Introduction

India is an agrarian economy, and products like spices and cashews are contributing a substantial amount to foreign earnings. India has the potential to offer goods at competitive prices not only in the developed markets but also in developing markets around the world. Some of the determinants, such as Aggarwal (2002) manufacturing sector, Chadha (2009) health care industry, Ganguly (2007) iron and steel industry, Adhikari et al. (2016) agriculture sector, Khan (1990) spices industry (Wadkar et al., 2005). as focused on only a few variables, whereas present study  considering both consumer and producer-level determinants.

The analysis of determinants was carried out at two levels. First, the determinants of exports are examined at the aggregated level for spices and cashews. The determinants of exports are identified as the exchange rate, domestic consumption, world demand and production. The selection of these regressors is theoretically consistent, as most studies on export have often identified the exchange rate, domestic consumption, world demand and production as key determinants of export performance. It is expected that the production and world export would have a positive impact on export, whereas domestic consumption and exchange rate would have a negative impact.

Empirical Model

For analysing the determinants of export performance of both spices and cashew, the following method has been estimated. The econometric specification, the fully modified ordinary least squares (FMOLS) method, is as follows

where ln represents the natural logarithmic transformation, ₀ is the intercept, P is production, WE is world export, DC is domestic consumption, ER is the exchange rate, and u_t is the error term.

Determinants of Spices and Cashew Export (CE) in India at Aggregated Level

The purpose of the analysis is to study the important variables that determine the spices and CE performance in India. For the purpose of analysis, the augmented Dickey–Fuller (ADF) unit root test is performed for stationary. The Johansen cointegration test is used for identifying the long-run relationship between exports and their determinants. The determinants of export performance are estimated through the FMOLS method. The variables are spices export (SE), CE, spices production (SP), cashew production (CP), spices world export (SWE), world cashew export (CWE), spices exchange rate (SER), cashew exchange rate (CER), spice domestic consumption (SDC), and cashew domestic consumption (CDS).

Table 1 shows the stationary test of the determinants variables for spices. At the level, all the variables are not stationary. Therefore, the researcher has taken the first difference. All variables are stationary at the first difference.

Table 1. Unit Root Test for Spices Augmented Dickey–Fuller (ADF).

Source: Estimation of the researcher.

Note: **Represents a stationary variable at 1% level of significance. *Denotes 5% level of significance. Dln: Variable differenced; ln: Natural logarithms of variables.

Table 2 shows the stationary test of the determinant variables for cashew. At the level, all the variables are not stationary. Therefore, the researcher has taken the first difference. All variables are stationary at the first order. The ADF test for the presence of a unit root indicates that variables are non-stationary at the level, whereas all the variables were integrated at order one.

Johansen and Juselius Cointegration for Spices and Cashew

The method used for cointegration analysis is drawn from Johansen and Juselius cointegration test. The results from the cointegration test are presented in the following tables. If the trace statistics and the max-eigen statistics are higher than the critical value, it means there is a long-run relationship between the variables.

The unit root test shown in the table clearly indicates that time series variables are integrated at order one. The next step is to examine whether a long-run relationship exists between the variables, which are presented in Table 3 and Table 4, for spices and cashew, respectively. Therefore, in order to examine the long-run relationship between variables, cointegration analysis is employed. The data series are of the same order of integration, as suggested by the unit root test.

The researcher has estimated trace statistics and max-eigen statistics. The trace values and max-eigenvalues are higher than the critical value for three variables with respect to spices and two variables with respect to CE, which indicates that there is a long-run relationship among variables. It can also be inferred from the analysis that the independent variables have an impact on spices and CEs in India.

Table 2. Unit Root Test for Cashew.

Source: Estimation of the researcher.

Note: **Represents a stationary variable at 1% level of significance. *Denotes 5% level of significance. Dln: Variable differenced; ln: Natural logarithms of variables.

Table 3. Johansen and Juselius Cointegration Test for Export Determinants of Spices.

Source: Estimation of the researcher.

Note: *Indicates significant at 0.05% level.

Table 4. Johansen and Juselius Cointegration Test for Cashew Export Determinants.

Source: Estimation of the researcher.

Note: *Indicates significant at 5% level.

FMOLS Estimation

The cointegration test indicates the existence of a long-run relationship among the variables included in the econometric model. To identify the exact influencing variables, the researcher has estimated the FMOLS method.

The result (Table 5) shows that variables like domestic consumption, exchange rate and production are the major determining factors for spice exports with a significance of 1%. The result shows that production has a positive influence on SE in India at a 1% level of significance, whereas the exchange rate and domestic consumption have statistically negative influences on SE. This implies that the increase in domestic consumption, declining production and the upward movement in the exchange rate adversely affect the performance of spice exports. Higher R² (0.799901) indicates that the overall goodness of fit of the model is perfect.

Table 6 shows that variables like domestic consumption and exchange rate are the major determining factors for CE. The result shows that the exchange rate and domestic consumption have a statistically negative influence on CE. This implies that the increase in domestic consumption and the upward movement in the exchange rate adversely affect the performance of CE. Higher R² indicates that the overall goodness of fit of the model is perfect.

Table 5. Fully Modified Ordinary Least Squares Result of Spices.

Source: Estimation of the researcher.

Table 6. Fully Modified Ordinary Least Squares Result of Cashew.

Source: Estimation of the researcher.

Conclusion

The present study suggests that policymakers have to devise a strong strategy, keeping in mind the variables that influence the export of spices and cashews, in order to improve the export performance of these products in light of the above analysis.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest regarding the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Ahamed Kabeer M  https://orcid.org/0009-0006-6264-0996