Introduction

Before the 18th century, investment in human capital was very low and a country was reluctant to invest in schooling, job training, and other opportunities that can enhance the abilities of human beings (Goldin, 2016). However, scenarios began to alter dramatically as science was used to the invention of new items and more efficient production processes, first in Great Britain and then gradually extending to other countries. A country possesses greater human capital if its residents are more educated, healthy, and skilled (Robeyns, 2006). In other words, educated, well-skilled, and healthy human beings play a significant role in economic growth (Offem et al., 2017). Therefore, an educated, innovative, and creative workforce can put the economy on track to high productivity and sustainable economic growth. Therefore, the global economy is being transformed from a resource economy to a knowledge economy where human skills and trade secrets are considered more essential economic resources than natural or physical resources. In this Knowledge era, economies are more dependent on intellectual qualities to determine the type and rate of their economic and social growth (Xiao et al., 2013). Furthermore, the ability, skill, and knowledge of human beings provide a strong foundation for the economic prosperity of developing nations (Brinkley, 2006). The prosperity of a nation does not depend on investment in physical and technological resources only but also depends on investment in human capital to upgrade their knowledge and skill level. Therefore, a significant mechanism for the up-gradation of human capital is necessary to identify sustainable economic development.

It has been recognized worldwide that human capital is one of the major sources of a nation’s wealth (Pieper, 2012). A country with more human capital is said to be developed more rapidly as compared to a country that owned low human capital (Benhabib and Spiegel, 1994). Without a solid foundation of human capital, a nation cannot grow. Human capital provides the basis for economic development and is highly appreciated in a developed nation. However, it has been observed that poor countries are facing great challenges to boost their human capital development (Del Carpio, 2018). Therefore, for developing countries like Pakistan, it is essential to take all possible initiatives at every level for human capital development.

Experienced, talented, committed, and punctual human beings play a significant role to increase productivity and economic profits, so to have better prospects of economic growth and sustainability, the developing nation needs to invest more in their human capital rather than physical capital (Marimuthu et al., 2009). In this context, the purpose of this research is to determine the significance of key elements in the development of human capital in Pakistan.

In labor-surplus countries, such as Pakistan, the target of economic development is achievable by providing adequate education and health facilities and converting raw human resources into human capital (Abbasa and Foreman-Peck, 2007). Human capital formation is a process that converted human beings into valuable assets rather than an unwanted burden on the economy (Hendricks, 2002). Human capital is considered a renewable source of productivity, so government should invest more in human capital to increase their productivity and innovativeness (Lanzi, 2007). Moreover, individuals also spend on their health and education to participate in economic activities more appropriately.

Individuals and government are the main pillars of society (Shetter, 1971). Society is shaped by a group of people who belong to the same geographic territory and have common beliefs, interests, and purposes, and Govt. is the main actor of the society that is responsible to develop the education system and policies to convert their unskilled labor into human capital (Jongbloed et al., 2008). It has been observed that the govt. plays an important role to influence or shape the educational system in all its ramifications; so to think about the education system without govt. intervention in poor countries like Pakistan is impossible. Durkheim (1956) was one of the first sociologists who introduced the role of govt. as a society in the education system. The Govt. of each country, whether a country is rich, poor, traditional, advanced, simple, or complex, has commitment and dedication to developing its education system (A. Green, 1997). However, their aims, contents, and techniques to develop education systems and policies differ from one country to another. Moreover, a good educational system causes a transformation in the social system through changes in cultural norms, industrial development, and political, religious, and family structure.

In the existing literature, most of the studies investigated that FDI, use of ICT, and economic growth are essential factors that help nations to promote human capital like Gupta et al. (2019), Badri et al. (2019), and Wang (2011), but the role of government spending in human capital formation is less researched area. Therefore, this study aims to investigate the impact of government spending on education and health on human capital development.

Background of the study

Pakistan is one of the most heavily and densely populated countries in the world but unfortunately, it did not have proper planning at the national lave to utilize its human resources efficiently to boost the speed of economic growth (Pelinescu, 2015). Moreover, the rulers of Pakistan have very low concerns to implement a proper educational policy that convert the youth into skilled professionals and engage them in profitable trade or business. No doubt educational policies not only train the youth to play their desired role in society but also guarantee to improve of the quality of human life that ensure socio-economic growth in the country (Afzal et al., 2011). Asian countries like South Korea, China, and India achieved remarkable growth pace through agricultural and educational reforms (Balatchandirane, 2003). Unfortunately, in Pakistan, three land reforms presented 1959, 1972, and 1977 failed to remove the concentration of land among a few big landlords. Moreover, Pakistan allocates only 3.7% of its GDP to education while 9.7% of its GDP is for health expenditures that are inadequate to provide schooling and health facilities to the masses in Pakistan (Chaudhry et al., 2006). Therefore, the progress of human capital (HC) is miserable in Pakistan, especially when we compare it with the rest of the countries in the South Asian region. For instance, based on child mortality rate, health, and education, World Bank ranked Pakistan, India, Bangladesh, and Sri Lanka at 134, 115, 106, and 74, respectively, out of 157 countries of the world. Moreover, government spending on education, health, and social protection activities in Pakistan is presented in Fig. 1. The behaviors of graphs indicate that during the last two decades, there are huge fluctuations in government health and education spending and overall there is a decreasing trend in health spending while there is an increasing trend in spending on social protection and educational activities but still the literacy rate is far below than 50% in the country (Khawaja, 2018). However, during the same period, East Asian countries like the Republic of Korea and Malaysia spend on their human capital and achieved 98% and 90% literacy rates, respectively (Abbasa and Foreman-Peck, 2007). It has been also observed that well-developed human capital played an important role to provide increasing momentum to economic progress in East Asian economies (Mamoon and Murshed, 2009).

Fig. 1: Government expenditure trends.
figure 1

Government spendings on education, health, and social protection.

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According to World Bank, human capital project, a child born in Pakistan today can utilize only 39% of their potential while their productivity can be increased if he or she is provided with adequate health and education facilities; so Pakistan has the potential to do wonders in economic growth by concentrating on its 60% unexplored human capital wealth (Gatti et al., 2020). No doubt, 10–30% differences in living standard among different countries is attributed to the difference in human capital in these countries (Le et al., 2003).

Although, Pakistan restated its education policy for the last two years and the Government of Pakistan revised its commitment to provide free and compulsory education to the country’s children aged 5–16 with limited resources but still there is a need to revisit its spending on education and health activities. No doubt, Govt. is an important pillar of society that is responsible to provide the basic opportunities of survival to its citizens. The Govt. of a country can overcome the shortage of tangible capital by increasing the pace of human capital development through private and Governmental spending on education and health activities (Quinn, 2017).

The provision of education and health facilities is a basic human right and the state is responsible to ensure and secure this basic right for its citizens (Fredman, 2006). Though Govt. of Pakistan invest in health and education facilities to increase HC and generate fuel for growth, to impart skills among youth to enhance their earning abilities, and to convert its personnel into responsible and good citizen but high debt repayment, high and continuously increasing defense expenditure in the presence of military conflict with India, and unproductive foreign tours expenditures by Govt. officials are the main obstacles that lower the commitment of the state toward enhancement of human capital wealth.

Education and health as a form of human capital were introduced by Schultz (1961); who emphasized the role of health and educational institutions in human capital formation. According to recent literature (Akram and Khan, 2007; Khaliq and Ahmed, 2018), Govt. plays an important role to improve schooling opportunities, skill development centers, and opportunities for good health for the citizens (Aad et al., 2012). A higher standard of education and health leads to a higher rate of human capital production as the Human Development Index (HDI) rises (Anand and Ravallion, 1993). Higher HDI implies that the country possesses high human capital with good health and education opportunities, and has greater chances to grow its per capita income more rapidly.

Existing literature (Afzal et al., 2011; M. A. Khan and Rehman, 2012) in the context of Pakistan, mostly highlighted the importance of human capital in the economic growth of Pakistan; for instance, Abbas and Mujahid-Mukhtar (2000) stated that human capital proxies by secondary school enrollment play a significant and positive impact on the economic growth of Pakistan. Similarly, Afzal et al. (2012) documented a direct relationship between school education and economic growth in Pakistan. However, a few studies described the role of government education spending in human capital, for instance, Jung and Thorbecke (2003), explained that Zambia and Tanzania can get maximum benefits from education expenditure only if they invest a sufficiently high level of investment in physical capital and a well-targeted pattern of education expenditure can be effective for poverty alleviation. Similarly, Patel and Annapoorna (2019) stated that public education expenditure causes human capital formation in India.

Therefore, existing research on human capital revealed that most of this subject remained a part of the theoretical debate (Anderson, 2008) and empirical literature investigated the impact of human capital on economic growth (Abbas and Foreman-Peck, 2008; Galor and Tsiddon, 1997; Pelinescu, 2015), but how human capital can be developed and what is the role of public spending on health and education sector in human capital extension is less researched area.

A few empirical studies were done in Pakistan on the determinants of human capital like N. H. Khan et al. (2019) who selected various variables namely ICT, economic growth, urbanization, trade, and FDI as a determinant of human capital. Whereas, Qasim and Chaudhary (2015) explained the role of social infrastructure, remittances, industrialization, and population density in human capital development. Therefore, this study aimed to bridge the gap in the literature and attempt to investigate the impact of government spending on human capital formation. Secondly, government spending on social protection and human capital formation is neglected in empirical research. Most of the empirical research highlighted the importance of social protection in poverty alleviation (Bakhshinyan et al., 2019; Zafar et al., 2021) and in economic growth (Piachaud, 2013) but the role of social protection programs in human capital formation is overlooked. However, a few empirical studies conceptualize a framework that suggests how social protection can play a key role in supporting human capital development in poor nations where people spend their income on their daily survival (Holzmann and Jørgensen, 2001). Moreover, World Bank’s Social Protection Strategy 2012–2022 emphasizes the importance of social protection policy in promoting and protecting human capital (Bank, 2013). Social protection programs help the poor’s against negative shocks and provide them with a sustainable source to support their children’s health and education activities (Devereux, 2002). Cash transfer from the government on regular basis to the poor can build the human capital foundations, health, nutrition, and education, which are necessary for people to have productive working lives. Therefore, this study aims to contribute to the literature by investigating empirically the link of social protection with human capital formation. Hence the aim of this study is twofold: first to examine the role of government expenditure on health and education in human capital formation and second to examine the role of social protection policy in human capital development.

Literature review

Education plays a significant role in the economic and social progress of a country; it imparts knowledge and skills to the general public and makes them beneficial for the country (Ozturk, 2008). Education has a critical role in conveying and fostering values, which, in turn, influence the behaviors, attitudes, and reactions of responsible citizens (Idris et al., 2012). As the Govt. invests more in education, a better-educated population leads to rapid and far more sustainable development. Ample studies are available on Govt. expenditure on education and economic growth (Abhijeet, 2010; Carpentier, 2003; Chandra, 2010; Hussin et al., 2012; Mallick and Dash, 2015; Muktdair-Al-Mukit, 2012; Ozatac et al., 2018; Urhie, 2014). However, this study found a few studies on Govt. expenditure on education and human capital that are reviewed in the following section.

Okafor et al. (2017) conducted a study on the effect of Govt. expenditure on human capital development in the country of Nigeria and found that Government expenditure on health and education does not significantly promote the human development index (HDI) in the context of Nigeria. On contrary, Patel and Annapoorna (2019) found in India by utilizing the data from the Ministry of Human Resource Development and UNDP from 1990 to 2014 that public education expenditure significantly influenced HDI in India. Similarly, Edeme (2019) conducted a study on the composition and distributional impacts of Public Expenditure on education and HDI by utilizing the data from UNDP and World Development Indicators from 2007 to 2017 and the study found that education, and health expenditures, significantly increase HDI. Similarly, Maharda and Aulia (2020) also concluded in the context of Indonesia that Government health expenditure (GEX) on education and health plays a significant role in the up-gradation of HDI. Therefore, overall the impact of Govt. spending on education on HC formation produces mixed results; so there is still a dire need to examine the role of Govt. expenditure on education in human capital.

Similarly, most of the studies examined and discussed the role of social protection policies in financial crises and how social protection develops resilience to adverse shocks (Bowen et al., 2020; Marzo and Mori, 2012). A few studies establish a theoretical link between social protection and human capital. For instance, Moroz (2020) presented a discussion paper on “The Role of Social Protection in Building, Protecting, and Deploying Human Capital in the East Asia and Pacific Region” and highlighted how social protection plays an important role to build, protect and deploy human capital. The study also painted that cash payments help to improve human capital foundations and health and education insurance to protect human capital during the period of negative shocks in East Asia and Pacific Regions. Béné et al. (2018) stated that adaptive protection policies help the household to overcome unwanted shocks and keep continuity in human capital formation activities. Another study conducted by Hidrobo et al. (2018) on social protection and its impact on food security and asset formation explained that social protection causes to increase in livestock, non-farm productive assets, and savings by individuals and increase their capacity to spend on their children education and health activities. Moreover, the study concluded that it has been observed that the quantity and quality of food intake by the social protection policies beneficiaries has been also improved which helps to improve the health and cognitive abilities to learn (Eide and Showalter, 2011).

Theoretical background

The Human Capital Theory was presented by Becker (1962) and Rosen (1976), who stated that the provision of training and education facilities enhances individual workers’ skill sets or abilities. The first book titled “A Theoretical and Empirical Analysis with Special Reference to Education” was written by Gary S. Becker to model that the development of knowledge or skills is a function of investment. The Human Capital Theory states that educational and training expenditures are investments made by individuals or by the Govt. to raise the current and expected level of human capital. According to Human Capital Theory, employees with a particular skill set and health are more efficient as they proved more regular and competent at running specific technology or manufacturing a type of product.

The conceptual framework is also supported by the Musgrave theory of Public finance which was presented by Musgrave (1969) who stated that at the initial stage of development, the demand for public goods like health and education facilities is low from the citizen of a country but as the per capita income starts to increase, the people started to demand more government investment on health and education facilities that contribute in the human capital of a country. Moreover, this theory established the importance of government expenditures in human capital, specifically theory stated that the government of a country can enhance its human capital by spending more on health and educational facilities. Similarly, Musgrave and Musgrave (1989) established that it is the role of the government to provide goods and services that cannot be provided through the transaction between the seller and the buyer in the market system due to market failure; for instance, the government should provide public goods like health and educational facilities to promote human development.

Moreover, in the private sector, the price of public goods like health and education is very expensive due to imperfect competition. This study contributes to the human capital theory and public finance theory in two ways; first, this study extends the human capital theory by establishing a link between human capital with government spending on education, health, and protection. Secondly, this study also contributes to public finance theory by establishing that government is not only spending on education and health facilities to promote human capital but it also spends on protection programs like scholarships and other financial schemes that help individuals against adverse and sudden shocks that limit the financial expenditures of an individual’s on their children, health, and education. Based on the above two theories, this study designed the conceptual framework of this study and presented it in Fig. 2.

Fig. 2: Conceptual framework.
figure 2

Factors affecting human capital formation.

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Data and results

Variables and sources of data

Variables used in the present study are human capital, private health expenditure, domestic government health expenditure, government education expenditure, social protection, population growth, and foreign direct investment. Human capital is a dependent variable while all other variables are the independent variable. Time series annual data is utilized from 1971 to 2020; whereas the data of all variables were collected from World Development Indicators (WDI). The detailed description and measurements of all variables are given in Table 1 (See appendix).

Unit root test

Before the empirical investigation, it is necessary to ensure the stationarity of the time series data; it implies that the mean and variance of the time series data remain the same over time. This study confirms the stationarity of each series by conducting Augmented Dickey–Fuller Test (ADF) proposed by Dickey and Fuller (1979). The augmented Dickey–Fuller Test produces highly reliable results if the study expects that the error term is not white noise and there is a high order of auto-correlation exists. This study reported the summary of the Unit Root Test with intercept and with the trend and intercept in Table 1. The results of the ADF indicate that all variables are stationary at first difference except GEE, FDI, and SP which are stationary at level. Stationary at level means that the mean, variance, and covariance of GEE, EFI, and SP are constant at a level while stationary at first difference means that mean, variance, and covariance of current health expenditure, domestic government health expenditure, and population growth are constant at first difference.

Table 1 Augmented Dickey–Fuller (ADF) unit root test.
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The results of descriptive statistics are presented in Table 2 and the probability value of Jerqa Bera indicates that all variables are normally distributed at a 5% level of significance. Moreover, the mean value indicates the on-average behavior of all variables that range from 0.76 to 96.70; on average net inflow of FDI (% of GDP) is 0.76 while the mortality rate, infant (per 1000 live births) on average is 96. The value of skewness is positive for HC1, HC2, HC3, HC5, CHE, SP, and FDI which indicates that all these variables are asymmetrically distributed with a long tail on the right side while other variables like HC4, DGHE, GEE, and population has negative values for skewness which indicate that these variables are asymmetrically distributed with a long tail on the left side. Similarly, the values of Kurtosis are positive for all variables which indicate that the distribution of data is more peaked than the Gaussian distribution. Moreover, this study presented the results of correlation analysis in Table 3. Results indicate that all values of correlation among modeled independent variables; CHE, DGHE, GEE, POP, and FDI, are <0.7 which indicates that there is no multi-collinearity among the independent variables (Mansfield and Helms, 1982), so this study can proceed with the model and model can produce reliable results. Moreover, the results of correlation in Table 3 indicated that HC1, HC2, HC3, HC4, and HC5 are highly correlated with each other because correlation values are high than the threshold value of 0.7; so to control the multi-collinearity, this study developed five models and utilized HC1, HC2, HC3, HC4 and HC5 in a separate model as the dependent variable.

Table 2 Descriptive statistics.
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Table 3 Correlation analysis.
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After testing the Unit Root Test, this study employed the ARDL Bound Test to examine the long-run Co-integration among modeled variables, and the results are reported in Table 4. Results of the ARDL bound test indicate that long-run Co-integration exists in five defined models because the F statistics value is more than the lower and upper bound of critical values for all five models. After confirmation of long-run Co-integration among series, ARDL Co-integrating and Long Run Form is utilized to investigate the long-run and short-run estimates. Moreover, the study employed residual diagnostic tests like the Breusch–Godfrey Serial Correlation LM Test and Breusch–Pagan–Godfrey LM Tests to test heteroscedasticity results are reported in Table 5. Results ensure no Serial Correlation and no Heteroscedasticity exists in models 1–5. Similarly, the results of the Ramsey reset test ensure that all models are appropriate and reliable. Moreover, Fig. 3 represents the CUSUM test that also ensures that models are stable.

Table 4 ARDL bound test.
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Table 5 Diagnostic test.
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Fig. 3: CUSUM test.
figure 3

CUSUM test for stability.

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Model specification

it is necessary to check the long-run co-integration among the series before empirical investigation. Traditionally, Engle and Granger (1987) or Johansen (1988) are used to investigate the long-run cointegration among series but Engle–Granger and the maximum-likelihood-based Johansen methods may produce biased results when some series are stationary at a level while some are stationary at the first difference (Engle and Granger, 1987; Johansen, 1988). However, ARDL bound test produces unbiased estimates related to long-run cointegration when series are stationary at mix order.

Autoregressive distributed lag (ARDL) bound test method was proposed by Pesaran et al. (2001), the simple form of ARDL (1.1) is given below

$$Y_t = \alpha _0 + \alpha _1\cdot Y_{t - 1} + \beta _0\,X_t + \beta _1\,X_{t - 1} + \varepsilon {{{\mathrm{t}}}}$$
(1)

In Eq. (1), on the right side of the equation, there are independent variables, independent variables with lags that can affect the dependent variable, and the autoregressive lagged value of the dependent variable that can affect its current value. Moreover, Eq. (1) indicates that both independent and dependent variables have the lag order of 1, so the regression coefficient of X in the long-run equation and ECM equation can be expressed as follow:

$$\begin{array}{l}{{{k}}} = \beta _0 + \beta _0/1 + \alpha _1\\ \Delta Y_t = \alpha _0 + \left( {\alpha _1 - 1} \right)\left( {Y_{t - 1}-{{{k}\,{X}}}_{t - 1}} \right) + \beta _0\,\Delta\, X_{t - 1} + \varepsilon {{{\mathrm{t}}}}\end{array}$$
(2)

Therefore, a general ARDL (p0, p1, p2, p3,…, pn) model for one dependent variable Y and a set of independent variables X1, X2, X3,…, Xn, in which p0 is lag of dependent variable and p1–pn are lags of the independent variable can be written as follows:

(3)

To explain the systematic impact of CHE, DGHE, GEE, SP, POP, and FDI on human capital proxied by HC1, HC2, HC3, HC4, and HC5; this study applied the following ARDL equations to examine the short-run and long-run relationship among modeled variable. The specific equation is given below:

(4)
(5)
(6)
(7)
(8)

This study utilized ARDL bound test procedure to identify the long-run co-integration among the variables based on the Unrestricted Error Correction Model (UECM) form of ARDL that can be written as follow for HC1, HC2, HC3, HC4, and HC5.

(9)

The null hypothesis of the Unrestricted Error Correction Model is, δ0 = δ1 = δ2 = δ3 = δ4 = +δ5 = +δ6 = 0 (there is no co-integration) while the alternative hypothesis is δ0 ≠ δ1 ≠ δ2 ≠ δ3 ≠ δ4 ≠ δ5 ≠ δ6 ≠ 0 (there is co-integration) and values of F statistics are reported in Table 4. This study adopts an alternative hypothesis because the values of F Statistics for all five Models are greater than lower and upper bound values.

After confirmation of long-run cointegration, this study applied ARDL co-integration technique to estimate short-run and long-run estimates among modeled variables for its several advantages; first ARDL produce statistically significant estimates in case of small sample size, second, it allows various lag order of all variables and third, it produces a single equation for short-run and long-run estimates than a set of the equation like other techniques. This study employed 5 models by utilizing five different measurements of human capital and the results are reported in Table 6. Moreover, this study utilized automatic lag selection criteriawhile employing ARDL Model 1,

Table 6 ARDL cointegrating and long run form.
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Model 1

In model 1, this study utilized education attainment at least primarily completed as an indicator of human capital to capture skilled labor and investigated the impact of CHE, GDHE, GEE, POP, and FDI on human capital in the short-run and long-run periods. Results indicate that CHE, DGHE, and SP have a positive and significant association with educational attainment in terms of primary completion in the short-run as well as in the long-run period (β = 2.73*, β = 2.97*, β = 2.41*, β = 13.12*, β = 5.93*, β = 11.59*) while the population has a significant and negative association with human capital in the short run and long run period (β = −8.44*, β = −8.29*). Moreover, FDI has a positive and significant association with human capital in the short run while a negative and insignificant association in the long run (β = 1.72*, β = −1.36). The results of model 1 explained that as CHE, GDHE, and SP increase, educational attainment in terms of primary completion will also increase while as population increases, educational attainment in terms of primary completion will decrease. Similarly, in the short run increases in FDI will significantly increase educational attainment in terms of primary completion while in the long run, FDI has no significant impact on educational attainment in terms of primary completion.

Model 2

In model 2, this study utilized education attainment at least secondary completed as an indicator of human capital to capture skilled labor and investigated the impact of CHE, GDHE, GEE, POP, and FDI on human capital in the short run and long-run period. The results indicate that CHE, and DGHE, have a positive and significant association with human capital in the short-run (β = 2.28*, β = 0.58*) as well as in the long-run period (β = 2.71*, β = 1.47*, β = 1.71*). However, FDI has a positive and significant association with secondary attainment in the short run (β = 0.81*) while in long run it has an insignificant association with human capital in terms of secondary completed (β = −0.31). Therefore, the results of model 2 explained that as CHE, and DGHE increase, HC will also increase in the short run and long run period while FDI significantly increases HC in the short run while in the long run period; it has no significant association with HC.

Model 3

In model 3, this study utilized educational attainment, at least completed short-cycle tertiary as an indicator of human capital to capture skilled labor and investigated the impact of CHE, GDHE, GEE, POP, and FDI on human capital in the short-run and long-run period. The results indicate that CHE, DGHE, SP, and FDI have a positive and significant short-run association with HC (β = 4.82*, β = 2.49*, β = 1.66*, β = 2.90*), while in the long run DGHE, GEE, SP has a positive and significant association with HC in term of a completed short-cycle tertiary education (β = 4.62*, β = 3.51*, β = 10.77*). Results of Model 3 explained that as CHE, DGHE, SP, and FDI increase, HC will also increase in the short run period while in the long run period, increases in DGHE, GEE, and SP will increases HC.

Model 4

In model 4, this study utilized life expectancy at birth total years as an indicator of human capital to capture the health of human beings and investigated the impact of CHE, GDHE, GEE, POP, and FDI on human capital in the short run and long-run period. The results indicate that CHE, GEE, and SP has positive and significant association with human capital in term of life expectancy at birth in the short-run period (β = 0.01*, β = 0.20*, β = 1.41*), while in the long run CHE, DGHE, GEE, and SP has a positive and significant association with human capital in term of life expectancy at birth (β = 4.18*, β = 4.43*, β = 2.13*, β = 7.11*). Results of Model 4 explained that as CHE, GEE, and SP will increase, life expectancy at birth will also increase while in the long run period, as CHE, DGHE, GEE, and SP will increase, life expectancy at birth will also increase.

Model 5

In model 5, this study utilized child mortality as an indicator of human capital to capture the health of human beings and investigated the impact of CHE, GDHE, GEE, POP, and FDI on human capital in the short run and long-run period. The results indicate that DGHE, GEE, and SP had a negative and significant association with child mortality in the short-run and long-run period (β = −0.22*, β = −1.11*, β = 7.44*; β = −3.43*, β = −1.13*, β = −7.11*) while CHE has insignificant impact on child mortality in the short run while in the long run period, it has a negative and significant association with child mortality (β = 0.11, β = −5.18*). Moreover, POP has a positive and significant association with child mortality in short-run and long-run periods (β = 7.23*, β = 6.04*). The results of model 5 explained that an increase in DGHE, GEE, and SP will significantly lower child mortality in the short run and long run period while the increase in CHE will significantly lower child mortality in the long run period while it has no impact in the short run period on child mortality. Similarly, results explained that an increase in population significantly increases child mortality in the short run as well as in the long-run period.

Moreover, the value of ECM for all five models indicated that models are stable and if any shock destabilizes the variables from their trend growth path models 1–5 will converge to the equilibrium at the speed of 20%, 115%, 15%, 0.4%, and 0.3%.

Discussion

This study examines the long-run impact of current health expenditure, domestic government health expenditure, government education expenditure, population growth, FDI, and social protection on human capital formation. The empirical investigation is done by utilizing ARDL bound test. The results of this study indicated that current government spending on health and education has a positive and significant association with human capital in terms of primary, secondary, and tertiary education attainment. Our results are consistent with Lafortune et al. (2018) and Jackson et al. (2015) who have established that government school spending strongly and positively affects student outcomes. Similarly, F. Green et al. (1999), advocated that government investment at the primary and tertiary levels is more effective for skill development and promotes knowledge, efficiency, and inventions that help the economies to progress (Maitra and Mukhopadhyay, 2012). Moreover, this study also found that government spending in the health and education sector positively and significantly increases life expectancy at birth and significantly decreases child mortality in Pakistan.

Our results are consistent with Kiross et al. (2020) who have conducted a study on public health expenditure and health outcomes in the context of Sub-Sahara Africa and established that the level of health expenditure plays a significant role to decrease child mortality among children under the age of 5 years. The logical inference suggests that in developing nations due to high poverty, people are unable to spend more on their children’s health; so the government of the poor countries is responsible to provide basic health services to their citizens and play a significant role to achieve the goal of reduction in child mortality and increase in the life expectancy at birth. Similarly, social protection has a positive and significant long-run association with human capital in terms of primary, and tertiary education attainment, and life expectancy at birth and a negative and significant association with child mortality in long run. In developing countries, education incurs certain direct, indirect, and opportunity costs that limit the access of the poor to education. Government social protection programs help to alleviate financial barriers and promote children’s access to schooling and health facilities (Adato and Hoddinott, 2008).

Furthermore, this study found that population growth has a negative and significant association with primary attainment. Results suggest that as population growth increases the human capital in terms of primary attainment will decrease. The reason behind these findings is as the population increases, more schools, more teachers, more hospitals and more funds will be required for the education and the health sector; so overcrowded schools may spread negative feelings and attitudes among the students and they left school without primary completion. Similarly, the overcrowded hospital may limit the health facilities available to a newly born baby which may cause severe infectious diseases to newly born babies (Fotso et al., 2007). Our results are consistent with Ding (1983) who established that high population growth exerted huge pressure on the education system and strongly affect the education outcome in China. Furthermore, results revealed that FDI is positively and significantly associated with human capital accumulation. Results indicate that more FDI will increase human capital. Results are consistent with Choudhuri and Desai (2021) who have established a link between FDI and human capital. It has been observed that there is a bidirectional relationship between FDI and human capital, for instance, the countries that possess human capital have more ability to attract FDI and FDI is more productive in the countries where human capital is higher.

Conclusion

This study examines the long-run impact of current health expenditure, domestic government health expenditure, government education expenditure, population growth, FDI, and social protection on human capital formation. The empirical investigation is done by utilizing ARDL bound test. The results of this study indicated that current health expenditure has a positive and significant association with educational attainment, at least completed primary (HC1), educational attainment, at least completed lower secondary (HC2), educational attainment, at least completed short-cycle tertiary (HC3), and Life expectancy at birth (HC4) in the short run period while in the long-run period, current health expenditure has a positive and significant association with educational attainment, at least completed primary, educational attainment, at least completed lower secondary, Life expectancy at birth, and mortality rate, infant (per 1000 live births) (HC5). Moreover, this study also concluded that domestic government health expenditure has a significant association with HC1, HC2, HC3, and HC5 while in the long run period, domestic government health expenditure has a significant association with Hc1, HC2, HC3, HC4, and HC5. This study also found that government education expenditure (GEE) has a significant association with HC4, and HC5 while an insignificant association with HC1, HC2, and HC3 in the short run period while GEE has a significant association with HC2, HC3, HC4, and HC5. Similarly, this study also found that social protection (SP) has a positive and significant association with HC1, HC3, and HC4 while a negative and significant association with HC5 in the short run and long run period. Moreover, the population has negative and significant associations with HC1 while positive and significant associations with HC5 in the short run and long-run periods. Furthermore, FDI has a positive and significant association with HC1, HC2, and HC3 in the short-run period.

Implications of the study

The findings of this study imply that government should adopt an objective-based expansionary fiscal policy to obtain better health and educational outcomes in Pakistan. Moreover, the finding has a clear implication that sustainable public finances for education and health activities help the country promote human capital. The finding of the study suggests that Govt. should allocate more budget to creating health facilities and educational opportunities for human capital development. Similarly, findings also suggest that social protection programs alleviate the financial barriers and can promote human capital development in Pakistan. Similarly, the findings of this study also suggest that government should adopt policies to control the population and to enhance foreign direct investment in Pakistan because population growth and FDI have a strong and significant impact on human capital in Pakistan.