Effect of Changes in Economic Variables on the Performance of Manufacturing Firms in Nigeria

A thriving manufacturing sector is crucial for a robust economy, driving both domestic consumption and exports, ultimately bolstering foreign reserves and maintaining a competitive local currency. Effective economic policies are vital in this regard, balancing the needs of consumers and producers. Key economic variables like inflation (measured by CPI), exchange rates, and interest rates all significantly impact production costs. Poorly designed economic policies negatively impact both producers and consumers, particularly in fragile economies. In Nigeria, the manufacturing sector is a significant driver of economic activity, comprising large, medium, and small firms that utilize land, labor, capital, and entrepreneurship. Government monetary policies must support this sector, mitigating the effects of fluctuating economic conditions. The study focuses on the impact of CPI and exchange rate changes on the earnings per share (EPS) of Nigerian manufacturing firms, addressing the mixed opinions on inflation's effects on financial performance.

The literature review explores the impacts of inflation and exchange rates on firm performance. Inflation, often measured using CPI or GDP deflator, impedes economic growth by eroding purchasing power and increasing production costs. While some firms can adjust profit margins to offset inflation, this isn't always possible, impacting profitability (Akers, 2014; Alimi, 2014; Bora, 2013; Boyd et al., 2001). High inflation reduces company performance due to increased input costs and reduced output demand (Stewart, 2001). Exchange rates influence trade and firm-to-firm transactions; naira appreciation reduces import costs, benefiting firms with limited foreign exchange risk-hedging capabilities (Kirui et al., 2014; Kuwornu, 2012). Both nominal and real effective exchange rates play significant roles in evaluating economic performance and competitiveness. Studies present mixed findings on inflation's effect on firm profitability, with some showing positive correlations (Chioma et al., 2015) and others revealing negative or insignificant relationships (Nnado and Ugwu, 2016). The review also touches upon the managerial theory of the firm, emphasizing efficient resource utilization and value creation for shareholders. Prior research by Ezeaku and Modebe (2016), Idaka et al. (2021), Ezu et al. (2020), Ghareli and Mohammadi (2016), Maimunah and Patmawati (2018), and Nnado and Ugwu (2016) provide varying perspectives on the relationships between macroeconomic variables and manufacturing firm performance in different contexts.

This study employed an ex-post-facto research design, utilizing quantitative data from the Central Bank of Nigeria (CBN), National Bureau of Statistics (NBS), and annual financial statements of manufacturing firms from 2004 to 2022. The population consisted of 20 consumable goods manufacturing firms listed on the Nigerian Exchange Group, with a purposive sample of 12 firms (60%). Panel data were analyzed using ordinary least squares (OLS) regression and descriptive statistics to assess the impact of economic parameters on firm performance. The model specified EPS as the dependent variable, with the average consumer price index (AVCPI) and average exchange rate (AVEXR) as independent variables. The equation used was: EPS = bo + b₁ μCPI + b2 μEX-R + e, where bo represents the intercept, b1 and b2 are the coefficients for AVCPI and AVEXR, respectively, and 'e' is the error term. Descriptive statistics were used to describe the characteristics of EPS, AVCPI, and AVEXR. The OLS regression analyzed the relationship between variables, testing for statistical significance at a 5% level. The R-squared and F-statistic were used to evaluate the model’s goodness-of-fit and overall significance. The Durbin-Watson statistic checked for autocorrelation. All data analysis was done using statistical software.

Descriptive statistics revealed mean values of 65.60 for EPS, 167.31 for AVCPI, and 231.34 for AVEXR. The OLS regression results showed a positive and statistically significant relationship (p=0.0045) between LOG(AVCPI) and EPS, indicating that a 1% increase in AVCPI leads to a 0.70% increase in EPS. Conversely, LOG(AVEXR) showed a negative and statistically significant relationship (p=0.0418) with EPS, meaning a 1% increase in AVEXR results in a 0.64% decrease in EPS. The adjusted R-squared of 0.54 indicated that the model explained 54.12% of the variation in EPS. The F-statistic was significant (p=0.000765), suggesting the model as a whole is significant. The Durbin-Watson statistic (1.53) fell into the inconclusive region regarding autocorrelation.

The findings align with the economic realities of Nigeria, where manufacturers often adjust prices to compensate for increased production costs resulting from inflation. This finding is consistent with studies such as Ezu et al. (2020) and Maimunah and Patmawati (2018) but contradicts Nnado and Ugwu (2016). The insignificant effect of the exchange rate on EPS, however, is in line with Ezeaku and Modebe (2016) and contrasts with Ezu et al. (2020). This suggests that while exchange rate fluctuations impact input costs, manufacturers absorb these costs and pass the burden onto consumers through price adjustments. The study's limitations include the sample size and the potential influence of uncaptured variables on EPS. Future research could explore a larger sample and incorporate additional factors like government policies and global economic conditions.

This study reveals that inflation significantly impacts the earnings of Nigerian manufacturing firms, with manufacturers adjusting prices to offset rising costs. The exchange rate, while affecting input costs, showed no significant direct impact on EPS, suggesting that firms absorb these costs. The government should encourage local production to reduce import dependency and mitigate the impacts of exchange rate volatility. Further research incorporating a broader range of variables and a larger sample size would enhance the understanding of these complex relationships.

The study's limitations include the use of a relatively small sample size of 12 firms, which might not fully represent the diversity of the Nigerian manufacturing sector. The model also does not account for all potential factors affecting firm performance. Omitted variable bias could be a concern, and unobserved heterogeneity among firms might also influence the results. The reliance on secondary data may limit the ability to capture all nuances of the dynamic interactions between economic variables and firm-specific factors.