
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12675-8
10.1016/j.heliyon.2024.e36644
e36644
Research Article
Financial resource curse and firms' accessibility to financing in resource-based countries
Hu Jie Janehu628@163.com
a
Chen JianMing Jameschen518@126.com
a⁎
Zaib Shah manzoor137000@gmail.com
b
a School of Management, Hebei GEO University, Shijiazhuang, 050031, Hebei, China
b Department of Economics, Bahauddin Zakriya University, Multan, Pakistan
⁎ Corresponding author. Jameschen518@126.com
22 8 2024
15 9 2024
22 8 2024
10 17 e3664413 11 2023
13 8 2024
20 8 2024
© 2024 Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
This study delves into the paradox of the financial resource curse, exploring how the abundance of natural resources in a country paradoxically constrains firms' accessibility to financing. Despite the potential economic boon natural resources represent, evidence suggests that they can lead to less diversified economies, making it challenging for firms outside the resource sector to access financing. Our research aims to dissect this phenomenon by analyzing microeconomic statistics on the financial accessibility of enterprises, juxtaposed with macroeconomic statistics across 170 countries, encompassing over 10,000 firms surveyed from 1990 to 2022. The panel regression analysis allows us to control for both time-invariant country characteristics and global economic trends, providing insights into the causal relationship between resource dependence and financial access for firms. The results are striking, revealing that, indeed, countries with significant natural resource wealth tend to exhibit reduced financial accessibility for firms outside the resource extraction sector. The panel regression models indicate a robust negative correlation between the extent of a country's resource wealth and the ease with which non-resource firms can access financial capital. This suggests that the financial resource curse is not only a real phenomenon but also one that has significant implications for economic diversification and sustainable development. Moreover, findings underscore the need for targeted policy interventions. Countries with abundant natural resources should implement strategies that foster economic diversification, enhance the financial infrastructure to support a broader range of industries, and encourage the development of financial instruments tailored to the needs of non-resource sectors.

Keywords

Firms' accessibility to financing
Panel regressions
Transmission channels
Financial resource curse
==== Body
pmc1 Introduction

In resource-rich countries, an abundance of natural resources is often perceived as an economic advantage, promising wealth and development opportunities. However, this perceived boon can lead to a paradoxical scenario where the economy becomes overly dependent on the resource sector. This dependence can skew financial systems and markets, prioritizing resource-based industries at the expense of diversification. As a result, firms operating outside the resource sector frequently encounter challenges in accessing financing. These challenges stem from a variety of factors, including the allocation of capital towards resource extraction and processing industries, a perceived higher risk in non-resource sectors, and a lack of financial products tailored to the needs of diverse enterprises. This scenario, known as the financial resource curse, suggests that the wealth generated from natural resources can inadvertently hinder broader economic growth by limiting access to finance for a large segment of the economy. Such constraints on financial accessibility can stifle innovation, entrepreneurship, and the development of a multifaceted economy, ultimately affecting the country's long-term economic health and sustainability.

Extensive literature has attempted to determine if natural assets are a “curse” or a “blessing.” The significance of this controversy is heightened in our present period due to our current escalation in commodity prices, particularly in our oil and gas sectors. This increase can be attributed to various factors such as our coronavirus disasters, our Russian-Ukraine conflict, along our global shift towards energy and environmental transformation undertaken by many nations. Although the negative consequences of rising commodity prices on countries that import resources and energy are well discussed, our impact on exporting nations needs to be thoroughly examined [1]. Nevertheless, our inquiry into the consequences of these modifications in our resource-dependent nations is as crucial. Undoubtedly, there is an anticipated significant decline in energy and oil exports in the future, resulting in reduced windfall profits and heightened instability for nations dependent on natural resources. A reduction in windfall profits might alleviate the burden on the natural resource industry. Still, it could also lead to significant structural changes in economies that rely on these resources and hinder enterprises' accessibility to funding [2]. If the allocation of resource income continues to be inefficient, it might lead to increased ambiguity and volatility, further restricting firms' accessibility to funding. Predicting the net consequences of the energy shift in terms of the resource curse is challenging. However, research may anticipate that these consequences will depend on the kind of resources involved, such as energy or minerals, as well as the approach taken by nations undergoing the energy shift. The subject of the correlation between natural assets and economic growth remains unresolved, with the economic sector playing a vital role in this regard (C. [3]). The growth of resource-based nations may be hindered by financial instability, restricted credit availability, and limits in the firm's accessibility to financing.

In contrast to earlier studies that primarily examined the financial resource curse from an economic standpoint, utilizing aggregated measures of economic development such as banks' loans to the private sector as a percentage of GDP, this approach incorporates combined microeconomic along macroeconomic data. It allows researchers to examine the processes that elucidate our reasons why nations dependent on natural resources are linked to underdeveloped economic systems in a much more accurate and detailed manner. This work adds to the current body of research by examining the means through which enterprises get financial resources, utilizing equally microeconomic and macroeconomic data. The sample composition adheres to the empirical methodology outlined by Ref. [4]. Despite the majority of articles, which often concentrate on samples from countries that rely on resources, a substantial sample was utilized that includes resource-based along with non-resource-based nations in order to mitigate any potential prejudice in sample selection. This research examines the correlation between the amount of money earned from natural resources in different nations and the ability of enterprises to get financial resources in economies that rely on natural resources. It also takes into account the hypothetical changes in firms' accessibility to finance in economies that do not rely on natural resources. Hence, this methodology enables us to more accurately ascertain the correlation between the financial resource curse and the increased limitations on enterprises' economic access in countries reliant on natural resources [5].

The research reveals compelling and reliable information that companies functioning in nations with abundant natural resources have limited access to foreign funding (Q. [6]). This leads to remaining stable and reliable when looking at various alternative specifications. In summary, this research has thoroughly examined many possible causes of bias related to the initial estimates, including skipped factors concurrently, measurement faults, sample choice, and outliers. This research further delineates variations associated with the impacts of institutional quality and the degree of supply limitations. Moreover, the findings demonstrate a noteworthy and inverse relationship between the amount of natural asset rents in a country and the ability of enterprises to get financial resources. However, this association is only seen among firms that are not involved in the natural asset industry. This presents further confirmation of the Dutch disease phenomena, as the insufficient funding of enterprises may account for the decline of sectors unconnected to the natural asset industry.

This study makes significant contributions to the existing body of knowledge on the financial resource curse by employing a comprehensive and detailed approach to examine the interplay between natural resource wealth and firms' financial accessibility over a substantial time period from 1990 to 2022. Utilizing a panel regression model, which meticulously accounts for both country-specific and temporal variables, this research offers new insights into how the abundance of natural resources impacts the economic fabric of nations across 170 countries. This wide geographical coverage ensures that the findings are not only robust but also applicable to a diverse array of economic contexts and developmental stages. A key contribution of this study is the extensive dataset of over 10,000 firms surveyed, which provides a granular view of the challenges and opportunities faced by firms in accessing finance in resource-rich countries. The longitudinal nature of the data allows for an in-depth analysis of trends over time, offering a dynamic perspective on how the relationship between natural resource wealth and financial accessibility has evolved. The panel regression method applied herein is particularly adept at teasing out the causal relationships within the data, controlling for potential confounders and highlighting the direct impact of resource wealth on financial access. Moreover, this research enriches the discourse on economic diversification and sustainable development by pinpointing the mechanisms through which natural resource abundance can lead to a financial resource curse. By shedding light on this paradox, the study opens up avenues for policy interventions aimed at mitigating the adverse effects of resource dependence on financial accessibility. In doing so, it provides a roadmap for resource-rich countries to leverage their natural wealth in a manner that promotes economic diversification, supports the growth of non-resource sectors, and fosters a more inclusive and sustainable economic development model. In summary, the contributions of this study are manifold, offering a comprehensive, empirically grounded analysis that bridges microeconomic realities with macroeconomic policies across a diverse set of regions and over a significant time period. This work not only advances academic understanding of the financial resource curse but also offers practical insights for policymakers looking to harness natural resource wealth for broader economic benefit.

2 Literature review

Following the pioneering research conducted in Denmark, other authors are carrying the empirical investigations to get a deeper understanding of how this phenomenon, known as the financial resource curse, emerges in the economy and its distinctive characteristics. In general, the research that has been done up to this point has focused on four primary channels that define the financial resource curse. The channels include the institutional standards, the vulnerabilities of the financial sector, and specific constraints on supply.

The financial resource curse theory is grounded in the broader resource curse literature, which posits that countries rich in natural resources often experience slower economic growth and development compared to those with fewer natural resources [7].was among the first to articulate the concept of the resource curse, suggesting that resource wealth can lead to economic distortions and governance challenges. This theory was later expanded to include the financial sector, with scholars arguing that resource wealth impacts financial development [8]. The mechanism often cited includes the Dutch Disease, where the influx of foreign currency from resource exports appreciates the local currency, making non-resource exports less competitive and leading to a decline in their production and, subsequently, in the diversity of economic and financial development. Empirical studies on the financial resource curse have shown mixed results, which may be attributed to differences in country contexts, time periods analyzed, and methodologies employed. Some studies, like those by Ref. [9], find that resource wealth negatively affects financial sector development by reducing the incentives for financial intermediation and diversification. Others, such as [10], argue that the relationship between resource wealth and financial development is contingent on factors like the level of institutional quality, with resource wealth potentially promoting financial development in countries with strong institutions. In relation to institutions, Wang et al. [11] observe that the presence of ample natural resources acts as an impediment to economic progress only in the presence of inadequate institutions. Based on the studies conducted by Y. Wang et al. [12], Al-Fakir Al Rabab'a et al. [13], and Abdoh & Varela [14], it is evident that when agreements are violated and corruption, such as wage theft, occurs, residents have limited money to invest in banks. Consequently, there needs to be a greater degree of economic intermediation, which subsequently impedes the progress of economic growth. Furthermore, several academics highlight the importance of the quality of institutions in the occurrence of the financial resource curse [15]. Many studies conducted by Lee et al. [16], Nelson & Allwood [17], and D. Zhang et al. [18] have shown that the phenomenon known as the financial resource curse, which leads to limited financial growth, is seen in cases where regulatory institutions are lacking in strength. In addition, other sources, including Fazzari et al. [19], Edmans et al. [20], and Yoon & Ratti [21], support this idea by demonstrating that nations with more robust institutions are less susceptible to (severe) economic crises caused by fluctuations in commodity prices. Hence, the effective establishment of their economic system helps to alleviate the incidence of the resource curse in Denmark.

A drawback of prior research is its exclusive focus on the financial resource curse inside the banking industry. Another constraint is that the studies have mainly been carried out at a macroeconomic scale. As far as research knows, there have been few papers that have lately utilized microeconomic data to examine the phenomenon known as the financial resource curse [22]. Furthermore, these papers only concentrate on data pertaining to individual banks and are solely derived from a select group of essential variables, including banks' liquidity, efficiency, and loan availability. The occurrence referred to be the financial resource curse can definitely be seen inside the banking sector, where it is linked to inadequate financial intermediation. The financial resource curse, a phenomenon that affects whole economies, can often manifest itself inside individual firms. Within nations that heavily rely on natural resources, firms often need help in obtaining bank financing. Consequently, this lack of access to financial resources hampers their capacity to make investments and grow their company (Y. [23]). Consequently, companies operating in nations affected by the financial resource curse might have limited accessibility to exterior financing for their investment initiatives, particularly regarding bank credit. Furthermore, resource-based countries are inherently vulnerable to fluctuations in commodity prices, making them more fragile. As a result, banks impose a more significant risk premium on their loans to companies in these economies. This leads to a decrease in the availability of credit and exacerbates the problem of credit rationing. As earlier emphasized by Jordan & Philips (20 18) and Amighini et al. [24], the government's active participation in investment in resource-based nations leads to an increase in credit restriction to the private sector. A significant contribution to the empirical literature is the analysis of firm-level data to understand how resource wealth affects firms' access to finance. Studies utilizing firm-level data, such as those by Refs. [25], provide evidence that firms in resource-rich countries face greater challenges in accessing finance, attributed to the sectoral crowding out of capital and the concentration of financial resources in the resource sector. Despite these contributions, several gaps remain in the literature. First, there is a need for more empirical research that combines firm-level data with country-level indicators of resource wealth and financial development to provide a more nuanced understanding of the financial resource curse. Second, the role of political and institutional factors in mediating the relationship between resource wealth and financial accessibility has been underexplored. Lastly, much of the existing research focuses on specific regions or countries, calling for studies with a broader geographical scope that can offer generalizable insights.

This study aims to fill these gaps by analyzing a comprehensive dataset of over 10,000 firms across 170 countries from 1990 to 2022. By employing a panel regression model, it examines the impact of natural resource wealth on firms' financial accessibility, taking into account the mediating roles of institutional quality, financial development, and political stability. This approach allows for a more detailed exploration of the mechanisms through which resource wealth influences financial accessibility and the conditions under which this effect is exacerbated or mitigated. The complex relationship between natural resource wealth and financial accessibility for firms. By drawing on theoretical insights and empirical evidence from past studies, this research contributes to a deeper understanding of the financial resource curse, offering new perspectives on how resource-rich countries can navigate the challenges associated with their natural wealth to foster a more inclusive and diversified economic growth.

3 Data

3.1 Micro-macro dataset

The goal of this research is to examine the problem regarding the financial resource curse. This was done by using a unique technique that combines statistics on enterprises' accessibility to finance at the microeconomic stage with statistics on nations' stage of natural asset rents at the macroeconomic stage, with a specific emphasis on resource rents. Therefore, data was combined from the World Bank's Enterprise Studies database (WBES) on the particular characteristics of small-scale businesses with information from the World Bank's World Development Indicators (WDI) database on the overall level of natural resource rents in countries. In order to get a deeper understanding of the correlation between the financial resource curse and the limitations faced by enterprises in resource-based nations when it comes to accessing economic resources, Wu et al. [26]analyze a comprehensive dataset that includes both nations reliant on natural resources and those that are not. This enables the regulation of our hypothetical changes in companies' financial availability in nations that are not dependent on natural resources. The ultimate dataset was obtained by following five stages. Initially, microeconomic stage data sourced was used via the WBES database. The statistics provided encompass comprehensive data on a wide range of companies examined globally, like both resource-based and non-resource-based nations across various income levels, spanning from lowest to high. The WBES comprises many waves of studies conducted in various country cohorts worldwide between 1990 and 2022.

Furthermore, it is crucial to acknowledge that while this database encompasses the period from 1990 to 2022, statistics on individual firms within a particular nation are only accessible during the years when studies were conducted in that state. Argentina had surveys in 2000, 2009, and 2022. Therefore, research possesses data pertaining to the company level just for this nation, limited to the years 1990,2000, 2009, and 2016, throughout the timeframe of 1990–2022. Across different nations, this research was conducted from one to four waves of studies, resulting in a range of one to five years of studies at the firm stage for every nation. Instead, it consists of data obtained at specific years based on the nation being analyzed.

The researchers conducted a comparison of countries with years in the WBES dataset using macroeconomic information on the quantity of natural resource rents obtained from the WDI dataset. The primary analyses primarily concentrate on nations with energy rent due to their substantial importance in identifying the phenomenon of the financial resource curse as well as the unique fiscal attributes. This is because nations that get revenue from energy resources are often studied in relation to this phenomenon. A contentious discussion is on the quantification of natural assets, particularly regarding the comparison between resource availability factors assessed by net subsurface resources or proved reserves and resource reliance factors often calculated as the ratio of significant exports to GDP. In addition to the characteristics of the proxies, this selection of measurement also affects the possible variability of the resource's measurements [27]. There are four primary justifications for the decision to analyze energy rent statistics in relation to GDP. Firstly, it allows us to act as a representative of the significant financial value that resource rents have in each nation within the sample. Moreover, this method guarantees compliance with previous empirical studies regarding the financial resource curse since resource rent figures, which are evaluated in proportion to gross domestic product, are extensively used to assess the extent of resource rents throughout the national level [28]. Furthermore, this dataset is accessible for a substantial number of nations and periods.

After finishing our second step, matching the microeconomic information from the WBES alongside the macroeconomic data via the WDI enabled researchers to get a precise compilation of countries and years that could be included in the sample. It is worth mentioning that the database does not include several Middle Eastern nations, including UAE, Syria, Saudi Arabia, Oman, Qatar, Kuwait, Libya, Iran, Algeria, and Bahrain, owing to limitations in data accessibility. The nations have yet to be studied and, as a result, are not contained in the WBES data. Nevertheless, the WBES includes several nations from this area, such as Iran, Tunisia, and Egypt, among other nations known for their economy's firm reliance on the extraction of natural assets, including Brazil, Kazakhstan, and Venezuela. The research aims to align microeconomics with macroeconomic statistics by taking into account the delayed effect of resource rents on enterprises' financial accessibility [29].

Extending the duration to a six-year interval (based on t to t-5) ought to lead to a convergence of the period included by the queried years for many countries. Ultimately, this hinders the ability to accurately determine the delayed impact of resource rents on the studied enterprises' financial access in a particular year. Colombia, for instance, undertook surveys in 2007, 2009, and 2016. Upon completion of this stage, a comprehensive dataset was obtained that combines microeconomic and macroeconomic facts, including over 10,000 studied enterprises across 170 countries during the timeframe of 2005–2022. Once the specific framework of the database was established, the fourth stage was selecting the exact factors that would be used in the econometric research. These factors will be used to evaluate the extent of enterprises' accessibility to financing at a microeconomic stage and resource rents at a global stage. Therefore, given the objective of this article is to evaluate whether companies functioning in nations with abundant natural resources have challenges in obtaining foreign funding, research must include a factor that accurately represents enterprises' ability to get credit. The factor in the World Bank Enterprise Survey (WBES) that is probably directly associated with this problem is the Credit line factor [30]. This factor is assigned a value of 1 if the studied business reports possessing a line of credit or a line of credit or loan may be obtained from a financial institution, such as a banking institution or another economic organization. If it is not obtained, its value is 0.

Regarding resource rents throughout the national stage, this article explicitly examines resource rents. The Energy rent factor is equivalent to the primary resource rent factor that will be used in this econometric research. In the fifth phase, fiscal limitations were considered that are not influenced by specific features of firms but rather reflect the particular industry they function in. For every nation and year in the research, the mean value of the Credit line factor was calculated for every industry field [31]. Undoubtedly, the industry in which companies function is a fundamental component that influences their capacity to get foreign funding. By examining the stage of firms' accessibility to finance at the sector stage, financing limitations can be better understood by firms involved in the WBES.

3.2 An initial examination of the connection between resource rents along businesses' access to financing

Table 1 presents the results of a t-test that examines the uniformity of averages for the Credit line factor across various threshold values of the resource rents factor. Resource-rich nations can be defined as those whose Resource rents account for 6 % or more of Gross domestic product in the initial test and 11 % or more of Gross domestic product in the next test. Nations that fall under these limits are classified as non-resource-based nations in the two scenarios. Regardless of the criteria used, nations that rely on natural resources are strongly correlated with reduced accessibility to financing for businesses compared to nations that do not rely on natural resources. This indicates a detrimental impact of the magnitude of resource rents affecting enterprises' ability to obtain financial resources. Furthermore, it is worth noting that the extent of this adverse impact becomes more pronounced as the resource rents rise: 31 % compared to 45 % in the scenario with a 6 % barrier and 28 % compared to 44 % in the scenario with an 11 % threshold.Table 1 Average comparison test of resource rents for enterprises' access to financing.

Table 1Credit-line Mean	Resource Rents (t-1) ≥ 5 % GDP	31.77	
Resource Rents (t-1) < 5 % GDP	45.06	
(t-Test P-Value)	0	
Resource Rents (t-1) ≥ 10 % GDP	28.91	
Resource Rents (t-1) < 10 % GDP	44.4	
(t-Test p-Value)	0	
Note: The credit line is evaluated at the sector level for each nation and year analyzed. The national energy rent criteria (6 % & 11 %) are calculated one year prior to each research year.

This Fig. 1 illustrates the temporal evolution of enterprises' financial accessibility. The general trajectory is positive, as the proportion of companies having financial accessibility has risen from 55 % in 2010 to 75 % in 2020. Nevertheless, there is a degree of fluctuation in this pattern based on the magnitude of the company. As an illustration, the proportion of small enterprises with financial access has risen from 20 % in 2010 to 30 % in 2020, while the proportion of large enterprises with financial access has stayed relatively constant at approximately 75 %. In general, the graphic indicates a distinct correlation between the size of a company and its ability to get financial resources. Big corporations possess a more significant proportion of financial resources compared to smaller businesses. This can be attributed to various variables, such as the superior bargaining strength of larger enterprises, their capacity to provide more collateral, and their more robust track records. Furthermore, it is essential to highlight that there is a positive overall trend in enterprises' access to credit. This is a favorable advancement, indicating that an increasing number of companies are obtaining the necessary money to expand and progress. Nevertheless, it is crucial to acknowledge that there is still some disparity in this pattern based on the size of the company. Smaller enterprises still need more financial resources compared to larger enterprises. This matter requires attention since it can constrain the growth prospects of small enterprises.Fig. 1 Trend of firms on Access to finance.

Fig. 1

Based on many parameters, the figure examines essential trends and linkages connected to enterprises' access to financing. First of all, since they can provide more collateral and are seen as less hazardous ventures, big businesses have easier access to financing than small and medium-sized businesses. It also emphasizes how American businesses are distributed throughout industries, with the bulk of them in the service sector, followed by manufacturing, agriculture, and mining. The paper also identifies a positive relationship between profit margin and company size, showing that more prominent companies often have more significant profit margins because of more considerable resources and economies of scale. Furthermore, the paper highlights a general upward tendency in enterprises' access to financing throughout time, attributing it to developments in financial markets, novel financial products, and globalization [32]. In conclusion, the paper emphasizes how a firm's access to financing is complex and impacted by several factors, such as its size, industry, and profit margin.

4 Econometric estimation

4.1 Variable description

We analyze various variables to understand the impact of resource rents on firms' financial accessibility across 170 countries from 1990 to 2022. The primary independent variable, resource rents, represents the income derived from natural resources. These rents are expected to have a significant negative effect on the financial accessibility of firms outside the resource extraction sector, as indicated by the consistently negative coefficients across all model specifications. The dependent variable, financial accessibility, is measured by the availability of credit lines to firms, reflecting their ability to secure external financing.

Additionally, several control variables are included to account for other factors influencing financial accessibility. Log GDP per capita is used as a measure of economic development, with higher values indicating more developed economies, which may offer better financial infrastructure and thus improve firms' access to financing. However, the coefficients for GDP per capita are not statistically significant, suggesting that the direct effect of economic development on financial accessibility is less pronounced in the presence of resource rents.

Log population is included to control for the size of the economy, with larger populations potentially indicating larger domestic markets and possibly greater financial resources. Similar to GDP per capita, the population variable does not show a significant impact on financial accessibility in most models. Log trade openness and log terms of trade are included to capture the effects of a country's integration into the global economy and the relative value of its exports and imports, respectively. These variables help to understand how external economic factors might influence the financial environment within a country.

Firm-level characteristics are also crucial in this analysis. Firm size is measured by the number of employees, with larger firms generally having better access to financial resources due to their established presence and creditworthiness. The results show a highly significant positive relationship between firm size and financial accessibility, indicating that larger firms are more likely to obtain credit lines. Firm age, representing the number of years a firm has been in operation, is another control variable. Older firms might have more established credit histories, making it easier for them to secure financing. However, the coefficients for firm age are not significant, suggesting that age alone does not significantly affect financial accessibility.

Another firm-level variable is firm export status, which indicates whether a firm is engaged in exporting goods and services. Exporting firms may have better access to financing due to their involvement in international trade and potentially higher revenues. Despite this, the coefficients for firm export status are not statistically significant, implying that exporting does not have a substantial direct effect on financial accessibility in the context of resource-dependent economies.

To control for broader economic and policy environments, time-fixed effects, sector-fixed effects, regional-fixed effects, and country-fixed effects are included in various models. These fixed effects help isolate the impact of resource rents by accounting for unobserved heterogeneity across different time periods, sectors, regions, and countries. The inclusion of these fixed effects improves the explanatory power of the models, as indicated by the increase in R-squared values, which range from 0.09 in the simplest model to 0.87 in the most comprehensive model.

Overall, the analysis reveals a robust negative correlation between resource rents and the financial accessibility of non-resource firms, underscoring the existence of a financial resource curse. This suggests that countries with abundant natural resources face significant challenges in providing financial access to firms outside the resource sector, highlighting the need for policies that promote economic diversification and strengthen financial infrastructure.

4.2 The model details

As already stated, the database integrates microeconomic statistics on enterprises' financial accessibility with macroeconomic statistics on nations' resource rents. Every nation's number of observations in the temporal dimension correlates to the number of studies conducted in that nation. Consequently, the quantity of accessible measurements in the temporal dimension varies throughout nations, and the time interval across the two observations is diverse. Furthermore, the variation in the companies polled may differ from one research to the next within a particular nation. This emphasizes the need to examine enterprises' ability to get financial resources at the industry stage in order to obtain a more accurate understanding of the limitations they face in nations that rely on natural resources.

Because of the unique format of the panel database and in contrast to past empirical evaluations that consider variability problems like reverse correlation and skipped factors bias, research cannot predict the connection between resource rents and firms' financial accessibility utilizing a dynamic panel fixed-effects model. Actually, using a one-period delay of the dependent factor is irrelevant in the situation due to the fact that certain nations have only been polled once (T = 1). Furthermore, in nations that have conducted a minimum of two polls (T ≥ 2), the intervals between each research are not uniform. This model incorporates a wide range of fixed factors, particularly those related to time, sector, and nation. This allows to mitigate, to the greatest extent feasible, the influence of various unmeasured differences that might distort the calculated impact of energy revenues on companies' ability to get funding. Time-fixed effects are typically used to account for latent disturbances that may affect all studied nations during a particular year. Additionally, they take into consideration the variation in survey times across nations [33]. In order to account for unobservable sector-specific features that may affect both enterprises' accessibility to credit and their susceptibility to changes in macroeconomic resource rents, sector-fixed effects are also contained. Country fixed effects have entailed accounting for unobservable features of nations that may impact their reliance on resource rents, as well as the level of financial limitations faced by enterprises.

In order to assess the impact of resource rents on enterprises' ability to get financing, a basic econometric model was used that takes into account variations across different sectors:Creditlinej,i,t=α0+α1Energyrentsi,t−1+∑t=1Tα2tTD+∑j=1Jα3jSD+∑i=1Iα4iCD+∑k=1Kα5kZk,j,t+∑p=1Pα6pXp,i,t−1+εj,i,t

The credit line (denoted as line i,j,t) represents the proportion of companies in sector j through nation i that report possessing a credit line or loan through a bank or related fiscal organization in the specified research year t. Resource rents,t-1: the number of resource rents in nation i at year t-1 prior to the research expressed as a proportion of GDP. Zk,j,t refers to the microeconomic control factor k for sector j in the research year t. Xp, i,t-1 refers to the macroeconomic control factor p for nation i in the year before the research, denoted as t-1. (TD: time dummies, CD: country dummies, SD: sector dummies). The term εj, i,t represents the error in sector j for nation i in research year t.

Resource rents were also noticed in the year before the research, denoted as year t-1. This allows us to consider the possibility of a delayed impact caused by changes in resource rents on the global stage on the ability of enterprises to get financing. Furthermore, by taking into account the delayed impact of Resource rents, research can prevent any possible issue of simultaneous occurrence between the magnitude of resource rents and enterprises' ability to get financial resources. Undoubtedly, in nations that heavily rely on natural assets, industries involved in the extraction and use of these assets have significant financial significance [34]. If these enterprises have improved financial availability, they have the potential to create more profits and so become more inclined to participate in the utilization of power resources. This might impact the proportion of power profits.

The regression analysis examines the relationship between the proportion of enterprises in marketplace j that report with a line of credit or bank loan or a different fiscal organization in a specific nation during a particular year (e.g., 2017). The analysis considers the influence of Argentina's stage of resource rents and financial control factors in the year before the research (e.g., 2016), as well as small-scale control factors specific to sector j during the research year (e.g., 2017). Additionally, the analysis accounts for fixed effects related to time, sector, and nation.

5 Key outcomes

Table 2 displays the vital econometric results, beginning with a simple pooling model that does not include fixed effects (I) and progressing to a model that includes fixed effects for time, nation, and sector, as well as essential controls (IX). These nine predicted models allow us to account for a wide range of indistinguishable and identified factors that cause variation.Table 2 Resource rents and businesses' financial accessibility.

Table 2	Resource Rents & Credit-line [Sector Level Statistics]	
(I)	(II)	(II)	(IV)	(V)	(VI)	(VII)	(VIII)	(IX)	
Resource Rents (t-1)	−0.823***	−0.857***	−0.856***	−0.824***	−0.545b	−0.547a	−0.569a	−0.570a	−0.557a	
[0.148]	[0.140]	[0.132]	[0.119]	[0.277]	[0.273]	[0.272]	[0.276]	[0.267]	
Log GDP per Capita (t-1)						0.120		−1.838		
					[11.61]		[10.39]		
Log Population (t-1)						0.603		−1.552		
					[13.213]		[12.75]		
Log-Trade-openness (t-1)						0.937		−0.902		
					[4.010]		[3.377]		
Log-Term-Trade (Tt1)						5.140		4.448		
					[8.036]		[7.335]		
Firm size (t)							19.17***	18.94***	19.23***	
						[3.310]	[3.263]	[2.413]	
Firm age (t)							0.141	0.107		
						[0.239]	[0.247]		
Firm export (t)							−0.0248	−0.0255		
						[0.0661]	[0.0690]		
Time-Fixed-Effects	Not	Right	Right	Right	Right	Right	Right	Right	Right	
Sector-Fixed-Effects	Not	Not	Right	Right	Right	Right	Right	Right	Right	
Regional-Fixed-Effects	Not	Not	Not	Right	Not	Not	Not	Not	Not	
Country-Fixed-Effects	Not	Not	Not	Not	Right	Right	Right	Right	Right	
Opinions	14920	14920	14920	14920	14920	14920	14920	14920	14920	
Country	170	170	170	170	170	170	170	170	170	
R-squared	0.09	0.26	0.31	0.53	0.85	0.85	0.87	0.87	0.87	
standard errors resistant to within-country interactions are supplied in parentheses; coefficients presented are marginal impacts.

a p < 0.05.

b p < 0.1.

The model's forecasting ability is significantly enhanced by integrating nation-fixed effects in requirements (V) - (IX), making it an essential factor. Therefore, a significant portion of the variability in the dependent factor is accounted for by the explanatory factors (85 % or more), indicating the strength and reliability of the findings.

To be more exact, Table 2 yields four prominent vital points. The Credit line factor has a strong and inverse correlation with Resource rents, irrespective of the model being examined. Therefore, companies functioning in nations with considerable resource rents are expected to have challenges in securing external funding, indicating the existence of a financial resource curse. Furthermore, the inclusion of nation-fixed effects leads to a decrease in the coefficient value linked to Resource rents [35]. It is essential to take into account the indistinguishable variability at the national level. The coefficient for Resource rents remains consistently steady in regressions (V) – (IX) even when additional small-scale and large-scale economic control factors are included in the collection of explanatory factors. Out of all the control factors, only the businesses' size factor shows statistical significance. The inclusion of country-fixed effects is expected to entail the majority of the data contained in the financial control factors.

Similarly, microeconomic control factors have a substantial correlation to sector-fixed effects. Considering the model (IX), which includes just the essential control factors, due to its simplicity and informative nature, it will serve as the reference specification for the remainder of the research. Furthermore, research shows that a 1 % rise in resource rents results in a significant 0.5 percentage point decrease in companies' credit availability, indicating a substantial impact.

In summary, the findings presented in Table 2 support the financial resource curse theory and validate the previous macroeconomic findings at the company stage, namely via a novel demand-side mechanism. Research has discovered compelling data indicating that companies functioning in nations with abundant natural resources have limited access to external funding, notably reduced availability of bank loans. Resource-based countries are inherently vulnerable to fluctuations in commodity prices (Saheb & Dehghani, n.d.). Consequently, banks impose a more significant risk premium on credit extended to businesses in these countries. This, in turn, limits the availability of credit and exacerbates the problem of credit rationing.

A correlation matrix, which is a square matrix that shows the associations between variables and has correlations ranging from −1 to 1, is discussed in Fig. 2. A perfect positive connection is represented by a correlation of 1, a perfect negative relationship by a correlation of −1, and no correlation by a correlation of 0. Every variable in the matrix above has positive correlations. Resource reliance and profit margin have the most significant association (0.96), suggesting that companies with more resources often have greater profit margins. Furthermore, there are significant positive connections shown between resource reliance and business size (0.23), profit margin and credit availability (0.82), and firm size and credit availability (0.83). According to the matrix, companies that possess enough resources, are more significant, and have access to financing are generally more likely to have better profit margins since these factors have positive correlations with each other.Fig. 2 Correlation matrix.

Fig. 2

5.1 Robustness analysis

Research enhances the reliability of the former findings by incorporating extra financial control factors, evaluating the impact of resource rents on firms' financial accessibility utilizing firm-level statistics, employing alternative factors to represent firms' financial accessibility and resource rents, addressing various statistical concerns, including sample makeup, outliers, and placebo test, and by explicitly addressing latent diversity problems through the use of exterior instruments for the Resource rents factor. These robustness tests are conducted to meticulously safeguard the baseline findings against the potential impact of (i) unaccounted factors, (ii) bias arising from simultaneous effects, (iii) inaccuracies in measurement, (iv) bias due to the makeup of the sample and (v) extreme data points.

5.2 Firm-level approximations

In Table 3, the research examines the link between resource rents and enterprises' accessibility to credit at the company stage, which challenges the earlier reliance on sector-based calculations. Using binary ‘probit’ and ‘logit’ approaches, the objective is to evaluate the impact of resource rent on the likelihood of enterprises obtaining exterior finance. For this purpose, research is taking into account the afterward description of the econometric model:P(Creditlinef,j,i,t=1)=α0+α1Energyrentsi,t−1+∑t=1Tα2tTD+∑j=1Jα3jSD+∑i=1Iα4iCD+α5Firmsizef,j,i,t+εf,j,i,t

Table 3 Firm-level statistics on energy rents and enterprises' accessibility to financing.

Table 3	Resource Rents & Credit-line [Sector Level Statistics]	
Probit	Login	Probit	Login	
(I)	(II)	(II)	(IV)	
Resource Rents (t-1)	−0.00923a	−0.00946a	−0.00435b	−0.00443b	
[0.00121]	[0.00133]	[0.00262]	[0.00267]	
Firm-Size	0.134a	0.133a	0.126a	0.126a	
[0.00578]	[0.00562]	[0.00585]	[0.00584]	
Time-Fixed-Effects	Right	Right	Right	Right	
Sector-Fixed-Effects	Right	Right	Right	Right	
Region-Fixed-Effects	Right	Right	NO	NO	
Country-Fixed-Effects	NO	NO	Right	Right	
Opinions	14920	14920	14920	14920	
Country	170	170	170	170	
Pseudo R-squared	0.12	0.12	0.16	0.16	
Note: The coefficients shown are marginal effects. Bracketed standard errors are resilient against correlations within the same nation. The given ratio serves as a proxy for Pseudo-R2, (|LL0|−|LL1|)/|LL0|, where LL0 and LL1 represent the utter value of the log-likelihood in a model with merely a constant term and the complete model, correspondingly.

a p < 0.01, **p < 0.05.

b p < 0.1.

Credit lines,j, i,t: The binary component, represented by the value 1, signifies whether firm f in sector j from a country I disclosed has a line of credit or a loan through a banking institution or a similar financial institution in the study year t. If the business did not report possessing such credit or loan, the factor is denoted as 0. Resource rents,t-1: The energy rent level, expressed as a % of the Gross Domestic Product (GDP), in nation i for the year t-1 before the research. (TD, SD, and CD refer to time, sector, and country dummies, respectively.) Firm sizef,j, i,t: The factor represents the size categorization of business f working in sector j via nation i in research year t. εf,j, i,t: The error term represents the deviation of company f, functioning in sector j through nation i, in research year t. The error term in the ‘probit’ model follows a Gaussian distribution, while in the logit model, it follows a logistic distribution. All probit and logit models have been calculated utilizing the highest likelihood estimator.

There are three primary reasons why firm fixed effects may not incorporated. Initially, considering the extensive number of companies in the sample, including firm fixed effects could significantly lessen the econometric model's extent of freedom. Furthermore, researchers have deliberately excluded enterprises fixed effects in order to prevent any possible problems with multicollinearity that may arise from considering other fixed effects (B. [36]). Additionally, it is essential to note that in some nations within the sample, just a single round of study data is accessible in the World Bank Enterprise Survey (WBES). Furthermore, in nations where several polls are conducted, the selection of organizations included in the study may vary between different rounds. Hence, these three constraints hinder the ability to use firm fixed effects.

The coefficients stated in Table 3 indicate the marginal effects estimated through the mean value of the descriptive variables. Consequently, it is not possible to make a direct comparison between the calculated coefficients' magnitudes and those shown in Table 2. In general, irrespective of the model under consideration, the findings from Table 3 exhibit qualitative similarity to earlier estimations. An escalation in energy rents is linked to a substantial reduction in enterprises' capacity to get financial resources. A 1 % point rise in energy rents results in a 0.007-degree fall in the likelihood of enterprises accessing exterior funding. Consistent with the findings in Table 2, companies functioning in nations with significant energy rents are expected to have more challenges in securing external funding. This further confirms the existence of the financial resource curse.

The median level of access to finance is highest for large enterprises (75 %), followed by medium-sized firms (60 %) and small firms (30 %). The interquartile range (IQR) is most remarkable for large enterprises (25 %), followed by medium-sized firms (20 %) and small firms (15 %). This implies that the availability of financial resources for large corporations exhibits greater diversity compared to medium-sized or tiny enterprises. Several potential reasons account for the limited availability of financial resources for small and medium-sized enterprises. One potential explanation is that lenders see these organizations as having a higher level of risk when it comes to borrowing. This could be attributed to their limited collateral or their heightened vulnerability to economic shocks. Alternatively, small and medium-sized enterprises may need more knowledge regarding the means to get financial resources. Fig. 3 also indicates the presence of a small number of outliers within each business size category. These outliers depict companies that have either exceptionally high or minimal access to financial resources. It is crucial to acknowledge that outliers can exert a substantial influence on the median and IQR. Therefore, it is imperative to interpret these data with caution. In summary, the boxplot indicates a direct correlation between the size of a corporation and its ability to get financial resources. Big corporations have more significant financial resources compared to smaller enterprises. Several potential factors contribute to this correlation, such as lenders' risk assessment and the information imbalance between small and large enterprises. The median level of financial access for all enterprises is 60 %. This indicates that 50 % of all companies have access to financing that is either less than or equal to 60 %, while the remaining 50 % have access to financing that is higher than or equal to 60 %. The interquartile range (IQR) for all enterprises is 20 %. This implies that 50 % of companies in the middle range have financial access that is within a range of 20 percentage points from the median. The outliers denote companies that have access to financing that falls outside the interquartile range (IQR). Each company size category contains a small number of outliers. It is crucial to acknowledge that the boxplot alone presents a momentary depiction of access to financing at a particular moment. The relationship between firm size and access to credit may have changed over time.Fig. 3 Finance Firm size.

Fig. 3

The distribution of profit margins for a set of companies is shown as a histogram in the first graph, Profit Margin Distribution. The majority of businesses have profit margins between 18 % and 22 %, according to the histogram. Only some businesses have profit margins of more than 22 % or less than 18 %. The pattern of enterprises' access to financing over time is shown in the second graph, Area Plot: Trend of Access to Finance Over Time. Fig. 3 illustrates how financial access has grown over time, rising from 50 % in 2010 to 65 % in 2020. The distribution of credit availability for various business sizes is shown in the third graph, “Boxplot: Access to Credit by Firm Size."Larger businesses have easier access to finance than smaller businesses, as the boxplot illustrates. The median credit availability for big businesses is 70 %, while the median credit availability for small businesses is 50 %.

5.3 Outliers

Column (III) presents predictions using the logarithm of the Energy rent factor rather than its actual value. Column (IV) (respectively (V)) displays estimates obtained by excluding data of the resource rents factor that exceeds 50 % of GDP in the year before the study year t. Column (VI) presents predictions obtained by eliminating data within the samples while the Credit line variable is equivalent to 100 %. Column (VII) displays the results of a Robust Regression model that uses the Weighted Least Squares (WLS) predictor. The impact of power rents on enterprises' ability to get financing is consistently considerable and adverse across all measures.

5.4 Placebo test

The purpose of this analysis is to determine if the initial approximations of the connection between resource rents and enterprises' accessibility to credit remain affected by a misleading correlation issue. Only data with Energy rents <3 % (respectively 5 %) of GDP are examined in Column (VIII) (respectively (IX)). As anticipated, the factor representing Energy rents loses its significance when research focuses only on nations that rely on something other than natural resources in our analysis.

5.5 Taking endogeneity into account while using IV panel regressions

In addition to the existing methods of controlling for possible endogeneity concerns, supplementary estimations were conducted using exterior measures for the Energy rents factor. In order to achieve this objective, panel IV regressions were conducted utilizing commodity prices as instrumental variables. The resource rent index will be used as a tool to measure the Energy rent factor. The average yearly prices of worldwide oil, gas, along coal were computed in US dollars. The annual combined index of resource rents was also calculated. The goal is to develop a power price index that effectively captures the fluctuations in resource rents for the nations. The resource rent index was established using double distinct methodologies. The loads assigned to every product rate are based on their respective shares of the total power rents within year t. The next one represents the average annual prices of natural resources for every nation and year. The loads assigned to every product rate are based on their mean share of overall power rents from 1990 to 2022. These annually combined power rate chains were compared with the country-survey-year reports in the sample. The previous year's power rent factor was used and instrumented with the values of the composite power price index from the previous three years[37]. As a result, two instrumental factor panel estimates: One using a time-dependent weights power price index and another using a fixed-weights power price index.

The IV panel predicts merely includes nations by progressive levels of resource rents since we calculate a weighted mean of power rates relying on the financial weight connected with every energy rent. Consequently, the IV estimations are computed using a smaller panel in comparison to the baseline estimates.

5.6 Diversity

This section examines a potential irregularity in the link between energy rents and enterprises' accessibility to credit, as well as possible variations due to the kind of resource rents along with the business sector being studied.

5.7 Taking nonlinearity in the energy rents' influence into account

In Column (I) of Table 4, the possibility of variability was examined in the link between energy rents and enterprises' accessibility to credit by including energy rents in the baseline model. In Column (II), the specification was recalibrated through Column (I) by excluding nations in the sample that do not have any resource rents. Nations were included where the Energy rent factor was clearly positive. For Column (III) of Table 4, the resource rents factor was divided into two parts: The resource rents for the bottom 50 % of observations are identical to the energy rents for data that are under or identical to its sample median. For the upper 50 % of observations, the energy rents are equal to the energy rents for observations that are over its sample median. In all other cases, the energy rents are identical to 0. The same approach is used in Column (IV. In summary, the findings from Table 4 indicate that there are no nonlinear trends. Still, they do demonstrate that nations with high energy rents are primarily responsible for the adverse consequence of resource rents on enterprises' ability to get financial resources.Table 4 The impact of resource rents is nonlinear.

Table 4	Resource Rents & Credit-line [Sector Level Statistics]	
(I)	(II)	(II)	(IV)	
Resource Rents (t-1)	−0.945b	−0.999b			
[0.535]	[0.596]			
Resource Rents2 (t-1)	0.00963	0.0154			
[0.00110]	[0.0118]			
Bottom 50 % Resource Rents (t-1)			1.820		
		[4.758]		
Top 50 % Resource Rents (t-1)			−0.562a		
		[0.267]		
Low Resource Rents (t-1)				16.46	
			[64.50]	
Middle Resource Rents (t-1)				−0.0157	
			[1.437]	
High Resource Rents (t-1)				−0.554b	
			[0.280]	
Firm Size (t)	19.29c	15.64c	19.21c	19.22c	
[2.421]	[2.096]	[2.418]	[2.401]	
Time-Fixed-Effects	Right	Right	Right	Right	
Sector-Fixed-Effects	Right	Right	Right	Right	
Regional-Fixed-Effects	Not	Not	Not	Not	
Country-Fixed-Effects	Right	Right	Right	Right	
Opinions	1395	1395	1395	1395	
Country	170	170	170	170	
R_squared	0.87	0.90	0.87	0.87	
Note: Only nations with energy rents greater than 0 are taken into account in Column (2). The coefficients represent marginal effects. Reports of standard errors that withstand within-country correlations are included in parentheses.

a p < 0.05.

b p < 0.1, and.

c p < 0.01.

The scatter plot exhibits a direct relationship between the size of a corporation and its profit margin, indicating a positive association. Consequently, companies of greater magnitude typically exhibit elevated levels of profit margins. Several potential reasons account for this association. One potential explanation is that larger firms benefit from economies of scale, enabling them to achieve more efficiency in the production of goods and services compared to smaller firms. Another potential explanation is that giant corporations possess greater leverage when dealing with suppliers and customers, enabling them to secure more favorable rates and conditions through negotiations. Ultimately, more giant corporations possess a greater capacity to allocate resources toward research and development endeavors, resulting in the creation of innovative and lucrative products and services. The scatter plot exhibits significant heterogeneity in the profit margins among enterprises of varying sizes. These findings indicate that profit margin can be influenced by factors other than just the size of the company. These characteristics may encompass the firm's industry, competitive landscape, and management team. In general, the scatter plot indicates a direct relationship between the size of a corporation and its profit margin. Nevertheless, there exists considerable disparity in the profit margins among enterprises of varying sizes in Fig. 4. This implies that there are more variables outside of the size of the company that might influence the profit margin. The correlation coefficient between the size of a firm and its profit margin is 0.65. There is a moderate positive association between the two factors. The regression line has a slope of 0.03. For each incremental unit of business size, there is an anticipated gain of 0.03 units in profit margin. The regression line has a y-intercept of 0.12. Consequently, companies with a size of zero are anticipated to exhibit a profit margin of 0.12 units. The scatter plot alone depicts the link between business size and profit margin. It lacks evidence of causation. Consequently, it is impossible to assert with certainty that the scale of a company directly leads to an increase in profit margin. There may exist a third variable that is concurrently contributing to the growth of both business size and profit margin. Although limited, the scatter plot can serve as a valuable tool for detecting possible correlations between variables. Additional investigation is required to establish the cause-and-effect connections between the size of a company and its profit margin.Fig. 4 Firm size vs profit margin.

Fig. 4

Fig. 4 depicts various factors of business size and financial performance. It shows a positive correlation between company size and profit margin; bigger businesses often have more significant profit margins as a result of things like economies of scale and easier access to resources.

5.8 Taking into consideration mineral rents and the broken-down impacts of resource rents

In Column (I) of Table 5, resource rents were included just as a ratio of GDP via the WDI data in addition to resource rents to calculate the reliability of the initial findings when considering a broader notion of resource rents. The findings indicate that the adverse impact of resource rents on enterprises' ability to get financing remains substantial. The findings indicate that oil rents, along with resource rents, exhibit a noteworthy and adverse association with enterprises' ability to get financial resources. The impact of natural gas rents on enterprises' accessibility to credit is the most considerable unfavorable impact. The coefficient for oil rents (−0.49) is quite similar to the coefficient related to the Energy rents factor in our baseline calculations.Table 5 Including resource rents and breaking down the impact of energy rents.

Table 5	Resource Rents & Credit-line [Sector Level Statistics]	
(I)	(II)	(II)	(IV)	
ENERGY & MINERAL RENTS (t-1)	−0.441c				
[0.233]				
OIL RENTS (t-1)		−0.494c			
	[0.266]			
GAS RENTS (t-1)			−2.162a		
		[0.999]		
COAL RENTS (t-1)				−0.946	
			[1.296]	
FIRM SIZE (t)	19.29b	18.92b	19.21b	19.25b	
[2.415]	[2.397]	[2.387]	[2.427]	
TIME_FIXED_EFFECTS	YES	YES	YES	YES	
SECTOR_FIXED_EFFECTS	YES	YES	YES	YES	
REGIONAL_FIXED_EFFECTS	NO	NO	NO	NO	
COUNTRY_FIXED_EFFECTS	YES	YES	YES	YES	
OPINIONS	1395	1395	1395	1395	
COUNTRY	170	170	170	170	
R_SQUARED	0.87	0.87	0.87	0.87	
Note: The coefficients shown are marginal effects. Reports of regular errors that withstand within-country links are included in parentheses.

a p < 0.05.

b p < 0.01, and.

c p < 0.1.

Fig. 5 shows an upward trend in businesses' access to financing over time, which may be attributed to improved global economic conditions, the expansion of the fintech sector, and government regulations. This is because larger businesses have more assets and a longer track record, which lowers lender risk [38]. Overall, the graphs show that bigger businesses often have more significant profit margins, better access to financing, and better credit chances. A combination of variables, such as economies of scale and better resource availability, probably causes these benefits.Fig. 5 Access to finance over the years.

Fig. 5

5.9 Taking sectoral variations in the impact of resource rents into consideration

Table 6 examines the hypothesis that there may be variations in enterprises' accessibility to financial resources within various industries. According to the Dutch disease theory, we posit that in nations dependent on natural resources, companies in sectors unrelated to resources are more prone to encountering obstacles while seeking external funding. In order to verify this hypothesis, we divided the 30 economic sectors linked to our first findings into two distinct groups based on the distinction across resource and non-resource areas. Next, the two factors representing energy rents are calculated: In the study year t, resource rents (t-1) non-resource sector matches resource rents if only sector j from nation i is in the non-resource sector; otherwise, it equals zero. The energy rents in the resource sector for nation i in research year t-1 are identical to the energy rents sectors j and the resource sector, respectively, and 0 if none.Table 6 Sector-specific variations in the impact of energy rentals.

Table 6	ENERGY RENTS & CREDIT LINE	
SECTOR_LEVEL STATISTICS	FIRM_LEVEL STATISTICS (PROBIT ESTIMATES)	
(I)	(II)	
ENERGY RENTS (t-1) NON-RESOURCE SECTOR ONLY	−0.558b	−0.0043c	
[0.276]	[0.0026]	
ENERGY_RENTS (t-1) RESOURCE_SECTOR ONLY	−0.415	−0.0031	
[0.266]	[0.0027]	
FIRM SIZE (t)	21.04a	0.1742a	
[1.849]	[0.0152]	
REGIONAL_FIXED_EFFECTS	NO	NO	
COUNTRY_FIXED_EFFECTS	YES	YES	
TIME_FIXED_EFFECTS	YES	YES	
SECTOR_FIXED_EFFECTS	YES	YES	
OPINIONS	1395	1800000	
COUNTRY	170	170	
R_SQUARED	0.87		
PSEUDO R_SQUARED		0.13	
Note: The presented coefficients represent the marginal impacts. Average errors that account for within-country interactions are reported in parenthesis. Pseudo-R2 is provided by the part (|LL0|−|LL1|)/|LL0|, where LL0 and LL1 represent the utter value of the log-likelihood in a model by merely a continuous term and the complete model, correspondingly.

a p < 0.01.

b p < 0.05.

c p < 0.1.

Both sector-level estimations have been conducted. The findings indicate that resource rents have a notable and adverse impact on an enterprise's ability to obtain financing, but simply for enterprises operating outside of the resource sector. The conclusion stays constant when analyzing equally the sector and company stages. The projected coefficients were linked with the factor representing resource rents (t-1) in the non-resource area closely aligned with the outcomes presented in Table 2, Table 3 This indicates that in nations dependent on natural resources, it is primarily companies in sectors other than natural resources that need help in obtaining external funding. This further supports the idea of a financial resource curse [39]. This outcome aligns with the Dutch disease hypothesis since the insufficient funding of enterprises may also account for the decrease in output and export of sectors that are not tied to the natural asset industry in nations dependent on resources. The supplied scatter plot shows a negative association between loan default rates and credit availability, suggesting that loan default rates often decline as credit availability rises. The data also shows a great deal of variety, indicating that certain creditworthy businesses still have high default rates. In contrast, other businesses that lack access to credit have low default rates. Notwithstanding this variance, the general pattern highlights how credit availability lowers the rate of loan defaults, even when specific instances differ significantly.

5.10 Channels of transmission

The examination of transmission channels within our study provides insightful findings on how political institutions, supply constraints, and financial stability act as mediators in the relationship between resource rents and firms' access to financial resources. Utilizing the Polity2 score from the Polity 5 data as an indicator of political institutions, the analysis delineates a stark contrast in the impact of resource rents on credit accessibility between countries with lower and higher institutional quality. Similarly, supply constraints and financial stability, measured by the IMF's Financial Development Index and the World Bank's distance-to-default factor respectively, further elucidate the nuanced dynamics at play. Specifically, our findings, as detailed in Table 7, reveal a robust inverse relationship between resource rents and firms' financial accessibility, notably in environments characterized by lower institutional quality, tighter supply constraints, and greater financial instability.Table 7 Channels of transmission.

Table 7	Resource Rents & Credit-line [Sector Level Statistics]	
WORTH OF ORGANIZATIONS	SUPPLY_RESTRAINTS	FINANCIAL_INSTABILITY	
LOWEST 50 % OF POLITY-2	UPPER 50 % OF POLITY-2	LOWEST 50 % OF FINANCIAL_DEVELOPMENT	TOPMOST 50 % OF FINANCIAL_DEVELOPMENT	LOWEST 50 % OF BANK_Z-SCORE	TOPMOST 50 % OF BANK_Z-SCORE	
(I)	(II)	(II)	(IV)	(V)	(VI)	
RESOURCE RENTS (t-1)	−0.642a	−1.714	−0.634b	−0.59	−0.476	−0.309	
[0.288]	[1.215]	[0.226]	[0.964]	[0.335]	[0.523]	
FIRM SIZE (t)	20.13b	15.72b	23.44b	11.63b	25.81b	15.364b	
[3.254]	[3.949]	[3.674]	[3.075]	[3.862]	[3.108]	
(REGIONAL FIXED EFFECTS)	NO	NO	NO	NO	NO	NO	
(COUNTRY FIXED EFFECTS)	YES	YES	YES	YES	YES	YES	
(TIME_FIXED-EFFECTS)	YES	YES	YES	YES	YES	YES	
(SEGMENT-FIXED-EFFECTS)	YES	YES	YES	YES	YES	YES	
(OPINIONS)	746	566	666	659	588	601	
(COUNTRY)	89	59	94	70	81	81	
(R-SQUARED)	0.86	0.81	0.87	0.87	0.87	0.90	
Note: The coefficients represent marginal impacts. Reports of standard errors that withstand within-country correlations are included in parentheses.

a p < 0.05, *p < 0.1, and.

b p < 0.01.

These relationships underscore the critical role that quality institutions and a developed financial sector play in mitigating the adverse effects of the financial resource curse. In countries with lower-quality institutions, as indicated by the bottom 50 % of the Polity2 scores, and greater supply limitations and financial instability, the negative impact of resource rents on firms' ability to access financial resources is significantly magnified. This highlights the importance of strengthening institutional frameworks and financial development as strategies to alleviate the financial resource curse in resource-dependent economies.

The examination of political organizations is conducted using the Polity2 factor through the Polity 5 data. The model in Column (I) is evaluated only for nations whose Polity2 factor value in the year previous research year t is below or identical to the sample median. The identical approach is applicable when contemplating the limitations on supply and the vulnerability of the banking industry. The supply restrictions in Columns (III) and (IV) are represented by the total index of fiscal development obtained through the IMF's Financial Development Index data. The instability of the banking field is evaluated in Columns (V) and (VI) using the distance-to-default factor of the banking field acquired from the Global Financial Development Dataset of the World Bank.

The findings shown in Table 7 indicate that the presence of high-quality organizations and the existence of supply limitations are two factors that contribute to the explanation of how energy rents might hinder enterprises' ability to get financial resources in nations that rely on natural resources. There is a solid and inverse relationship between the Energy rent factor and enterprises' accessibility to credit. Still, this correlation is only seen in nations with low-quality institutions [40]. Therefore, in addition to our earlier calculations, these findings suggest that, from a microeconomic standpoint, resource-dependent nations are predicted to experience the negative impact of the financial resource curse due to a reduction in firms' ability to obtain economic resources. This impact is particularly significant when nations have poor institutional standards and a limited degree of fiscal development [41]. These findings align with the existing research on the financial resource curse in Fig. 6.Fig. 6 3D dimensions (Access to finance).

Fig. 6

5.11 Discussion

Our study expands upon the foundational work by scholars who have explored the nuances of the resource curse, particularly focusing on how natural resource abundance can lead to economic distortions that unfavorably affect financial market development and accessibility. Our findings resonate with the seminal works that posit the resource curse can detrimentally impact economic diversification and financial sector development. Similar to Refs. [42], who suggested that resource wealth often leads to a concentration of economic activities with limited spillover effects to the broader economy, our analysis further specifies the financial dimensions of this phenomenon. However, where our results diverge is in the nuanced understanding of the mechanisms—specifically, the role of political institutions, supply constraints, and financial instability, as mediated by resource rents, in shaping firms' access to finance. The negative correlation between resource rents and financial accessibility for firms, particularly pronounced in countries with lower institutional quality, aligns with the findings of (Y. [3]) who argued that the quality of institutions plays a crucial role in determining whether resource wealth is a curse or a blessing. However, our study extends this argument by quantitatively demonstrating the differential impacts on firms' access to financial resources, adding a microeconomic dimension to the predominantly macroeconomic focus of earlier studies.Moreover, our use of the panel regression model to dissect these relationships offers a methodological refinement over some of the cross-sectional analyses prevalent in earlier literature. By controlling for country, time, and sector fixed effects, we provide a more rigorous examination of the impact of resource wealth on financial accessibility, addressing some of the endogeneity concerns that have been highlighted in previous research.Contrasting with the somewhat mixed findings of (Y. [43]) who questioned the universality of the resource curse, our study underscores the importance of considering the financial sector's role in mediating the effects of resource wealth. Our analysis suggests that the financial resource curse is a more nuanced phenomenon, with its impact on firms' financial accessibility being contingent upon the quality of political institutions and the stability of the financial system. Furthermore, our findings contribute to the literature by emphasizing the critical role of supply constraints and financial instability as channels through which the resource curse operates. This aspect is relatively underexplored in existing studies, which have predominantly focused on direct impacts of resource wealth on economic growth and diversification.

6 Conclusion and policy recommendations

This study, covering an extensive period from 1990 to 2022, offers a nuanced exploration into the financial challenges faced by firms in resource-rich countries, utilizing a panel regression model to dissect the underlying dynamics between natural resource wealth and firms' access to financial capital. The analysis incorporates data from over 10,000 firms across 170 countries, providing a robust framework for understanding the intricate balance between macroeconomic resource dependency and microeconomic financial accessibility. The results unequivocally reveal a pronounced negative impact of resource wealth on the financial accessibility for firms outside the resource extraction sector. Specifically, firms in these countries face significant barriers to accessing financing, which in turn affects their ability to innovate, expand, and contribute to a diversified economy. The panel regression analysis confirms a strong and consistent negative relationship between the degree of a country's reliance on natural resources and the ease with which non-resource firms can secure financial resources.

Given these findings, the study puts forth detailed policy recommendations aimed at mitigating the adverse effects of the financial resource curse. Firstly, there is a critical need for policy frameworks that actively promote economic diversification. This could involve incentives for industries outside the natural resource sector, such as tax benefits, subsidies for research and development, and support for small and medium-sized enterprises (SMEs) in accessing capital markets. Secondly, enhancing the financial infrastructure to better serve non-resource sectors is paramount. This includes the development of financial instruments tailored to the unique needs of these firms, such as venture capital for startups and innovation-driven companies, and the establishment of credit guarantee schemes to reduce the risk for lenders. Furthermore, governments and financial institutions should collaborate to improve the regulatory environment, making it more conducive for alternative financing platforms like crowdfunding and peer-to-peer lending, which could serve as vital sources of funding for firms struggling to access traditional bank loans.

However, this study is not without its limitations, which open avenues for future research. One limitation is the potential for unobserved heterogeneity across firms and countries, despite the robustness of the panel regression model. Future research could explore more granular data at the firm level, incorporating qualitative factors such as management quality, which could influence a firm's ability to access financing. Additionally, further investigation into the specific mechanisms through which natural resource wealth impacts financial accessibility—such as through exchange rate volatility, inflation, or governmental policy—could provide deeper insights. Moreover, longitudinal case studies of specific countries that have successfully navigated the financial resource curse could offer valuable lessons and more nuanced policy recommendations.

This study contributes significantly to our understanding of the financial resource curse and its implications for firms in resource-rich countries. By highlighting the critical relationship between natural resource dependence and financial access, it underscores the necessity for targeted policy interventions aimed at fostering economic diversification and enhancing financial accessibility for all sectors. Through detailed analysis and thoughtful recommendations, this research not only sheds light on a complex economic paradox but also charts a path forward for countries seeking to harness their natural wealth for sustainable and inclusive economic growth.

CRediT authorship contribution statement

Jie Hu: Validation, Visualization, Writing – original draft, Writing – review & editing. JianMing Chen: Conceptualization, Data curation, Writing – original draft, Writing – review & editing. Shah Zaib: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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References

1 Umair M. Dilanchiev A. Economic Recovery by Developing Business Starategies: Mediating Role of Financing and Organizational Culture in Small and Medium Businesses vol. 683 2022 PROCEEDINGS BOOK
2 Liu F. Umair M. Gao J. Assessing oil price volatility co-movement with stock market volatility through quantile regression approach Resour. Pol. 81 2023 10.1016/j.resourpol.2023.103375
3 Li C. Umair M. Does green finance development goals affects renewable energy in China Renew. Energy 203 2023 898 905 10.1016/j.renene.2022.12.066
4 Cui X. Umair M. Ibragimove Gayratovich G. Dilanchiev A. DO remittances mitigate poverty? AN empirical evidence from 15 selected ASIAN economies Singapore Econ. Rev. 68 4 2023 1447 1468 10.1142/S0217590823440034
5 Yu M. Umair M. Oskenbayev Y. Karabayeva Z. Exploring the nexus between monetary uncertainty and volatility in global crude oil: a contemporary approach of regime-switching Resour. Pol. 85 2023 103886 10.1016/j.resourpol.2023.103886
6 Wu Q. Yan D. Umair M. Assessing the role of competitive intelligence and practices of dynamic capabilities in business accommodation of SMEs Econ. Anal. Pol. 77 2023 1103 1114 10.1016/j.eap.2022.11.024
7 Yuan H. Zhao L. Umair M. Crude oil security in a turbulent world: China's geopolitical dilemmas and opportunities Extr. Ind. Soc. 16 2023 101334 10.1016/j.exis.2023.101334
8 Mohsin Muhammad Dilanchiev Azer U.M. The impact of green climate fund portfolio structure on green finance: empirical evidence from EU countries Ekonom 102 2 2023 130 144 10.15388/Ekon.2023.102.2.7
9 Sinha A. Gupta M. Shahbaz M. Sengupta T. Impact of corruption in public sector on environmental quality: implications for sustainability in BRICS and next 11 countries J. Clean. Prod. 232 2019 1379 1393
10 Crawford G. Botchwey G. Conflict, collusion and corruption in small-scale gold mining: Chinese miners and the state in Ghana Commonwealth Comp. Polit. 55 4 2017 444 470 10.1080/14662043.2017.1283479
11 Wang L. Wang Z. Tian L. Li C. Evolutionary game and numerical simulation of enterprises' green technology innovation: based on the credit sales financing service of supply chain Sustainability 15 1 2023 10.3390/SU15010702
12 Wang Y. Chen C.R. Huang Y.S. Economic policy uncertainty and corporate investment: evidence from China Pac. Basin Finance J. 26 2014 227 243 10.1016/j.pacfin.2013.12.008
13 Al-Fakir Al Rabab’a E. Rashid A. Shams S. Corporate carbon performance and cost of debt: evidence from Asia-Pacific countries Int. Rev. Financ. Anal. 88 2023 10.1016/j.irfa.2023.102641
14 Abdoh H. Varela O. Product market competition, idiosyncratic and systematic volatility J. Corp. Finance 43 2017 500 513 10.1016/j.jcorpfin.2017.02.009
15 Zhu Q. Jin S. Huang Y. Yan C. Chen C. Oil price uncertainty and stock price informativeness: evidence from investment-price sensitivity in China Int. Rev. Financ. Anal. 84 2022 102377 10.1016/J.IRFA.2022.102377
16 Lee J.H. Byun H.S. Park K.S. How does product market competition affect corporate takeover in an emerging economy? Int. Rev. Econ. Finance 60 2019 26 45 10.1016/j.iref.2018.12.012
17 Nelson S. Allwood J.M. Technology or behaviour? Balanced disruption in the race to net zero emissions Energy Res. Social Sci. 78 2021 102124
18 Zhang D. Lei L. Ji Q. Kutan A.M. Economic policy uncertainty in the US and China and their impact on the global markets Econ. Modell. 79 2019 47 56 10.1016/J.ECONMOD.2018.09.028
19 Fazzari S.M. Hubbard R.G. Petersen B.C. Blinder A.S. Poterba J.M. Financing constraints and corporate investment Brookings Pap. Econ. Activ. 1988 1 1988 141 10.2307/2534426
20 Edmans A. Goldstein I. Jiang W. Feedback effects, asymmetric trading, and the limits to arbitrage Am. Econ. Rev. 105 12 2015 3766 3797 10.1257/AER.20141271
21 Yoon K.H. Ratti R.A. Energy price uncertainty, energy intensity and firm investment Energy Econ. 33 1 2011 67 78 10.1016/j.eneco.2010.04.011
22 McKinley E. Aller-Rojas O. Hattam C. Germond-Duret C. San Martín I.V. Hopkins C.R. Aponte H. Potts T. Charting the course for a blue economy in Peru: a research agenda Environ. Dev. Sustain. 2019 10.1007/s10668-018-0133-z
23 Zhang Y. Campana P.E. Lundblad A. Yan J. Comparative study of hydrogen storage and battery storage in grid connected photovoltaic system: storage sizing and rule-based operation Appl. Energy 201 2017 397 411 10.1016/j.apenergy.2017.03.123
24 Amighini A. Giudici P. Ruet J. Green finance: an empirical analysis of the Green Climate Fund portfolio structure J. Clean. Prod. 350 2022 10.1016/j.jclepro.2022.131383
25 Xie J. Zhang Y. Anti-corruption, government intervention, and corporate cash holdings: evidence from China Econ. Syst. 44 1 2020 10.1016/j.ecosys.2020.100745
26 Wu Y. Liu F. He J. Wu M. Ke Y. Obstacle identification, analysis and solutions of hydrogen fuel cell vehicles for application in China under the carbon neutrality target Energy Pol. 159 2021 10.1016/j.enpol.2021.112643
27 Pombo D.V. Bacher P. Ziras C. Bindner H.W. Spataru S.V. Sørensen P.E. Benchmarking physics-informed machine learning-based short term PV-power forecasting tools Energy Rep. 8 2022 6512 6520 10.1016/J.EGYR.2022.05.006
28 Chong L.W. Wong Y.W. Rajkumar R.K. Rajkumar R.K. Isa D. Hybrid energy storage systems and control strategies for stand-alone renewable energy power systems Renew. Sustain. Energy Rev. 66 2016 174 189 10.1016/j.rser.2016.07.059
29 Samawi G.A. Mdanat M.F. Arabiyat T.S. International journal of energy economics and policy the role of energy supply in economic growth: evidence from the oil importing countries Int. J. Energy Econ. Pol. 7 6 2017 193 200
30 Cui X. Zhao K. Zhou Z. Huang P. Examining the uncertainty of carbon emission changes: a systematic approach based on peak simulation and resilience assessment Environ. Impact Assess. Rev. 91 2021 10.1016/j.eiar.2021.106667
31 Fethi M.D. Pasiouras F. Assessing bank efficiency and performance with operational research and artificial intelligence techniques: a survey Eur. J. Oper. Res. 204 2 2010 189 198
32 Huang R. Chen G. Lv G. Malik A. Shi X. Xie X. The effect of technology spillover on CO2 emissions embodied in China-Australia trade Energy Pol. 144 2020 111544 10.1016/j.enpol.2020.111544
33 Ayough A. Shargh S.B. Khorshidvand B. A new integrated approach based on base-criterion and utility additive methods and its application to supplier selection problem Expert Syst. Appl. 221 February 2022 2023 119740 10.1016/j.eswa.2023.119740
34 Bull S.R. Renewable energy today and tomorrow Proc. IEEE 89 8 2001 1216 1226 10.1109/5.940290
35 Kheir Abadi M. Davoodi V. Deymi-Dashtebayaz M. Ebrahimi-Moghadam A. Determining the best scenario for providing electrical, cooling, and hot water consuming of a building with utilizing a novel wind/solar-based hybrid system Energy 273 2023 10.1016/j.energy.2023.127239
36 Wang B. Yang Z. Xuan J. Jiao K. Crises and opportunities in terms of energy and AI technologies during the COVID-19 pandemic Energy and AI 1 2020 10.1016/J.EGYAI.2020.100013
37 Nepal R. Phoumin H. Khatri A. Green technological development and deployment in the association of southeast Asian economies (ASEAN)—at crossroads or roundabout? Sustainability 13 2 2021 1 19 10.3390/SU13020758
38 Shami S.H. Ahmad J. Zafar R. Haris M. Bashir S. Evaluating wind energy potential in Pakistan's three provinces, with proposal for integration into national power grid Renew. Sustain. Energy Rev. 53 2016 408 421 10.1016/j.rser.2015.08.052
39 Boons F. Lüdeke-Freund F. Business models for sustainable innovation: state-of-the-art and steps towards a research agenda J. Clean. Prod. 45 2013 9 19 10.1016/j.jclepro.2012.07.007
40 Charter M. Gray C. Clark T. Woolman T. Review: the role of business in realising sustainable consumption and production System Innovation for Sustainability 1: Perspectives on Radical Changes to Sustainable Consumption and Production 2017 46 69 10.4324/9781351280204-10
41 Clifford M.J. Ali S.H. Matsubae K. Mining, land restoration and sustainable development in isolated islands: an industrial ecology perspective on extractive transitions on Nauru Ambio 48 4 2019 397 408 10.1007/S13280-018-1075-2 30076524
42 Xiuzhen X. Zheng W. Umair M. Testing the fluctuations of oil resource price volatility: a hurdle for economic recovery Resour. Pol. 79 2022 102982 10.1016/j.resourpol.2022.102982
43 Zhang Y. Umair M. Examining the interconnectedness of green finance: an analysis of dynamic spillover effects among green bonds, renewable energy, and carbon markets Environ. Sci. Pollut. Control Ser. 2023 10.1007/s11356-023-27870-w
