Do Exchange Rate Volatility and Public Expenditures Influence Selected Crop Output in Nigeria?

Introduction

Macroeconomic variables such as the exchange rate and public expenditure, among others, are characterized by frequent fluctuations, rendering their values and behaviour volatile or unpredictable (Ayeni and Oluwasegun, 2020). The Exchange rate is a strong predictor of the competitiveness of a country's currency; it influences the international flow of goods and services in a country (Uzoma-Nwosu and Orekoya, 2019). Nigeria used a fixed exchange rate system until 1998 when it was changed with a fluctuating exchange rate system, and our exchange rates have been highly unpredictable for almost a decade. Osuegbu (2013) ascribed this unpredictable exchange rate to our poor level of performance (deficiency) in international global trade. Exchange rate volatility describes variations in the exchange rate of a country. Kanu and Nwaduibu (2020) itemized the main factors that cause exchange rate volatility as interest rate, inflation, government intervention, and balance of payments, among others. This volatility of exchange rates has been a constant source of concern for policymakers and scholars, particularly after the collapse of the Bretton Woods agreements (Héricourt & Poncet, 2012).

To fully understand the changes and the economy's financial stability, significant attention has been paid to exchange rate volatility by market participants, investors, and policymakers (Thorlie et al., 2015). To learn about market expectations and uncertainty about policy, policymakers are interested in measuring exchange volatility. For example, it is important for asset pricing, portfolio allocation, and risk management to understand and estimate exchange volatility. (Erdemlioglu et al. 2012).

Trade in agricultural commodities continues to play a prominent role in the economic growth of Nigeria. Our traditional agricultural commodities include cocoa, cotton, sesame, rubber, cassava, groundnuts, and rice. Our export crops are sold in markets in the Gulf States, Europe, the United States of America, and other countries (Alegwu et al., 2018). The crops selected for use in this study were carefully selected because of their contribution to export earnings in Nigeria (NBS, 2018). Nigeria has an advantage of massive and fertile land with proximity to European markets by air or sea; therefore, with the right policies in place, more money can be gotten from farming and exporting cash crops (Sasore, 2004). Abiodun and Salau (2010) opined a necessary and sufficient condition for increased output in price reforms. The agricultural sector's poor output in recent years due to poor pricing from poorly developed policies have necessitated this work.

This research aims to determine the influence of exchange rate volatility, and public expenditure on selected crop produces in Nigeria between 1980 and 2019. This study contributes to the literature in several ways. Firstly, over 70% of Nigeria's population is employed by the agricultural sector. The majority of these farmers are small-scale farmers who depend upon banks and financial institutions for loans to expand their farming. Therefore, a study like this that gives estimates of volatility and correlations will significantly help these farmers in risk management. Secondly, since banks and financial institutions invest in foreign exchange instruments, this study will help them accurately model and measure volatility to maintain the payment system and boost economic growth. Thirdly, our study focuses on the simultaneous effect of exchange rate and public expenditure on selected cash crops like sesame, groundnuts, cassava, rice, and cocoa, an area that has not been researched. Finally, this study uses the recently available data, which spans up to 2019, to capture the devaluation of the naira in 2017, after which the exchange rate has been very volatile. The study uses the autoregressive conditional heteroscedastic (ARCH) model promoted by Engle (Engle, 1982) alongside the generalized autoregressive conditional heteroskedastic (GARCH) model built independently by Bollerslev (1986) and Taylor (1986), which have become standard tools in measuring volatility. Both models can capture symmetry and asymmetry in volatility and dynamics of conditional heteroscedasticity.