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Factors affecting economic growth: empirical evidence from developing countries

The question around factors affecting economic growth in developing nations is widely debated as it influences the policies to promote economic welfare. This paper, therefore, determines the factors influencing economic growth in developing countries. By estimating a fixed-effect model with a panel dataset for 62 developing countries from 2010 to 2018, the study found that government spending and natural resource rents have a favourable impact on per capita GDP growth. In contrast, rising labour force participation and inflation stifle economic growth in these countries.

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Financial Factors in Economic Development

Financial factors have been assigned strategic importance in economic development. But very different factors have been isolated in the respective experiences: in Asia unrepressed financial markets in mobilizing saving and allocating investment have been given prominence. In Latin America the central question is the role of inflationary finance, the scope for deficits to enhance growth and, increasingly, the feedback from high and unstable inflation to poor economic performance. This paper reviews and contrasts the two approaches and concludes that the strong claims for the benefits of financial liberalization are not supported by evidence. Financial factors are important, but probably only when financial instability becomes a dominant force. The scope for inflationary finance is small and the risks are larger than commonly accepted. When hyperinflation takes over and foreign exchange crises disrupt the price system, and shorten the economic horizon to a week or a month, normal economic development is suspended. Moreover, difficult to reverse capital flight puts savings outside the home economy. Attention should focus on these extreme cases and explore deeper the thresholds at which financial factors become dominant and the channels through which this occurs. Superior growth performance, in this perspective, may be more a reflection of adaptability than financial deepening.

Acknowledgements and Disclosures

MARC RIS BibTeΧ

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The carbon emission reduction effect of green fiscal policy: a quasi-natural experiment

Shuguang Wang 1 ,
Zequn Zhang 1 ,
Zhicheng Zhou 2 &
Shen Zhong 2

Scientific Reports volume 14 , Article number: 20317 ( 2024 ) Cite this article

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Climate-change impacts
Climate-change mitigation
Environmental impact

Carbon emission reduction is crucial for mitigating global climate change, and green fiscal policies, through providing economic incentives and reallocating resources, are key means to achieve carbon reduction targets. This paper uses data covering 248 cities from 2003 to 2019 and applies a multi-period difference-in-differences model (DID) to thoroughly assess the impact of energy conservation and emission reduction ( ECER ) fiscal policies on enhancing carbon emission ( CE 1 ) reduction and carbon efficiency ( CE 2 ). It further analyzes the mediating role of Green Innovation ( GI ), exploring how it strengthens the impact of ECER policies. We find that: (1) ECER policies significantly promote the improvement of carbon reduction and CE 2 , a conclusion that remains robust after excluding the impacts of concurrent policy influences, sample selection biases, outliers, and other random factors. (2) ECER policies enhance CE 1 reduction and CE 2 in pilot cities by promoting green innovation, and this conclusion is confirmed by Sobel Z tests. (3) The effects of ECER policies on CE 1 reduction and the improvement of CE 2 are more pronounced in higher-level cities, the eastern regions and non-resource cities. This research provides policy makers with suggestions, highlighting that incentivizing green innovation through green fiscal policies is an effective path to achieving carbon reduction goals.

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Introduction.

Efforts to mitigate global climate change through the reduction of CE 1 have emerged as a shared objective among nations globally 1 . From the initiation of the United Nations Framework Convention on Climate Change to the enactment of the Kyoto Protocol and the adoption of the Paris Agreement, these pacts reflect the unified resolve of nations to tackle global climate change 2 , 3 . With the acceleration of global industrialization and the continuous increase in energy demand, there has been a significant rise in the emissions of greenhouse gases, especially carbon dioxide, posing an unprecedented challenge to the Earth’s climate system 4 . These issues encompass the escalation of average global temperatures, a surge in severe weather occurrences, accelerated glacier melt, and a persistent increase in sea levels 5 , 6 , 7 , which threaten the balance of natural ecosystems and have profound impacts on the economic development and well-being of human societies. Therefore, adopting effective carbon reduction strategies to slow these climate change trends has become an urgent task faced globally.

In the current field of CE 1 reduction research, the focus is mainly on implementing policies such as carbon emission trading 8 , smart city pilot policies 9 , and low-carbon city pilot policies 10 . Among these policies, green fiscal policy, as a core strategy to mitigate the impact of climate change, is increasingly recognized by the academic community and policymakers for its importance in promoting CE 1 reduction 11 , 12 . This policy directly impacts CE 1 in economic activities through adjustments in the tax system, provision of fiscal subsidies, and increased investments in renewable energy and low-carbon technologies 13 . Green fiscal policies differ from traditional environmental protection measures by employing a mechanism that combines incentives and constraints, aiming to encourage enterprises to adopt emission reduction measures. In the implementation process of green fiscal policies, governments encourage enterprises to reduce CE 1 by adjusting tax policies 14 . Specifically, the ECER policy impacts the carbon emissions of demonstration cities through a combination of financial incentives and target constraints. The demonstration period lasts for three years, during which the central government provides reward funds for demonstration projects. The amount of these rewards is determined by the category of the city: 600 million RMB annually for municipalities and city clusters, 500 million RMB annually for sub-provincial cities and provincial capitals, and 400 million RMB annually for other cities. Local governments have the discretion to decide how to utilize these funds, while the central government is responsible solely for project record management. Additionally, the central government conducts annual and overall target assessments of the demonstration cities. The results of the annual assessment influence the reward funds for the following year: cities that perform excellently will receive an additional 20% of reward funds, while those that fail to meet the standards will have 20% of their funds withdrawn. The overall assessment results are linked to the demonstration qualification and reward funds; cities that fail to meet the overall targets or have serious issues will lose their demonstration status and have all reward funds withdrawn. This financial incentive mechanism ensures that local governments have sufficient financial support when implementing green technologies and projects, promoting increased energy efficiency and the widespread adoption of clean energy. Simultaneously, through the target constraint mechanism, the central government strictly supervises and incentivizes local governments’ efforts to reduce emissions, ensuring effective policy implementation. Under the dual pressure of financial incentives and performance assessments, local governments actively adopt various measures to promote energy conservation and emission reduction, including investing in green infrastructure, promoting energy-saving technologies, and optimizing energy structures, thereby achieving significant reductions in carbon emissions.

Furthermore, innovation and technological breakthroughs significantly enhance the effectiveness of green fiscal policies in reducing carbon emissions. Specifically, technological advancements improve energy efficiency, reducing the energy consumption per unit of output; they lower the production costs of clean energy, promoting its widespread adoption; and they advance carbon capture and storage technologies, directly reducing industrial carbon dioxide emissions. These technological improvements bolster the impact of green fiscal policies, making them more effective in achieving carbon reduction targets. However, the implementation of green fiscal policies also faces some challenges. Firstly, balancing the relationship between economic development and environmental protection to avoid potential negative impacts such as job losses and industrial relocation during policy execution is an issue that policymakers need to consider. Secondly, the effective implementation of green fiscal policies requires strong policy support and regulatory mechanisms to ensure that policy measures are effectively executed and can adapt to constantly changing economic and environmental conditions. Therefore, evaluating the carbon reduction effect of such policies is of significant importance for achieving long-term environmental sustainability and promoting the green economic transformation.

This paper analyzes the impact of green fiscal policies on carbon emissions and carbon efficiency. Relevant research mainly focuses on the following two areas: studies on the factors influencing carbon emissions, and research related to environmental regulations and energy conservation and emission reduction fiscal policies.

Firstly, a substantial body of literature focuses on the factors influencing carbon emissions, with some studies specifically examining the impact of government intervention and environmental regulation on CO2 emissions. These studies are closely related to the theme of this paper. From an economic perspective, numerous studies have demonstrated that economic growth significantly impacts carbon emissions 15 , 16 , 17 . Generally, increased economic activity is associated with higher energy consumption, leading to higher carbon emissions. However, as economies reach a certain level of development, the Environmental Kuznets Curve (EKC) phenomenon may occur, where carbon emissions begin to decrease after reaching a certain economic threshold 18 , 19 . Research has also confirmed that economic growth increases the ecological footprint, leading to environmental degradation 20 . For example, economic growth, income inequality, and energy poverty have increased environmental pressure in BRICS countries 21 . In Pakistan, institutional quality has led to higher CO 2 emissions, but economic development can help reduce these emissions 22 . From a social perspective, the acceleration of urbanization is typically accompanied by increased energy consumption, thereby raising carbon emissions. There is a long-term and short-term U-shaped relationship between urbanization and the environment 23 . Upgrading existing infrastructure can enable various sectors to produce minimal waste that impacts emissions 24 . Changes in consumption levels and population structure also significantly affect carbon emissions 25 . From a policy perspective, government-enacted environmental regulations and policies, such as carbon taxes, carbon trading markets, emission standards, and renewable energy subsidies, play a crucial role in reducing carbon emissions. Innovations and environmental policies contribute to emission reductions both in the long and short term. Additionally, carbon pricing can reduce emissions in specific regions, although its impact is often more targeted at specific countries 26 . Carbon taxes and mitigation technologies are helping to achieve sustainable development goals for carbon mitigation 27 . Green energy investments are significantly associated with greenhouse gas emissions and support environmental quality 28 . However, these studies often overlook the impact of energy conservation and emission reduction fiscal policies on carbon emissions.

Secondly, there is a body of literature focusing on environmental regulation, which can be divided into two main areas: the impact of environmental regulation on the environment and its impact on the economy. On the one hand, extensive research has explored the environmental impact of regulation. Studies generally agree that stringent environmental regulations help reduce pollutant emissions and improve environmental quality. Environmental regulations significantly enhance the synergy between carbon reduction and air pollution control 29 . Target-based pollutant reduction policies effectively constrain the sulfur dioxide emissions of regulated enterprises, lowering their sulfur dioxide emission intensity, thereby demonstrating that stringent environmental regulations facilitate green transitions for businesses 30 . However, in some developing countries or regions with weak enforcement, the effectiveness of environmental regulations may be compromised. Despite strict regulatory policies being in place, inadequate enforcement or a lack of regulatory capacity may result in actual pollutant reduction falling short of expectations. On the other hand, part of the literature examines the economic impact of environmental regulation. Some studies suggest that environmental regulation can drive technological innovation and industrial upgrading, thereby promoting economic growth 31 . Strict environmental standards force companies to improve production processes and develop new environmental technologies, which can create new economic opportunities and growth points 32 . Environmental regulations significantly enhance green technological innovation 33 , and they have notably promoted green innovation across European countries 34 . Conversely, environmental regulations may increase operational costs for businesses, particularly in the short term due to compliance costs, which could inhibit economic growth. This is especially true for regions or countries that rely heavily on high-pollution, high-energy-consumption industries, where environmental regulation might lead to a slowdown in economic growth. Given that energy conservation and emission reduction fiscal policies are a form of environmental regulation, it is necessary to evaluate their effectiveness.

Thirdly, some literature evaluates the governance effectiveness of energy conservation and emission reduction fiscal policies. From an environmental perspective, these policies can reduce pollutants and enhance efficiency. On average, such policies have reduced industrial SO2 (sulfur dioxide) emissions by 23.8% and industrial wastewater discharge by 17.5% 35 . Additionally, energy conservation and emission reduction fiscal policies can effectively improve green total factor carbon efficiency 36 . From an economic perspective, these policies can promote investment and economic growth 37 . They have significantly improved green credit for enterprises and can facilitate sustainable urban development 38 .

In summary, there are two significant gaps in the existing literature. Firstly, although numerous studies have extensively explored the factors influencing carbon emissions from economic, social, and policy perspectives, relatively few have examined the relationship between ECER policies and carbon emissions. Specifically, most of the existing literature focuses on the impact of macroeconomic policies, industrial structure adjustments, and technological innovation on carbon emissions. However, there is a lack of systematic empirical analysis on how specific fiscal incentives directly affect carbon emissions, limiting our comprehensive understanding of the actual effects of fiscal policies on emission reduction. Secondly, most of the existing studies investigate carbon dioxide emissions from a single perspective, such as focusing on total carbon emissions, carbon intensity, or carbon efficiency. These studies lack a multi-faceted exploration of the relationship between a single policy and carbon emissions. Typically, research adopts a specific metric to measure policy effects, but this approach overlooks how different metrics might reveal various aspects of policy impact. Consequently, these studies fail to capture the multi-dimensional effects of policies on reducing carbon emissions comprehensively. This single-perspective research methodology cannot adequately reflect the multiple impacts of policies on carbon emissions across different scenarios and time periods. This paper aims to evaluate the impact of the ECER policy, jointly introduced by the Ministry of Finance and the National Development and Reform Commission in 2011, on CE1 and CE2. Given that the ECER policy was implemented in three batches of pilot cities, this study employs a multi-period Difference-in-Differences (DID) model for analysis. The advantage of this model lies in its ability to compare the effects of the policy before and after its implementation across multiple time points, thereby capturing the dynamic impacts of the policy. Furthermore, this article explores the mediating role of green innovation in the impact process of the ECER policy, revealing the policy’s varying effects on CE 1 and CE2 across different regions through heterogeneity analysis.The marginal contributions of this article: Firstly, this paper evaluates the relationship between ECER policies and carbon emissions, addressing a significant gap in the existing research. Although numerous studies have explored various factors influencing carbon emissions from different perspectives, there is a lack of systematic research on the actual effects of specific fiscal policies on energy conservation and emission reduction, particularly their direct impact on carbon emissions. Through empirical analysis and data validation, this study thoroughly investigates the specific mechanisms and effects of ECER policies on carbon emissions in practice, thus filling this research gap. Secondly, this paper systematically assesses the relationship between ECER policies and carbon emissions from two key perspectives: total carbon emissions and carbon efficiency. By considering these two important indicators, this study not only examines the impact of ECER fiscal policies on overall carbon emissions but also analyzes their role in improving carbon efficiency. Through an in-depth analysis of these two metrics, this paper provides a more comprehensive and multi-dimensional view, systematically evaluating the effectiveness and mechanisms of ECER policies.

The remainder of the article is organized as follows: the second part discusses the policy background and theoretical analysis; the third part details the model settings and variable explanations; the fourth part presents the empirical analysis; the fifth part analyzes regional heterogeneity; and the last part concludes with conclusions and policy recommendations.

Policy background and theoretical analysis

Policy background.

In 2011, the Ministry of Finance and the National Development and Reform Commission issued the “Notice on Conducting Comprehensive Demonstration Work of Fiscal Policies for Energy Conservation and Emission Reduction,” deciding to carry out comprehensive demonstrations of fiscal policies for ECER in some cities during the “Twelfth Five-Year” period. Beijing, Shenzhen, Chongqing, Hangzhou, Changsha, Guiyang, Jilin, and Xinyu were selected as the first batch of demonstration cities. In the subsequent years of 2013 and 2014, 10 and 12 cities were respectively chosen as pilot cities for the fiscal policies on ECER . Specifically, this policy uses cities as platforms and integrates fiscal policies as a means to comprehensively carry out urban ECER demonstrations in various aspects, including industrial decarbonization, transportation clean-up, building greening, service intensification, major pollutant reduction, and large-scale utilization of renewable energy. Its main goal in terms of CE 1 reduction is to establish a concept of green, circular, and low-carbon development in the demonstration cities, achieve widespread promotion of low-carbon technologies in industries, construction, transportation, and other fields, lead the pilot cities in ECER efforts across society, and significantly enhance their capacity for sustainable development. Figure 1 presents the spatial distribution of ECER policy pilot cities in the years 2011, 2013, and 2014 (This figure was created using ArcMap software).

Distribution of ECER Policy Pilot Areas (Plan Approval Number GS(2019)1822).

Theoretical analysis

Carbon emission reduction effect of green fiscal policy.

Green fiscal policy, as a significant environmental governance tool, promotes the transformation of the economic and social system towards low-carbon, sustainable development through fiscal measures 39 . Its CE 1 reduction effects can be described from the following aspects. Firstly, green fiscal policy encourages the research and application of green technologies through economic incentives (such as tax reductions and fiscal subsidies) 40 . These technologies include energy efficiency improvement technologies, clean energy technologies, and carbon capture and storage technologies, which directly reduce energy consumption and CE 1 in economic activities. Secondly, green fiscal policy influences the behavior of consumers and producers by affecting the price mechanism. The imposition of a carbon tax raises the cost of CE 1 , reflecting the external cost of CE 1 on the environment, encouraging enterprises to take emission reduction measures, and prompting consumers to prefer low-carbon products and services 41 . The change in price signals promotes the transformation of the entire society’s energy consumption structure towards more efficient and low-carbon directions. Furthermore, green fiscal policy can support CE 1 reduction-related infrastructure construction and public service improvements through the guidance and redistribution of funds. This includes the construction and optimization of public transportation systems, urban greening, and forest conservation projects, which not only directly or indirectly reduce CE 1 but also enhance the carbon absorption capacity of cities and regions. Lastly, green fiscal policies, by raising public environmental awareness and participation, create a conducive atmosphere for all sectors of society to join in carbon reduction efforts 42 . Governments can increase public awareness of climate change and inspire a low-carbon lifestyle through the promotion and education of fiscal policies, providing broader social support for carbon reduction 43 .

Green fiscal policies not only drive a reduction in CE 1 but also stimulate sustainable economic growth. By taxing high-carbon activities, offering financial subsidies and incentives for green projects, these policies channel capital towards low-carbon and green industries. This not only mitigates negative environmental impacts but also fosters the development of emerging green technologies and sectors. As the green industry expands and low-carbon technologies become more widespread, economic growth increasingly relies on clean and efficient energy use 44 , thereby enhancing the CE 2 . Thus, the implementation of green fiscal policies demonstrates a commitment to transitioning towards a low-carbon economy, playing a crucial role in the global response to climate change, achieving a win–win for environmental protection and economic growth.

Based on this, the article proposes hypothesis 1: Green fiscal policies can promote CE 1 reduction effects and enhance CE 2 .

Mechanism analysis

Green innovation is a key factor in driving sustainable development, particularly playing a significant role in CE 1 reduction and efficiency enhancement. By introducing and adopting new environmentally friendly technologies and processes, green innovation not only significantly reduces greenhouse gas emissions but also enhances the efficiency of energy use and resource management, thus promoting a harmonious coexistence between economic activity and environmental protection. Green innovation, through the development and adoption of renewable energy technologies such as solar, wind, and biomass energy, directly reduces reliance on fossil fuels and the corresponding CE 1 . The application of these technologies not only reduces the carbon footprint but also promotes the diversification of energy supply and enhances energy security 45 . Green innovation also plays an essential role in improving energy efficiency. By adopting more efficient production processes and energy-using equipment, businesses and households can accomplish the same tasks or meet the same living needs with lower energy consumption, thus reducing CE 1 46 . Additionally, green innovation encompasses the concepts and practices of the circular economy, which encourages the reuse, recycling, and recovery of materials, reducing the extraction and processing of new materials and further lowering CE1s in the production process 47 . Green innovation includes the development of Carbon Capture, Utilization, and Storage (CCUS) technologies, which can directly capture carbon dioxide from industrial emissions and either convert it into useful products or safely store it, thereby reducing the carbon content in the atmosphere 48 . On the policy and management level, green innovation also involves establishing and refining mechanisms such as carbon pricing, green taxes, and carbon trading, which promote the adoption of low-carbon and environmentally friendly technologies and behaviors among businesses and individuals through economic incentives 49 . Based on this, the article proposes hypothesis H2: Green fiscal policies can promote CE 1 reduction effects and CE 2 by fostering green innovation.

In conclusion, the theoretical framework, as shown in Fig. 2 .

Theoretical framework.

Model setting and variable description

To address the limitations faced by traditional regression models in evaluating policy implementation effects, this study utilizes DID model for analysis. Given the variation in the policy implementation years in this paper, the traditional DID model cannot be used 50 . Accordingly, this paper draws on the approach of Beck et al. 51 , employing a DID with multiple time periods to assess the policy effects, with the model set up as follows:

Y in the model is the explained variable, indicating CE 1 and CE 2 of the city i in the annual t . Treated i is the group variable, where it takes the value 1 if city i belongs to the treatment group, and 0 if it belongs to the control group; Post it is the post-treatment period dummy variable, where it takes the value 1 for city i in year t if ECER policy has been officially implemented, and 0 if it has not been officially implemented. This study investigates the impact of energy conservation and emission reduction fiscal policies on urban CE 1 and CE 2 by examining the effect of the interaction term Treated × Post it on the dependent variable. The coefficient β 1 measures the impact of the policy on the dependent variable. Controls in this study represent control variables, specifically urbanization rate ( lnur ), foreign direct investment level ( lnfdi ), industrial structure ( lnis ), level of scientific and technological expenditure ( lnsst ), and fiscal revenue and expenditure level ( lnfre ), among others. \(\nu\) , \(\tau\) and \(\varepsilon\) represent city fixed effects, time fixed effects, and random error terms, respectively.

Considering the three-year implementation period of green fiscal policies, it is necessary to establish an exit mechanism for the treatment group. Drawing on existing literature 12 , this paper constructs the following treatment groups: the first batch of pilot cities from 2011 to 2014 is set to 1; the second batch of pilot cities from 2013 to 2016 is set to 1; the third batch of pilot cities from 2014 to 2017 is set to 1, with other years set to 0. The pilot cities are shown in Fig. 3 .

ECER policy implementation period.

Variables and data sources

Explained variables.

Carbon Emissions: Drawing from existing literature, this article utilizes current CE 1 data to calculate CE 1 52 , 53 . It follows the guidelines on greenhouse gas emission allocations by the IPCC , taking into account the emissions of carbon dioxide within the administrative boundaries of each city. Territorial emissions refer to emissions occurring within the managed territory and maritime areas under the jurisdiction of a region 54 , including emissions from socio-economic sectors and direct residential activities within regional boundaries 55 .

Carbon Efficiency: Following existing literature, this paper measures CE 2 using the ratio of CE 1 to GDP 56 .

In examining the correlation between CE 1 and economic efficiency, Fig. 4 a provides an overview of the evolution of CE 1 from 2003 to 2019, while Fig. 4 b offers a detailed portrayal of the progress in CE 2 over the same period. Figure 4 a reveals a steady increase in total CE 1 beginning in 2002, with a notable acceleration post-2009, peaking in 2017. Despite some fluctuations and a slight dip in 2018, the figures for 2019 remained just below the peak, overall indicating an upward trajectory. In contrast, Fig. 4 b demonstrates a year-on-year improvement in CE 2 , measured in tens of thousands of yuan output per ton of carbon emitted, starting in 2003. The pace of growth accelerated significantly after 2011, reaching its zenith in 2019. This signifies a substantial rise in the economic output efficiency per unit of carbon emitted, revealing a reduction in carbon dependency within economic activities. The combined analysis of both figures indicates that, alongside economic growth, there has been a notable advancement in optimizing CE 2 .

Trends in CE 1 ( a ) and CE 2 ( b ) (2003–2019).

Control variables

To eliminate the interference of omitted variables on the research results, this article selects the following control variables 57 , 58 : Urbanization rate ( lnur ), which refers to the ratio of urban population to total population; Level of foreign direct investment ( lnfdi ), the ratio of actual foreign investment to the GDP ; Industrial structure ( lnis ), the proportion of the secondary industry in GDP ; Level of science and technology expenditure ( lnsst ), the ratio of science and technology expenditure in ten thousand to GDP in hundred billion; Fiscal revenue and expenditure level ( lnfre ), the sum of local fiscal budget revenue and expenditure to GDP . To reduce heteroscedasticity in the data, this article takes the logarithm of all control variables. Table 1 reports the definitions of the main variables in this paper.

Sample selection and data source

We selects cities at the prefecture level in China from 2003 to 2019 as the research sample. Considering that missing data can affect the results, this paper excludes samples with missing data, ultimately obtaining 3134 samples. The CE 1 data in this paper comes from the China Emissions Accounts and Datasets (CEADs), which provides CE 1 data from 1997 to 2019, so the sample period for this paper ends in 2019. The control variable data are all sourced from the China City Statistical Yearbook covering the years 2004 to 2020. Table 2 provides descriptive statistics for the main variables in this paper.

Eliminating interference

In a quasi-natural experiment, various factors may influence the relationship between the implementation of green fiscal policies and the reduction of carbon emissions. To address this, we employed multiple methods to control for these potential confounding variables. Firstly, we introduced control variables to eliminate or reduce the interference of external factors on the main research relationship, ensuring the accurate estimation of the effects of green fiscal policies. Secondly, we adopted a two-way fixed effects model to control for time-invariant city characteristics and potential common time trends. Thirdly, we conducted parallel trend tests to verify whether the trends of the treatment and control groups were consistent before the policy implementation, ensuring the validity of the Difference-in-Differences (DID) estimates. Additionally, we performed multiple robustness checks, including propensity score matching and excluding the effects of other concurrent policies, to test the robustness of the results. Finally, we confirmed the reliability of the results through placebo tests. These methods collectively help to effectively reduce the interference of external variables, ensuring the accuracy and reliability of the research findings.

Empirical results

Benchmark regression analysis.

We employs a two-way fixed effects model for the empirical analysis of the CE 1 reduction effects of ECER policies, with the estimation results presented in Table 3 . Columns (1) to (3) of Table 3 report the estimation results of green fiscal policies on CE 1 . The results show that, when the model does not include control variables, the implementation of green fiscal policies has an estimated coefficient of − 0.070 for CE 1 , significant at the 1% level, indicating that the CE 1 of pilot cities are 7.0% lower than those of non-pilot cities. After adding control variables, the results do not change significantly. Columns (4) to (6) report the estimation results of green fiscal policies on CE 2 . The results indicate that, when the model does not include control variables, the implementation of green fiscal policies has an estimated coefficient of 0.099 for CE 2 , significant at the 1% level, suggesting that the CE 2 of pilot cities is 9.9% higher than that of non-pilot cities. After including control variables, the results remain largely unchanged. This provides evidence for Hypothesis 1: ECER policies have a significant CE 1 reduction effect and also significantly promote CE 2 .

To further illustrate the step-by-step changes in the coefficients, this paper presents Fig. 5 . The horizontal axis of Fig. 5 represents the number of control variables, while the vertical axis indicates the coefficients, with the grey area denoting the error bars. As evident from Fig. 5 , the coefficients and error bars exhibit minimal variation with the increase in control variables, indicating a negligible impact of the number of control variables on the coefficients and highlighting their stability. This finding suggests that the primary regression coefficients remain consistent even when more control variables are included in the analysis, underscoring the model’s robustness.

Plot of coefficient variation based on the step by step method.

Parallel trend test

The prerequisite for using DID model to evaluate policies is the parallel trends assumption. This implies that, before the policy intervention, the treatment group and the control group should exhibit similar trends without systematic differences. After the policy intervention, the trends between these two groups should diverge significantly. Following existing literature 50 , 59 , 60 , this paper employs an event study approach to analyze the effects before and after the policy implementation.

In Eq. ( 2 ), the variable Treated still represents cities that have been approved to establish pilot ECER policies. To avoid perfect multicollinearity, this paper uses the year before policy implementation as the baseline group, meaning that k = − 1 is not included in the regression equation, and the other parts of the model are consistent with the baseline model. If the coefficient is not significant when k < 0 , it indicates that the estimated results satisfy the parallel trends assumption. Figure 6 shows that, before the implementation of the policy, all coefficients are not significant, and in the fifth year after policy implementation, the coefficients start to become significant. This indicates that the implementation of ECER policies has a significant promotional effect on CE 1 reduction and CE 2 in the pilot areas, but this effect has some lag.

Parallel trend test of CE 1 ( a ) and CE 2 ( b ).

Robustness test

Exclusion of contemporaneous policies.

The smart city construction policy began with the “Notice on Carrying out the National Smart City Pilot Work” issued by the Ministry of Housing and Urban–Rural Development in 2012, with smart city pilots being established in 2012, 2013, and 2014 61 . This paper excludes all smart pilot cities and re-runs the regression, with results shown in columns (1) and (2) of Table 4 . The results indicate that contemporaneous policies during the sample period caused some interference with the estimated coefficients, but the extent is very limited. The implementation of ECER policies still has statistically and economically significant effects on promoting CE 1 reduction and CE 2 in pilot cities.

We employs the Propensity Score Matching (PSM) method to process the data, aiming to reduce data bias and the impact of confounding factors 62 , 63 . Through PSM-DID analysis, the results show that after matching, the absolute bias (|bias|) of all variables decreases by more than 70%, and the p -values are not statistically significant. This comparative analysis reveals the effectiveness of PSM in reducing the initial bias between the treatment and control groups. Therefore, the matching process successfully achieves balance in characteristics between the two groups across key indicators, making the assessment of the treatment effect more accurate and reliable.

Table 4 reports the results of the PSM. The propensity score matching results show a substantial decrease in |bias| for variables, highlighting an enhanced balance between treated and control groups post-matching. For instance, the absolute bias for “lnur” dropped from 86.0% to just 3.3%, showcasing a 96.2% reduction in bias, which underscores the effectiveness of the matching process. Similarly, other variables like “lnfdi”, “lnis”, and “lnsst” experienced significant reductions in bias. The p >|t| values, mostly above 0.05 post-matching, indicate that the differences between groups are not statistically significant, affirming the success of the matching in minimizing discrepancies and improving comparability.

Figure 7 displays the matching results of PSM. The results indicate that after the matching process, the percentage bias (%bias) for the control variables all remain below 10%. This finding fully confirms the effectiveness of the PSM method in balancing key characteristics between the experimental and control groups, thereby ensuring the accuracy and reliability of subsequent analyses.

Balance test.

This paper conducts an empirical analysis using matched data, with the results shown in columns (3) and (4) of Table 5 . The results indicate that ECER policy still has a significant CE 1 reduction effect and also significantly promotes CE 2 . This suggests that there is no significant impact of self-selection bias on the regression results in this study.

To reduce the impact of outliers on regression analysis, this paper adopts a winsorization process 39 , 64 , which involves replacing observations below a certain threshold with the 1st percentile and those above the threshold with the 99th percentile before conducting the regression. Columns (5) and (6) of Table 5 display the analysis results after this treatment, showing that the impact of outliers on the regression results is not significant.

Replacement sample time

Considering the potential unique impact of the COVID-19 pandemic on CE 1 and CE 2 in 2019, this paper decided to exclude data from 2019 to ensure the robustness of the research results, thus avoiding the interference of pandemic-related outliers in the analysis. Subsequently, the paper conducted an empirical analysis based on the updated dataset, with the analysis results presented in columns (7) and (8) of Table 5 . The analysis results indicate that after excluding the special impact of the COVID-19 pandemic, the CE 1 reduction effect of the green fiscal policy remains significant, and there is still a significant promotional effect on CE 2 .

Placebo test

The DID model is based on the common trends assumption, which posits that, in the absence of an intervention, the trends of the treatment and control groups would have been similar 65 . By conducting a placebo test on data from before the intervention, this assumption can be tested for validity. If significant ‘intervention effects’ are also found during the placebo test conducted before the intervention or at irrelevant time points, this indicates that the effects estimated by DID are actually caused by other unobserved factors, rather than the intervention itself 66 . Referencing the placebo practices in existing literature 59 , this paper tests for the impact of unobservable factors on the estimation results. The study randomizes the impact of ECER policies across cities, selecting treatment groups randomly from 248 cities, with the remaining cities serving as control groups. This randomization process is repeated 500 times to generate a distribution graph of the regression coefficients, where the dashed line in the graph represents the actual regression coefficient, as specifically shown in Fig. 8 . Figure 8 a represents the placebo test for CE 1 , and Fig. 8 b for CE 2 . From Fig. 8 , it is evident that after randomizing the core explanatory variables, the mean of the coefficients is close to 0, and the mean of the coefficients after randomization significantly deviates from their true values. This indicates that, excluding the interference of other random factors on the empirical results, the green fiscal policy has a significant effect on CE 1 reduction and significantly promotes CE 2 .

Placebo test of CE 1 ( a ) and CE 2 ( b ).

Mechanism test

The analysis results presented earlier indicate that the ECER policy has significantly promoted CE 1 reduction and the improvement of CE 2 in pilot cities. Accordingly, this study will further explore the mechanism of action of ECER policy and has constructed the following model:

GI refers to green innovation. Following existing literature, this study uses the number of green invention patent grants ( lngi_invention ) and the total number of green patents per 10,000 people ( lnpgi_total ) as proxy variables for green innovation 67 , 68 . Due to the evident causal inference flaws in the three-stage mediation mechanism test 69 , this study refers to the mediation effect test model by Niu et al. 70 and employs the Sobel test to further evaluate the regression results, thereby enhancing the completeness and credibility of the mechanism test 71 . The regression results are shown in Table 6 . Columns (1) and (4) report the impact of the ECER policy on green innovation, with significant results. This confirms hypothesis H2: green fiscal policies can promote CE 1 reduction effects and CE 2 by fostering green innovation. Moreover, the Sobel Z coefficients are greater than 2.58, indicating that the mediating variable has a sufficiently strong explanatory power for the total effect.

Heterogeneity analysis

By city grade.

In the process of urbanization and industrialization, a city’s level often reflects its level of economic development, capacity for technological innovation, infrastructure completeness, and the comprehensiveness of its public services. This paper categorizes the sample cities based on their tier into higher-level cities (provincial capitals, sub-provincial cities, and municipalities directly under the Central Government) and general cities, and conducts regression analysis. The regression results shown in Table 7 , specifically in columns (1), (2), (6), and (7), indicate that in higher-tier cities, the coefficients of the ECER policy on CE 1 and CE 2 for pilot cities are -0.098 and 0.118, respectively, significant at the 1% level. However, in general cities, the absolute values of the coefficients are smaller and not significant. From this, we can conclude that the ECER policy’s effect on CE 1 reduction and the enhancement of CE 2 is more significant in higher-tier cities compared to general cities. Higher-level cities, with their advanced economic structures, abundant fiscal resources, high levels of technological innovation, and strong policy enforcement capabilities, make the green fiscal policy more effective in these areas in terms of CE 1 reduction and the promotion of CE 2 . Firstly, economically developed higher-tier cities have more sufficient fiscal funds and investment capacity, which can support large-scale green infrastructure construction and green technology R&D, thereby directly reducing urban CE 1 and improving energy use efficiency. Secondly, technological innovation is a key factor in improving CE 2 . As centers of technological innovation and information exchange, higher-level cities are more likely to attract and gather high-tech companies and research institutions, promoting the development and application of green technologies, and effectively reducing CE 1 . Additionally, higher-tier cities usually have more comprehensive laws, regulations, and policy enforcement mechanisms, ensuring the effective implementation and regulation of green fiscal policies. Also, residents in these cities often have higher environmental awareness and a preference for green consumption, which helps to create a favorable social atmosphere for the implementation of green fiscal policies. Finally, due to their strong regional influence and exemplary role, higher-tier cities can promote green transformation and low-carbon development in surrounding areas and even the entire country through policy guidance and market incentives, further amplifying the CE 1 reduction effect and enhancing the impact on CE 2 of green fiscal policies.

By geographic location

Given the significant differences in economic development levels, resource endowments, and institutional environments across regions in China, the implementation effects of the ECER policy may exhibit heterogeneity. Therefore, this paper divides the sample into eastern, central, and western regions for analysis and conducts regressions separately. The regression results are presented in Table 7 . Columns (3) to (5) and (8) to (9) of Table 7 show the regression results for CE1s and CE 2 , respectively, with columns (3) and (8) representing the results for the eastern region. The analysis indicates that, in the eastern region, the ECER policy significantly promotes carbon reduction and CE 2 . Although the policy’s effects in the central region are less than those in the eastern region, they still exhibit a positive impact. In contrast, in the western region, the ECER policy’s promotional effects on carbon reduction and CE 2 are not significant.

This analysis reveals that, within the regional development pattern of China, the eastern regions exhibit more significant outcomes in terms of the CE 1 reduction effect and the enhancement of CE 2 under green fiscal policies compared to the central and western regions. Firstly, as the most economically developed area in China, the eastern region, with its leading total economic output, industrialization, and urbanization levels, provides a solid fiscal support and technological foundation for the implementation of green fiscal policies. This economic advantage enables the eastern region to allocate more resources to the research, development, and application of green technologies, as well as related infrastructure construction, thereby effectively promoting CE 1 reduction and energy efficiency improvement. Secondly, environmental policies and regulations in the eastern region are generally stricter and more advanced. Coupled with a higher public awareness of environmental protection, this creates a favorable social environment and policy atmosphere for the implementation of green fiscal policies and carbon reduction. Additionally, the industrial structure in the eastern region is more optimized and high-end compared to the central and western regions, with a larger proportion of the service industry and high-tech industries, which typically have lower energy consumption intensity and CE 1 , facilitating the improvement of overall CE 2 . Furthermore, as an important gateway for international trade and investment, the eastern region is more open to adopting and introducing advanced green technologies and management practices from abroad, accelerating the pace of green transformation. Lastly, the dense urban network and well-developed transportation and logistics systems in the eastern region provide convenient conditions for the effective implementation of green fiscal policies. Therefore, due to comprehensive advantages in economic development level, industrial structure, policy environment, technological innovation capability, and infrastructure, the eastern region demonstrates more significant performance in the CE 1 reduction effect and the promotion of CE 2 under green fiscal policies.

Figure 9 reports the main regression coefficients and error bars from the heterogeneity analysis, clearly illustrating the distribution of coefficients.

Results of heterogeneity analysis.

Classification by resource-based city

Resource-based cities center on industries involved in the extraction and processing of local natural resources, including minerals and forests 72 , 73 , 74 . Due to their unique urban characteristics, these cities may have a specific impact on the efficacy of ECEP policy. Consequently, this paper follows the guidelines set forth by the State Council in the “National Plan for Sustainable Development of Resource-based Cities (2013–2020),” dividing the sample into resource-based and non-resource-based cities for separate regression analyses, the results of which are presented in Table 8 . Columns (1) and (2) detail the regression outcomes for CE 1 , while columns (3) and (4) address CE 2 . The findings reveal that, compared to resource-based cities, the effect of ECEP policies on carbon reduction is more pronounced in non-resource-based cities, with a similarly more substantial impact on the promotion of CE 2 .

Upon conducting a thorough analysis of the disparities in how non-resource-based cities and resource-based cities respond to ECER policies, a significant finding emerges: non-resource-based cities, due to their diversified industrial structures and lower reliance on highly polluting and energy-intensive heavy industries and mineral resource extraction, demonstrate a stronger capacity to adopt and promote new energy, clean energy, and energy-efficient technologies. This characteristic of their industrial structure not only facilitates effective carbon reduction efforts but also propels a shift in economic growth models towards services, high-tech industries, and innovation-driven sectors, which are associated with lower energy consumption and carbon intensities. Therefore, the potential for ECER policies to enhance CE 2 and reduce CE 1 is greater in these cities. In contrast, resource-based cities, due to their long-standing dependence on resource extraction, exhibit significant inertia in their economic structure, technological levels, and employment opportunities. This inertia not only complicates their transition and industrial restructuring but also increases the associated costs. Against this backdrop, non-resource-based cities are more likely to achieve notable successes in implementing ECER policies compared to their resource-based counterparts.

Conclusions and policy recommendations

Conclusions.

Based on the city-level dataset from 2003 to 2019, this paper employs a multi-time point difference-in-differences model to thoroughly explore the impact of the ECER policy on CE 1 reduction and CE 2 , reaching the following conclusions:

The ECER policy is confirmed to play a significant role in promoting the reduction of CE 1 and enhancing CE 2 . This conclusion remains robust even after controlling for factors that might affect the accuracy of the assessment, such as contemporaneous policy interferences, sample selection biases, extreme value treatments, and other random factors. This indicates that the ECER policy has important practical implications in mitigating climate change impacts, and its effects are not significantly influenced by the aforementioned potential interferences. The ECER policy effectively promotes CE 1 reduction and CE 2 improvements by incentivizing the research and application of green technologies. This finding underscores the mediating role of green innovation in environmental policies, highlighting that fiscal incentives such as tax breaks and subsidies are crucial for promoting technological innovation and application, and further achieving environmental benefits. The CE 1 reduction effect and CE 2 enhancement of the ECER policy are more pronounced in economically developed, higher-tier cities and in the eastern regions. This may be due to these areas having better infrastructure, higher technological innovation capabilities, more abundant fiscal resources, and stronger public environmental awareness, which all provide strong support for the effective implementation of the ECER policy. Moreover, this variation also suggests that policymakers need to consider regional characteristics when implementing relevant policies to maximize policy effectiveness.

Existing literature has explored the role of energy conservation and emission reduction fiscal policies in environmental protection, such as green credit 37 , ESG performance 75 , green total factor carbon efficiency 36 , and sustainable urban development 38 . These studies report the positive impact of such policies on the environment. However, they do not directly examine the impact of these policies on pollutants. Our study extends the existing literature by investigating the relationship between these policies and carbon emissions. Green fiscal policies significantly promote the reduction of carbon emissions (CE1) and the improvement of carbon efficiency (CE2) through economic incentives, price mechanisms, infrastructure support, and increasing public environmental awareness. Specifically, these policies encourage the research and application of green technologies, change consumer and producer behavior, optimize energy consumption structures, support related infrastructure construction, and increase public participation in low-carbon living. Additionally, green fiscal policies promote sustainable economic growth by directing funds towards low-carbon and green industries, fostering the development of green technologies and industries. Overall, green fiscal policies have not only achieved significant environmental protection results but also played a crucial role in realizing the dual goals of economic growth and environmental protection.

Despite the significant findings, our study has some limitations. Firstly, the data is limited to 248 cities from 2003 to 2019, which may not fully capture the long-term impact of ECER policies. Secondly, reliance on existing data may introduce biases, as not all relevant factors could be considered. Future research could address these limitations by expanding the dataset, including more diverse regions, and employing alternative methods to validate these findings.

Policy recommendations

Based on the above analysis, the policy recommendations of this paper are as follows:

Continue to increase fiscal support. The government should continue to enhance fiscal support for the ECER policy, including expanding the scope of tax reductions and increasing the level of fiscal subsidies, especially for those projects and technologies that can significantly improve energy efficiency and reduce CE 1 . This will further stimulate the innovation motivation of enterprises and research institutions, accelerating the research and development (R&D) and application of low-carbon technologies.

Optimize policy design and implementation mechanisms. Considering the robustness of the ECER policy effects, the government should further refine the policy design to ensure that measures precisely target sectors and aspects with high CE 1 . Concurrently, it is crucial to establish and enhance the supervision mechanism for policy execution, ensuring effective implementation of policy measures. This approach also necessitates timely adjustments and optimizations of the policy to tackle new challenges effectively.

Establish a dedicated Green Technology Innovation Fund. This fund aims to provide financial support specifically for R&D and promotion of green technologies with high CE 2 . By offering startup capital, R&D subsidies, and rewards for the successful commercialization of green technologies, the fund can not only stimulate the innovation drive of enterprises and research institutions but also accelerate the transformation of green technologies from theory to practice. Consequently, this will promote CE 1 reduction and CE 2 enhancement on a broader scale. This initiative directly responds to the importance of fiscal incentive measures for promoting technological innovation and application emphasized in the research, ensuring the ECER policy maximizes its benefits in promoting green development.

Differentiated policy design. Given the variations in the effects of the ECER policy across different regions, policymakers should design and implement differentiated energy-saving and emission reduction policies based on regional factors such as economic development level, industrial structure, and resource endowment. For economically more developed areas with a stronger technological foundation, CE 1 reduction can be promoted by introducing higher standards for environmental protection and mechanisms for rewarding technological innovation. For regions that are relatively less economically developed, the focus should be on providing technical support and financial assistance to enhance their capacity for CE 1 reduction.

Green fiscal policies play a crucial role in reducing carbon emissions and promoting sustainable economic growth, but their impact on social and income inequality needs careful consideration. Firstly, while policies like carbon taxes are effective in reducing emissions, they may place a significant burden on low-income households, as a larger proportion of their income goes towards energy and basic necessities. To mitigate this inequality, governments can implement redistributive measures, such as using carbon tax revenues for direct subsidies or tax reductions for low-income families, ensuring social equity while achieving emission reductions. Secondly, green fiscal policies encourage investment in green technologies and the implementation of green projects. However, these incentives often favor businesses and wealthy families capable of making such investments, potentially widening income disparities. Therefore, policy design should consider inclusive growth by providing green job training and encouraging small and medium-sized enterprises to participate in green projects, ensuring that various social strata benefit from the green economy. Furthermore, in terms of public investment, governments should prioritize low-income and marginalized communities, ensuring they also benefit from the construction of green infrastructure. This includes prioritizing the development of public transportation and renewable energy projects in these areas, thereby reducing living costs and improving the quality of life for these communities. By adopting these redistributive measures and inclusive policy designs, green fiscal policies can achieve the goals of environmental protection and economic growth while effectively mitigating their negative impacts on social and income inequality, promoting sustainable and inclusive development.

When evaluating various policy tools for achieving carbon reduction goals, it is evident that carbon taxes, renewable energy subsidies, ECER policies, emissions trading systems, and energy efficiency standards each have their unique advantages (see Table 9 ). Carbon taxes leverage price mechanisms to encourage emissions reduction and provide redistribution opportunities, while renewable energy subsidies promote technological advancement and market development. ECER policies offer direct incentives and support for infrastructure, resulting in long-term environmental benefits. Emissions trading systems combine cap-and-trade controls with market flexibility, and energy efficiency standards provide direct pathways to emissions reduction. In practical applications, the integrated use of multiple policy tools, fully utilizing their respective advantages, can more effectively achieve carbon reduction goals and drive the transition to a low-carbon economy. Policymakers must consider equity, economic impact, and public acceptance when designing these policies to balance environmental protection with economic growth. Through careful integration and balanced implementation, green fiscal policies can significantly reduce carbon emissions while promoting sustainable and inclusive economic development.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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This study is supported by the National Social Science Fund Major Project: “Research on the Policy System and Implementation Path to Accelerate the Formation of New Productive Forces,” Project Number: 23&ZD069.

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by ZC. Z, S. Z. The first draft of the manuscript was written by ZQ. Z and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Macroeconomic factors, firm characteristics and financial performance: A study of selected quoted manufacturing firms in Nigeria

Asian Journal of Accounting Research

ISSN : 2459-9700

Article publication date: 7 November 2018

Issue publication date: 12 December 2018

The purpose of this paper is to explore the interrelationship between macroeconomic factors, firm characteristics and financial performance of quoted manufacturing firms in Nigeria. Specifically, the study investigates the effect of interest rate, inflation rate, exchange rate and the gross domestic product (GDP) growth rate, while the firm characteristics were size, leverage and liquidity. The dependent variable financial performance is measured as return on assets (ROA).

Design/methodology/approach

The study used the ex post facto research design. The population comprised all quoted manufacturing firms on the Nigerian Stock Exchange. The sample was restricted to companies in the consumer goods sector, selected using non-probability sampling method. The study used multiple linear regression as the method of validating the hypotheses.

The study finds no significant effect for interest rate and exchange rate, but a significant effect for inflation rate and GDP growth rate on ROA. Second, the firm characteristics showed that firm size, leverage and liquidity were significant.

Practical implications

The study has implications for regulators and policy makers in formulating policy decisions. In addition, managers may better understand the interplay between macroeconomic factors, firm characteristics and profitability of firms.

Originality/value

Few studies have addressed the interplay of macroeconomic factors and firm characteristics in determining the profitability of manufacturing firms in the country and developing countries in general.

Financial performance
Manufacturing firms
Firm characteristics
Macroeconomic

Egbunike, C.F. and Okerekeoti, C.U. (2018), "Macroeconomic factors, firm characteristics and financial performance: A study of selected quoted manufacturing firms in Nigeria", Asian Journal of Accounting Research , Vol. 3 No. 2, pp. 142-168. https://doi.org/10.1108/AJAR-09-2018-0029

Emerald Publishing Limited

Published in Asian Journal of Accounting Research . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Background of the study

Micro and macroeconomic factors affect the performance of a firm. Microeconomic factors exist within the company and under the control of management; they include product, organizational culture, leadership, manufacturing (quality), demand and factors of production ( Broadstock et al. , 2011 ; Adidu and Olanye, 2006 ). Macroeconomic factors exist outside the company and not under the control of management; they include social, environmental, political conditions, suppliers, competitors, government regulations and policies ( Adidu and Olanye, 2006 ). Key economic factors include the Consumer Price Index (CPI), unemployment, gross domestic product (GDP), stock market index, corporate tax rate and interest rates ( World Bank Group, 2015 ; Broadstock et al. , 2011 ). These factors (i.e. macro) can pose a positive or negative threat to the performance of a firm. While micro factors are within the control of management, the macro factors are beyond the control of management ( Dioha et al. , 2018 ).

This was evidenced from the crises in Latin America, East Asia, Russia and the global financial crisis in 2007 ( Issah and Antwi, 2017 ). And presently, the recession witnessed in Nigeria, which business analysts opined that led to the delisting of some companies, has brought to limelight the implications of macroeconomic factors on corporate performance ( Zeitun et al. , 2007 ).

For instance, the monetary policy of a country affects all sectors through the cost of debt and the availability of money/credit, which could affect a firm’s ability to access external sources of fund. Fiscal policies affect a firm’s after tax net cash flow, its cost of capital, and potentially the demand for its products, and survival ( Zeitun et al. , 2007 ). Also, increases in the nominal interest rate and inflation rate intensify the aggregate rates of failure or default ( Robson, 1996 ; Davis, 1995 ; Wadhwani, 1986 ). In most developing countries, for instance Nigeria, macroeconomic factors, such as hyperinflation and increasing exchange rates, are some of the factors affecting the performance of manufacturing firms ( Owolabi, 2017 ).

However, the performance of a firm is not affected by macroeconomic factors. According to the resource-based view (RBV), the internal attributes of an organization determine its position in the competitive environment ( Denizel and Özdemir, 2006 ). The attributes of a firm’s physical, human and organizational capital enable the firm conceive of and implement strategies that improve its efficiency and effectiveness ( Barney, 1991 ). Industry and corporate specific factors have been shown to be significant determinants of corporate performance ( Oyebanji, 2015 ; Rajkumar, 2014 ; Akinyomi, 2013 ; Akintoye, 2008 ).

The subject of financial performance has received significant attention from scholars ( Kaguri, 2013 ). It has been of primary concern to various stakeholders in all forms of businesses because of its implications on organizational health and ultimate survival. Therefore, its measurement and determining factors have gained increased attention, more especially in developing countries in the area of business and corporate finance literature ( Dioha et al. , 2018 ). High performance reflects management effectiveness and efficiency in making use of company’s resources and this, in turn, contributes to the country’s economy at large ( Naser and Mokhtar, 2004 ).

2. Statement of the problem

Firms make several operational and strategic decisions which are usually moderated by the macroeconomic environment; these include financing decision, investing decision and operational decision ( Owolabi, 2017 ). Thus, performance is often gauged from stability in the macro economy, such as exchange rate and inflation rate fluctuations, the CPI, level of government expenditure, interest rates, among others. However, macroeconomic volatility is much higher in developing countries than developed ones ( Owolabi, 2017 ). For instance, the Nigerian economy has shown volatility in exchange rate, inflation, interest rate, among several others ( Agu et al. , 2014 ; Ogbole et al. , 2011 ). Analysts opine that growth in the manufacturing sector is hindered negatively from high lending rates, which invariably is responsible for high cost of production ( Rasheed, 2010 ).

Studies have extensively examined the effect of macroeconomic factors on firm performance in developed countries ( Barakat et al. , 2016 ; Broadstock et al. , 2011 ; Kandir, 2008 ; Stock and Watson, 2008 ; Ibrahim and Aziz, 2003 ). However, there is little empirical evidence how macroeconomic variables impact on the performance of manufacturing firms in Nigeria ( Owolabi, 2017 ).

In Nigeria, major macroeconomic indicators have shown significant fluctuations over time, more especially as the country emerges from recession. For instance, inflation rate as measured by the CPI is presently at double-digit level 14.33 as at February 2018. Exchange rate increased tremendously from to over 300 as at April 2018. In a communiqué issued in April 2018, the Central Bank of Nigeria (CBN) Governor Mr Godwin Emefiele raised its money supply growth forecast for 2018 to 10.98 percent. The CBN had earlier projected a money supply growth of 10.29 percent for 2018 ( Vanguard, 2018 ). The GDP at current basic prices has also steadily increased. Studies have extensively focused on the banking sector ( Ogunbiyi and Ihejirika, 2014 ; Osamwonyi and Michael, 2014 ).

However, survival and growth of firms also depend on interaction of macroeconomic factors and firm characteristics. Using data from nine African countries, Lemma and Negash (2013) found evidence that income level, growth rate and inflation influence the capital structure of firms. However, this is further affected by industry- and firm-specific characteristics. Ghareli and Mohammadi (2016) reported mixed findings for the effect of firm-specific characteristics on financial reporting quality. Studies have also substantiated the effect of firm characteristics on financial performance ( Dioha et al. , 2018 ). For instance, firm characteristics such as firm age ( Swiss, 2008 ), firm size ( Malik, 2011 ), liquidity ( Dogan, 2013 ) and leverage ( Mule and Mukras, 2015 ) have been associated with profitability.

The recent study by Foyeke et al. (2015) on a sample of firms from both financial and non-financial sectors in Nigeria revealed a significant positive relationship between financial performance and firm size with the level of corporate governance disclosure. Thus, given the interaction of the two factors in determining performance, there is a need for additional evidence on the joint association between macroeconomic factors, firm characteristics and financial performance in developing countries ( Adeoye and Elegunde, 2012 ). More so, Izedonmi and Abdullahi (2011) have shown that the influence of macroeconomic factors varied from sector to sector. Therefore, there is a need to examine using such firms from the consumer goods sector.

Therefore, the thrust of this study is to examine macroeconomic factors, firm characteristics and financial performance of selected manufacturing companies in Nigeria.

3. Objective of the study

to examine the effect of interest rate on return on assets (ROA) of consumer goods manufacturing firms;

to ascertain the effect of inflation rate on ROA of consumer goods firms;

to examine the effect of exchange rate on ROA of consumer goods manufacturing firms;

to determine the effect of GDP growth rate on ROA of consumer goods manufacturing firms;

to examine the effect of firm size on ROA of consumer goods manufacturing firms;

to analyze the effect of leverage on ROA of consumer goods manufacturing firms; and

to analyze the effect of liquidity on ROA of consumer goods manufacturing firms.

4. Review of related literature

4.1 conceptual framework, 4.1.1 macroeconomic factor(s)..

The word “macroeconomics” is derived from the Greek prefix makro meaning “large” and economics, and is a branch of economics which deals with the performance, structure, behavior and decision making of the economy as a whole ( Sullivan and Sheffrin, 2003 ). The macro environment looks at forces surrounding a firm that have the potential to affect the way it operates ( Davis and Powell, 2012 ). The Institute of Chartered Accountants (ICAN) opined that it can be viewed as a set of factors or conditions that are external to the firm but which can influence the operations of the firm.

The macro environment refers to those conditions and forces which are external to the firm and are beyond the individual business unit, but they all operate within it ( Taher et al. , 2010 ). Duncan (1972) opined that the external business environment refers to the totality of factors outside an organization that are taken into consideration by an organization in its decision making. These factors depend largely on the complexity and dynamism of the environment ( Duncan, 1972 ; Dess and Beard, 1984 ). The external business environment is classified as being stable when it does not show any changes, unstable when it shows relative changes and dynamic when it shows changes continuously ( Aguilar, 1967 ).

Studies have indicated changes in the value of financial assets to be responsive to macroeconomic factors such as inflation rate, exchange rate, interest rates, GDP, money supply, unemployment rate, dividends yields and so forth ( Fosu et al. , 2014 ). The study focused on the following selected macroeconomic variables: interest rate, inflation, exchange rate, money supply and GDP ( Table I ).

4.1.1.1 Interest rate

Crowley defined interest rate as the price a borrower pays for the use of money they borrow from a lender or fee paid on borrowed assets. Ngugi (2001) described interest rate as a price of money that reflects market information regarding expected change in the purchasing power of money or future inflation. Economists argue that interest rate is the price of capital allocation over time; monetarist use the interest rate as an important tool to attract more saving, as increases in the interest rates attract more savings and the decrease in interest rate will encourage investors to look for another investment that will generate more return accordingly ( Murungi, 2014 ). That interest rates are important because they control the flow of money in the economy. High interest rates curb inflation but also slow down the economy. Low interest rates stimulate the economy, but could lead to inflation.

The lending interest rate (percent) in Nigeria was reported at 17.58 percent in 2017, according to the World Bank collection of development indicators, compiled from officially recognized sources. The rate was marginally higher than periods prior. In Nigeria, Acha and Acha (2011) examined the implication of interest rates on savings and investment and reported that interest rate was a poor determinant of savings and investment. While Obamuyi and Olorunfemi (2011) proved that financial reform and interest rates had significant impact on economic growth in Nigeria. At the firm level, Khan and Mahmood (2013) showed that the financial structure of some industry makes firms in that industry more susceptible to interest rates volatilities than others. Mnang’at et al. (2016) found a significant relationship between interest rate and financial performance of micro enterprises in Kenya. Barnor (2014) found a significant negative effect of interest rate on stock market returns of listed firms in Ghana.

4.1.1.2 Inflation rate

Jhingan (2002) defined inflation as a persistent rise in the general level of prices. Akers (2014) stated that inflation rate measures changes in the average price level based on a price index. Inflation can be measured in several ways; however, two commonly used measures are the GDP Deflator or a CPI indicator. The GDP Deflator is a broad index of inflation in the economy; the CPI measures changes in the price level of a broad basket of consumer products. The CPI measures average retail prices that consumers pay. A high or increasing CPI indicates existence of inflation. Higher prices tend to reduce overall consumer spending which, in turn, leads to a decrease in GDP while inflation itself is not negative, rapidly increasing rates of inflation signal the possibility of poor macroeconomic health. Economists distinguish between two types of inflation: demand-pull inflation and cost-push inflation. Demand-pull inflation occurs when aggregate demand for goods and services in an economy rises more rapidly than an economy’s productive capacity. Cost-push inflation, on the other hand, occurs when prices of production process inputs increase. Rapid wage increases or rising raw material prices are common causes of this type of inflation.

Inflation rate is primarily measured in Nigeria as the percentage change in the CPI which has the food and core index, to give the headline inflation. The CPI measures the price of the representative food and services components such as food, alcoholic beverages, energy, housing, clothing, transport, health, communication, transport, etc. ( Figure 1 ).

Several studies have shown a negative effect of inflation on economic growth. For instance, the study by Usman and Adejare (2013) in Nigeria reported a negative relationship between market all share index, market volume and GDP with inflation. Similarly, Alimi (2014) reported a deleterious effect of inflation on financial development; proxied as broad definition of money as ratio of GDP; quasi money as share of GDP; and credit to private sector as share of GDP. The study by Djalilov and Piesse (2016) found a negative relationship with profitability of early transition countries and positive relationship in late transition countries.

4.1.1.3 Exchange rate

According to Business Dictionary, exchange rate is the price for which the currency of a country can be exchanged for another country’s currency. Harvey (2012) described exchange rate as the value of two currencies relative to each other. It is the price of one currency expressed in terms of another currency. It is the price at which the currency of one country can be converted to the currency of another. Exchange rates are either fixed or floating. Fixed exchange rates are decided by central banks of a country, whereas floating exchange rates are decided by the mechanism of market demand and supply ( The Economic Times , 2017 ). Factors that influence exchange rate include: interest rates; inflation rate; trade balance; political stability; internal harmony; general state of economy; and quality of governance.

Martin and Mauer (2003) showed that understanding the impact of foreign exchange risk is a critical element for purposes of firm valuation and risk management. The study by Barnor (2014) found a significant positive effect of exchange rate on stock market returns of listed firms in Ghana.

4.1.1.4 Gross domestic product (GDP)

GDP is the total market value of goods and services produced by a country’s economy during a specified period of time. It includes all final goods and services, that is, those that are produced by the economic agents located in that country regardless of their ownership and that are not resold in any form. According to Mwangi (2013) , GDP is a most commonly used macroeconomic indicator to measure total economic activity within an economy; its growth rate reflects the state of the economic cycle. It is used throughout the world as the main measure of output and economic activity.

In economics, the final users of goods and services are divided into three main groups: households, businesses and the government. One-way GDP is calculated – known as the expenditure approach – by adding the expenditures made by those three groups of users. Accordingly, GDP is defined by the following formula: GDP = Consumption + Investment + Government spending + Net exports [ GDP = C + I + G + N X ] , where Consumption ( C ) represents private-consumption expenditures by households and non-profit organizations; Investment ( I ) refers to business expenditures by businesses and home purchases by households; Government spending ( G ) denotes expenditures on goods and services by the government; and Net exports ( NX ) represents a nation’s exports minus its imports. The idea behind the expenditure approach is that the output that is produced in an economy has to be consumed by final users, which are either households, businesses or the government.

Tan and Floros (2012) on a sample of banks in China reported a negative relationship between GDP growth and bank profitability. Sinha and Sharma (2016) also documented a positive relationship between profitability and GDP in India, while Trujillo-Ponce (2013) on a sample of banks in Spain reported a positive impact of GDP growth on ROA and return on equity (ROE).

4.2 Firm characteristics

Firm Size has become dominant in empirical corporate finance studies and has been widely established among the most significant variables ( Kioko, 2013 ). Studies, however, document mixed results on the effect of size, while some confirm ( Tarawneh, 2006 ; Sarkaria and Shergill, 2000 ); others find mixed or no effect at all ( Goddard et al. , 2006 ; Mariuzzo et al. , 2003 ). There is a positive significant relationship between size and profitability ( Liargovas and Skandalis, 2008 ; Akhavein et al. , 1997 ; Smirlock, 1985 ). More recently, Lopez-Valeiras et al. (2016) revealed that the relationship between size and financial performance is negatively mediated by indebtedness.

Leverage refers to the proportion of debt to equity in the capital structure of a firm ( Omondi and Muturi, 2013 ). It strives to measure what portion of the total assets is financed by debt funds. Leverage ratios are used to measure business and financial risks of a firm ( Okwoli and Kpelai, 2006 ). Studies have shown a positive significant relationship between leverage and firm size ( Booth et al. , 2001 ; Wald, 1999 ; Rajan and Zingales, 1995 ; Marsh, 1982 ). Leverage is the amount of debt used to finance other capital expenditure that can improve firm financial performance ( Lin et al. , 2006 ; Pandey, 2005 ).

Liquidity refers to the firm’s ability to convert its short-term assets into cash in order to meet its day-to-day operation ( Douglas, 2014 ). Liquidity is used to measure firm’s ability to meet its current maturing liabilities ( Okwoli and Kpelai, 2006 ). Liargovas and Skandalis (2008) opined that firms can use liquid asset to finance its activities and investment when external finance is not available. According to Katchova and Enlow (2013) , liquidity ratios measure the firm’s ability to pay off its short-term debt obligations. Examples are the current ratio and quick ratio, which measure the health of a firm in the short run.

Sales growth refers to increase in sales over a specific period of time. Sustainable growth is defined as the annual percentage growth in sales that is consistent with the firm’s financial policies ( Pandey, 2005 ). The amount a company derives from sales compared to a previous, corresponding period of time in which the latter sales exceed the former. Several studies such as Omondi and Muturi (2013) and Rehana et al. (2012) measure sales growth as the current year sales minus prior year sales and dividing by prior year sales.

4.3 Financial performance

Performance is multi-faceted, and the appropriate measure selected to assess corporate performance depends on the type of organization evaluated, and the objectives to be achieved through that evaluation ( Kaguri, 2013 ). Firm performance encompasses three specific areas: financial performance (profits, ROA, return on investment, etc.); product market performance (sales, market share, etc.); and shareholder return (total shareholder return, economic value added) ( Richard et al. , 2009 ).

performance is a set of financial and non-financial indicators which offer information on the degree of achieving of objectives and results; and

performance is dynamic, requiring judgment and by using a causal model that describes how current actions may affect future results.

There are two kinds of performance: financial performance and non-financial performance. Company’s performance is evaluated in three dimensions. The first dimension is company’s productivity, or processing inputs into outputs efficiently. The second is profitability dimension, or the level of which company’s earnings are bigger than its costs. The third dimension is market premium, or the level of which company’s market value is exceeding its book value ( Walker, 2001 ).

Liquidity ratios: measure the availability of cash to pay debt.

Activity ratios: measure how quickly a firm converts non-cash assets to cash.

Debt ratios: measure the firm’s ability to pay long-term debt.

Profitability ratios: measure the firm use of its assets to generate the acceptable rate of return.

Market ratios: measure investors’ response to owning a firm’s stock and the cost of stock. They are concerned with the return on investment for shareholders.

4.4 Theoretical framework

The study is anchored on systems theory to explain the interaction of the external environment with the performance of the firm; and the RBV to explain how internal factors (firm characteristics) determine the outcome of the firm.

4.4.1 Systems theory.

Nwachukwu (2006) defined a system as “a set of interrelated and interdependent parts arranged in a manner that produces a united whole.” Kühn (1974) considered a system as “any pattern whose elements are related in sufficiently regular way to justify attention.” Kühn (1974) extended the theory to include the fact that the knowledge of a part of a system facilitates the knowledge of another part. A system can either be controlled (cybernetic) or uncontrolled. A controlled system sensed information (detector), applies rules to take decision on what is sensed (selector) and makes some transaction or communication between the system (effector). According to Kühn (1974) , the aim of decision (communication and transaction) between systems is to achieve equilibrium. A system can either be a closed system in which case interactions occur only between elements within the system and not with any system outside it, or an open system where interactions occur both within the system and outside it. Closed systems tend toward negative entropy with the likelihood of decaying due to the absence of exchanges with outside systems.

According to Laszlo and Krippner (1998) , “Systems theory promises to offer a powerful conceptual approach for grasping the interrelation of human beings and the associated cognitive structures and processes specific to them in both society and nature.” It is “concerned with the holistic and integrative exploration of phenomena and events.” The term conveys “a complex of interacting components together with the relationships among them that permit the identification of a boundary-maintaining entity or process.” The general systems theory aims at looking at the entire world as a composite of co-existing, interacting and interrelating elements. This is not to undermine or downplay the value of studying units, subsystems or even systems within a larger context (a reductionist approach) as is done in specialization, but to place all disciplines within proper perspective of the whole.

4.4.2 Resource-based view (RBV).

The RBV posits a link between firms’ internal resources and performance ( Denizel and Özdemir, 2006 ). According to RBV, the competitive advantage of a firm can be built on a firm’s resources ( Bharadwaj et al. , 1993 ; Hunt, 1999 ) that meet some important conditions such as value, heterogeneity, rareness, durability, imperfect mobility, unsubstitutability, imperfect imitability and ex ante limits to competition ( Čater, 2001 ). Barney (1991) further observed that a little amount of heterogeneity should certainly exist within different firms in order to be able to explain the observed performance differences between firms. Otherwise, all firms possessing identical resources would conceive of and implement the same strategies and could only improve their effectiveness and efficiency to the same extent, ending up with no sustained competitive advantage or performance superiority ( Denizel and Özdemir, 2006 ).

Lately, the RBV has focused on the relationship with environmental threats and opportunities ( Barney, 1986, 1996 ; Mahoney and Pandian, 1992 ).

Being valuable (enabling a firm to conceive of and implement strategies that will improve its effectiveness and efficiency).

Being rare (By this assertion RBV does not dismiss the importance of valuable but common resources. However, it claims that such resources can help to ensure a firm’s survival but cannot lead to competitive superiority for the firm).

Being imperfectly imitable (due to unique historical conditions; causal ambiguity between the competitive advantage and the resource giving rise to it; and social complexity of the resource generating competitive advantage).

Absence of strategically equivalent substitutes.

4.5 Review of empirical studies

4.5.1 macroeconomic factors and firm performance..

Issah and Antwi (2017) investigated the role of macroeconomic variables on firm’s performance in the UK. Multiple regression was used to analyze the data. They studied a total of 59 macroeconomic variables, subjected to principal component analysis for variable reduction. The full sample model showed adjusted R 2 value of 0.91, and the following variables were significant: lagged ROA; adjusted unemployment rate; benchmarked unit labor costs; real GDP and exchange rate. And five out of the six studied industries had significant F -values.

Owolabi (2017) examined the relationship between economic characteristics and financial performance in Nigeria. The economic characteristics were: government expenditure, inflation, interest rate and exchange rate. The sample comprised 31 manufacturing firms listed on the Nigeria Stock Exchange. The duration of the study was from 2010 to 2014. The effect of government expenditure, inflation, interest rate and exchange rate on EPS and ROA was not significant. Interest rate was significant for only ROE, while all the variables (government expenditure, inflation, interest rate and exchange rate) were significant for Tobin’s Q .

Mwangi and Wekesa (2017) examined the influence of economic factors on firm performance in Kenya. They study used a descriptive research design, and the sample comprised 74 staff working in Kenya Airways Finance Department. The economic factors were interest rate and taxation; the dependent variables of the study were efficiency and growth. The study used primary data. They used multiple regression technique in testing the hypotheses. They found that economic factors had significant effect on performance.

Rao (2016) examined the relationship between macroeconomic factors and financial performance in Nairobi. The sample comprised five firms listed under the energy and petroleum sector of the Nairobi Stock Exchange. The study was from 2004 to 2015. The study found a significant negative effect of interest rate and oil price on financial performance. However, GDP growth, exchange rate and inflation rate were not significant.

Otambo (2016) examined the effect of macroeconomic variables on financial performance of banks in Kenya. The duration of the study was from 2006 to 2015. ROA was used to measure financial performance while quarterly interest rates, quarterly exchange rates (USD/KSH), quarterly GDP and quarterly inflation rates were used to measure interest rates, exchange rates, GDP and inflation rates. The study found that interest rates and exchange rates affect financial performance negatively while GDP affects financial performance positively. Inflation rates were not significant.

Udu (2015) examined the influence of environmental factors on business operations in Nigeria. The duration of the study was from 1981 to 2013. The variables studied were inflation rate, interest rate, unemployment rate, and exchange rate, and business operations proxied as real GDP was the dependent variable. Ordinary least squares method of analysis was employed to test the hypothesis. The study found that interest rate and unemployment rate were positive and significant.

Gado (2015) examined the impact of macro environment on performance in Nigeria. The sample comprised 20 most capitalized companies. The study used ordinary least squares and correlation. The results showed that collectively the macro-environmental variables have significant and positive impact on performance. Specifically, government expenditure and inflation have a positive impact while exchange and interest rate have a negative impact.

Murungi (2014) examined the relationship between macroeconomic variables and financial performance in Kenya. The sample comprised 46 Insurance firms listed on Kenya Stock Exchange. The study duration was from 2009 to 2013. The data were analyzed using multiple regression. The study found that interest rate and GDP were statistically significant. Others such as inflation rate, exchange rate, money supply and size of assets were not statistically significant.

Kiganda (2014) examined the effect of macroeconomic factors on profitability of banks in Kenya. The study focused on Equity Bank. The studied macroeconomic factors were: real GDP, inflation and exchange rate. The study used the Cobb–Douglas production function transformed into natural logarithm and used annual data from 2008 to 2012. The results showed that the macroeconomic factors (real GDP, inflation and exchange rate) have insignificant effect on profitability of Equity Bank at 5 percent level of significance. The study focused on a single bank which limits the generalizability of the findings.

Ogunbiyi and Ihejirika (2014) examined the effect of interest rates on profitability of Deposit Money Banks in Nigeria. They used country-level aggregate annual data over a period of 13 years from 1999 to 2012. They employed multivariate regression analysis. The results showed that maximum lending rate, real interest rate and savings deposit rate have negative and significant effect on profitability of banks as measured by ROA at 5 percent level of significance. However, no significant relationship was found between interest rate and net interest margin of banks.

Osamwonyi and Michael (2014) investigated the impact of macroeconomic variables on profitability of banks in Nigeria from 1990 to 2013. They used pooled ordinary least squares (POLS) regression. The macroeconomic variables were: GDP, interest and inflation rate; profitability was proxied using ROE. The study reported a positive effect of GDP on ROE. Interest rate had a significant negative effect on ROE, while inflation was not significant at all levels of significance.

Enyioko (2012) examined the effect of interest rate policies on performance of banks in Nigeria. The sample comprised 20 banks that emerged from the consolidation exercise of 2004. They applied regression and error correction models to analyze the relationship. The study reported that interest rate policies have not affected the performance of banks significantly.

Izedonmi and Abdullahi (2011) studied the effect of three macroeconomic variables, i.e. inflation, exchange rate and market capitalization on the performance of 20 sectors of the Nigerian Stock Exchange (NSE) for the period 2000–2004. The study reported that the extent to which a factor affected the various sectors varied from one sector to another. Jointly the study found no significant influence of macroeconomic factors on the NSE.

Kandir (2008) investigated the effect of macroeconomic factors on stock returns in Turkey. The sample comprised all non-financial firms listed on the Istanbul Stock Exchange for the period 1997–2005. Macroeconomic variables in the study were: growth rate of industrial production index, change in CPI, growth rate of narrowly defined money supply, change in exchange rate, interest rate, growth rate of international crude oil price and return on the MSCI World Equity Index. Multiple regression was employed in data analysis. The study finds that exchange rate, interest rate and world market return affect all of the portfolio returns, while inflation rate is significant for only 3 of the 12 portfolios. On the other hand, industrial production, money supply and oil prices do not have any significant effect on stock returns.

4.5.2 Firm characteristics and firm performance.

Dioha et al. (2018) examined the effect of firm characteristics on profitability in Nigeria. The sample consisted of 18 listed consumer goods companies for the period 2011–2016. Profitability was proxied by ROS, while firm characteristics were proxied by firm age, firm size, sales growth, liquidity and leverage. Multiple regression was used to analyze the data. The study found that size, sales growth and leverage have significant effect on profitability. However, age and liquidity were not significant.

Bist et al. (2017) examined the impact of firm characteristics on financial performance in Nepal. They studied 18 Nepalese insurance companies from 2008 to 2016. Multiple regression was used to analyze the data. The regression analysis showed that the coefficients of leverage and premium growth were positive and significant at 1 percent level. However, the coefficients of diversification, size, liquidity and claim payments were negative and insignificant.

Lasisi et al. (2017) examined the determinants of profitability of listed agricultural companies in Nigeria. The sample comprised four agricultural firms listed on the Nigeria Stock Exchange for the period 2008–2016. The independent variables were leverage, liquidity, sales growth and operating expenses efficiency. They analyzed the panel data using multiple regression technique. The study findings revealed that liquidity and sales growth have a positive and significant effect on profitability (ROE), leverage had a negative and significant effect on profitability, and operating expenses efficiency revealed an insignificant negative effect on the profitability. The study was, however, delimited to firms in the agricultural sector.

Mohammed and Usman (2016) examined the impact of corporate attributes on share price in Nigeria. The sample comprised five listed pharmaceutical firms for a period of 10 years (2004–2013). Multiple regression was used to analyze the data. They found that size, leverage and growth have a positive and significant impact on profitability.

Bhutta and Hasan (2013) examined the impact of firm-specific and macroeconomic factors on profitability of firms in Pakistan. The sample comprised firms listed on the food sector of Karachi Stock Market for the period 2002–2006. The firm-specific factors include debt to equity, tangibility, growth and size, and the macroeconomic factor was food inflation. They found a significant negative relationship between size and profitability, and an insignificant positive relationship between tangibility, growth, food inflation and profitability. Similarly, an insignificant negative relationship is observed between debt to equity ratio and firm profitability.

Chandrapala and Knápková (2013) studied the effect of firm-specific factors on financial performance in Czech Republic. The sample comprised 974 firms over the period 2005–2008, using data from Albertina database. They used pooled and panel designs for the analysis. They found that the firm size and sales growth had significant positive impact on ROA. However, debt ratio and inventory had significant negative impact on ROA.

Kaguri (2013) examined the relationship between firm characteristics and financial performance in Kenya. The sample comprised 17 life insurance companies over the period of 2008–2012. The studied firm characteristics were: size, diversification, leverage, liquidity, age, premium growth and claim experience of life insurance companies in Kenya. Regression analysis was used to analyze the data. All variables were found to be statistically significant.

Mehari and Aemiro (2013) examined firm-specific factors that determine performance in Ethiopia. The sample comprised nine insurance companies for the period 2005–2010. The firm characteristics were: size, leverage, tangibility, loss ratio (risk), premium growth, liquidity and age. Performance was proxied as return on total assets (ROA). The results of regression analysis revealed that size, tangibility and leverage were positive and statistically significant; however, loss ratio (risk) was negative and statistically significant. Premium growth, age and liquidity were statistically non-significant.

Similarly, Sumaira and Amjad (2013) examined determinants of profitability in Pakistan. The sample comprised 31 insurance firms (life and non-life insurance) from 2006 to 2011. The study found that leverage, size and age of the firm were significant determinants of profitability, while sales growth and liquidity were not significant.

Sambasivam and Ayele (2013) studied the performance of insurance companies in Ethiopia. The sample comprised nine listed insurance companies from 2003 to 2011. The firm-specific factors were: age, size, volume of capital, leverage, liquidity, growth and tangibility of assets, while profitability was proxied by ROA. They found that growth, leverage, volume of capital, size and liquidity were significant determinants of performance. While liquidity and leverage are negative, age and tangibility were not significant.

4.5.3 Macroeconomic factors, firm characteristics and firm performance

Rani and Zergaw (2017) examined bank-specific, industry-specific and macroeconomic factors on profitability of Ethiopian commercial banks. Profitability was proxied by ROE and net interest margin. They used secondary data from 2005 to 2015. Multiple regression was used to analyze the data. The study results showed that capital adequacy, management efficiency, earnings and liquidity ratios significantly affected ROE, while net interest margin significantly affected capital adequacy and earnings. The industry-specific variable proxied by industry growth rate had significant impact on net interest margin. All the macroeconomic factors (inflation, GDP, tax rate and exchange rate) had positive but insignificant impact both on ROE and net interest margin.

Ghareli and Mohammadi (2016) studied the effect of macroeconomic factors and firm characteristics on quality of financial reporting in Iran. The macroeconomic factors in the study were exchange rates, inflation rates, interest rates and GDP. The firm characteristics included working capital, size of firm and financial leverage. The sample comprised 91 firms listed on the Tehran Stock Exchange. The duration of the study was from 2005 to 2013. Multiple linear regression and Spearman correlation test were used to test the hypotheses. The results showed that exchange rate, interest rate and leverage were positive and significant, while GDP was negative and significant. Inflation rate was negative but not significant, while firm size was not significant.

Owoputi et al. (2014) examined the impact of bank-specific, industry-specific and macroeconomic factors on profitability of banks in Nigeria. They found that inflation rate was significant for both ROA and ROE. Interest rate was significant for ROA and NIM. The real growth rate of GDP was not significant. Among the bank-specific variables, size was found significant for the profitability measures: ROA, ROE and NIM.

Mirza and Javed (2013) examined macro and micro determinants of financial performance in Pakistan. The sample comprised 60 Pakistani firms listed on Karachi Stock Exchange for the period 2007–2011. The results showed that income per capita was significant and positive, inflation was significant but negative. Firm characteristics showed that debt to equity ratio was significant and positive, both short-term and long-term debt to total assets was significant and negative. Firm size was significant and positive, while liquidity (current ratio) was significant but negative.

Riaz and Mehar (2013) investigated the impact of bank-specific variables and macroeconomic indicators on profitability of commercial banks in Pakistan from 2006 to 2010. The variables studied were: asset size, credit risk, total deposits to total assets ratio, interest rate (discount rate) and the profitability measures were: ROA and ROE. The sample included all 32 commercial banks. They employed regression for data analysis. They reported a significant impact of the bank-specific variables (asset size, total deposits to total assets and credit risk) and interest rate on ROE, while credit risk and interest rate had a significant impact on ROA.

Kanwal and Nadeem (2013) investigated the impact of macroeconomic variables on profitability of public limited commercial banks in Pakistan for years 2001–2011. They used POLS to examine the effect of three major external factors: inflation rate, real GDP and real interest rate on profitability indicators: ROA, ROE and equity multiplier (EM) ratios in three separate models. The study finds that there is a strong positive relationship of real interest rate with ROA, ROE and EM. Second, real GDP is found to have an insignificant positive effect on ROA, but an insignificant negative impact on ROE and EM. Inflation rate, on the other hand, has a negative link with all three profitability measures.

Charles (2012) investigated the performance of monetary policy on manufacturing sector in Nigeria, using econometrics test procedures. The result indicates that money supply positively affects manufacturing index performance while company lending rate, income tax rate, inflation rate and exchange rate negatively affect the performance of manufacturing sector.

Zeitun et al. (2007) examined macro and microeconomic determinants of corporate performance and failure in Jordan. The sample comprised 167 Jordanian companies from 1989 to 2003. The key macroeconomic indicators studied were nominal interest rate, changes in money supply, production manufacturing index, inflation, exports and availability of credit, including Islamic credit. They found that interest rate negatively and significantly affects firm performance measured by ROA. Both production manufacturing index and growth of Islamic credit facilities positively and significantly affected firm’s performance. The significant microeconomic variables were size, age and total debt to total assets.

5. Methodology

5.1 research design.

Research design refers to the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure ( Claire et al. , 1962 ). The study made use of ex post facto research design. Kerlinger and Rint (1986) observed that an ex post facto investigation seeks to reveal possible relationships by observing an existing condition or state of affairs and searching back in time for plausible contributing factors. Ex post facto design is deemed appropriate for the study because the study is non-experimental, and seeks to investigate causal relationship between the dependent and independent variables of the study ( Owolabi, 2017 ).

5.2 Population of the study

Population is defined as all the members of a real or hypothetical set of people, events or objects to which a researcher wishes to generalize the results of the study ( Borg and Gall, 1989 ). The population of the study is made up of firms quoted on the floor of the NSE as at end of 2017. The number of firms included in the various sectors on the NSE is shown in Table II .

5.3 Sample size of the study

The study focused on firms in the consumer goods sector of the NSE. The study employed a variant of non-probability sampling, namely, the purposive sampling technique and included all the firms in the consumer goods sector into the sample.

DN Tyre & Rubber Plc.

Champion Breweries Plc.

Golden Guinea Breweries Plc.

International Breweries Plc.

Nigerian Breweries Plc.

7-Up Bottling Company Plc.

Dangote Flour Mills Plc.

Dangote Sugar Refinery Plc.

Flour Mills Nigeria Plc.

Honeywell Flour Mill Plc.

Multi-Trex Integrated Plc.

N. Nigeria flour mills plc.

Union Dicon Salt Plc.

Cadbury Nigeria Plc.

Nestle Nigeria Plc.

Nigerian Enamelware Plc.

Vitafoam Nigeria Plc.

P.Z. Cussons Nigeria Plc.

Unilever Nigeria Plc.

McNichols Plc.

Nascon Allied Industries Plc.

5.4 Sources of data

The study employed secondary data. These are described as data previously obtained for purposes other than the present study. The sources utilized include annual financial reports, such as Statement of Comprehensive Income and the Statement of Financial Position, of the selected companies for the period 2011–2017. Secondary data for economic factors were obtained from the Statistical Bulletin of the CBN.

5.5 Technique of data analysis

The study employed multiple linear regression technique. This is a “statistical technique which analyses the relationship between a dependent variable and multiple independent variables by estimating coefficients for the equation on a straight line” ( Hair et al. , 2006 ). A multiple linear regression model was used to understand the relationships between the dependent variable and the independent variables ( Malhotra and Birks, 2000 ).

5.5.1 Model specification.

The model is stated in its implicit form below as follows: ROA = F ( Macro ‐ economic factors , Firm characteristics ) .

The estimation approach leads to the following estimation equations: (1) ROA i t = α + IntR t + InfR t + ExcR t + GDPR t + Firm Size i t + Leverage i t + Liquidity i t + μ .

5.5.2 Robustness test

(2) ROE i t = α + IntR t + InfR t + ExcR t + GDPR t + Firm Size i t + Leverage i t + Liquidity i t + μ .

5.5.3 Description of variables.

ROA it : measured as the proportion of net income to total assets in the period ( t );

ROE it : measured as the proportion of net income to total equity in the period ( t ); and

NPM it : measured as the proportion of net profit to revenue in the period ( t ).

IntR t : measured as the official lending rate during a year;

InfR t : measured as the annual change in the CPI;

ExcR t : measured as the official exchange rate during a year;

GDPR t : the variable is an indication of economic growth. Measured as the annual change in GDP;

Firm Size it : measured as the natural logarithm of total assets in the period ( t );

Leverage it : measured as the proportion of debt to equity in the period ( t ); and

Liquidity it : measured as the proportion of debt to equity in the period ( t ).

6. Data analysis

6.1 descriptive statistics and model results.

The descriptive statistics are shown in Table III . The number of observations was 146; while the p -value of the Jarque–Bera statistics showed that all variables were not normally distributed. The model’s degree of goodness of fit was estimated and evaluated using multiple coefficients denoted by R 2 and the adjusted R 2 . R 2 is the square of this measure of correlation and indicates the proportion of the variance in the dependent variable that is explained by the independent variables in the model. However, the disadvantage of R 2 is that it tends to over-estimate the success of the model in some cases when applied to the real world, so an adjusted R 2 value takes into account the number of variables in the model and the number of observations is used ( Ahmed, 2006 ). The R 2 value is 0.28; and the adjusted R 2 is 0.24; therefore the independent variables explain approximately 24 percent of the variation in the dependent variable ( Table IV ).

The F -statistic measures the statistical significance of the model; the F -value is 7.60 ( p <0.05); therefore, the model is statistically significant. The properties of both the standardized and unstandardized regression coefficients were used in assessing each independent variable ( Issah and Antwi, 2017 ). The unstandardized coefficient measures the average change in the dependent variable associated with one unit change of the independent variable, holding other independent variables constant. Standardized coefficient (also known as beta) measures the contribution of each independent variable on the dependent variable.

6.1.1 Analysis of H1

There is no significant effect of interest rate on ROA of consumer goods manufacturing firms.

“List of consumer goods manufacturing companies” showed that interest rate had a negative but non-significant effect on ROA ( t : −0.375893; p >0.05). The study therefore rejects the alternate hypothesis and accepts the null of “no significant effect of interest rate on ROA of consumer goods manufacturing firms.”

6.1.2 Analysis of H2

There is no significant effect of inflation rate on ROA of consumer goods manufacturing firms.

“List of consumer goods manufacturing companies” showed that inflation rate had a negative but significant effect on ROA ( t : 1.799492; p <0.10). The study therefore rejects the null hypothesis and accepts the alternate of “a significant effect of inflation rate on ROA of consumer goods manufacturing firms.”

6.1.3 Analysis of H3

There is no significant effect of exchange rate on ROA of consumer goods manufacturing firms.

“List of consumer goods manufacturing companies” showed that exchange rate had a negative but non-significant effect on ROA ( t : −0.902177; p >0.05). The study therefore rejects the alternate hypothesis and accepts the null of “no significant effect of exchange rate on ROA of consumer goods manufacturing firms.”

6.1.4 Analysis of H4

There is no significant effect of GDP growth rate on ROA of consumer goods manufacturing firms.

“List of consumer goods manufacturing companies” showed that GDP growth rate is positive and had a significant effect on ROA ( t : 1.710709; p <0.10). The study therefore rejects the null hypothesis and accepts the alternate of “a significant effect of GDP growth rate on ROA of consumer goods manufacturing firms.”

6.1.5 Analysis of H5

There is no significant effect of firm size on ROA of consumer goods manufacturing firms.

“List of consumer goods manufacturing companies” showed that firm size is positive and had a significant effect on ROA ( t : 1.922123; p <0.10). The study therefore rejects the null hypothesis and accepts the alternate of “a significant effect of firm size on ROA of consumer goods manufacturing firms.”

6.1.6 Analysis of H6

There is no significant effect of leverage on ROA of consumer goods manufacturing firms.

“List of consumer goods manufacturing companies” showed that leverage is positive and had a significant effect on ROA ( t : 3.519396; p <0.05). The study therefore rejects the null hypothesis and accepts the alternate of “a significant effect of leverage on ROA of consumer goods manufacturing firms.”

6.1.7 Analysis of H7

There is no significant effect of liquidity on ROA of consumer goods manufacturing firms.

“List of consumer goods manufacturing companies” showed that liquidity is positive and had a significant effect on ROA ( t : 3.390252; p <0.05). The study therefore rejects the null hypothesis and accepts the alternate of “a significant effect of liquidity on ROA of consumer goods manufacturing firms.”

6.2 Discussion of findings

The study explored the interrelatedness of macroeconomic factors, firm characteristics and financial performance. The macroeconomic factors showed inconsistent results; interest rate had negative but non-significant effect, while inflation rate had a negative and significant effect. Exchange rate was negative but non-significant, while GDP growth rate was positive and significant. The mixed results may partially be attributed to the proxy for financial performance used in a study. The study by Issah and Antwi (2017) in the UK found that real GDP and exchange rate were significant. Otambo (2016) in Kenya also reported that GDP positively affected ROA. Inflation rates were not significant. Owolabi (2017) in Nigeria showed that inflation, interest rate and exchange rate had no significant effect on ROA. The interest rate and exchange rate behavior were in line with the present study of non-significant effect. Similarly, Rao (2016) in Nairobi reported a non-significant effect of exchange rate on financial performance. Gado (2015) in Nigeria found a positive effect for inflation while exchange and interest rate had negative effects.

This is contrary to Mwangi and Wekesa’s (2017) study conducted in Kenya, which showed that interest rate had a significant effect on performance. And Rao (2016) in Nairobi reported a significant negative effect of interest rate on financial performance. But the GDP growth and inflation rate were not significant. Otambo (2016) in Kenya also reported a negative effect of interest rates and exchange rates on ROA; inflation rates were not significant.

The study by Udu (2015) in Nigeria which proxied business operations as real GDP found that interest rate had a positive and significant effect on real GDP. On a sample of Deposit Money Banks in Nigeria, Ogunbiyi and Ihejirika (2014) found that real interest rate has negative and significant effect on ROA. Also, Osamwonyi and Michael (2014) who measured profitability using ROE reported a positive effect for GDP and a significant negative effect for interest rate, while inflation was not significant. Contrary to this, Enyioko (2012) found that interest rate has not affected performance of banks significantly. In conclusion, the effect of macroeconomic factors on performance may be sector based. This supports the study by Izedonmi and Abdullahi (2011) that the extent to which a factor affected a particular sector varies from one sector to another.

In other African countries such as Kenya, the study by Murungi (2014) on a sample of insurance firms found that interest rate and GDP had significant effects on performance, while inflation and exchange rates were not statistically significant. This is contrary to the study by Kiganda (2014) conducted in Kenya but with a focus on Equity Bank, which reported that real GDP, inflation and exchange rate had insignificant effect on profitability. Similarly, Kandir (2008) investigating the effect of macroeconomic factors on stock returns in Turkey reported that exchange rate and interest rate affect all the portfolio returns, while inflation rate was significant for 3 out of the 12 portfolios.

The analysis of firm characteristics showed that firm size, leverage and liquidity had positive and significant effect. The study by Dioha et al. (2018) in Nigeria found that size and leverage have significant effect on profitability; but liquidity was not significant. This is consistent with the study by Bist et al. (2017) in Nepal that showed that leverage had a positive and significant effect; but, size and liquidity were negative and insignificant. Chandrapala and Knápková (2013) in Czech Republic found that firm size has a significant positive impact on ROA. However, contrary to the present study, they found that debt ratio had significant negative impact on ROA.

Using firms from the agricultural sector the study by Lasisi et al. (2017) in Nigeria revealed that liquidity has a positive and significant effect on ROE, but leverage had a negative and significant effect on ROE.

The study by Mohammed and Usman (2016) in Nigeria showed that size and leverage have a positive and significant effect on share price. In Pakistan, the study by Bhutta and Hasan (2013) on firms listed on the food sector of Karachi Stock Market reported a significant negative relationship between size and profitability, and a positive insignificant relationship between food inflation and profitability. Also, debt to equity ratio had insignificant negative relationship.

Studies conducted on other sectors also show similar and mixed findings. Kaguri (2013) on a sample of life insurance companies in Kenya found that size, leverage and liquidity were statistically significant. On a sample of insurance companies in Ethiopia, Mehari and Aemiro (2013) revealed that size and leverage were positive and statistically significant; however, liquidity was statistically non-significant. Similarly, Sumaira and Amjad (2013) in Pakistan found that leverage and size were significant determinants of profitability, while liquidity was not significant. Sambasivam and Ayele (2013) in Ethiopia, which proxied profitability as ROA, found that leverage and liquidity were significant and negative.

The F -statistic which tests the significance of the model was significant ( p <0.05). Therefore, jointly macroeconomic factors and firm characteristics interact to determine firm performance. Studies such as Rani and Zergaw (2017) on the banking sector in Ethiopia showed that macroeconomic factors (inflation, GDP and exchange rate) had positive but insignificant impact on ROE. Earnings and liquidity ratios significantly affected ROE. An additional industry-specific variable proxied by industry growth rate had also a significant impact on net interest margin. Also, the study by Owoputi et al. (2014) on banks in Nigeria found that inflation rate was significant for both ROA and ROE. Interest rate was significant for ROA and NIM. The GDP growth rate was not significant. Size was significant for ROA, ROE and NIM. From an Islamic perspective, Zeitun et al. (2007) in Jordan found that interest rate negatively and significantly affects ROA. The significant microeconomic variables were size and total debt to total assets.

Riaz and Mehar (2013) in Pakistan reported a significant impact of asset size and interest rate on ROE; and interest rate had a significant impact on ROA. Kanwal and Nadeem (2013) found that there is a strong positive relationship of real interest rate with ROA, ROE and EM. Second, real GDP is found to have an insignificant positive effect on ROA, but an insignificant negative impact on ROE and EM. Inflation rate, on the other hand, has a negative link with all three profitability measures.

Using samples drawn from manufacturing firms, studies by Ghareli and Mohammadi (2016) on firms in Iran showed that exchange rate, interest rate and leverage had positive and significant effect, while GDP was negative and significant. Inflation rate was negative but not significant, while firm size was not significant. Mirza and Javed (2013) in Pakistan found that inflation was significant but negative. Leverage was significant and positive, firm size was significant and positive, while liquidity (current ratio) was significant but negative. Specifically, Charles (2012) in Nigeria reported a positive relationship between money supply and manufacturing index performance, while inflation rate and exchange rate had negative effect on the performance of manufacturing sector.

7. Conclusion and recommendations

7.1 conclusion.

The study was undertaken to explore the interrelationship between macroeconomic factors, firm characteristics and financial performance of manufacturing firms in Nigeria. Studies have shown that both micro and macro factors interact to determine the financial performance of a firm. While micro factors are under the control of management, the macro factors are outside the company and not under the control of management. The Nigerian economy has shown volatility in macroeconomic factors, such as exchange rate, inflation, interest rate, etc. These have hindered performance of manufacturing firms over time; however, firm performance also depends on interaction of such factors with firm characteristics. As decisions regarding financing and liquidity are purely within the ambit of the manager. This then calls for a need to provide evidence on the joint association between macroeconomic factors, firm characteristics and financial performance in developing countries.

7.2 Recommendations

managers should effectively consider interest rates in making borrowing decisions, as this may affect the cost of debt;

government should be wary of the prevailing inflation rate because of its negative effect on manufacturing capacity utilization;

government should endeavor to maintain a stable exchange rate to enable firms secure the needed resources from foreign countries;

the government and regulatory authorities should make sustainable effort at ensuring a sustainable GDP growth rate by providing policies which favor the growth of domestic manufacturing firms;

managers should seek efforts at expansion and diversification; this is because of the positive benefits of firm size on growth potential of a firm;

the leverage position of a firm should be adequately monitored by managers because a highly geared firm may experience a negative performance over time; and

the liquidity posture of a firm should be monitored by managers; emphasis on industry and across firm comparison may be used in monitoring the status of a firm in relation to competitors.

Nigeria’s inflation rate from 2000 to 2012

Selected macroeconomic variables

Year	GDP per capita (USD)	GDP (USD billion)	Money (annual variation in %)	Inflation rate (CPI, annual variation in %)	Exchange rate (vs USD)	Policy interest rate (%)
2013	3,082	522	1.3	8.5	155.2	12.00
2014	3,312	576	20.6	8.1	167.5	13.00
2015	2,766	494	5.8	9	196.5	11.00
2016	2,206	405	17.8	15.7	304.5	14.00
2017	1,995	376	1.7	16.5	305.5	14.00

S no.	Sector	Number of firms
1	Agriculture	5
2	Consumer goods	21
3	Conglomerates	6
4	Financial services	57
5	Health care	11
6	ICT	7
7	Industrial goods	15
8	Natural resources	4
9	Oil and gas	12
10	Services	25
	Total	163

	IntR	InfR	ExcR	GDPR	Firm Size	Leverage	Liquidity
Mean	12.56164	11.50645	211.9160	3.273973	24.18891	0.536857	2.405487
Median	12.00000	10.80000	188.4524	4.300000	24.77322	0.293164	0.495891
Maximum	14.00000	16.50000	305.5000	6.300000	27.01342	5.950043	161.7999
Minimum	11.00000	8.047411	159.2632	−1.600000	18.04201	−1.020951	−87.86760
SD	1.050272	3.175347	58.61567	2.616975	2.038982	0.858093	21.45939
Skewness	0.194952	0.490764	0.822211	−0.711094	−1.218807	3.351398	5.349119
Kurtosis	1.754506	1.672726	1.873250	2.215223	4.337613	18.97932	45.02500
Jarque–Bera	10.36163	16.57741	24.17328	16.05085	47.03127	1,826.619	11,440.03
Probability	0.005623	0.000251	0.000006	0.000327	0.000000	0.000000	0.000000
Sum	1,834.000	1,679.942	30,939.74	478.0000	3,531.581	78.38117	351.2011
Sum sq. dev.	159.9452	1,462.010	498,190.6	993.0411	602.8298	106.7668	66,773.30
Observations	146	146	146	146	146	146	146

Variable	Coefficient	SE	-Statistic	Prob.
	−0.259721	0.204524	−1.269880	0.2063
IntR	−0.006350	0.016894	−0.375893	0.7076
InfR	0.009470	0.005263	1.799492	0.0741
ExcR	−0.000413	0.000457	−0.902177	0.3685
GDPR	0.015564	0.009098	1.710709	0.0894
Firm Size	0.013125	0.006829	1.922123	0.0567
Leverage	0.059902	0.017021	3.519396	0.0006
Liquidity	0.000678	0.000200	3.390252	0.0009

	0.278248	Mean dependent variable	0.366369
Adjusted	0.241638	SD dependent variable	0.555485
SE of regression	0.423122	Sum squared resid.	24.70646
-statistic	7.600210	Durbin–Watson stat.	1.214496
Prob. ( -statistic)	0.000000

-squared	0.052610	Mean dependent variable	0.101658
Sum squared resid	34.68503	Durbin–Watson stat.	1.233974

Question #1	Question #2	Question #3
What are the essential PED DNAs? Can generic PED archetypes be created based on them?	What are the categories of quality-of-life indicators relevant for PED development?	How would you use a database tool to learn about PED development process (e.g., using static information for dynamic decision-making)?
Which future technologies would you expect to be adopted in PEDs and cities?	What can be the challenges and the barriers in the future (regarding e.g., control, smart solutions, modeling, technologies) to PED development and diffusion?	What is your expectation for urban and district energy modeling? How can models help to shape PEDs and cities?
What is the impact of stakeholders in the PED design/decision process, what are their interests and how are stakeholders likely to be involved in the overall process?	What costs do you expect to bear and what revenues do you expect to realize from the PED implementation? Which aspects should be included in the organizational/business models?	What would you prioritize in terms of energy aspects or efficiency and social implications of living in a PED? Which aspects are more relevant for you?
Annex 83 together with other PED initiatives is developing a database of PEDs and PED-Labs: what would be your main interest in consulting the database?	Having the outcomes from PED guidelines analysis, what information would be the most interesting for you to see?	Who can benefit from the PED research studies and Annex 83 results? Which stakeholders are interested?

Categories	Key Characteristics
Facts and Figures	Physical sizes/population size
Geographical location
Climate
Density
Built form
Land use
Energy demand
Renewable energy potential
Technologies	Renewable energy supplies
Energy-efficiency measures
Energy distribution (e.g., co-generation, district network)
Energy storage
Mobility solutions
Quality of Life	User comfort
Social-economic conditions
Health impacts (e.g., air pollution, noise pollution)
Accessibility to green space
Accessibility to services (e.g., bike lane, public transportation)
Local value/sense of community
Others	Regulations/Policies
Stakeholder involvement
Local targets and ambitions
Local challenges
Impacts of PEDs

Type	Quality Categories
Tangible	Indoor and outdoor environmental quality	Physical quality and comfort of the environment
Security and safety
Level and accessibility of servicing	Public and active transport facilities including walkability, energy services (access to affordable energy including access to energy efficiency), sustainable waste management
Access to daily life amenities including education, culture, sports, coworking and study places, provisions for children, but even common gardens or community kitchens
Aesthetic quality
Functional mix
Future-proofness
Acceptable cost of life (affordability, inclusivity)
Equity and just transition
Functional links to realizing circularity and reducing emissions
Citizen engagement	Involvement in decision-making
Social diversity in participation
Access to greenery	The possibility to reconnect with nature
Sufficient open space
Information flow	From creating awareness over enhancing knowledge and literacy up to capacity of control
Transparency on energy flows and information for the end prosumer
Insight in applicable PED solutions and in healthy lifestyles
Intangible	Sense of well-being
Quality of social connections
Sense of personal achievement
Level of self-esteem
Sense of community
Degree of cooperation and engagement for the common interest
Time spent with friends (outdoor)
Budget available at the end of the month to spend freely
Not being aware or realizing of living in a PED

Technology Groups	Solutions
Energy efficiency	New energy-efficient buildings and building retrofitting.
Nature-based solutions (natural sinks) and carbon capture solutions (CCS)
Efficient resource management
Efficient water systems for agriculture (smart agriculture, hydroponics, agrivoltaics, etc.)
Organic photovoltaics and a circular approach (second life materials, like batteries)
Energy flexibility	Hardware	Storage (long-term and short-term)
Monitoring systems (sensors, smart meters, PLCs *, energy management systems, etc.)
Vehicle to grid
Heat pumps
Electronic devices like IoT * technologies
Buildings fully automated with real time monitoring behind-the-meter and automated actions
Cybersecurity, data rights and data access
Demand management and remote control of devices
Software	Edge computing
Machine learning
Blockchain
Digital twins
5G
City management platform and platforms for city planning (space, refurbishment, climate change, etc.)
E-mobility	Promotion of shared vehicles over individual car use, lift sharing, and alternative ways (like micromobility) to collective transports
Soft mobility	Promotion of a lifestyle that require less use of cars, i.e., “soft mobility” solutions like low emission zones or banning the entrance of some type of car (e.g., Singapore and Iran have policies in place to allow only certain car groups to drive freely in certain periods)
E-vehicle charging stations and vehicle-to-grid solutions
Low-carbon generation	Photovoltaics
Energy communities
Electrification of heating and cooling (H&C) using heat pumps, district heating networks utilizing waste heat, or solar thermal technologies
Virtual production
Fusion technology

Challenges and Barriers	Key Topics
Capacity building and policy issues	Political and legal barriers
Regulatory frameworks and policy constraints
Tailored legislation
Bridging the knowledge gap
Inadequate data sharing practices
Securing sufficient financial resources
Lack of clear regulations defining PED classification
Active involvement of policymakers
Widespread dissemination of knowledge
Collaborative data-sharing efforts
Securing adequate funding
Establishing supportive policies and regulations
Social challenges and considerations	Cultural barriers
Access to affordable and sustainable energy for all
Building social agreements and fostering collaboration
Energy literacy
Addressing personal behavior acceptance
Transition strategy for inclusivity
Social inclusion and trust-building
Data sharing and privacy concerns
Overcoming public opposition and promoting knowledge dissemination
Financial barriers	Long-term storage investment and space competition
Insufficient investment
High upfront costs
Allocation of costs among stakeholders
Incentives for participation
Addressing investment challenges for different stakeholders
Accounting for battery costs
Data management	Data standardization
Data security measures and protocols
Sustainability and maintenance of data infrastructure
Privacy regulations and data anonymization techniques
Sustainable business models and ownership structures	Standardization of control technologies and replication strategies
Grid management approaches
Deep penetration of sustainable technologies
Implementation of predictive models Long-term maintenance activities and resident data collection
Balancing diverse requirements
Addressing grid operation challenges
Managing multiple independent energy districts
Inclusivity strategies for digital technology reliance
Managing production peaks and defining the role of buildings and districts
Effective management strategies for grid congestion and stability

Categories of Innovation	Innovation Types	Possible Revenues/Advantages in PED Business Model/Governance	Possible Costs/Drawbacks in PED Business Model/Governance
Configuration	Profit Model	Providing thermal comfort instead of a certain amount of thermal energy to inhabitants	Misconducts or rebound effect
Network	Inclusion of the PED into larger projects and international networks, possibility of co-financing and knowledge sharing	Misalignment or delay of the PED project to the original timeline due to constrains related to international activities and networking
Structure	Participation of the real estate companies/investors in the development and management of the energy infrastructure and EV mobility services as well as building management	Lack of knowledge, involvement in activities out of the usual business of investors
Free or almost free thermal energy supply from “waste energy” sources	Failure of the network due to unliteral decisions of a member in ceasing the provision of energy
Process	Involvement of future inhabitants in the design phase of the energy community since the early stage, to share the sense of belonging and ownership	Reluctancy of inhabitants to participate in additional expenses or being involved in “entrepreneurial” activities or bored by the participation in boards and governance structures
Offering	Product Performance	Investors and companies involved in the PED development take profit from their role of frontrunner placing them before the competitors or entering in new market niches	Hi-tech BA and BEM systems may result costly in O&M, because of digital components, cloud and computing services, rapid aging of technology
Product System	Including EV available for PED users may generate new incomes and reduce the need of individual cars. The integration of EV in the energy system may offer “flexibility services”	Lack of knowledge, involvement in activities out of the usual business of investors/real estate companies. Low interest of users in participating to the flexibility market, because of discomfort (unexpected empty battery of the EV)
Experience	Services	Provision of high tech and high-performance buildings, with outstanding energy performances (lower heating/cooling costs) and sophisticated Building Automation and Energy Management systems	Sophisticated Building Automation and Energy Management systems may result “invasive” to users, asking for continuous interaction with complicate systems, or leaving them not enough freedom to choose (e.g., opening the windows is not possible to achieve some energy performance)
Channel	The PED is promoted as a rewarding sustainable investment, this allows the city to attract more clean investments (public funds, investment funds, donors), speeding up the energy transition	The communication of the characteristics of the PED is not done in the proper way
Brand	Gold class rated buildings may have an increased value on the market, resulting in higher selling and rental costs, occupancy rate. The high architectural quality is appreciated by the market	The Branding/certification of the PED is not recognized by the market as an added value.
The development of the PED takes longer as expected. Technology failures during the implementation or operation phase create a bad reputation and discourage future similar activities
Customer Engagement	The PED is available as a digital twin, users are engaged via a dedicated app, allowing interaction, communication, reporting, monitoring of bills, etc.	The PED is perceived by users (e.g., social housing tenants) as a hassle and not responding to their needs, because they have not been involved in the identification of peculiar traits since the beginning

Category	Beneficiaries
Citizens and communities	Citizens, inhabitants, residents, general public, local communities and neighborhoods, municipalities and provinces, energy communities, and socially disadvantaged groups.
City decision-makers and planners	City decision-makers, city planners, local authorities, policy-makers, public administrations, politicians, local and national governments.
Research	Scientists, publishers, and research organizations.
Private companies and technology developers	Private companies of RES technologies, ICT companies, start-ups and new companies, entrepreneurs, technology developers and other companies involved in local development (tech development and evaluation).
Energy providers	Energy providers, grid operators.
Education stakeholders	Students and teachers.
Non-governmental organizations (NGOs)	NGOs and other civil society groups

Category	Comments
Strategies	Most comments dealt with the strategies on how to achieve PEDs, that should focus on success factors of PED initiatives, technologies and stakeholders rather than a standardized approach
References	Useful information, special attention to Liwen Li, planning principles for integrating community empowerment into zero-carbon transformation
Definitions	Help to reduce uncertainty
Boundaries	Energy balance calculations, mobility, definition (of buildings)
Finance	Financial mechanisms, support schemes
Citizen engagement	From engagement to empowerment
Management	Process management, organizing involvement, information provision
Policy	Incentives, regional policies
Flexibility/Grid interaction	Timesteps, credit system
Form	Dissemination through video and other forms (not only written information)

Category	Comments
Lessons learned	Special reference to real life implementation
Results	Data analysis and potential research on the field
Metadata as the useful information that can the real goal of consultation
Benchmarking to compare PEDs
Need to normalize results depending on a number of factors (size, location…) to really compare different initiatives
Privacy and data protection
Sets of technologies and solutions	-
Economic parameters	As a way to benchmark the different PED technologies
Citizen engagement	Energy poverty
Prosumers
From engagement to empowerment
Definition and boundaries	Need to standardize and have a reference framework to establish the energy balance
Contact persons	It is very valuable to have a contact address to ask more about the initiative
Regulatory framework	Drivers and Enablers

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HUMAN RESOURCE ECONOMICS: IMPACT OF SOCIAL AND ECONOMIC FACTORS ON EMPLOYER-EMPLOYEE RELATIONSHIP

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3.1. Positive Energy Districts Definitions and Fundamentals

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Control variables

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Macroeconomic factors, firm characteristics and financial performance: A study of selected quoted manufacturing firms in Nigeria

Design/methodology/approach

Practical implications

Originality/value

1. Background of the study

2. Statement of the problem

3. Objective of the study

4. Review of related literature

4.1.1.1 Interest rate

4.1.1.2 Inflation rate

4.1.1.3 Exchange rate

4.1.1.4 Gross domestic product (GDP)

4.2 Firm characteristics

4.3 Financial performance

4.4 Theoretical framework

4.4.1 Systems theory.

4.4.2 Resource-based view (RBV).

4.5 Review of empirical studies

4.5.2 Firm characteristics and firm performance.

4.5.3 Macroeconomic factors, firm characteristics and firm performance

5. Methodology

5.2 Population of the study

5.3 Sample size of the study

5.4 Sources of data

5.5 Technique of data analysis

5.5.1 Model specification.

5.5.2 Robustness test

5.5.3 Description of variables.

6. Data analysis

6.1.1 Analysis of H1

6.1.2 Analysis of H2

6.1.3 Analysis of H3

6.1.4 Analysis of H4

6.1.5 Analysis of H5

6.1.6 Analysis of H6

6.1.7 Analysis of H7

6.2 Discussion of findings

7. Conclusion and recommendations

7.2 Recommendations

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