In Search of Pakistan’s Inclusive Growth: Evidence from Income and Non-Income Dimensions

Abstract

Inclusive growth (INCL_GRTH) is a process through which economic opportunities are available for all segments of society without any discrimination between rich and poor. The aim of this study is to find how socio-economic factors affect poverty and how we may achieve INCL_GRTH and reduce poverty in a country like Pakistan. The study has used a poverty headcount ratio as a proxy for INCL_GRTH while the number of income and non-income poverty factors, including, per capita GDP, FDI inflows, agriculture value added, health expenditures, income inequality and education expenditures are highlighted as explanatory factors. The results show that poverty is substantially reduced by increasing education expenditures and FDI inflows. The pro-poor growth estimates have confirmed that except agriculture value added, the remaining factors show anti-poor growth in a country, while agriculture value added gives marginal benefits to the poor as compared to the non-poor between the two time periods studied, that is 1980 and 2016.

Keywords

Inclusive growth poverty income inequality health expenditures education expenditures pro-poor growth Pakistan

Introduction

Inclusive growth (INCL_GRTH) is a process in which the economy grows in such a way that it creates new opportunities that are available to all, irrespective whether people are rich or poor (World Economic Forum, 2017). The fundamental requirement of INCL_GRTH is that an economy should strive hard to achieve a balanced economic growth (ECO_GRT), with a judicious income distribution, and high social expenditures that favour the poor as compared to the non-poor (Akinyemi et al., 2019; Bhattacharya & Haldar, 2017). The development of the agriculture sector is vital for overall ECO_GRT as more than 60 per cent of Pakistan’s population, directly or indirectly, is associated with this sector and their livelihood depends upon farming and agricultural raw products. Indeed, it is possible that this sector may create economic harmony by stimulating employment, especially for rural areas where the population depends upon agricultural income for sustenance and reduce poverty (POV) levels by enlarging agricultural yield for the country (Ghanem, 2015).

Achieving INCL_GRTH is a dream for Asian countries desperate to reduce income disparity between the rich and the poor, and reduce their POV profile by encouraging a balanced ECO_GRT. Asian countries largely lag behind the INCL_GRTH process due to a wide inequality in income (INC_INEQ) and a rising POV profile which negatively impacts the pro-poor growth (PPG) process in a country. Pakistan is no exception as high INC_INEQ and POV sabotages the process of INCL_GRTH (Khan et al., 2020) in this nation. There are a number of scholarly works available on the growth-inequality-poverty (GIP) nexus across other countries too. Zaman and Khilji (2014a) in their studies confirm the role of sectoral value added in Pakistan’s INCL_GRTH and have proposed the need for effective social action policies that support the poor as compared to those available to the non-poor. Zaman and Khilji (2014b) have expanded the pro-poor growth index (PPGI) by including non-income POV factors, and have proposed a new PPGI, called a pro-poor social index (PPSI). They have concluded that the index is a better tool in terms of assessing a pro-growth and INCL_GRTH phenomena as it notes varied different non-income factors, including, health, education, social infrastructure and so on that give support to the poor as compared to the non-poor. Zaman (2015) has considered different household surveys for Pakistan by evaluating non-income determinants by expanding the pro-poor tool box and confirming both the absolute and relative PPGI in social indicators, human related factors, social safety nets and infrastructure. That study has proposed certain policy implications to sustain the lives of the poor by following pro-growth and pro-equality policies in a country. Hassan et al. (2015) have considered the role of the GIP triangle in Pakistan’s environmental degradation and found long-term and causal mechanisms between them. Zaman et al. (2015a) have estimated national, rural and urban working POV for Pakistan and has concluded that the working poor headcount increases over a period of time in proportion to the decrease in the pace of inclusive growth policies in a country. Zaman (2016a) has argued that POV globally is intensified with an imbalance in economic activities, including low spending on social expenditures, poor healthcare reforms and low spending on education; thus it is imperative to concentrate on social policies and provide a social safety net for economic resource balance between the rich and the poor, hence the process of INCL_GRTH may be achieved with strategic and policy-oriented action across countries.

Zaman and Shah (2016) have examined the relationship between INC_INEQ, ECO_GRT and social expenditures in the context of Pakistan. They have concluded that income disparities between the rich and poor may slowdown the process of ECO_GRT through the channel of low social expenditures which may trickle down by foreign and domestic investment for long-term sustained growth, and it may support PPG policies with judicious income distribution. Thus, social channels might be helpful in achieving INCL_GRTH across countries. Zaman (2016b) has further supported the non-linear PPG model and refined this index for more policy-oriented and robust interventions to evaluate PPG in Pakistan. A recent study by Zaman and Shamsuddin (2018) has further investigated the GIP nexus among select countries of Latin America and the Caribbean and proposed a non-linear model for the assessment of PPG. They concluded that social expenditures may reduce POV by the inclusion of the non-income pro-poor tool box which is desirable to support a judicious income distribution across countries. Khan et al. (2017) have supported the pro-equality growth arguments in a diversified panel of countries and argued that the large disparity of income between rich and poor substantially affects the PPG process which directly leads to increased imperfections in education, health and labour markets. Thus, it is imperative to reduce INC_INEQ to achieve INCL_GRTH across countries.

Shaheen et al. (2017) has examined the long-term relationship between POV, terrorism and ECO_GRT through a simultaneous equations modelling in the context of Pakistan and found that POV increases with growing incidences of terrorist attacks through the channel of education and ECO_GRT. The reasoning is that as terrorists largely use educated persons, capable of devising a plan for terrorist activities, it is therefore desirable to control the socio-economic and human cost factors which may be used in terrorist activities for achieving wrong objectives. Thus, the process of INCL_GRTH may impact the killing of innocents by wrong terrorist acts which should be stopped by putting into place strategic plans and consistent efforts by the establishment. The proposed index provides a basis of INCL_GRTH and has emphasised the need to use non-linear dynamics in the GIP relationship.

Table 1 shows the recent literature available on the GIP triangle and PPG across countries. The cited literature confirms the role of income and non-income POV factors that support INCL_GRTH.

Table 1.

Current Literature on GIP Triangle and PPG Estimates.

Authors	Time Period	Country	Results	Conclusion
Ashrafi et al. (2019)	1995–2016	Iran	The two different time periods exhibit PPG, that is, 1995–1999 and 2000–2004 while the remaining periods, that is., 2005–2010 and 2011–2016 shows anti-poor growth (ANTIPG) in a country.	Inclusive growth (INCL_GRTH) can be achieved by PPG and pro-equality growth arguments in a country.
Khan et al. (2019a)	1975–2016	Pakistan	The PPG is found between 1975–1985 and 2005–2016 while ‘immiserising growth’ is found between 1985–1995 and 1995–2005.	Achieving INCL_GRTH is possible through environmental sustainable development in a country.
Khan et al. (2019b)	2007–2013	Bolivia	Only the service sector shows PPG between the two stated time periods.	Income inequality is the main factor that sabotaged the process of INCL_GRTH in a country.
Zaman et al. (2019)	2010–2013	124 countries	75 countries show PPG, 8 countries show ANTI-PG while the remaining countries show ‘immiserising growth’.	Increasing job opportunities and poor income can breakdown the vicious cycle of POV to achieve INCL_GRTH.
Young et al. (2019)	1970–2018	Nigeria	Young et al. (2019) 1970–2018 Nigeria In general, the country shows anti-poor ANTI-PG between the two time periods, that is, 1970 and 2018	Rationale income distribution, continued economic growth, and PPG policies are imperative for INCL_GRTH.
Michálek and Výbošt’ok (2019)	2005–2015	28 EU countries	Few countries have a successful story to reduce inequality and POV by their aggregated wealth, while in majority of the case, the growth process is not fairly PPG.	Global financial crunch led to decrease countries ECO_GRT that resulting impact on increase POV and INC_INEQ, which is the hurdle against achievement of INCL_GRTH.
Doumbia (2019)	1975–2012	112 countries	In general, growth is fairly pro-poor; however, it is not inclusive, as low income group unable to get the fruits of aggregated wealth to reduce POV.	The role of government effectiveness could play a role to achieve INCL_GRTH across countries.
Arabiyat et al. (2020)	1990–2015	Jordan	Pro-poor trade policies helpful to reduce POV and inequality in a country.	Trade liberalisation policies could be achieved INCL_GRTH agenda.
Liu et al. (2020)	1980–2017	Pakistan	Agriculture and foreign remittances both play a vital role to reduce rural POV in a country.	To achieve INCL_GRTH, country should increase agriculture value added and attract more foreign remittances to alleviate POV.
Asongu and Odhiambo (2020)	2000–2012	44 sub-Saharan African countries	Governance dynamics negatively impact on environment in the form of exacerbation of carbon emissions that further negatively affect the inclusive growth process across countries.	Regulation quality and institutional governance framework should be improved in order to progress towards inclusive development.

Source: Author’s extraction.

This study has proposed the following research questions in order to devise long-term PPG policies to achieve INCL_GRTH agenda in a country: first, does a country’s per capita income decreases its levels of poverty and inequality? This question is important in terms of evaluating the INCL_GRTH agenda, as a country’s income is considered to be the main factor that may increase job opportunities and pro-equality arguments in order to alleviate poverty and inequality in a country. The second research question: to what extent do social expenditures decrease poverty and achieve an INCL_GRTH agenda? It is evident that social expenditures will provide educational opportunities and healthcare facilities to the poor and non-poor income group which ultimately benefit the poor more as compared to the non-poor. A trickle-down impact will help achieving an INCL_GRTH agenda in a country. Finally, the third question: does agriculture value add and FDI inflows support PPG and poverty reduction in a country? Pro-poor agriculture growth policies may be helpful in increasing the income of the poor with the availability of fair agricultural food prices and subsidies that could remove the impoverished out of a poverty trap.

The objective of the study is to examine the role of per capita income, inequality, health expenditures, education expenditures, agriculture value added and FDI inflows on POV reduction in Pakistan by using a consistent time series data from 1980 to 2016. The study has further used a poverty interdependence growth index (PIGI) as proposed by Zaman and Shamsuddin (2018) to assess the PPG process between the two time periods, that is, 1980 and 2016.

Material and Method

Table 2 shows the list of variables and their expected relationships. A poverty headcount has served as a dependent variable, while explanatory variables are per capita incomes, INC_INEQ, health expenditures, education expenditures, FDI inflows and agricultural value added. The data is taken from World Bank (2016) for Pakistan economy.

Table 2.

List of Variables

Variable	Symbol	Measurement	Expected Sign
Poverty	POV	Headcount ratio in %
GDP	GDP_PC	Per capita GDP in constant 2010 US$	Negative
GINI coefficient	GINI	%	Positive
Health expenditure	HEXP	% of total health expenditures	Negative
Education expenditure	EDU	% total education expenditure	Negative
FDI	FDI	% of GDP	Negative
Agriculture value added	AGRGDP	% of GDP	Negative

Source: World Bank (2016).

The growth-inequality-poverty (GIP) triangle is extensively calculated by earlier literature. Bourguignon (2004) is one who emphasised the need of effective intervention in economic policies to get benefits for the poor in the form of provision of social safety nets and spending on pro-poor infrastructure which ultimately transferred into a lower income strata group. Figure 1 shows the Bourguignon style of the GIP triangle formulation and its transmissions mechanism which was further extended by including social expenditures, agriculture value added, and FDI inflows, which give more insights to formulate the PPG index (PPGI).

Figure 1.

Source: Adapted by Bourguignon (2004) and extended by the author.

Figure 1 shows that the GIP triangle has a wide connection between them. As Kakwani and Son (2002) have rightly mentioned all three factors altogether form a PPGI, and even if one of them is out of the nexus, the PPG formulation cannot get a meaningful inference. Bourguignon (2004) has presented the arithmetic form of the GIP triangle where the poverty headcount can be influenced by continued ECO_GRT and INC_INEQ. Bourguignon has concluded that ECO_GRT will be helpful in decreasing poverty, but it’s negatively affected by high INC_INEQ thus pro-growth and pro-poor policies need a judicious income distribution to reduce poverty across countries. The study has extended the same concept and has included agriculture value added, social expenditures and FDI inflows in the GIP nexus in order to gain more insights in the given relationship that will make it easier to formulate PPG policies for a country like Pakistan. The results have indicated that the inclusion of the three stated factors in the GIP triangle will have a positive impact on ECO_GRT and will be helpful in decreasing INC_INEQ, thus ultimately decreasing poverty incidence in a country. For this purpose, the study has used the following equation to analyse the INCL_GRTH agenda in a country:

\begin{matrix} \ln (P O V) = α_{0} + α_{1} \ln (G D P_{P C}) + α_{2} \ln (G I N I) + α_{3} \ln (H E X P) + \\ α_{4} \ln (E D U) + α_{5} \ln (A G R G D P) + α_{6} \ln (F D I) + ε \end{matrix}

(1)

where POV shows poverty headcount, GDP_PC shows per capita GDP, GINI shows income inequality, HEXP shows health expenditures, EDU shows education expenditures, Agriculture Gross Domestic Product (AGRGDP) shows agriculture value added, FDI shows FDI inflows, ‘ln’ shows natural logarithm, and ε shows an error term.

Equation (1) is estimated by a unit root test, cointegration process and robust least square regression. The Augmented Dickey-Fuller (ADF) unit root test is employed to check the order of integration of the given variables, that is, if the given variable is at a significant level, while it is significant at the first difference, then the integration process will be the first order of integration, that is, I(1). The reverse is true for a zero order of integration as if the given variable is significant. After analysing the order of integration by a unit root test, the study proceeds for a Johansen cointegration test and has found that the number of cointegration equations in a given model, that is, if there is more than one cointegration equations, then we consider that a long-run relationship exists between the variables. Finally, the study has employed a robust least square regression which gives three different estimation strategies, including (a) an ‘S’ estimation that minimises the possible outliers from the ‘response variable’, (b) an ‘M’ estimation that accounts for explanatory variables to absorb possible outliers and (c) a, ‘MM’ estimation that reduces outliers from both the dependent and explanatory variables. This study has used an ‘MM’ estimation technique to reduce the possible outliers from the given model for robust inferences. After the parameter estimates, the study has used a non-linear PPGI which is borrowed from the works of Zaman and Shamsuddin (2018) for two different time periods, that is, 1980 and 2016. The following way estimates Zaman’s identity of PPGI, called PIGI that includes both the linear and non-linear growth-inequality factors which have proclaimed that the proposed index is fairly stable and gives a pragmatic approach to assess PPG across countries:

T P E = (\frac{Δ P / P}{Δ G / G} + \frac{Δ P / P}{Δ G^{2} / G^{2}} + \frac{Δ P / P}{Δ (G \times I / G \times I)}) + (\frac{Δ P / P}{Δ I / I} + \frac{Δ P / P}{Δ I^{2} / I^{2}} + \frac{Δ P / P}{Δ (G \times I / G \times I)})

(2)

where TPE shows total poverty elasticity, P shows poverty, G shows per capita income and I shows income inequality.

The PIGI index can be calculated by the following formula:

P I G I = \frac{(\frac{Δ P / P}{Δ G / G} + \frac{Δ P / P}{Δ G^{2} / G^{2}} + \frac{Δ P / P}{Δ (G \times I / G \times I)}) + (\frac{Δ P / P}{Δ I / I} + \frac{Δ P / P}{Δ I^{2} / I^{2}} + \frac{Δ P / P}{Δ (G \times I / G \times I)}}{(\frac{Δ P / P}{Δ G / G} + \frac{Δ P / P}{Δ G^{2} / G^{2}} + \frac{Δ P / P}{Δ (G \times I / G \times I)})}

(3)

If the value of PIGI is greater than the value of unity, the growth process is considered as PPG, while less than the unity shows an ANTI-PG between the two time periods. Thus, this index may conclude to assess an INCL_GRTH phenomenon for a country. The study has further estimated gains/losses from the growth process and has estimated a ‘poverty interdependence equivalent growth rate (PIEGR)’:

P I E G R = \frac{(\frac{Δ P / P}{Δ G / G} + \frac{Δ P / P}{Δ G^{2} / G^{2}} + \frac{Δ P / P}{Δ (G \times I / G \times I)}) + (\frac{Δ P / P}{Δ I / I} + \frac{Δ P / P}{Δ I^{2} / I^{2}} + \frac{Δ P / P}{Δ (G \times I / G \times I)}}{(\frac{Δ P / P}{Δ G / G} + \frac{Δ P / P}{Δ G^{2} / G^{2}} + \frac{Δ P / P}{Δ (G \times I / G \times I)})} \times A G R

(4)

where AGR shows annual growth rate between two time periods.

This index is different from the existing PPG indices seen in the given literature, Kakwani and Pernia (2000), McCulloch and Baulch (2000), Kakwani and Son (2002), Ravallion and Chen (2003), Kakwani et al. (2010), Klasen (2008), Klasen and Reimers (2017). These studies are based on the linear components of PPGI, while some other studies have extended the linear PPGI in the non-income poverty dimensions across countries. This study has borrowed the methodology of PIGI by Zaman and Shamsuddin (2018) which states that it gives more conclusive findings to assess PPG at a countrywide level.

Results and Discussions

Table 3 shows the descriptive statistics of the variables. The POV headcount has a maximum value of 36.800 per cent and a minimum of 17.320 per cent with an average value of 26.048 per cent which shows that more than a quarter of Pakistan’s population lives below the national poverty line. The income INC_INEQ inequality has a maximum value of 41.100 per cent and a minimum value of 28.670 per cent with an average value of 32.740 per cent which implies that the income disparity is larger than the POV headcount in a country that may affect the PPG process in a country. The mean value of per capita income, education expenditures, health expenditures, agriculture value added and FDI inflows are US$ 886.929, 2.447 per cent, 26.552 per cent of the GDP, 25.838 per cent of the GDP and 0.906 per cent of the GDP, respectively.

Table 3.

Descriptive Statistics

Statistics	POV	GDPPC	GINI	EDU	HEXP	AGRGDP	FDI
Mean	26.048	886.929	32.740	2.447	26.552	25.838	0.906
Maximum	36.800	1,174	41.100	3.022	36.904	31.564	3.668
Minimum	17.320	598.534	28.670	1.837	19.587	21.465	0.102
Std. Dev.	4.692	172.992	3.147	0.330	4.380	2.321	0.836
Skewness	0.474	0.117	1.308	−0.431	1.118	0.542	2.045
Kurtosis	2.727	1.824	3.786	2.128	3.481	3.130	6.590
Observations	37	37	37	37	37	37	37

Source: Economic survey of Pakistan (various issues) and World Bank (2016).

Table 4 shows the estimates of the ADF unit root and Johansen cointegration and has found that the candidate variables exhibit non-stationary series at their first difference with the lag length varying between 0 and 2. Thus, we confirm that the given variables hold the first order of integration properties, that is, I(1) variables.

Table 4.

Unit Root Test and Johansen Cointegration Estimates

Variable	Level	First Deference
POV	−0.252 (2)	−4.580^a (1)
GDP_PC	2.330 (1)	−2.299^b (0)
GINI	−1.181 (2)	−3.679^a (1)
EDU	0.196 (0)	−4.582^a (0)
HEXP	0.564 (0)	−7.234^a (0)
AGRGDP	−0.720 (0)	−5.632^a (0)
FDI	−1.661 (1)	−4.046^a (0)
Johansen Cointegration Estimates
Cointegrating equations	Trace statistics	Maximum eigenvalue statistics
Й₀	232.421^a	55.246^b
Й₁	177.175^a	47.732^b
Й₂	129.442^a	42.977^b
Й₃	86.465^a	36.560^b
Й₄	49.905^a	27.719^b
Й₅	22.185	13.837
Й₆	8.3476	8.347

Source: Author’s extraction

Note: ^a and ^b show 1 per cent and 5 per cent, respectively, significance level. Small bracket shows the lag length of the respective variable.

Further, we have proceeded to analysing the long-run cointegration relationship by the Johansen cointegration test and have found five cointegration equations both from the trace test and the maximum Eigen value test. The result implies that both the tests have confirmed the long-run relationship between the variables; hence, the study has proceeded for parameter estimates for conclusive findings in a country. After a cointegration test, the study proceeds to the robust least square regression by using an ‘MM’ estimation technique. The results are presented in Table 5 .

Table 5.

Estimates of Robust Least Square (RLS) Regression

Variables	RLS-1	RLS-2	RLS-4
ln(GDP_PC)	0.783^a	0.527^b	0.495^c
ln(GINI)	0.132	0.267	0.172
ln(EDU)	−0.627^a	–	−0.839^a
ln(HEXP)	–	–	0.529^c
ln(AGRGDP)	1.048^a	–	1.101
ln(FDI)	–	−0.094^b	0.066
Constant	−5.368^c	−1.300	−5.249
Statistical Tests
R ²	0.457	0.267	0.505
Adjusted R²	0.389	0.200	0.407

Source: Author’s extraction.

Note: Dependent variable: ln (POV). ^a, ^b, and ^c indicate 1 per cent, 5 per cent and 10 per cent level of significance. RLS shows robust least squares regression.

The results show that the GDP_PC has a positive and significant association with the POV headcount, that is, if there is 1 per cent increase in the GDP_PC of the country, the POV will increase by 0.783 per cent in the RLS(1), 0.527 per cent in RLS(2) and 0.495 per cent in RLS which implies that the country needs to make PPG policies and strengthen pro-poor infrastructure in order to alleviate poverty. The result is contrary to the findings of Dollar & Kraay (2002), avallion & Chen (2003) and Dollar, et al. (2016). All of the stated studies have confirmed that the ECO_GRT supports largely the poor in an absolute sense. However, it is evident from the results that Pakistan’s economy is still lagging behind in the PPG process where the higher GDP_PC does not translate into a lower income strata group to achieve the INCL_GRTH agenda in a country. The role of the INC_INEQ is quite invisible in the given scenario; because of the insignificant explanatory power we may not conclude the results in a given time period. There is a negative relationship between EDU and POV which implies that a higher EDU substantially decreases POV in a country, that is, if there is 1 per cent increase in the EDU, the POV decreases by −0.627 per cent in RLS(1) and −0.839 per cent in RLS(3), thus the PPG process is achieved by higher education expenditures that have been transmitted into the lower income strata group to sustain livelihoods with a decent living. The results are consistent with the previous studies of Zaman and Khilji (2014a, 2014b), Zaman et al. (2015b) and so on. These studies have confirmed the positivity of educational reforms to reduce POV across countries. The healthcare expenditures (HEXP) largely increases POV in a country: if there is a one per cent increase in the HEXP, the POV increases by 0.529 percentage points, confirming the anti-poor health reforms in a country. The results emphasises the need of pro-poor health expenditures to reduce poverty in a country (Gottret et al., 2009). The impact of AGRGDP on POV is direct which shows that along with an increase in AGRGDP, the POV substantially increases as this relationship almost has a one-to-one equation. Thus, it is necessary to devise pro-poor agricultural policies that give benefits to the poor and support a pro-growth and PPG process (Dorward et al., 2004).

However, there is a negative relationship between FDI inflows and POV which implies that along with 1 per cent increase in FDI inflows, poverty decreases by 0.094 per cent, thus confirming that financial liberalisation can reduce POV in a country (Ali et al., 2009). Table 6 shows the estimates of PIGI in different decades and years.

Table 6.

PPG Estimates for the Periods Between 1980 and 2016

Statistics	FDI	EDU	GDP_PC	HEXP	AGRGDP
1980	0.20775	2.01367	598.5347	24.21	29.51729
2016	0.32	2.7	1174	35.01	26.1
Actual growth rate	54.03129	34.08354	96.14569	44.60967	−11.5772
Non-linear growth elasticity	−0.08286	−0.13281	−0.04617	−0.10076	0.34503
Non-linear inequality elasticity	0.201258	0.203497	0.199582	0.202066	0.179576
PIGI	–1.42902	−0.53225	−3.32231	−1.00546	1.520466
PIEGR	−77.2118	−18.1411	−319.426	−44.8534	−17.6028
Decision	ANTI-PG	ANTI-PG	ANTI-PG	ANTI-PG	PPG

Source: Author’s extraction

The PPG estimates have confirmed that between 1980 and 2016, the growth process has been is unstable and ANTI-PG, except agriculture value added, gives marginal benefits to the poor as compared to the non-poor. However, the agriculture PPG estimate is surprising as the coefficient value of agricultural value added in a robust least square regression does not confirm the poverty reducing impact under different social expenditures, while if we assume other constant factors, the growth largely gives benefits to the poor as compared to the non-poor. The estimate of PIEGR confirms that the poor have received marginal gains from the growth process, that is, 17.602 per cent of the GDP in absolute terms. Thus we conclude that for achieving an INCL_GRTH agenda, the country needs to focus on more pro-poor agricultural policies that benefit the poor as compared to the non-poor.

Conclusion

The main objective of the study is to analyse the INCL_GRTH agenda in Pakistan’s context by evaluating different socio-economic factors, including POV, INC_INEQ, GDP_PC, AGRGDP, FDI inflows, EDU and HEXP by using a consistent time series data from 1980 to 2016. The study has further analysed the PPG assessment in two time periods, that is, 1980 and 2016. The results have shown that EDU and FDI inflows substantially reduce POV, while PPG is achieved through enhanced agriculture production in a country. The results point to the need of substantial investments in agriculture value added and increased mechanisation process, agriculture marketing, research and development and using high-yield variety of seeds which largely supports the poor as compared to the non-poor.

The study shows that the following three policies will help government officials to make growth pro-poor policies:

Income support programmes: The country needs to initiate income support programmes like the earlier Benazir Income Support Programme so that the marginalised people can benefit.

Labour market regulations: There is a need to regulate labour market issues and ensure legislation for a minimum wage rate, social security contribution, health facilities, and old-age pension in private and public organisations.

Pro-poor policies: The government needs to spend on infrastructure that supports the poor, including rural support programmes, the provision of clean water and sanitation facilities, community services, free education, free health facilities, subsidised house facilities, and so on. These factors will be helpful in promoting PPG policies in a country.

Social safety programmes, including education and healthcare facilities, may further enhance the abilities and capabilities of the poor to get a marginal benefit from them. The financial liberalisation process further supports the poor to give an opportunity for employment in healthy economic system. Rational income distribution is one of the desired policy instruments that need strategic economic decisions to reduce income disparities between the rich and the poor, while ECO_GRT further may expedite the process of shared prosperity in a country and trickle down to the poor as compared to the non-poor. Thus, these factors may be helpful to achieve INCL_GRTH agenda in a developing country like Pakistan.

There are some limitations to this study that one can point out that can be incorporated in future studies. First, governance indicators and financial factors can be further utilised in a GIP triangle in order to gain more insights in PPG policies. Second, the second-degree coefficient values can be used to get an inverted U-shaped relationship between growth and poverty incidence in a country. Finally, the GIP triangle can be effectively used in a panel of Asian countries to get more robust inferences for making PPG policies.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

References

Akinyemi

, Magareth

, & Oluwafemi

(2019). Poverty and inequality in Nigeria: Implications for inclusive growth. Nile Journal of Business and Economics, 4(9), 30–51.

Ali

, Nishat

, & Anwar

(2009). Do foreign inflows benefit Pakistani poor? [with Comments]. The Pakistan Development Review, 48(4), 715–738.

Arabiyat

T. S.

, Mdanat

, & Samawi

(2020). Trade openness, inclusive growth, and inequality: Evidence from Jordan. The Journal of Developing Areas, 54(1). https://doi.org/10.1353/jda.2020.0008

Ashrafi

, Behbudi

, Vaez Mahdavi

M. R.

, & Panahi

(2019). The triangular relationship between poverty, inequality and growth in Iran during the development plans. Social Welfare Quarterly, 18(71), 9–46.

Asongu

S. A.

, & Odhiambo

N. M.

(2020). Governance, CO₂ emissions and inclusive human development in sub-Saharan Africa. Energy Exploration & Exploitation, 38(1), 18–36.

Bhattacharya

, & Haldar

S. K.

(2017). Social sector development and inclusive growth in India—An empirical analysis. Indian Journal of Regional Science, 49(2), 1–14.

Bourguignon

(2004). The poverty-growth-inequality triangle. Paper presented at the Indian Council for Research on International Economic Relations, New Delhi, 4 February.

Dollar

, Kleineberg

, & Kraay

(2016). Growth still is good for the poor. European Economic Review, 81, 68–85.

Dollar

, & Kraay

(2002). Growth is good for the poor. Journal of Economic Growth, 7(3), 195–225.

10.

Dorward

, Kydd

, Morrison

, & Urey

(2004). A policy agenda for pro-poor agricultural growth. World Development, 32(1), 73–89.

11.

Doumbia

(2019). The quest for pro-poor and inclusive growth: The role of governance. Applied Economics, 51(16), 1762–1783.

12.

Ghanem

(2015). Agriculture and rural development for inclusive growth and food security in Morocco. Brookings Global Working Paper Series. https://www.brookings.edu/wp-content/uploads/2016/07/Agriculture_WEB_Revised.pdf

13.

Gottret

, Gupta

, Sparkes

, Tandon

, Moran

, & Berman

(2009). Protecting pro-poor health services during financial crises: Lessons from experience. Advances in Health Economics and Health Service Research, 21, 23–53.

14.

Hassan

S. A.

, Zaman

, & Gul

(2015). The relationship between growth-inequality-poverty triangle and environmental degradation: Unveiling the reality. Arab Economic and Business Journal, 10(1), 57–71.

15.

Kakwani

, Neri

M. C.

, & Son

H. H.

(2010). Linkages between pro-poor growth, social programs and labor market: The recent Brazilian experience. World Development, 38(6), 881–894.

16.

Kakwani

, & Pernia

(2000). What is pro-poor growth. Asian Development Review, 16(1), 1–22

17.

Kakwani

, & Son

(2002). Pro-poor growth: Concept, measurement, and application [Unpublished mimeo]. University of New South Wales.

18.

Khan

H. U. R.

, Khan

, Zaman

, Nabi

A. A.

, Hishan

S. S.

, & Islam

(2017). Gender discrimination in education, health, and labour market: A voice for equality. Quality & Quantity, 51, 2245–2266.

19.

Khan

H. U. R.

, Nassani

A. A.

, Aldakhil

A. M.

, Abro

M. M. Q.

, Islam

, & Zaman

(2019a). Pro-poor growth and sustainable development framework: Evidence from two step GMM estimator. Journal of Cleaner Production, 206, 767–784.

20.

Khan

, Saboor

, & Ahmad

(2020). An empirical analysis of monetary and multidimensional poverty: Evidence from a household survey in Pakistan. Asia Pacific Journal of Social Work and Development, 30(2), 1–16.

21.

Khan

H. U. R.

, Zaman

, Yousaf

S. U.

, Shoukry

A. M.

, Gani

, & Sharkawy

M. A.

(2019b). Socio-economic and environmental factors influenced pro-poor growth process: New development triangle. Environmental Science and Pollution Research, 26(28), 29157–29172.

22.

Klasen

(2008). Economic growth and poverty reduction: Measurement issues using income and non-income indicators. World Development, 36(3), 420–445.

23.

Klasen

, & Reimers

(2017). Looking at pro-poor growth from an agricultural perspective. World Development, 90, 147–168.

24.

Liu

, Amin

, Rasool

S. F.

, & Zaman

Q. U.

(2020). The role of agriculture and foreign remittances in mitigating rural poverty: Empirical evidence from Pakistan. Risk Management and Healthcare Policy, 13, 13–26.

25.

McCulloch

, & Baulch

(2000). Simulating the impact of policy upon chronic and transitory poverty in rural Pakistan. The Journal of Development Studies, 36(6), 100–130.

26.

Michálek

, & Výbošťok

(2019). Economic growth, inequality and poverty in the EU. Social Indicators Research, 141(2), 611–630.

27.

Ravallion

, & Chen

(2003). Measuring pro-poor growth. Economic Letters, 78(1), 93–99.

28.

Shaheen

, Zaman

, Mushtaq

, Ain

, Naz

, Bibi

, Akhter

, Bibi

, & Kousar

(2017). Simultaneous equations modeling for terrorism, poverty, and economic growth: Evidence from Pakistan. International Journal of Economics and Financial Issues, 7(5), 233–244.

29.

World Bank (2016). World development indicators. World Bank.

30.

World Economic Forum (2017). The inclusive growth and development report 2017: Insight report. World Economic Forum, Geneva. http://www3.weforum.org/docs/WEF_Forum_IncGrwth_2017.pdf

31.

Young

A. O.

(2019). Does reduction in inequality of income distribution matter for poverty reduction? Evidence from Nigeria’s poverty trends. International Research Journal of Finance and Economics, 172, 48–72.

32.

Zaman

(2015). Measurement issues of income and non-income welfare indicators: Assessment of Pakistan’s pro-poor growth. International Journal of Economics and Financial Issues, 5(3), 802–811.

33.

Zaman

(2016a). Poverty intensive curve: A new form of pro-poor growth index. Forensic Research & Criminology International Journal, 2(3), 1–2.

34.

Zaman

(2016b). Examination of non-linear dynamics in pro-poor growth framework: A case study of Pakistan. International Journal of Economics and Empirical, Research (IJEER), 4(4), 207–211.

35.

Zaman

, Al-Ghazali

B. M.

, Khan

, Rosman

A. S. B.

, Sriyanto

, Hishan

S. S.

, & Bakar

Z. A.

(2019). Pooled mean group estimation for growth, inequality, and poverty triangle: Evidence from 124 countries. Journal of Poverty, 24(3), 1–19.

36.

Zaman

, Ashraf

, Sajjad

, Saleem

, & Raja

U. M.

(2015b). Quality versus quantity in health care and educational reforms: Combating poverty. Quality & Quantity, 49(1), 267–293.

37.

Zaman

, & Khilji

B. A.

(2014a). Sectoral decomposition of changes in Pakistan’s poverty: The new interface. Journal of Poverty, 18(4), 453–476.

38.

Zaman

, & Khilji

B. A.

(2014b). A note on pro-poor social expenditures. Quality & Quantity, 48(4), 2121–2154.

39.

Zaman

, & Shah

I. A.

(2016). Income inequality, investment, public expenditures and economic growth: The case of Pakistan. International Journal of Economics and Empirical, Research (IJEER), 4(2), 112–123.

40.

Zaman

, Shah

I. A.

, & Naseem

(2015a). Estimating working poverty in Pakistan evidence from national, rural and urban population. Journal Transition Studies Review, 22(2), 69–78.

41.

Zaman

, & Shamsuddin

(2018). Linear and non-linear relationships between growth, inequality, and poverty in a panel of Latin America and the Caribbean countries: A new evidence of pro-poor growth. Social Indicators Research, 136(2), 595–619.