Version 1
: Received: 15 August 2023 / Approved: 15 August 2023 / Online: 15 August 2023 (15:48:41 CEST)
Version 2
: Received: 22 September 2023 / Approved: 25 September 2023 / Online: 26 September 2023 (13:53:32 CEST)
Caetano Joao, M.A.; de Castro, A.M. The Impact of Agricultural Credit on the Growth of the Agricultural Sector in Angola. Sustainability2023, 15, 14704.
Caetano Joao, M.A.; de Castro, A.M. The Impact of Agricultural Credit on the Growth of the Agricultural Sector in Angola. Sustainability 2023, 15, 14704.
Caetano Joao, M.A.; de Castro, A.M. The Impact of Agricultural Credit on the Growth of the Agricultural Sector in Angola. Sustainability2023, 15, 14704.
Caetano Joao, M.A.; de Castro, A.M. The Impact of Agricultural Credit on the Growth of the Agricultural Sector in Angola. Sustainability 2023, 15, 14704.
Abstract
The ultimate goal of this paper was to examine the degree of elasticity between two variables, namely, agricultural credit and agricultural growth, in Angola in the period 2003–2022. Time series data were fitted into the ARDL test using various econometric techniques such as the ADF stationarity test, Granger causality and the ordinary least squares method as well as a vector error correction model (VECM) to analyze the relationship between agricultural credit and agricultural economic growth, showing a causal relationship. Both the impacts through elasticities and the optimal point existing in this relationship were estimated. It was concluded that the impact of agricultural credit on agricultural GDP was 14.41%. Granger causality shows signs of a positive linkage between agricultural credit and agricultural GDP. However, there is a causal relationship between agricultural credit and agricultural GDP, in a unidirectional aspect. This result is consistent with most of the earlier studies reviewed in the literature, confirming that credit-oriented monetary policies can boost economic growth and, consequently, development in Angola. It is important for agricultural credit systems to be designed in a way that ensures equitable access, fair interest rates, and appropriate risk management mechanisms. Additionally, monitoring and evaluation mechanisms should be in place to assess the environmental and social impacts of credit programs on agricultural sustainability. It is worth noting that this is a first-of-a-kind study on the matter of the Angolan credit experience, specifically for the agricultural sector. Angola is still searching for a sustainable credit model that could be used as a catalyzer to boost growth and contribute to economic development.
Business, Economics and Management, Econometrics and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
26 September 2023
Commenter:
Mario Joao
Commenter's Conflict of Interests:
Author
Comment: We have added in chapter 3 more detail on the literature used, especially paragrapgh 6 (Moura), and added paragpahs 9 and 10 with two more literature references (Pham and Nguyen (2020) and Kaleemuddin and Masih (2017))
We have explained the reasons for chosing the ARDL model with paragraphs 1 and 2 of the subchapter 4.1 on econometric model. The ARDL model has become an important tool for detecting cointegration relationship based on the work of Pesaran and Shin (1999). The authors demonstrate that with an ARDL representation, it is possible to identify cointegration relationships in a system formed by variables that are all I(1), all I(0), or a mixture of stationary variables and variables I(1). This constitutes a great advantage compared to the Johansen cointegration method and even the FMOLS estimator, as both assume tha all variables in the system are I(1).
in paragraph 6 of the conclusions, we have explained the differences or similarities between using this method and previous literature. In this study, a literature review found that most studies assessed reported a positive impact of agricultural credit on the formation of agricultural GDP, with particular similar findings with King and Levine (1993) and Kaleemuddin and Masih (2017), this latter also employed the ARDL model. It was found that most of them are in agreement regarding the positive impact that agricultural credit has on the growth of agricultural GDP. Therefore, in terms of analysis between the present study and previous literature regarding the method used, a comparative method was established with criteria that described the Differences: in the explanatory variables and model estimation test; as for Similarities: object of study, explained variable, Granger causality and results found. Overall, the present study is in line with the vast majority of research presented in the literature review section.
We have added a new paragraph in chapter 4 (Methodology) explaining the rationale for chosing data modeling, making clear the data origins, time series. In clonclusion, we have added in the last two paragraphs the novelty, explaining that although there are several studies related to the history of the Angolan economy, its economic growth, financial system, credit models, etc., it is worth noting, that this is the first of a kind study on this matter on the Angolan credit experience, specifically for the agricultural sector. Angola is still searching for sustainable credit model that could be used as a catalyzer to boost growth and contribute to economic development.
The Introduction has been substantially adjusted to reflect many gaps, including the novelty of this article (last two sentences of the before last paragraph of the Introduction). We have removed the subchapters, but we think it would be better to maintain the literature review separated from the introduction as in many articles.
We have introduced a completely new subchapter on the data gathering (4.1) and introduced a new paragraph in chapter 4, before subchapter 4.1.
We have introduced an alignment with the line of the journal (sustainability). In the last 3 sentences of the abstract, first paragraph of the methodology, in the results chapter, above the table 6 and new paragraphs 7 and 8 in the conclusions.
The Abstract has been substantially adjusted and we analyzed the ADL/ARDL model in the literature review chapter. We have added in chapter 3 more detail on the literature used, especially paragraph 6 (Moura), and added paragraphs 9 and 10 with two more literature references (Pham and Nguyen (2020) and Kaleemuddin and Masih (2017)). We have also completely reviewed the methodological chapter to include the rationale of using the ARDL model. The ARDL model has become an important tool for detecting cointegration relationship based on the work of Pesaran and Shin (1999). The authors demonstrate that with an ARDL representation, it is possible to identify cointegration relationships in a system formed by variables that are all I(1), all I(0), or a mixture of stationary variables and variables I(1). This constitutes a great advantage compared to the Johansen cointegration method and even the FMOLS estimator, as both assume tha all variables in the system are I(1). Finally we have correct ADL (p, p) to ADL (p, k).
Commenter: Mario Joao
Commenter's Conflict of Interests: Author
We have explained the reasons for chosing the ARDL model with paragraphs 1 and 2 of the subchapter 4.1 on econometric model. The ARDL model has become an important tool for detecting cointegration relationship based on the work of Pesaran and Shin (1999). The authors demonstrate that with an ARDL representation, it is possible to identify cointegration relationships in a system formed by variables that are all I(1), all I(0), or a mixture of stationary variables and variables I(1). This constitutes a great advantage compared to the Johansen cointegration method and even the FMOLS estimator, as both assume tha all variables in the system are I(1).
in paragraph 6 of the conclusions, we have explained the differences or similarities between using this method and previous literature. In this study, a literature review found that most studies assessed reported a positive impact of agricultural credit on the formation of agricultural GDP, with particular similar findings with King and Levine (1993) and Kaleemuddin and Masih (2017), this latter also employed the ARDL model. It was found that most of them are in agreement regarding the positive impact that agricultural credit has on the growth of agricultural GDP. Therefore, in terms of analysis between the present study and previous literature regarding the method used, a comparative method was established with criteria that described the Differences: in the explanatory variables and model estimation test; as for Similarities: object of study, explained variable, Granger causality and results found. Overall, the present study is in line with the vast majority of research presented in the literature review section.
We have added a new paragraph in chapter 4 (Methodology) explaining the rationale for chosing data modeling, making clear the data origins, time series. In clonclusion, we have added in the last two paragraphs the novelty, explaining that although there are several studies related to the history of the Angolan economy, its economic growth, financial system, credit models, etc., it is worth noting, that this is the first of a kind study on this matter on the Angolan credit experience, specifically for the agricultural sector. Angola is still searching for sustainable credit model that could be used as a catalyzer to boost growth and contribute to economic development.
The Introduction has been substantially adjusted to reflect many gaps, including the novelty of this article (last two sentences of the before last paragraph of the Introduction). We have removed the subchapters, but we think it would be better to maintain the literature review separated from the introduction as in many articles.
We have introduced a completely new subchapter on the data gathering (4.1) and introduced a new paragraph in chapter 4, before subchapter 4.1.
We have introduced an alignment with the line of the journal (sustainability). In the last 3 sentences of the abstract, first paragraph of the methodology, in the results chapter, above the table 6 and new paragraphs 7 and 8 in the conclusions.
The Abstract has been substantially adjusted and we analyzed the ADL/ARDL model in the literature review chapter. We have added in chapter 3 more detail on the literature used, especially paragraph 6 (Moura), and added paragraphs 9 and 10 with two more literature references (Pham and Nguyen (2020) and Kaleemuddin and Masih (2017)). We have also completely reviewed the methodological chapter to include the rationale of using the ARDL model. The ARDL model has become an important tool for detecting cointegration relationship based on the work of Pesaran and Shin (1999). The authors demonstrate that with an ARDL representation, it is possible to identify cointegration relationships in a system formed by variables that are all I(1), all I(0), or a mixture of stationary variables and variables I(1). This constitutes a great advantage compared to the Johansen cointegration method and even the FMOLS estimator, as both assume tha all variables in the system are I(1). Finally we have correct ADL (p, p) to ADL (p, k).