This paper analyzes the effects of economic policy on firm performance in developed and developing countries, using a sample of firms classified by turnover. The methodology refers to the estimation of the dynamic panel fixed effects model. The results show that the priority economic policy tools based on price, balance of payments and employment control do not influence firm performance. However, debt increases the performance of firms in Africa. In high-income countries and China, it is savings and exchange rate policies that have a positive effect on firm performance. It is recommended that policymakers in Africa adopt a debt policy based on borrowing in compliance with ceilings and concessional interest rates so as not to increase debt service and suffocate the government’s cash flow. In developed countries, the savings policy must be maintained at the same pace as the mobilization of savings to meet the needs of growth sectors. Exchange rate policy must be able to absorb shocks and always adapt to the market.
State interventions in economic affairs are controversial because of their effects. However, one of the objectives of state interventions is to improve the macroeconomic performance of states and the international competitiveness of companies. The effects on the performance of firms in particular are of great interest since firms are a source of employment, income and revenue both domestically and for export (OCDE, 2008) .
The literature highlights the institutional environment or business climate. A poor business climate (poor governance, corruption and insecurity) has a negative impact on firm performance (Fernandes, 2008) . It is impacted by a number of factors related to public policies.
Other works show that firms’ activities are influenced by specific instruments such as employment policy (Thomas, 2007; Aeberhardt et al., 2011) , trade policy (Mage-Bertomeu, 2006; Rieber & Tran, 2002; U.S. House of Representatives, Committee on Small Business, 2012) , tax policy (Fernandez-Villaverde et al., 2011; Valenduc, 2019) , education policy (Maisonnave & Decaluwé, 2010) , industrial policy (Carré & Levratto, 2009) , monetary policy (Blot et al., 2017) , etc.
Monetary policy, for example, by facilitating access to credit contributes to the performance of firms through improved productivity and expected profitability of projects (Beck & Laeven, 2006) . Allen & Gale (2003) argue that when risk transfer is proven for firms, policy makers do not need to determine whether assets are bubbles to justify intervention. The main possible interventions against asset bubbles have ambiguous welfare implications: tightening monetary policy raises interest rates, lowers asset prices and reduces the amount spent on assets. Their effects reduce the output available to agents, unlike the cap on total lending, which eliminates speculation without affecting output.
The study by Onaran and Ozturk (2008) analyzes the effects of exchange rate policy, export credits and export promotion on export earnings. It concludes that anti-inflationary policies dampen domestic demand, negatively impacting firms’ industrial capacity. The transfer of domestic demand to export markets offsets the decline in capacity utilization. The success and effectiveness of the application of these policies play an important role in the credibility of a country’s economy in the face of international competition.
According to Mage-Bertomeu (2006) , a model that takes into account market imperfections and dynamic behavior shows that openness policies reduce the monopoly power of firms, ruling out the possibility of exploiting economies of scale. The effects in terms of reduced welfare are also demonstrated.
In a study concerning the USA and for the period 1970-2010, Fernandez-Villaverde et al. (2011) analyze the evolution of uncertainty about future fiscal policy on aggregate economic activity. They show that fiscal volatility shocks have a negative effect on economic activity by reducing output.
Regarding performance, it is defined as the ideal of doing better than others (Bouquin & Kuzla, 2013) . It is assimilated to the achievement of strategic objectives defined by the company. These objectives are measured by indicators that can be economic, human, organizational, financial, commercial, etc.
Performance is explained by elements centered on the strategies of the firms themselves, notably the theories of value creation and success of firms in strategy (Arrègle & Powell, 2009) , the differences in the technological level of countries, the theory of resources (Ray et al., 2004) according to which internal and intangible resources play an important role in the success of firms.
Thus, the errors or failures of firms are considered as a determining factor in understanding the differentiation of performance between firms at the national and international level. Firms that make fewer errors obtain good results (Arrègle & Powell, 2009) . We can add to this, competitive disadvantages, knowledge-action gaps and execution gaps. It can also be differences in strategic weaknesses or the multiplication of avoidable errors (March, 2006; Arrègle & Powell, 2009) .
The best performing companies worldwide are those from developed countries. Most of them are American and European companies. Asian companies, especially those from emerging countries, follow. Survey rankings, such as the one compiled by Fortunes magazine, show powerful groups such as Walmart, State Grid, Sinopec Group, China National Petroleum, Royal Dutch Shell, etc. among the top 10 global companies in 2019.
Africa ranks very low in this area. Its companies are confronted with the problem of insufficient infrastructure (electricity, water, roads, etc.) necessary for the manufacture and distribution of their products, abusive taxation, difficulties in accessing finance (high interest rates, insufficient money markets and preponderance of money as the only financial asset), weak legislation on property rights, lack of information on the markets, especially financial markets, and weak incentives. African economies are generally characterized by a large number of very small, small and medium-sized enterprises (VSES). There is a notorious dualism between the formal and informal sectors, whose weight is such that they contribute a large share of GDP and 90% of jobs (Benjamin & Aly Mbaye, 2012) .
Nevertheless, since 2008, African companies have been included in the list of the world’s top 2000 companies (OCDE, 2008) thanks in particular to their international expansion. The highly selective Fortune 500 ranking remains highly coveted.
It appears from the literature presented that the work is not sufficiently edifying on the types of transmission channels of economic policy on firm performance, nor on how this policy influences performance. Performance is explained more by factors internal to the firm or by the business climate.
We assume that some economic policy instruments, because of their magnitude, can generate economic flows at both the national and international levels, which in turn impact firm performance. For example, the external balance (balance of payments) as an objective of economic policy implies an equality between savings and investment, including those of firms. The same is true of the deficit, resulting from an increase in investment and therefore a low savings rate. The consequences are excessive indebtedness and a reduction in consumption, with an impact on production and business performance. Under these conditions, recourse to external borrowing to finance the deficit is inevitable. From this perspective, we focus on the specific policy objectives as defined by Kaldor (1971) , namely growth, employment, price stability and external balance.
This article aims to analyze the effects of economic policy on the performance of firms. Its specificity and its contribution compared to the existing literature is that it could provide more information on the type of instrument to be put forward as a channel of influence of economic policy and which will serve as a lever for the recovery of companies.
Regarding the structure of the article, the introduction is the first point. The second is devoted to the methodology. The third point presents the results and the fourth the conclusion and the economic policy implications.
The methodology is based on a regression of the variable representing the performance of the companies according to the variables characteristic of the economic policy. It essentially includes the model, the estimation technique chosen and the data used.
The model used is a dynamic panel fixed individual effects model (Doucouré, 2016) . In such a model, the one-period lagged endogenous variable is considered among the explanatory variables of the model. It is a linear model written as follows:
y ˙ i t = α y ˙ i t − 1 + ∑ k = 1 k β k X k i t + f i + ε i t (1)
With α and β parameters to be estimated, i = 1, …, N is the number of individuals, t = 1, …, T determines the study period; y ˙ the endogenous value, y ˙ i t − 1 is the explanatory variable derived from the endogenous variable lagged by one period, X is a vector of other explanatory variables, f i the fixed effect or heterogeneity factor of the individuals, taking into account all the (unobserved) factors constant over time that have an impact on the endogenous variable.
Two (2) panels are estimated. Each panel contains five individuals i (i = 1, …, 5), respectively South Africa, Angola, Egypt, Tunisia and Senegal (panel 1), then the United States, China, Germany, the Netherlands and the United Kingdom (panel 2).
Performance - value explained - is represented by the total value added of firms. It is a performance indicator. There is a whole range of indicators: return on equity, gross operating surplus, labour productivity, return on sales or net income, turnover, value added, market share, etc.
Value added is a performance indicator resulting from the determination of total production from which intermediate consumption is subtracted. Nevertheless, while turnover and profit are among the most widely used performance indicators, profit remains the most relevant and its calculation is inseparable from the company’s added value. It reflects the wealth actually created by companies in all branches of the economy of a country.
It is expected that the variable added one period later will have a positive effect on the endogenous variable. The variables of interest are the balance of payments (current account), inflation and unemployment rates, which, along with GDP, are among the four priority objectives of economic policy (Kaldor, 1971) . GDP was excluded in order to avoid a bias with the endogenous variable, in particular the risk of taking into account simultaneously the value added as an endogenous variable and as an explained variable in GDP. Indeed, GDP is calculated from the point of view of production as the sum of value added, value added tax and customs duties.
The positive balance of payments is beneficial to the formation of the value added. The expected sign is positive. High inflation has adverse effects on producers and consumers. The expected sign of this variable is negative. Similarly, increased unemployment harms production and thus the creation of value added.
Among the control variables are final consumption expenditure and the public debt ratio, whose expected signs on value added are negative, while the savings ratio has a positive effect. The official exchange rate plays an ambiguous role. It is sometimes considered as a tool for boosting production when its value falls, allowing an increase in demand for export products, and sometimes as an element conferring strength on the national economy.
The estimation technique used is the Generalized Moment Method (GMM) in a dynamic panel (Blundell & Bond, 1998; Bond, 2002) . It provides an efficient estimation and controls for individual (and time) specific effects while compensating for the endogeneity bias of the variables.
The series of inflation, savings, balance of payments, unemployment rate, value added and exchange rate were taken from the World Bank’s database (World Development Indicators - WDI 2022). Total value added was obtained by adding the value added of the primary, industrial, and tertiary sectors. Final consumption expenditure and debt ratio data were taken from the International Monetary Fund’s database (World Economic Outlook - WE0 2022).
Surveys published by Fortune magazine (https://fortune.com/global500/) and Jeune Afrique (https://www.jeuneafrique.com) were used to select the sample countries. Each year, Fortune and Jeune Afrique publish their rankings of the top companies by country (top global companies by Fortune and top African companies by Jeune Afrique). Companies are ranked by revenue.
The first sample (panel 1: South Africa, Angola, Egypt, Tunisia and Senegal) is composed of the top five African countries with the best performing companies in 2019 from the Jeune Afrique ranking. The second sample (panel 2: United States of America, China, Germany, the Netherlands and the United Kingdom) is taken from the Fortunes Global ranking of the top 500 companies in 2019. Companies are ranked by revenue. China is associated with the panel of developed economies because its companies are among the top ten in the world, alongside companies from developed countries.
The data cover the study period 1985-2019. This period was chosen because of the availability of data, especially the start year (1985), compared with earlier years when there were many missing data for a large number of countries. The final year of the study period (2019) was chosen to avoid a bias in the data due to the Covid-19 pandemic, which could have an impact on the data from the year 2020 until 2021 or even 2022. Data for the year 2023 are not yet available. Missing data for a few years have been filled by the moving average technique. Countries with too much missing data have been replaced by the countries immediately following them in the ranking.
This section is structured as follow: descriptive and correlation statistics, results of panel estimations, effects of economic policy in African countries and effects of economic policy in rich countries.
| Variables | Moyenne | Ecart-type | Minimum | Maximum | Observations | |
|---|---|---|---|---|---|---|
| Curachal | Panel 1 | −1,640,000,000 | 5,300,000,000 | −21,400,000,000 | 13,800,000,000 | n = 5 |
| Panel 2 | −30,100,000,000 | 207,000,000,000 | −817,000,000,000 | 421,000,000,000 | T = 35 | |
| finconsex | Panel 1 | 74,700,000,000 | 95,000,000,000 | 3,580,000,000 | 372,000,000,000 | n = 5 |
| Panel 2 | 3,130,000,000,000 | 3,930,000,000,000 | 105,000,000,000 | 17,400,000,000,000 | T = 35 | |
| gengovdeb | Panel 1 | 59.71 | 30.42 | 0.07 | 133.91 | n = 5 |
| Panel 2 | 54.64 | 21.53 | 20.60 | 108.46 | T = 35 | |
| grossav | Panel 1 | 16,200,000,000 | 17,200,000,000 | 110,000,000 | 73,800,000,000 | n = 5 |
| Panel 2 | 1,020,000,000,000 | 1,320,000,000,000 | 36,800,000,000 | 6,250,000,000,000 | T = 35 | |
| inflati | Panel 1 | 74.03 | 391.54 | −4.14 | 4165.10 | n = 5 |
| Panel 2 | 2.89 | 3.31 | −1.40 | 24.25 | T = 35 | |
| ofexch | Panel 1 | 113.62 | 202.53 | 2.99e−08 | 732.39 | n = 5 |
| Panel 2 | 2.16 | 2.40 | 0.49 | 8.61 | T = 35 | |
| unemp | Panel 1 | 13.03 | 8.62 | 2.86 | 33.29 | n = 5 |
| Panel 2 | 5.79 | 2.06 | 2.12 | 11.17 | T = 35 | |
| addval | Panel 1 | 5,920,000,000,000 | 22,300,000,000,000 | 5,960,000,000 | 137,000,000,000,000 | n = 5 |
| Panel 2 | 5,220,000,000,000 | 5,460,000,000,000 | 357,000,000,000 | 19,300,000,000,000 | T = 35 |
Source: Author, using WDI and WEO 2022 data. Curachal, finconsex, grossav, and addval are in current USD; Gengovdeb, inflati, ofexch, and unemp in %.
| Variables | curachal | finconsex | gengovdeb | grossav | inflati | Ofexch | unemp | addval | |
|---|---|---|---|---|---|---|---|---|---|
| Curachal | Panel 1 | 1.000 | |||||||
| Pane 2 | 1.000 | ||||||||
| Finconsex | Panel 1 | −0.6995* | 1.0000 | ||||||
| Pane 2 | −0.6904* | 1.0000 | |||||||
| Gengovdeb | Panel 1 | 0.0114 | −0.1209 | 1.0000 | |||||
| Panel 2 | −0.3704* | 0.5565* | 1.0000 | ||||||
| Grossav | Panel 1 | −0.4529* | 0.9034* | −0.1983* | 1.0000 | ||||
| Panel 2 | −0.2072* | 0.7521* | 0.2508* | 1.0000 | |||||
| Inflati | Panel 1 | 0.0839 | −0.1241 | 0.3746* | 0.1290 | 1.0000 | |||
| Panel 2 | −0.0137 | −0.1433 | −0.3937* | −0.1204 | 1.0000 | ||||
| Ofexch | Panel 1 | 0.1305 | −0.3264* | −0.3731* | −0.358* | −0.0988 | 1.0000 | ||
| Panel 2 | 0.3087* | −0.1341 | −0.5417* | 0.2835* | 0.2835* | 1.0000 | |||
| Unemp | Panel 1 | −0.3167* | 0.5530* | −0.3665* | 0.5003* | −0.176* | 0.4020* | 1.0000 | |
| Panel 2 | 0.0769 | 0.0210 | 0.3022* | −0.1377 | −0.2414* | −0.451* | 1.0000 | ||
| Addval | Panel 1 | 0.0689 | −0.1887* | 0.5778* | −0.200* | 0.219* | −0.1476 | −0.2665* | 1.0000 |
| Panel 2 | −0.5936* | 0.8991* | 0.3377* | 0.759* | −0.1119 | 0.0021 | −0.0109 | 1.0000 |
Source: Author, using WDI and WEO 2022 data.
| Variables | Panel 1 | Panel 2 |
|---|---|---|
| addval L1. | 0.786016 (0.000)*** | 0.8067084 (0.000)*** |
| curachal | −3.326999 (0.942) | 0.084957 (0.878) |
| finconsex | −3.908059 (0.354) | 0.0512219 (0.248) |
| gengovdeb | 8.36e+10 (0.035)** | 6.27e+08 (0.0816) |
| grossav | 99.79896 (0.180) | 0.413275*** (0.000) |
| Inflati | 1.84e+08 (0.805) | −2.53e+10 (0.348) |
| Ofexch | −5.35e+09 (0.202) | 6.01e+10** (0.014) |
| Unemp | 7.13e+10 (0.512) | −8.64e+09 (0.604) |
| Annees | −1.31e+11 (0.343) | −8.56e+09 (0.530) |
| AR(1) | 0.298 | 0.305 |
| AR(2) | 0.231 | 0.305 |
| Hansen | 1.000 | 1.000 |
| Sargan | 0.319 | 0.310 |
Source: Author, using WDI and WEO 2022 data. *** p < 0.01, ** p < 0.05, p < 0.1.
In panel 1, the standard deviation of 22.3 trillion USD shows a high dispersion of value added values from the mean, compared to panel 2 with a standard deviation of 5.46 trillion USD. Also, the debt, inflation, unemployment, and exchange rate series are more dispersed in panel 1 than in panel 2. The government expenditure and savings series are weakly dispersed relative to their mean in both panels.
The low dispersion observed in the variables of panel 2 illustrates the similarities between the economies of rich countries in terms of final consumption expenditure (finconsex), debt (gengovdeb), savings (grossav), inflation (inflati), exchange rate (ofexch), unemployment (unemp) and business performance (addval). In contrast, African economies are characterized by disparities, both among themselves and relative to rich countries.
The correlation statistics are presented in the
The presumptions of effects of the correlated variables at the conventional threshold of 1% justify the regressions carried out. Moreover, the recap of the results of the IPS and LM stationarity tests in the appendix (
The results of the GMM estimations are presented in
The validity conditions are met for both panels:
The number of observations greater than the number of instruments in the models;
The correct identification of the instruments. The Sargan test reveals an absence of correlation between the instruments and the residuals (p-value = 0.319 for panel 1; p-value = 0.310 for panel 2);
The absence of first-order or second-order auto-correlation of the errors in the difference equation in the panels according to the AR(1) and AR(2) tests.
| Level | 1st difference | Decision | |||
|---|---|---|---|---|---|
| p-value | p-value | ||||
| LLC | IPS | LLC | IPS | ||
| Curacbal | 0.0390 | 0.4311 | 0.0000 | 0.0035 | I(1) |
| Finconsex | 0.9202 | 0.9985 | 0.0000 | 0.0000 | I(1) |
| Gengovdeb | 0.7735 | 0.8671 | 0.0092 | 0.000 | I(1) |
| Addval | 1.0000 | 1.0000 | 0.0051 | 0.0182 | I(1) |
| Grossav | 0.9731 | 0.9969 | 0.0002 | 0.0000 | I(1) |
| Inflati | 0.0026 | 0.0001 | 0.0000 | 0.0000 | I(0) |
| Ofexch | 1.0000 | 1.0000 | 0.0678 | 0.0001 | I(1) |
| Realint | 0.5739 | 0.6562 | 0.0192 | 0.0007 | I(1) |
| Unemp | 0.2877 | 0.5723 | 0.0004 | 0.0000 | I(1) |
Source: Author, based on data used.
| Level | 1st difference | Decision | |||
|---|---|---|---|---|---|
| p-value | p-value | ||||
| LLC | IPS | LLC | IPS | ||
| Curacbal | 0.5234 | 0.8583 | 0.0000 | 0.0783 | I(1) |
| Finconsex | 0.9984 | 1.0000 | 0.0000 | 0.0000 | I(1) |
| Gengovdeb | 0.9297 | 1.0000 | 0.0004 | 0.0001 | I(1) |
| Addval | 0.9986 | 0.0000 | 0.0125 | 0.0003 | I(1) |
| Grossav | 0.9613 | 1.0000 | 0.0000 | 0.0000 | I(1) |
| Inflati | 0.0000 | 0.0023 | 0.0000 | 0.0000 | I(0) |
| Ofexch | 0.2937 | 0.2211 | 0.0000 | 0.0049 | I(1) |
| Realint | 0.0000 | 0.0000 | 0.0000 | 0.0000 | I(0) |
| Unemp | 0.1686 | 0.9323 | 0.0000 | 0.0000 | I(1) |
Source: Author, based on data used.
| Test | Panel 1 | Panel 2 | ||
|---|---|---|---|---|
| statistics | probability | statistic | probability | |
| Fisher | 21.72 | 0.0000 | 50.26 | 0.0000 |
| Breusch-Pagan | 39.43 | 0.0000 | 38.47 | 0.000 |
| Hausman | 8.78 | 0.2109 | 3.65 | 0.1517 |
Source: Author, based on data used.
In the first panel of the best performing firms in the African zone, the lagged value added of one period has a positive and significant effect on the value added of the current period at the 1% threshold. The public debt ratio has a positive and significant effect on the value added of firms at the conventional 5% threshold. The rest of the variables have no significant effect on value added.
In panel 2, the lagged value added of one period has a positive and significant effect on the value added of the current period at the 1% threshold. In addition, gross savings and the exchange rate have a positive and significant effect of 1% and 5% respectively on the value added of firms.
The positive correlation between the public debt ratio and total firm value added indicates the positive effect of public debt on firm value added. Fiscal policy through debt contributes to the performance of firms. The linear evolution of these two variables combined is observable in all the individuals in the panel between 1985 and 2019, except in Angola where value added begins to fall from the 2000s. In practice, public debt is sometimes used to finance the public deficit. The effects on firm performance are explained by the fact that public debt is a source of financing that in fact leads to increased spending. The latter is an instrument for boosting demand. This reassures firms that they can produce and sell their goods.
These results are in line with the work of Nersisyan and Wray (2011) , who highlight the positive impact of public debt on the economy. This is an overhaul of the work of Reinhart and Rogoff (2009, 2010) , based on Ricardian equivalence theory, and arguing that debt poses a problem on growth.
The main trends of the endogenous variable and significant variables in the rich country panel 2 are presented in
The effect of gross savings and exchange rate targets on firms’ value added is noticeable for all individuals in panel 2. The increase in the savings rate leads to an increase in value added in the same direction. The latter follows the same trend as the exchange rate for all individuals in the panel. The relationship is more pronounced in the German, Dutch and American economies.
The increase in performance following the increase in savings may seem paradoxical, as it is commonly accepted that excess savings lead to a decrease in consumption expenditure. Nevertheless, the result in panel 2 above can be explained by the fact that saving precedes investment, which ultimately triggers future production and consumption. The increase in savings results from the increase in income that is capable of stimulating demand. In addition, in the financial system of developed countries, a large part of savings is prudently managed by institutions other than banks: investment funds, pension funds and life insurance companies. The latter are obliged to diversify their portfolios in order to secure more income while minimizing risk.
As for the exchange rate, its increase is supposed to boost productivity. The results of panel 2 are therefore consistent with the strong currency approach supported by Porter (1993) , according to which the depreciation of the real exchange rate slows down growth and productivity. Exporting firms have an incentive to produce more in order to increase their revenues.
The economic policy framework characterized by the instruments used in countries with the best performing firms has led to differential effects in developed and developing countries. The results obtained are based on economic dynamics that are not the same in the two types of countries studied.
The power of savings in high-income countries and China (USD 1,020,000,000 on average compared to USD 16,200,000,000 in African countries) allows them to finance investment with relative ease, compared to African countries that are forced to go into debt. The average debt ratio of panel 1 (59.71%) is higher than that of panel 2 (54.64%). The effects on firm performance of a stimulus package are obtained through two different modes of financing: debt for African countries and savings for developed countries.
The economic power of rich countries is also reinforced by the exchange rate of their currency, giving them greater purchasing power than other countries. This constitutes a tool for recovery in case of crisis and allows to increase the performance of their companies.
The objective of this research was to highlight the effects of economic policy on firm performance in developed and developing countries. The policy instruments highlighted are those defined by Kaldor (1971) and used as priority objectives of economic policy compared to other instruments such as government spending, debt, savings or exchange rate.
The estimation of the panel fixed effects model by the dynamic GMM method shows that the priority instruments of economic policy, i.e. inflation, employment and balance of payments, do not have significant effects on the performance of firms. The effects are differentiated by country. Debt has a positive and significant effect on firm performance in Africa, while savings and foreign exchange have a positive and significant effect on performance in rich countries.
The various results show that economic policies remain anchored to the context or environment of the country or region. It is recommended that policymakers in Africa adopt a debt policy based on borrowing in compliance with ceilings and concessional interest rates so as not to increase debt service and suffocate the government’s cash flow.
As for developed countries, the savings policy must be maintained at the same pace as the mobilization of savings to meet the needs of growth sectors. Exchange rate policy must be able to absorb shocks and always adapt to the market.
The author declares no conflicts of interest regarding the publication of this paper.
Ondaye, W. G. (2023). Effects of Economic Policy on Firm Performance in Developed and Developing Countries. Theoretical Economics Letters, 13, 169-182. https://doi.org/10.4236/tel.2023.131010