The Nonlinear Relationship Between Entrepreneurship and Natural Resource Rents

Abstract

To examine a nonlinear relationship between entrepreneurship density and natural resource rents, this study applies the panel-corrected standard errors (PCSE) estimator to a sample of 87 countries over the period of 2006–2016. Estimation results show strong evidence of the nonlinear relationship between the two variables. The influence of entrepreneurship density on natural resource rents is subject to two different regimes. In low- and upper-middle-income countries, an increase in entrepreneurship density increases natural resource rents to a certain level, but it reduces the rents afterwards. In high-income countries, the relationship is reversed. Policymakers should adapt their entrepreneurship policy to prevailing economic circumstances to enhance economic sustainability.

Keywords

Entrepreneurship natural resources rent seeking panel data

Natural resources have a special role in the economy as an important factor of production and consumption, development and pollution, and blessing and curse. Attention to natural resources has increased in recent years due to global warming. Natural resources are still exploited severely even though they have been used more efficiently from 1990 to 2010 than in previous decades (United Nations, 2019b). Lately, researchers focus on the role of entrepreneurship as it affects the rent-seeking of natural resources.

Entrepreneurial activity is usually linked with a new business model that enhances the efficiency of natural resource usage. So, it is presumed that entrepreneurship helps to reduce natural resource rents, which contributes to the environment through innovation (Fuentelsaz et al., 2018). However, studies show that there is heterogeneity in the types and influences of entrepreneurial activity on the environment across countries (e.g., see Asongu et al., 2018; Bizri, 2018; Inkizhinov et al., 2021). These studies suggest that entrepreneurs can abuse natural resources as well as save them.

Baland and Francois (2000) argue that an increase in natural resources enhances domestic rent-seeking activity when there is a large initial proportion of rent-seeking agents. Torvik (2002) explains that the natural resource curse can result from an entrepreneur’s rent-seeking behaviour for natural resources. In this regard, greater entrepreneurial activity can induce natural resource rents.

The relationship between entrepreneurial activity and natural resource rents is ambiguous (Canh et al., 2020, 2021; Chambers & Munemo, 2019). The nexus is a significant topic with increasing emphasis on sustainability and climate change. However, studies investigating the role of entrepreneurship on natural resource rents are still underexplored. Chambers and Munemo (2019) consider the relationship between natural resource rents and new business creation and find that heavy natural resource extraction lowers entrepreneurial activity, depending on the quality of governance. Further, Youssef et al. (2018) show that entrepreneurship is conditioned on heavy energy use, which deteriorates the environmental quality and sustainability of African countries by estimating the Environmental Kuznets Curve (EKC) hypothesis. Chowdhury et al. (2019) suggest that entrepreneurial activity and natural resource rent-seeking are strongly affected by institutional settings.

Building on the existing literature, this study examines the influence of entrepreneurship density on natural resource rents, focusing on a nonlinear relationship between the two variables. Entrepreneurship at its low level is more likely to exploit natural resources because firms face lower competition in a larger market (Buenstorf, 2016; Chell, 2000; Prieger et al., 2016). Entrepreneurship at its high level must focus on innovation because it faces higher competition in a smaller market (Lafuente et al., 2018; Prieger et al., 2016). As a result, there might be a nonlinear relationship between entrepreneurship density and natural resource rents. This study attempts to investigate the existence of the nonlinear relationship empirically by using a global sample.

There is some ambiguity regarding the role and nature of entrepreneurship even though it is a significant agent in determining the rent-seeking of natural resources and economic sustainability. Previous studies assume that the relationship is linear, despite its possible nonlinearity. For instance, Canh et al. (2020) conclude that increases in entrepreneurship density would increase natural resource rents in a sample of 60 countries from 2006 to 2016. This study can shed some light on the mechanism through which entrepreneurship affects natural resource rents by examining the nonlinear relationship.

This study is organized as follows: The second section develops the empirical model based on previous literature. The third section discusses data and empirical results. The fourth section presents the conclusions.

Literature Review and Empirical Model

Literature Review

Natural Resource Rents and Its Determinants

Economic activity is considered one of the main causes of environmental change. Researchers investigated the environmental impact of consumption, population and technology (Dietz & Rosa, 1997; Ehrlich & Holdren, 1971). They examined the influence of various economic activities on the environment such as urbanization, trade openness and FDI inflows (Bekun et al., 2021; Khan et al., 2022). Most of these studies proxied environmental degradation by CO₂ (carbon dioxide) emissions (Jafri et al., 2022; Nguyen, 2022).

In the backdrop of climate change, sustainable consumption and production become significant to mitigate climate change and global warming (United Nations, 2019a). Natural resources can be a blessing or a curse for economic development, but natural resource rents would lead to higher environmental degradation as they cause higher natural exploitation (Balsalobre-Lorente et al., 2018; Hodler, 2006). According to Gerelmaa and Kotani (2016), the efficiency of natural resource usage improved during the period 1990–2010 compared with previous decades. However, heavy natural resource exploitation and inefficient resource consumption remain major concerns (United Nations, 2019b). Human activity such as economic development, investment and urbanization can determine natural resource rents (Yang et al., 2018). Interestingly, a policy aimed at reducing accumulated fossil resource rents might cause side effects on scarcity rents, providing a dilemma in natural resource management (Kalkuhl & Brecha, 2013). That is a policy intended to reduce the demand for fossil fuel can decrease scarcity rents, in turn boosting its consumption. However, studies on the economic determinants of natural resource rents are still limited, and further investigation is needed.

Natural resources are not only important pillars in the economic ecosystem but are an essential inputs to production and consumption. Economic literature indicates that natural resources could be a positive factor in economic development, but they can also be a curse due to natural resource rent-seeking (Abdulahi et al., 2019; Hodler, 2006). According to the meta-analysis by Havranek et al. (2016), 40% of empirical papers show negative economic effects of natural resources on development, 40% find no effects, and 20% show positive effects. However, most economists agree that heavy natural resource rents can lead to a resource curse, social issues, political risk, economic instability and environmental degradation (Balsalobre-Lorente et al., 2018; Belaid et al., 2021; Li et al., 2021).

Farzanegan et al. (2018) find evidence of the positive impacts of natural resource rents on internal conflicts in a sample of more than 90 countries over the period 1984–2004. Borge et al. (2015) show a decrease in the efficiency of public goods provision from natural resource revenue in Norway. Bhattacharyya and Hodler (2010) warn that corruption is fed by natural resource rents in a sample of 124 countries over the period 1980–2004. Recent studies have raised much concern about natural resource rents, especially in rich-resource developing countries like African countries (Henry, 2019; Manzano & Gutiérrez, 2019). Badeeb et al. (2017) conclude that the natural resource curse still exists and is particularly strong in developing economies. Other studies show a link between natural resource rents and environmental degradation (Ahmad et al., 2021; Shittu et al., 2021).

Researchers show a nonlinear relationship between economic development and environmental quality in the EKC hypothesis (Gill et al., 2018; Sun, 1999). The environment deteriorates initially as economic development takes off in low-income countries. Thus, they adopt a strategy of ‘grow first and clean up later’ (Rock & Angel, 2007). The demand for a better environment intensifies as development progresses beyond a certain threshold. Then, the government tries to improve environmental quality, and people change their attitudes towards sustainable development. As a result, environmental quality starts to improve. However, empirical tests are mixed (Pata & Samour, 2022; Tenaw & Beyene, 2021). The EKC hypothesis is supported in some studies, for example, Cambodia, China, Indonesia, Korea, Lao, Malaysia, Mongolia, Philippines, Thailand, Timor-Leste and Vietnam (Hanif, 2018), in Turkey (Pata, 2018). The hypothesis is rejected in other studies (Ajmi et al., 2015; Liu et al., 2017).

Entrepreneurship and Natural Resource Rents

Entrepreneurship is one of the most pervasive concepts in economics. Entrepreneurship is an important factor for economic development and sustainability (Douglas & Prentice, 2019; Heiskanen et al., 2019; Pedeliento et al., 2018). Specifically, it shows a positive link with innovation, livelihoods and poverty reduction, social well-being and income inequality reduction (Halvarsson et al., 2018; Matthews & Brueggemann, 2015; Shir et al., 2018; Sutter et al., 2019). Recent studies investigated the link between entrepreneurship and sustainable development (Youssef et al., 2018). Some studies showed that entrepreneurship contributes to the natural environment (Heiskanen et al., 2019; Vernet et al., 2019). Other studies reported the existence of a significant difference in entrepreneurial activity across countries. They revealed a wide variety of entrepreneurship types and their influences on the economy (e.g., Asongu et al., 2018; Bizri, 2018). The link between entrepreneurship and the economic system is not uniform (Castaño et al., 2015).

According to Torvik (2002), the natural rent-seeking behaviour of entrepreneurs causes a natural resource curse. According to him, natural resource abundance may lead to rent-seeking activity of new entrepreneurs, while productive firms might compete and crowd out the market. This link between entrepreneurship and natural resource rents shows a good starting point for the relationship between entr- epreneurship and the environment through natural resource rents. Furthermore, Polzin et al. (2018) show that investors and entrepreneurs are challenged in early-stage entrepreneurial finance due to the high-risk profile of entrepreneurship. Entrepreneurs face many constraints like financial constraints (Markatou, 2015), which may induce entrepreneurs to exploit any available natural resources to sustain their new businesses. Thus, entrepreneurship activity is associated with natural resource rent-seeking behaviour and heavy natural resource rents.

On the other hand, recent studies document an important role of entrepreneurship in sustainable development as a good actor in solving environmental issues (Heiskanen et al., 2019; Youssef et al., 2018). For instance, Douglas and Prentice (2019) show that entrepreneurs recently pay more attention to sustainable economic activity. This entrepreneurial activity is regarded as social corporate or environmental entrepreneurship (Douglas & Prentice, 2019; Youssef et al., 2018). In addition, the risk-taking attitude of entrepreneurs may benefit the environment since entrepreneurs can take risks in solving the social challenges and issues of the economy, society and environment (Mthanti & Ojah, 2017). Entrepreneurial activity is also strongly related to innovations (Fuentelsaz et al., 2018). Thus, entrepreneurs can be important agents in creating new products and production processes that require natural resources less than before (Gerelmaa & Kotani, 2016). In this respect, entrepreneurship can reduce natural resource rents. However, there is likely no study on the non-linear relationship between entrepreneurship and natural resource rents.

Thus, this study attempts to explore the influence of entrepreneurship on natural resource rents. Particularly, the study aims at investigating the potential nonlinearity between entrepreneurship and natural resource rents. The study focuses on the non-linear effect of entrepreneurship density on natural resource rents in a global sample.

Empirical Model

To examine the influence of entrepreneurship on natural resource rents, the following empirical model is employed.

\begin{array}{l} N R R_{i t} = α_{0} + β_{1} I n c_{i t} + β_{2} P o p_{i t} + β_{3} E n t r e p_{i t} + β_{4} E n t r e p_{i t}^{2} + \\ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ β_{5} C a p_{i t} + β_{6} U r b_{i t} + β_{7} O p e n_{i t} + β_{8} F D I_{i t} + ε_{i t} \end{array}

(1)

where NRR is natural resources rents; Inc is income level; Pop is population; Entrep is entrepreneurship density; Cap is capital formation; Urb is urbanization; Open is trade openness, and FDI is foreign direct investment inflows. All variables are observed for a country i at time t.

Notice that the square term of entrepreneurship is included to estimate the nonlinearity in the relationship between entrepreneurial activity and natural resource rents. In estimation, three additional variables of inflation (Inf), government expenditures (Gov) and world commodity price (Pcom) are considered to control changes in the rents caused by changes in the price level, changes in the fiscal policy and changes in the world commodity price, respectively.

Figure 1 summarises the framework of the relationship between entrepreneurship with natural resource rents.

Figure 1.

Source: The authors.

Data and Empirical Results

Data

The data is compiled from the World Development Indicators (WDI) in the World Bank’s database, excluding the world commodity price index that is collected from the Federal Reserve Economic Data of the Federal Reserve System of St. Louis (Fred).

Regarding two main variables, the percentage share of the total natural resource rents in GDP denotes natural resource rents (NRR). Entrepreneurship density is represented by the log of new business density (new registrations per 1,000 people ages 15–64) as a proxy for entrepreneurial activity (Entrep).

Regarding control variables, the log of real GDP per capita represents the economic development level (Inc) of sample countries. The log of population density per square km of land area represents population density (Pop). The percentage ratio of GDP denotes gross capital formation (Cap). The percentage share of the urban population in the total population represents urbanization (Urban). The percentage ratio of the total trade value in GDP proxies trade openness (Open). The percentage ratio of net FDI inflows in GDP proxies FDI (FDI). Annual percentage change in the GDP deflator represents inflation (Inf). The percentage share of general government final consumption expenditure in GDP is a proxy for government expenditure (Gov). The annual percentage change of world commodity price represents a change in commodity price (Pcom).

Since the data on entrepreneurship density from the WDI of the World Bank is available from 2006 to 2016, the period of 2006–2016 is chosen as the sampling period. The sample comprises 87 countries, which is divided into three subsamples 24 LMEs, 26 UMEs and 37 HIEs.¹ Table 1 presents variables, abbreviations, measurements and basic statistics along with p-values from the cross-section independence (CD) test.

Table 1.

Variables, Abbreviations, Measurements, CD Tests and Summary Statistics.

Variables	Abb.	Measurements	Mean	SD.	CD-test^a
Natural resource rents	NRR	Total natural resource rents (% of GDP)	5.22	9.43	46.07^***
Economic development level	Inc	Log of GDP per capita (constant 2010 US$)	9.01	1.35	88.78^***
Population density	Pop	Log of population density (people per sq. km of land area)	4.29	1.28	89.09^***
Entrepreneurship density	Entrep	Log of new business density (new registrations per 1,000 people ages 15–64)	0.56	1.40	33.83^***
Capital formation	Cap	Gross capital formation (% of GDP)	25.03	6.90	25.54^***
Urbanization	Urb	Urban population (% of total)	62.51	20.29	105.8^***
Trade openness	Open	Trade (% of GDP)	94.15	55.43	27.75^***
FDI net inflows	FDI	Foreign direct investment, net inflows (% of GDP)	5.77	13.61	29.45^***
Inflation	Inf	Inflation, GDP deflator (annual %)	4.99	6.73	64.07^***
Government expenditure	Gov	General government final consumption expenditure (% of GDP)	16.61	4.58	41.41^***
World commodity price	Pcom	Yearly change in global price index of all commodities (%)	2.24	19.74	202.8^***

Sources: All data are compiled from World Development Indicators database (World Bank, version Apr/2019), except the world commodity price taken from Federal Reserve Economic Data, Federal Reserve system of St. Louis.

Notes: ^adenotes p-values of the cross-sectional independence (CD) tests. The CD test is based on the null hypothesis of cross-section independence, CD ~ N(0,1). *** is significant at 1% level of significance.

Table 2 reports correlation coefficients between explanatory variables. Entrepreneurship density has a significant negative correlation with natural resource rents. The rents have a significant negative correlation with income level, population density, trade openness, FDI net inflows and government expenditure. A change in world commodity prices is significantly correlated with inflation. To avoid multicollinearity arising from correlated explanatory variables, they are included separately in estimation.

Table 2.

Unconditional Correlation Matrix.

	Inc	Pop	Entrep	Cap	Urb	Open	FDI	Inf	Gov	Pcom
Inc	1.00
Pop	0.04	1.00
Entrep	0.65^***	−0.01	1.00
Cap	−0.18^***	−0.15^***	−0.15^***	1.00
Urb	0.77^***	−0.01	0.49^***	−0.21^***	1.00
Open	0.37^***	0.30^***	0.35^***	0.05	0.27^***	1.00
FDI	0.16^***	0.10^***	0.18^***	−0.02	0.13^***	0.37^***	1.00
Inf	−0.35^***	−0.15^***	−0.26^***	0.16^***	−0.19^***	−0.11^***	−0.01	1.00
Gov	0.45^***	−0.18^***	0.33^***	−0.09^***	0.33^***	0.07^**	0.04	−0.26^***	1.00
Pcom	−0.02	−0.02	−0.04	0.10^***	−0.03	0.03	0.02	0.31^***	−0.07**	1.00

Source: The authors.

Notes: *(**) *** Coefficient estimate is significant at the 10(5)1% level of significance.

Empirical Results

Before estimation, the CD test is employed to test if sample observations are dependent across countries. The Pesaran (2004)’s CD test is adopted because the sample consists of a large cross-section (87 countries) relative to the time series (2006–2016). The tests reject the null hypothesis of the non-existence of cross-sectional dependence for all the variables (Table 1). Thus, the PCSE estimator is utilized to deal with standard errors that are heteroskedastic and contemporaneously correlated across panels. Coefficient estimates of the PCSE estimator are robust against cross-sectional dependence (Bailey & Katz, 2011; Jönsson, 2005; Marques & Fuinhas, 2012). Furthermore, there may be a mutual causality between natural resource rents and economic factors on the right-hand side of Equation (1), which causes the problem of endogeneity in estimation. To check for the robustness of estimation results, all independent variables are included as one-year lagged as well².

To check the robustness of PCSE estimates, several different estimators are also used such as the feasible generalized least squares (FGLS), the Pooled OLS, the Robust Pooled OLS and the Pooled OLS with year effects estimators.

Estimation results are checked further as follows: First, the square term of the income level is added as an explanatory variable to test the EKC hypothesis to examine the sensitivity of the results against a different theoretical framework. Second, the total sample is divided into three subsamples according to the income level of the sample countries. Third, variables measured in per capita terms are used like natural resource rents per capita, capital formation per capita and trade openness per capita instead of the original variable measures.

Estimation results from the PCSE estimator are consistent with those from the FGLS, the Pool OLS and the other estimators. Tables 3 and 4 present the main results derived from the PCSE estimation method.³ Tables A2 and A3 in the appendix summarize estimation results with the EKC hypothesis and the alternative variable measurements.

Full Sample

Table 3 presents coefficient estimates about the nonlinear relationship between natural resource rents (NRR) and entrepreneurship density (Entrep) in the full sample. Coefficient estimates of entrepreneurship density are significantly negative, regardless of model specifications. Additionally, the square terms of entrepreneurship density (Entrep^2) are significantly negative. These results suggest that an increase in entrepreneurship density reduces natural resource rents at a decreasing rate as entrepreneurship density increases. The relationship can be described as a decreasing quadratic function, that is, the relationship is concave.

Table 3.

Entrepreneurship Density and Total Natural Resource Rents: Non-Linear Relationship in the Total Sample.

Dep. Var: NRR	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
Income	−0.782^***	−0.380^***	−0.378^***	−0.488^***	−2.108^***	−2.166^***	−2.173^***	−1.769^***	−1.677^***	−2.185^***
	[0.062]	[0.101]	[0.101]	[0.099]	[0.146]	[0.136]	[0.137]	[0.482]	[0.261]	[0.132]
Population	−2.385^***	−2.407^***	−2.410^***	−2.466^***	−2.326^***	−2.428^***	−2.428^***	−2.301^***	−2.626^***	−2.419^***
	[0.154]	[0.157]	[0.156]	[0.103]	[0.098]	[0.106]	[0.106]	[0.105]	[0.148]	[0.107]
Entrep		−0.598^***	−0.523^***	−0.486^***	−0.497^***	−0.557^***	−0.549^***	−0.507^***	−0.458^***	−0.534^***
		[0.119]	[0.127]	[0.180]	[0.175]	[0.184]	[0.184]	[0.149]	[0.169]	[0.180]
Entrep^2			−0.180^***	−0.198^***	−0.097^***	−0.106^***	−0.099^**	−0.085	−0.167^***	−0.096^**
			[0.028]	[0.034]	[0.038]	[0.038]	[0.045]	[0.053]	[0.046]	[0.046]
Capital				−0.113^***	−0.078*	−0.088**	−0.089^**	−0.106*	−0.095^**	−0.098^**
				[0.041]	[0.041]	[0.043]	[0.043]	[0.056]	[0.044]	[0.046]
Urbanization					0.143^***	0.142^***	0.142^***	0.132^***	0.134^***	0.143^***
					[0.009]	[0.009]	[0.009]	[0.014]	[0.009]	[0.008]
Openness						0.007^***	0.009^***	0.008^***	0.008^***	0.008^***
						[0.002]	[0.002]	[0.002]	[0.002]	[0.002]
FDI inflow							−0.018^*	−0.022^*	−0.018^*	−0.019^*
							[0.011]	[0.012]	[0.010]	[0.012]
Inflation								0.177
								[0.177]
Government									−0.279^***
Expenditure									[0.090]
Commodity										0.037^***
Price										[0.012]
Cons.	22.51^***	19.31^***	19.67^***	23.84^***	27.80^***	28.45^***	28.45^***	24.51^***	30.23^***	28.62^***
	[0.963]	[0.809]	[0.825]	[1.054]	[1.170]	[1.238]	[1.242]	[3.681]	[1.522]	[1.292]
Observation	957	957	957	923	912	912	912	912	910	912
R-squared	0.120	0.125	0.127	0.131	0.162	0.164	0.164	0.177	0.177	0.170
No. of country	87	87	87	85	84	84	84	84	84	84

Source: The authors.

Notes: Panel-corrected standard errors are in parentheses. *(**)***Coefficient estimate is significant at the 10(5)1% level of significance.

Estimations show that a nonlinear relationship exists between entrepreneurial activity and natural resource rents. Overall, an increase in entrepreneurship density enhances the productivity of entrepreneurs, which reduces natural resource rents. Also, the benefits of increasing entrepreneurship density rise as the density increases. The results are consistent with the general presumption that entrepreneurship helps to reduce natural resource rents, which contributes to the environment through innovation.

To observe the non-linear relationship clearly, predictive margins regarding the influence of entrepreneurship density on natural resource rents are estimated. Figure 2 presents fitted natural rents against the log of entrepreneurship density in the total sample along with their 95% confidence interval. A concave curve depicts the relationship between entrepreneurship density and natural resource rents.

Figure 2.

Source: The authors.

The sensitivity of the estimation results is checked by including the square term of the income level as a regressor to allow for the EKC hypothesis (Table A2 in Appendix). Some variables are measured in terms of per capita and present estimation results in the per capita analysis (Table A3 in Appendix). Through various exercises, the same relationship between entrepreneurship density and natural resource rents can be replicated.

Regarding control variables, real GDP has a significant negative impact on natural resource rents, which implies economic growth is one of the main drivers of natural resource rents. Both population and urbanization have a significantly positive impact on natural resource rents as well. This suggests that both population growth and the urbanization process require greater natural resource rents to support them. FDI inflows have a significantly negative effect on natural resource rents. These results are surprising considering that many studies documented the rent-seeking of natural resources by FDI firms (e.g., Hajzler, 2014; Ndikumana & Sarr, 2019). The results might reflect that countries become more interested in hosting the environment-friendly firms. Finally, an increase in government expenditure reduces the rents, but commodity price inflation increases the rents. Government expenditure might reflect the strength of the institution to enforce environmental policies. Commodity price inflation implies that the volume of natural resource rents in a country is stable against the fluctuating commodity prices, which causes the value of the rents to fluctuate together with the prices. Natural resource consumption is time-persistent, which suggests the inherent difficulty of reducing the rents in a country.

Three Subsamples

Table 4 presents coefficient estimates for the nonlinear relationship between natural resource rents (NRR) and entrepreneurship density (Entrep) for three subsamples. In LMEs, entrepreneurship density has a negative (mostly insignificant) impact on natural resource rents, while its square term has a significant negative impact. Entrepreneurship density reduces natural resource rent at a decreasing rate. There is a non-linear relationship between entrepreneurship density and natural resource rents in LMEs. Because coefficient estimates of the single term of entrepreneurship density are mostly insignificant, the shape of nonlinearity depends on the square term of entrepreneurship density which is significantly negative. Thus, the nonlinear relationship takes an inverted-U shape in LMEs.

Table 4.

Entrepreneurship Density and Natural Resource Rents: Non-Linear Relationship in Three Subsamples.

Dep. var: NRR	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Groups	LMEs			UMEs			HIEs
Income	−3.509*	−5.068***	−4.830***	−5.197***	−5.033***	−4.675***	−0.687	−0.601	−1.004***
	[1.798]	[1.616]	[1.637]	[0.852]	[0.762]	[0.731]	[0.564]	[0.522]	[0.333]
Population	−1.813***	−1.887***	−1.694***	−2.648***	−3.195***	−2.769***	−3.195***	−3.381***	−3.310***
	[0.240]	[0.215]	[0.241]	[0.221]	[0.317]	[0.211]	[0.213]	[0.241]	[0.248]
Entrep	−0.925	−0.253	−1.038*	4.456***	4.758***	4.384***	−8.951***	−8.983***	−9.020***
	[0.613]	[0.584]	[0.606]	[0.706]	[0.813]	[0.724]	[0.740]	[0.757]	[0.776]
Entrep^2	−0.682***	−0.456***	−0.686***	−1.754***	−1.907***	−1.773***	2.480***	2.416***	2.509***
	[0.178]	[0.161]	[0.174]	[0.372]	[0.379]	[0.374]	[0.249]	[0.259]	[0.252]
Capital	0.080*	0.140***	0.100**	−0.606***	−0.616***	−0.590***	−0.132	−0.146*	−0.125
	[0.043]	[0.042]	[0.043]	[0.143]	[0.136]	[0.123]	[0.095]	[0.088]	[0.083]
Urbanization	0.198***	0.267***	0.239***	0.104***	0.106***	0.115***	0.230***	0.240***	0.242***
	[0.055]	[0.052]	[0.052]	[0.018]	[0.016]	[0.018]	[0.023]	[0.017]	[0.017]
Openness	−0.092***	−0.058***	−0.083***	0.017**	0.030***	0.021**	0.026***	0.023***	0.026***
	[0.020]	[0.019]	[0.020]	[0.008]	[0.010]	[0.008]	[0.003]	[0.004]	[0.003]
FDI inflow	0.291	0.306	0.339	−0.432***	−0.403***	−0.442***	−0.035*	−0.030*	−0.031*
	[0.337]	[0.307]	[0.327]	[0.116]	[0.122]	[0.133]	[0.019]	[0.018]	[0.018]
Inflation	0.310***			0.150			0.241
	[0.080]			[0.143]			[0.306]
Government		−0.460***			−0.354***			−0.198
Expenditure		[0.092]			[0.110]			[0.136]
Commodity			0.028			0.067***			0.029***
Price			[0.020]			[0.024]			[0.010]
Cons.	33.409***	47.153***	41.775***	70.559***	76.724***	65.937***	12.792***	17.125***	15.923***
	[11.675]	[10.554]	[10.592]	[9.940]	[11.521]	[9.184]	[4.920]	[3.958]	[3.959]
Observations	245	243	245	260	260	260	407	407	407
R-squared	0.331	0.296	0.270	0.305	0.308	0.309	0.462	0.456	0.454
No. of countries	23	23	23	24	24	24	37	37	37

Source: The authors.

Notes: Panel-corrected standard errors are in parentheses. *(**)*** Coefficient estimate is significant at the 10(5)1% level of significance.

In UMEs, estimations show that entrepreneurship density has a significantly positive impact on natural resource rents, but the square term of entrepreneurship density has a significantly negative impact. Thus, entrepreneurial activity induces higher natural resource rents to a certain level, but the activity reduces natural resource rents afterwards. Again, the nonlinear relationship shows an inverted-U shape in UMEs.

In HIEs, estimations show that entrepreneurship density has a significantly negative impact on natural resource rents and its squared term has a significantly positive impact. Entrepreneurial density reduces natural resource rents initially as the density increases, but it increases the rents after a certain threshold. The nonlinear relationship shows an inverted-U shape in HIEs.

To illustrate the relationship clearly, Figure 3 presents estimated predictive margins regarding the influence of entrepreneurship density on natural resource rents for the three subsamples. The nonlinear relationship shows an inverted-U shape in both LMEs and UMEs, but a U-shape in HIEs. This is consistent with the estimation results in Table 4.

Figure 3.

Source: The authors.

A nonlinear relationship between entrepreneurial activity and natural resource rents is observed in all three subsamples, but the shape of the relationship varies across income groups. An increase in entrepreneurial activity is likely to increase natural resource rents in low and middle-income countries. This is significantly true in upper-middle-income countries. In both cases, the activity reduces the rent after a certain threshold. However, the relationship is reversed in high-income countries.

These results suggest that entrepreneurs in LMEs and UMEs pursue the rent-seeking of natural resources initially, but they are engaged more with productive activity as more entrepreneurs enter the market, fostering competition and innovation. On the contrary, entrepreneurs in HIEs turn to seek natural resource rents as the number of entrepreneurs increases. This results from a decreasing return from productive activity.

The results confirm that entrepreneurial activity in developing countries is relatively resource-oriented, and its innovation to save natural resources is relatively weaker. However, entrepreneurial activity in more developed countries is technology-oriented, and their innovation results are significantly stronger (Reynolds et al., 2001; Simón-Moya et al., 2014). However, the results suggest that entrepreneurial activities vary even within a country from exploitive to innovative as the economic environment changes with entrepreneurship density.

For sustainable development, the results suggest that entrepreneurial activity in developing countries should be directed toward relatively technology-oriented ones. For this, the countries should emphasize education that nurtures innovative entrepreneurs. They should also enhance the free-market environment to promote competition and innovation.

Concluding Remarks

This study investigated the influence of entrepreneurship density on the environment through natural resource rents, which focused on its possible nonlinear relationship. The study applied the panel-corrected standard errors (PCSE) estimator to the global sample of 87 countries over the period of 2006–2016, and it included 24 low and lower- middle-income economies (LMEs), 26 upper-middle-income economies (UMEs) and 37 high-income economies (HIEs).

Estimation results of this study show strong evidence of a nonlinear relationship between entrepreneurship density and natural resource rents both in the full sample and all three subsamples. For the total sample, the relationship between entrepreneurship density and natural resource rents has a decreasing quadratic functional form. This suggests that an increase in entrepreneurship density reduces natural resource rents at a decreasing rate. This is consistent with the general presumption that entrepreneurship helps to reduce natural resource rents, which contributes to the environment through innovation (Gavrila Gavrila & De Lucas Ancillo, 2022; Potts et al., 2010; Youssef et al., 2018).

For the subsamples, the relationship has an inverted-U shape in LMEs and UMEs, but it has a U-shape in HIEs. The influence of entrepreneurship density on natural resource rents is subject to two different regimes depending on the income level. In LMEs and UMEs, an increase in entrepreneurship density reduces natural resource rents to a certain level, but it reduces the rents afterwards. In HIEs, the relationship is reversed.

The estimation results suggest that entrepreneurs in LMEs and UMEs pursue the rent-seeking of natural resources initially, but they are engaged more with productive activity as more entrepreneurs enter the market, fostering competition and innovation. On the contrary, entrepreneurs in HIEs turn to seek natural resource rents as the number of entrepreneurs increases. This results from a decreasing return from productive activity.

The results confirm that the nature of entrepreneurial activities varies across countries. For example, studies show that entrepreneurial activity in less developed countries is weaker in innovation. However, the activity in developed countries is significantly stronger in innovation (Reynolds et al., 2001; Simón-Moya et al., 2014). However, the results suggest that the activity within a country also varies from exploitive to innovative as the economic environment changes with entrepreneurship density.

The results show that managers are tempted to lean on taxing natural resources to sustain and grow their firms not only because they do not have enough resources to innovate in developing countries but also because they face severe competition in developed countries. This provides temporary survival because their firms cannot compete with productive firms. Thus, managers should turn their focus from rent-seeking of natural resources to innovative activities to grow their business. This is especially important because environmental management becomes a necessity rather than a choice for the growth of a firm as it is the main component of corporate social responsibility (CSR) these days.

Policymakers should adapt their entrepreneurship policy to prevailing economic circumstances. For example, policymakers in LMEs and UMEs must implement suitable regulations at the low level of entrepreneurship density to limit natural resource rents. Meanwhile, policymakers in HIEs should pay attention to excessive entrepreneurial activity, resulting from deteriorating market conditions. It should create conditions for good entrepreneurship while exercising caution in rent-seeking activity for sustainable development.

This study uses natural resource rents to represent the environmental impact of entrepreneurial activity. Rents are represented by the difference between the price of a commodity and the average cost of extraction or harvesting it. Natural resource rents used in the study are proxied by the share of total rents for oil, natural gas, coal, mineral and forest in GDP. This proxy is used to denote a comprehensive measure of environmental degradation. However, human emissions of CO₂ and other greenhouse gases are considered the main driver of climate change. In this regard, further study can utilize CO₂ emission as a measure of environmental degradation to investigate the relationship between entrepreneurship and the environment.

Lastly, it is worth noticing that the Pandemic (COVID-19) has been creating several consequences and effects on human behaviours. In this trend, it would be interesting to see how entrepreneurship activity in the pre- and post-COVID (e.g., 2017–2021) affects the natural resources rents.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

Appendix

Notes

References

Abdulahi

M. E.

, Shu

, & Khan

M. A.

(2019). Resource rents, economic growth, and the role of institutional quality: A panel threshold analysis. Resources Policy, 61, 293–303. 10.1016/j.resourpol.2019.02.011

Ahmad

, Draz

M. U.

, Chang

W.-Y.

, Yang

S.-C.

, & Su

(2021). More than the resource curse: Exploring the nexus of natural resource abundance and environmental quality in northwestern China. Resources Policy, 70, 101902. 10.1016/j.resourpol.2020.101902

Ajmi

A. N.

, Hammoudeh

, Nguyen

D. K.

, & Sato

J. R.

(2015). On the relationships between CO2 emissions, energy consumption and income: The importance of time variation. Energy Economics, 49, 629–638.

Asongu

S. A.

, Nwachukwu

J. C.

, & Orim

S.-M.

I. (2018). Mobile phones, institutional quality and entrepreneurship in sub-Saharan Africa. Technological Forecasting and Social Change, 131, 183–203. 10.1016/j.techfore.2017.08.007

Badeeb

R. A.

, Lean

H. H.

, & Clark

(2017). The evolution of the natural resource curse thesis: A critical literature survey. Resources Policy, 51, 123–134. 10.1016/j.resourpol.2016.10.015

Bailey

, & Katz

J. N.

(2011). Implementing panel corrected standard errors in R: The pcse package. Journal of Statistical Software, 42, 1–11.

Baland

J.-M.

, & Francois

(2000). Rent-seeking and resource booms. Journal of Development Economics, 61(2), 527–542.

Balsalobre-Lorente

, Shahbaz

, Roubaud

, & Farhani

(2018). How economic growth, renewable electricity and natural resources contribute to CO2 emissions? Energy Policy, 113, 356–367. 10.1016/j.enpol.2017.10.050

Bekun

F. V.

, Alola

A. A.

, Gyamfi

B. A.

, & Yaw

S. S.

(2021). The relevance of EKC hypothesis in energy intensity real-output trade-off for sustainable environment in EU-27. Environmental Science and Pollution Research, 28(37), 51137–51148. https:/dx.doi.org/10.1007/s11356-021-14251-4

10.

Belaid

, Dagher

, & Filis

(2021). Revisiting the resource curse in the MENA region. Resources Policy, 73, 102225. 10.1016/j.resourpol.2021.102225

11.

Bhattacharyya

, & Hodler

(2010). Natural resources, democracy and corruption. European Economic Review, 54(4), 608–621. 10.1016/j.euroecorev.2009.10.004

12.

Bizri

O. F.

(2018). Innovation and entrepreneurship in the Arab countries. In Bizri

O. F.

(Ed.), Science, technology, innovation, and development in the Arab countries (pp. 455–502). Academic Press.

13.

Borge

L.-E.

, Parmer

, & Torvik

(2015). Local natural resource curse? Journal of Public Economics, 131, 101–114. 10.1016/j.jpubeco.2015.09.002

14.

Buenstorf

(2016). Schumpeterian incumbents and industry evolution. Journal of Evolutionary Economics, 26(4), 823–836. https://link-springer-com-443.web.bisu.edu.cn/article/10.1007/s00191-015-0423-7

15.

Canh

N. P.

, Kim

, & Thanh

S. D.

(2020). Entrepreneurship and natural resource rent-seeking: The roles of institutional quality. Economics Bulletin, 40(2), 1159–1177.

16.

Canh

N. P.

, Nguyen

, Thanh

S. D.

, & Kim

(2021). Entrepreneurship and natural resource rents: Evidence from excessive entrepreneurial activity. Sustainable Production and Consumption, 25, 15–26. 10.1016/j.spc.2020.07.010

17.

Castaño

M.-S.

, Méndez

M.-T.

, & Galindo

M.-Á.

(2015). The effect of social, cultural, and economic factors on entrepreneurship. Journal of Business Research, 68(7), 1496–1500. 10.1016/j.jbusres.2015.01.040

18.

Chambers

, & Munemo

(2019). Natural resource dependency and entrepreneurship: Are nations with high resource rents cursed? Journal of International Development, 31(2), 137–164.

19.

Chell

(2000). Towards researching the ‘opportunistic entrepreneur’: A social constructionist approach and research agenda. European Journal of Work and Organizational Psychology, 9(1), 63–80. https://dx-doi-org-s.web.bisu.edu.cn/10.1080/135943200398067

20.

Chowdhury

, Audretsch

D. B.

, & Belitski

(2019). Institutions and entrepreneurship quality. Entrepreneurship Theory and Practice, 43(1), 51–81.

21.

Dietz

, & Rosa

E. A.

(1997). Effects of population and affluence on CO2 emissions. Proceedings of the National Academy of Sciences, 94(1), 175–179.

22.

Douglas

, & Prentice

(2019). Innovation and profit motivations for social entrepreneurship: A fuzzy-set analysis. Journal of Business Research, 99, 69–79. 10.1016/j.jbusres.2019.02.031

23.

Ehrlich

P. R.

, & Holdren

J. P.

(1971). Impact of population growth. Science, 171, 1212–1217.

24.

Farzanegan

M. R.

, Lessmann

, & Markwardt

(2018). Natural resource rents and internal conflicts: Can decentralization lift the curse? Economic Systems, 42(2), 186–205. 10.1016/j.ecosys.2017.05.009

25.

Fuentelsaz

, Maicas

J. P.

, & Montero

(2018). Entrepreneurs and innovation: The contingent role of institutional factors. International Small Business Journal, 36(6), 686–711.

26.

Gavrila Gavrila

, & De Lucas Ancillo

(2022). Entrepreneurship, innovation, digitization and digital transformation toward a sustainable growth within the pandemic environment. International Journal of Entrepreneurial Behavior & Research, 28(1), 45–66. https://dx-doi-org-s.web.bisu.edu.cn/10.1108/IJEBR-05-2021-0395

27.

Gerelmaa

, & Kotani

(2016). Further investigation of natural resources and economic growth: Do natural resources depress economic growth? Resources Policy, 50, 312–321. 10.1016/j.resourpol.2016.10.004

28.

Gill

A. R.

, Viswanathan

K. K.

, & Hassan

(2018). The environmental kuznets curve (EKC) and the environmental problem of the day. Renewable and Sustainable Energy Reviews, 81, 1636–1642.

29.

Hajzler

(2014). Resource-based FDI and expropriation in developing economies. Journal of International Economics, 92(1), 124–146. 10.1016/j.jinteco.2013.10.004

30.

Halvarsson

, Korpi

, & Wennberg

(2018). Entrepreneurship and income inequality. Journal of Economic Behavior & Organization, 145, 275–293. 10.1016/j.jebo.2017.11.003

31.

Hanif

(2018). Impact of fossil fuels energy consumption, energy policies, and urban sprawl on carbon emissions in East Asia and the Pacific: A panel investigation. Energy Strategy Reviews, 21, 16–24. 10.1016/j.esr.2018.04.006

32.

Havranek

, Horvath

, & Zeynalov

(2016). Natural resources and economic growth: A meta-analysis. World Development, 88, 134–151. 10.1016/j.worlddev.2016.07.016

33.

Heiskanen

, Kivimaa

, & Lovio

(2019). Promoting sustainable energy: Does institutional entrepreneurship help? Energy Research & Social Science, 50, 179–190. 10.1016/j.erss.2018.11.006

34.

Henry

(2019). Transmission channels of the resource curse in Africa: A time perspective. Economic Modelling, 82, 13–20. 10.1016/j.econmod.2019.05.022

35.

Hodler

(2006). The curse of natural resources in fractionalized countries. European Economic Review, 50(6), 1367–1386. 10.1016/j.euroecorev.2005.05.004

36.

Inkizhinov

, Gorenskaia

, Nazarov

, & Klarin

(2021). Entrepreneurship in emerging markets: Mapping the scholarship and suggesting future research directions. International Journal of Emerging Markets, 16(7), 1404–1429. https://dx-doi-org-s.web.bisu.edu.cn/10.1108/IJOEM-11-2019-0988

37.

Jafri

M. A. H.

, Abbas

S. M. Y.

, & Ullah

(2022). Caring for the environment: Measuring the dynamic impact of remittances and FDI on CO2 emissions in China. Environmental Science and Pollution Research, 29(6), 9164–9172. https://dx-doi-org-s.web.bisu.edu.cn/10.1007/s11356-021-16180-8

38.

Jönsson

(2005). Cross-sectional dependency and size distortion in a small-sample homogeneous panel data unit root test. Oxford Bulletin of Economics and Statistics, 67(3), 369–392.

39.

Kalkuhl

, & Brecha

R. J.

(2013). The carbon rent economics of climate policy. Energy Economics, 39, 89–99. 10.1016/j.eneco.2013.04.008

40.

Khan

, Weili

, & Khan

(2022). Environmental innovation, trade openness and quality institutions: An integrated investigation about environmental sustainability. Environment, Development and Sustainability, 24(3), 3832–3862. https://dx-doi-org-s.web.bisu.edu.cn/10.1007/s10668-021-01590-y

41.

Lafuente

, Szerb

, & Acs

Z. J.

(2018). The entrepreneurship paradox: More entrepreneurs are not always good for the economy—The role of the entrepreneurial ecosystem on economic performance in Africa. https://dx-doi-org.web.bisu.edu.cn/10.2139/ssrn.3307617

42.

, Rizvi

S. K. A.

, Rubbaniy

, & Umar

(2021). Understanding the dynamics of resource curse in G7 countries: The role of natural resource rents and the three facets of financial development. Resources Policy, 73, 102141. 10.1016/j.resourpol.2021.102141

43.

Liu

, Zhang

, & Bae

(2017). The impact of renewable energy and agriculture on carbon dioxide emissions: Investigating the environmental Kuznets curve in four selected ASEAN countries. Journal of Cleaner Production, 164, 1239–1247.

44.

Manzano

, & Gutiérrez

J. D.

(2019). The subnational resource curse: Theory and evidence. The Extractive Industries and Society, 6(2), 261–266. 10.1016/j.exis.2019.03.010

45.

Markatou

(2015). Incentives to promote entrepreneurship in Greece: Results based on the ‘New innovative entrepreneurship’ program. Procedia Social and Behavioral Sciences, 195, 1113–1122. 10.1016/j.sbspro.2015.06.157

46.

Marques

A. C.

, & Fuinhas

J. A.

(2012). Is renewable energy effective in promoting growth? Energy Policy, 46, 434–442.

47.

Matthews

C. H.

, & Brueggemann

(2015). Innovation and entrepreneurship: A competency framework. Routledge.

48.

Mthanti

, & Ojah

(2017). Entrepreneurial orientation (EO): Measurement and policy implications of entrepreneurship at the macroeconomic level. Research Policy, 46(4), 724–739. 10.1016/j.respol.2017.01.012

49.

Ndikumana

, & Sarr

(2019). Capital flight, foreign direct investment and natural resources in Africa. Resources Policy, 63, 101427. 10.1016/j.resourpol.2019.101427

50.

Nguyen

C. P.

(2022). The ‘karma’ of impact on the Earth: Will humans take responsibility? Evidence of energy consumption and CO₂ emissions. Environmental Science and Pollution Research, 29(33), 50686–50703. https://dx-doi-org-s.web.bisu.edu.cn/10.1007/s11356-022-19461-y

51.

Pata

U. K.

(2018). Renewable energy consumption, urbanization, financial development, income and CO₂ emissions in Turkey: Testing EKC hypothesis with structural breaks. Journal of Clean Production, 187, 770–779. 10.1016/j.jclepro.2018.03.236

52.

Pata

U. K.

, & Samour

(2022). Do renewable and nuclear energy enhance environmental quality in France? A new EKC approach with the load capacity factor. Progress in Nuclear Energy, 149, 104249. 10.1016/j.pnucene.2022.104249

53.

Pedeliento

, Bettinelli

, Andreini

, & Bergamaschi

(2018). Consumer entrepreneurship and cultural innovation: The case of GinO12. Journal of Business Research, 92, 431–442. 10.1016/j.jbusres.2018.04.019

54.

Pesaran

M. H.

(2004). General diagnostic tests for cross section dependence in panels. Empirical Economics, 60, 13–50.

55.

Polzin

, Sanders

, & Stavlöt

(2018). Do investors and entrepreneurs match?—Evidence from The Netherlands and Sweden. Technological Forecasting and Social Change, 127, 112–126. 10.1016/j.techfore.2017.07.016

56.

Potts

, Foster

, & Straton

(2010). An entrepreneurial model of economic and environmental co-evolution. Ecological Economics, 70(2), 375–383. https://dx-doi-org-s.web.bisu.edu.cn/10.1016/j.ecolecon.2010.09.009

57.

Prieger

J. E.

, Bampoky

, Blanco

L. R.

, & Liu

(2016). Economic growth and the optimal level of entrepreneurship. World Development, 82, 95–109. 10.1016/j.worlddev.2016.01.013

58.

Reynolds

P. D.

, Camp

S. M.

, Bygrave

W. D.

, Autio

, & Hay

(2001). GEM global entrepreneurship report, 2001 summary report. Kauffman Center for Entrepreneurial Leadership at the Ewing Marion Kauffman Foundation.

59.

Rock

M. T.

, & Angel

D. P.

(2007). Grow first, clean up later? Industrial transformation in east Asia. Environment: Science and Policy for Sustainable Development, 49(4), 8–19.

60.

Shir

, Nikolaev

B. N.

, & Wincent

(2018). Entrepreneurship and well- being: The role of psychological autonomy, competence, and relatedness. Journal of Business Venturing, 34(5). 10.1016/j.jbusvent.2018.05.002

61.

Shittu

, Adedoyin

F. F.

, Shah

M. I.

, & Musibau

H. O.

(2021). An investigation of the nexus between natural resources, environmental performance, energy security and environmental degradation: Evidence from Asia. Resources Policy, 73, 102227. 10.1016/j.resourpol.2021.102227

62.

Simón-Moya

, Revuelto-Taboada

, & Guerrero

R. F.

(2014). Institutional and economic drivers of entrepreneurship: An international perspective. Journal of Business Research, 67(5), 715–721.

63.

Sun

(1999). The nature of CO2 emission Kuznets curve. Energy Policy, 27(12), 691–694.

64.

Sutter

, Bruton

G. D.

, & Chen

(2019). Entrepreneurship as a solution to extreme poverty: A review and future research directions. Journal of Business Venturing, 34(1), 197–214. 10.1016/j.jbusvent.2018.06.003

65.

Tenaw

, & Beyene

A. D.

(2021). Environmental sustainability and economic development in sub-Saharan Africa: A modified EKC hypothesis. Renewable and Sustainable Energy Reviews, 143, 110897. 10.1016/j.rser.2021.110897

66.

Torvik

(2002). Natural resources, rent seeking and welfare. Journal of Development Economics, 67(2), 455–470. 10.1016/S0304-3878(01)00195-X

67.

United Nations. (2019a). The sustainable development agenda. https://www.un.org/sustainabledevelopment/development-agenda/

68.

United Nations. (2019b). The sustainable development goals report 2019. https://unstats.un.org/sdgs/report/2019/

69.

Vernet

, Khayesi

J. N. O.

, George

, & Bahaj

A. S.

(2019). How does energy matter? Rural electrification, entrepreneurship, and community development in Kenya. Energy Policy, 126, 88–98. 10.1016/j.enpol.2018.11.012

70.

Yang

, Xia

, Zhang

, & Yuan

(2018). What matters for carbon emissions in regional sectors? A China study of extended STIRPAT model. Journal of Cleaner Production, 180, 595–602.

71.

Youssef

A. B.

, Boubaker

, & Omri

(2018). Entrepreneurship and sustainability: The need for innovative and institutional solutions. Technological Forecasting and Social Change, 129, 232–241.