant at the 95% level test (P > 0.05), implying that PHF was not a significant predictor of the poverty level index in EAC. The results also indicated an overall R-squared positive change of 1.86%. However, the overall model was not statistically significant; there was no need for further analysis. The mediation process was stopped at the second step since the link between PHF and the poverty level Index is insignificant, and it was reported that the poverty level index had no mediation effect on PHF and tax revenue linkage. The null hypothesis failed to be rejected, and it was concluded that poverty level has no significant intervening effect on the linkage of public healthcare financing and tax revenues in East African member countries.

The lack of relationship between public healthcare funding, as projected by theory, is explainable by the fact that several other factors are at play that led to rising poverty levels, most of which are not health related. The explanation could also be pegged on the reason that some citizens fail to seek care when sick, not entirely because of healthcare funding but other factors such as culture, accessibility, and mental or physical incapacity, among others.

Recommendations

In line with the study results, the following recommendations were made: The government should provide affordable and accessible healthcare to its people through national health insurance schemes or other agencies by consolidating allocations to the healthcare sector that will reach out to the urban and rural folks.

The poverty level was reported to be high across the three EAC member countries. The governments should strive to improve the macro-economic environment to allow the citizens to engage in productive activities that generate income to alleviate household poverty and ultimately become empowered to contribute to the tax basket.

To policymakers in the health sector, funding allocations towards healthcare need to be increased. Other players, such as corporations and NGOs, can be approached to support the health sector through various resources or idea contributions.

The government should also aggressively promote productive economic activities by addressing the poverty level of the households to empower more potential taxpayers to contribute to the tax basket.

Suggestions for Future Research

The study applied fiscal /budget allocation as a percentage of the total budget framework to compute the public health care financing score. Future research could consider other methods of computing the public health care financing scores encompassing grants and donations that could yield different tax return results.

The study was based on East African countries pegged on longitudinal data sets for 1982–2022. Future studies could concentrate on other countries using shorter periods that could factor in the countries with incomplete data to increase the population and increase the accuracy of the results.

The study also analyzed public spending for health in composite; it would be useful to break down the PHF into different spending categories, including primary health care, terminal disease, preventive, curative care, and age brackets, among other categorizations, to see the direct relationship between those investments and tax revenues. As postulated by Grossman’s theory, one would anticipate that the highest contribution towards tax expansion would be the health of working-age citizens. There exists a need to granulate the PHF across various demographics and analyze the causal effect on the tax revenues.