The Investment development path theory: Evidence from developing countries’ agricultural sector

Background We examined the investment development path (IDP) through the perspective of developing countries’ agricultural sector. Our analytical approach indirectly accounts for interactions among countries regarding cross-border resource transfers. Aside from providing knowledge on testing the IDP by inferential statistics, the information would be relevant for policymaking. Identifying the stage(s) in the IDP not only highlights the global appeal of agriculture but also guides firms seeking to expand beyond borders. This information is essential for developing an effective economic strategy. Methods We employed data from 1991 to 2021 for 55 countries from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) and applied a fixed effects estimator corrected for serial correlation and non-constant variances. Results and conclusions We found that agriculture in developing countries is currently in stages I and II of the IDP. Broadly, agricultural production requires policies that would increase outward foreign direct investment and inward foreign direct investment. Domestic agricultural businesses in developing countries must develop capacity by learning from foreign multinationals. This would enable agricultural businesses to invest abroad. Such a move would lead to an increase in outward FDI. As this would have resulted from increased GDP per capita, it will lead to movement from the existing stage to higher ones.


Introduction
The net outward and direct investment situation of a country methodically correlates with its economic progress (Dunning, 1981a).Stages I, II, III, IV and V have been identified.These reflect the international attractiveness of the economy and firms' cross-border expansion and give guiding steps for an appropriate economic strategy (Buckley and Castro, 1998;Djokoto and Pomeyie, 2021;Dunning, 1981aDunning, ,b, 1986;;Dunning and Narula, 1996;Gorynia et al., 2019).
The investment development path is established by the interaction of net foreign direct investment and the gross domestic product per capita.Sectors of the economy possess net foreign direct investment and the gross domestic product per capita, so the IDP can be established for sectors.Additionally, as the levels of these indicators can change, the IDP for each sector can change in ways that the IDP of the total economy can change.Further, Gorynia, Nowak and Wolniak (2009) demonstrated some sectoral perspectives on the IDP, including manufacturing.Hence, as in the case of the total economy, the IDP could apply to the agriculture sector which continues to be important for many developing countries.Indeed, as the agricultural sector has been studied across economies in the world, a similar study of the IDP in the agricultural sector is in place.The case of agriculture is interesting because the United Nations Food and Agricultural Organisation (FAO) has a dedicated website and database for agriculture with data including foreign direct investment and agricultural gross domestic product.Please see https://www.fao.org/faostat/en/#data/FDI. The World Bank notes that developing agriculture is an extremely effective instrument to stop severe lack, promote collective wealth and feed an anticipated 9.7 billion persons by the year 2050.Compared to other sectors, the growth of the agricultural sector is two and four times more operative in boosting incomes amongst the most deprived (World Bank, 2022).Thus, developing countries have sought agricultural development through policies to entice foreign direct investment (FDI) into the agricultural sector (Djokoto, 2022;Ju et al., 2022;Tian, 2023;Wardhani and Haryanto, 2020).As a result of knowledge gained from foreign firms, agricultural multinationals (AM) have emerged in some developing countries (Chen and Guo, 2017).The outcome of the FDI policies and the progress of the AMs from developing countries have resulted in both inward and outward FDI into and out of developing countries (Chen and Guo, 2017;Ju et al., 2022;Tian, 2023;Wardhani and Haryanto, 2020).Indeed, from 2008 to 2019, $68.7 billion and $63.75 billion respectively were recorded as inward and outward FDI into agriculture.This is within the global investment need of $5 to $7 trillion per year (United Nations, 2014).How does the interaction between the inward and the outward FDI on one hand and agricultural development on the other explain the level of development in developing countries' agriculture?
Our study intended to fill these gaps by first, examining the agricultural sector of developing countries.Secondly, we considered other variables that explain net outward FDI other than the gross domestic product (GDP) per capita.Thirdly, we employed econometric estimations to support the trend line based on the scatter plot, which is the origin of the IDP.In our contribution, we found global agricultural production is in the IDP's stages I and II.Broadly, agricultural production requires policies that would increase both outward and inward FDI.These include strong institutional support for FDI and improving the macroeconomic environment as these drive both inward and outward FDI.Promoting trade in agricultural commodities is essential in this regard.
In what follows, we review the IDP and present some empirical evidence on it.Next, we outline our empirical strategy and data.In the results and discussion section, we show the results of the selection of the appropriate model based on the

REVISED Amendments from Version 2
In this version, we provided further justification for a sectoral analysis of the IDP for the agricultural sector and acknowledged the immaterial effect of multicollinearity on the estimates.
Any further responses from the reviewers can be found at the end of the article information criteria, test for endogeneity and check the robustness of the IDP to estimators, charts, and control variables.The last section contains the conclusions and some policy recommendations.

Literature review
The theory of investment development path According to Kumar and McLoed (1981), the theory of the IDP was propounded by John H. Dunning in 1979.This theory holds that the net outward direct investment situation of an economy is methodically correlated with its economic progress, vis-à-vis other countries.Revisions of the theory are contained in Dunning (1981a, 1986, 1988a, 1993), Narula (1993).Essentially, patterns of the relationship between GDPs per capita (GDPPC) and outward FDI less inward FDI define five idealised stages of development (Figure 1).The FDI pattern is in turn governed by the ownership, location, and internationalisation advantages of the indigenous and foreign firms (Dunning, 1988a(Dunning, , 1993(Dunning, , 1995(Dunning, , 1998)).
In stage I, the place-specific merits of a country are inadequate to entice IFDI (Dunning, 1988a(Dunning, , 1993(Dunning, , 1995(Dunning, , 1998)).Those attributable to non-artificial assets are, however, exempted.Limited local markets, unsuitable economic arrangements, or political management strategies are evidence of the lack of place-bound assets.These can reflect insufficient infrastructure and poor capacity of the labour force.The theory notes that in stage I, outward direct investment is less probable (Narula, 1993).
The continual little outward investment signal of a rise in inward direct investment is symptomatic of stage II (Dunning, 1988a(Dunning, , 1993(Dunning, , 1995(Dunning, , 1998)).Foreign firms respond to the import restriction policies adopted in response to happenings in stage I.This is reflected in foreign firms investing in import substitution industries because of either an increase in the economy's size or the citizens' purchasing power.Foreign firms take advantage of the existing level of imperceptible resources including technology, registered marks, and management skills (Narula, 1993).The export sector would respond by increasing exports of largely non-artificial resources and prime products with some level of backward and forward linkage into labour-exhaustive little technical knowledge (Narula, 1993).By implication, a country should have some looked-for place features to entice IFDI, contingent on its development strategy, and preference for technical capacity development of local businesses.
In stage III, IFDI decelerates whilst outward foreign direct investment (OFDI) accelerates moving the negative net OFDI towards zero.Building on the capacity and lessons from stage II, in stage III, the technological capabilities of the country are more and more directed to the production of standardised goods (Narula, 1993).As the rise in incomes that started in stage II continues, citizens would clamour for superior attribute goods, inspired partly by the increasing keenness amid the delivering organisations (Narula, 1993).Dunning and Narula (1996) postulate the following: 1. Relative merits in labour-exhaustive undertakings will decline.2. Local wages will increase.3. OFDI will be sought more and more by countries at earlier stages of the IDP. 4. The initial possession recompenses of foreign businesses also start to get worn out, as local businesses obtain the modest merits and vie with them.
In stage III, firms' ownership merits influenced by control of trademarked assets will be as those of foreign firms in the country.As domestic firms develop capacity, the role of state-engendered ownership merits is probably less important.Stage III is typical of emerging economies (Frenken and Mbuvi, 2017).
As the OFDI continues to rise from stage III, the level rises to equal to or exceeds that of the IFDI (Dunning, 1988a(Dunning, , 1993(Dunning, , 1995(Dunning, , 1998)).This is stage IV.In this stage, domestic firms can now effectively vie for market and resources with foreignowned firms in the home country and can enter foreign markets as well (Narula, 1993).With increased human capital and technology, capital-intensive production techniques will be employed in the production of contemporary products.In the light of ownership, location and internationalisation paradigm, the location advantages will be built largely on created assets (Dunning, 1988a(Dunning, , 1993(Dunning, , 1995(Dunning, , 1998)).In the view of Dunning (1993), the ownership merits will be more of a 'transaction' than an 'asset'.OFDI will continue to grow relative to IFDI in stage IV.
The last stage, V, is described as a home for developed countries.This stage has some features (Dunning, 1988a(Dunning, , 1993(Dunning, , 1995(Dunning, , 1998)). 1.There is a rising tendency for international business dealings to be internalised and become intramultinationals rather than across borders.2. No one country has complete domination of produced assets.3. The multinationals' ownership merits will depend less on their economy's non-artificial assets' endowment but increasingly on their capacity to obtain resources and on the capacity of businesses to form their merits competently and to feat the gains of international joint control.4. Firms become globalised, and their countries of origin become indistinct.5.The multinationals link geo-political gulfs and integration as such they no longer function with the interests of their countries of origin.They trade, acquire resources and process these in various countries, taking advantage of created and natural assets based on their lead best interest.6.The ownership and geographical boundaries of the organisation become unclear as they get involved in a more and more interlocking network of trans-border supportive arrangements.7.As the placebound assets of countries increase in similarity, the inward and outward foreign direct investments are likely to match each other.The movements in the IFDI and OFDI would lead to unusable or fluctuating net OFDI.Essentially, the peaks and troughs of net OFDI become transient (Narula, 1993).
It is obvious from Figure 1 that a rise in GDPPC is connected to a rise in the position of the IDP.To some extent, this supports the common practice of using GDP per capita as an indicator of development (Abd Hakim et al., 2022;Macek, 2014;McNabb, 2018;Minh Ha et al., 2022;Neog and Gaur, 2020;Ranis, 2004;Radulović and Kostić, 2020;Santiago et al., 2020), measured as the human development index (HDI).Recently, Djokoto (2021b) found that the IDP explains human development (HD).Further, using the GDP per capita as an indicator for IDP, Djokoto (2022) showed that GDP per capita is positively related to human development.It is worth noting that although GDP per capita is an ingredient in the construction of the HDI, it is not the only ingredient.

Empirical evidence
The empirical literature is summarised in Table 1.Since the seminal paper of Dunning, the earliest empirical work was published in 1996 and the latest in 2022.The scope of the studies included single countries, regions, and development groups.The data structure varied from time series through cross-sectional to panel.Charts have been the main analytical tool.Others have been tables and regression analyses.There is evidence of all stages of the IDP.Whilst some empirical evidence confirmed the theoretical stages, others were inconsistent with the theory.One of our previous research projects studied the IDP of Small States and segregated Small States into both developed and developing countries (Djokoto, 2021a).The exchange rate influenced the net outward foreign direct investment per capita (NOFDIPC) for the developing, the developed and the combined sample.Inflation significantly influenced NOFDIPC for developing and developed Small States.Whilst the effect of the latter was negative, that of the former was positive.In the combined sample, the effect of inflation was neutral.Regarding human capital, the effect was significant for developing Small States and the combined sample but not for developed Small States.In the case of trade openness, the coefficient was negative and statistically significant for developed Small States but negative and statistically insignificant for developing Small States and the combined sample.
It can be observed from Table 1 that no IDP study focused on agriculture.Their assessments avoided higher-level statistical analysis.Our study goes beyond the diagrammatic illustration of the IDP to include econometric analysis with control variables.Our data covers developing countries' agriculture.

Study design
The quantitative research approach is employed by relying on secondary data.This has a cross-sectional dimension (countries) and a time dimension (years).Hence, panel data was used.The data were obtained from public sources.

Model
Before the statistical analysis, we created charts of the IDP for agriculture.This involved a scatterplot of NOFDIPC on the vertical axis and GDPPC on the horizontal axis and fitted with a polynomial trend line.Although the origins of the IDP lie in the chart depiction of the nexus between NOFDIPC and GDPPC (Figure 1) (Dunning and Narula, 1996), statistical assessments tend to provide a more rigorous outcome (Dai, 2021;Djokoto, 2021a;Djokoto and Pomeyie, 2021;Fonseca et al., 2016;Satoglu, 2017).
The theory of IDP supposes the relationship between NOFDI and GDPPC (Boudier Bensebaa, 2008;Djokoto and Pomeyie, 2021;Dunning, 1981aDunning, , 1986;;Durán andUbeda, 2001, 2005;Frenken and Mbuvi, 2017;Gorynia et al., 2012Gorynia et al., , 2019;;Iacovoiu and Panait, 2014), we specify model 1, NOFDIPC is the stock of OFDI less than the stock of IFDI divided by the total population (male and female) of a country for the corresponding year.The data on OFDI and IFDI for agriculture was reported as flows and not stocks.Consequently, the first observation for every country was considered the initial flow.The subsequent flows were added consecutively to build the stock.The NOFDIPC was used to reduce the magnitude of the values to be comparable especially to the GDPPC as with in existing studies (Djokoto, 2021a;Djokoto and Pomeyie, 2021;Djokoto, 2022).The use of logarithms is avoided in NOFDIPC as it would mask the nonlinearity that we seek to measure.GDPPC is nominal GDP divided by the total population.We follow Dunning (1981aDunning ( , 1986Dunning ( , 1988aDunning ( , 1993) ) in this regard.
However, other factors could explain NOFDIPC other than GDPPC (Andreff, 2003;Djokoto, 2021a;Durán andUbeda, 2001, 2005;Frenken and Mbuvi, 2017).These can be discussed from two perspectives: the host country and the home country (Paul, 2014).We focus on the home country determinants based on the data employed.In a review of IDP studies, Sawitri and Brennan (2022) identified factors including international trade, exchange rate, human capital and inflation as other determinants of OFDI. Hence, Where the other variables are controls, namely trade openness (AGTO), the exchange rate (EXRATE), human capital (HC) and inflation (INFLA).AGTO is defined as the sum of exports and imports to the ratio of the gross domestic product for the agricultural sector.Both inward and outward FDI engenders trade.Accessing new markets by multinationals (MNEs) could start with exports of finished products to the to-be host country.Whilst in the host country, the AM could export intermediate finish products to the home country as well as other countries.Where resources are the attraction for the AM, trade could involve imports from the to-be host country.Thus, trade influences both outward FDI and inward FDI (Buckley et al., 2007;Djokoto, 2021a;Frenken and Mbuvi, 2017;Tolentino, 2008).EXRATE is the official exchange rate captured as the annual average of the local currency per US dollar.The increase in the value of the local currency will cause a decline in local currency resources to invest abroad (Buckley et al. 2007;Paul, 2014) which enhances the level of outward FDI.Also, the increase in value of the local currency, makes products and services more expensive.This decreases the attractiveness of exports relative to FDI; hence, positively influences FDI going out.Thus, the effect of the local currency's value may harm FDI going out (Bhasin and Jain 2013;Djokoto, 2021a, Paul, 2014).
HC was captured as the secondary school enrolment as a percentage of gross enrolment following Djokoto (2022).A skilled labour force is an ownership advantage that firms must possess to engage in outward FDI and to support inward FDI.Thus, human capital influences both outward and inward FDI (Djokoto, 2021a;Stoian, 2013;Tolentino, 2008).
INFLA was defined as the annual growth rate of the consumer price index following Djokoto (2023), Amal and Tomio 2012; Djokoto, 2021a;Paul, 2014 andWorld Bank (2023a).In the presence of low macroeconomic stability, businesses will probably seek stable economic environments outside the home country.Proxying economic stability by inflation, a less volatile or more volatile inflation rate points to a positive business environment, that encourages a firm's outward relocation (Amal and Tomio 2012; Djokoto, 2021a;Paul, 2014).We specified equation 2, thus: Equation 3 was estimated for the appropriate powers of the GDPPC that were established econometrically.

Data
The data consists of a panel of 55 developing countries in  , b).The countries included in the data were based on the availability of data from the sources.Further, to ensure consistency, all data was extracted from the United Nations data system.
Estimation procedure First, we established the polynomial order of the GDPPC using the information criteria (Akaike, 1974;Schwarz, 1978).Second, we expressly tested for the existence of endogeneity between our key variables, NOFDIPC and GDPPC.
Thirdly, we estimated equation 3 using panel fixed effects (FE) and random effects (RE) estimators, selected the appropriate specification based on the Hausman test (Hausman, 1978) and tested for violations of the classical regression; serial correlation (Wooldridge, 2002) and heteroscedasticity using the modified Wald test for heteroskedasticity (Greene, 2000).The third step was applied to each model during the robustness check of the estimates of the GDPPC, GDPPC 2 and GDPPC 3 to the control variables.

Descriptive Statistics
The minimum NOFDIPC is -180.1437(Uruguay, 2008) with a maximum of 32.5600 (Malaysia, 2011) (Table 3).The mean of -4.4210 is close to that of Mozambique in 2008.Based on the standard deviation of 17.2928, the variance is more than the mean suggesting overdispersion of the data.Similar overdispersion can be observed with GDPPC 2 .Except for GDPPC and HC for which the standard deviation is less than the respective means, for all other control variables, the standard deviation exceeds the mean.
Determining the order of the polynomial As the observation in Figure 2 is based on the scatter plot of NOFDIPC and GDPPC, it may well be that incorporating other control variables could change this.Thus, we estimated models 1 -3 to test this.The AIC and BIC for model 3 are the lowest (Table 4).This suggests that model 3, with the cubic functional form, a polynomial of order 4 is appropriate for the curve based on the estimations.This conforms to the cubic curve shown as the trend line in Figure 2.This is in line with our specification of equation 3 to be modelled.

Test for endogeneity
In an earlier study with other colleagues, we found that agricultural FDI explained agricultural GDP (Djokoto et al., 2022).Other studies made similar findings ( (Djokoto, 2013;Gunasekera et al., 2015) whilst the reverse is also true (Djokoto, 2012;Kassem and Awad, 2019;Lv et al., 2010), hence there can be an endogeneity problem.Rather than anticipate and model accordingly, we proceeded to test for endogeneity (Table 5).First, we modelled GDPPC as the dependent variable with all others as exogenous variables.We predicted the errors GDPPC_ue and did the same for GDPPC 2 and obtained GDPPC 2 _ue as well as GDPPC 3 and obtained GDPPC 3 _ue.In Model 7, the predicted terms were introduced as additional explanatory variables.We then tested the significance of the GDPPC_ue, GDPPC 2 _ue and  GDPPC 2 _ue with a chi-square test.Failing to reject the null hypothesis suggests that the error terms are not correlated with the NOFDIPC, hence the suspicion of endogeneity is not borne out by the data.Following the absence of endogeneity, equation 3 was estimated with FE and RE estimators.The Hausman test showed that the hull hypothesis that the differences between the matched coefficients are not systematic could not be rejected.Consequently, the RE estimation was preferred to fixed effects.The errors were found to be serially correlated and non-constant (heteroscedastic).We addressed this with robust standard errors.The serial correlation was resolved by introducing a first lag of NOFDIPC, d.NOFDIPC as appropriate.

Robustness
The estimates of GDPPC, GDPPC 2 and GDPPC 3 in 8 are consistent with those of models 9 -13 (Table 6).The estimates of AGTO, EXRATE, HC and INFLA are also consistent across models 9 -12 and model 13.
We acknowledge the role of multicollinearity especially when using powers of the same variable as explanation variables.
An important consequence of multicollinearity is the inflation of the standard errors leading to the invalidation of the hypotheses tests.Our hypothesis tests are not appreciably invalidated, moreover, the estimates are robust and consistent with theory, thus, we did not consider the impairment of our results due to multicollinearity.

Discussion of the IDP for agriculture
We focus on model 13 for discussions.The negative and statistically significant coefficient of GDDPC confirms the curve declines initially as in Figure 2. The statistically insignificant coefficients of GDPPC 2 and GDPPC 3 suggest points of inflexion.The positive and negative signs respectively show the rise and fall before and after the points of inflexion.The estimates correctly depict Figure 2 notwithstanding the introduction of the control variables.It is informative to note that notwithstanding the cubic function in Figure 2, confirmed by the model selection in Table 2 and estimates in Table 5, the curve remains below the GDPPC axis.Indeed, the curve does not show any significant rise after its first decline.
The statistical significance of the GDPPC is in line with the evidence that the size of the economy determines FDI in agriculture (Djokoto, 2012;Djokoto, 2021c;Husmann and Kubik, 2019;Rashid and Razak, 2017).It also confirms the outcome of early studies of Dunning (1981aDunning ( , 1986Dunning ( , 1988aDunning ( , 1993) ) and Narula (1993).The statistical significance of the GDPPC also confirms the existence of the IDP.This should be viewed within the framework of the evidence that GDPPC, IDP and HD are positively correlated (Djokoto, 2021a(Djokoto, ,b,c, 2022;;Djokoto and Pomeyie, 2021).
The continual low or negligible OFDI and rise in IFDI are observable.The outcome means that the location-specific advantages of developing countries are inadequate to attract IFDI.These can be attributable to inadequate infrastructure and poor capacity of the labour force.Foreign firms may be interested in investing in import substitution industries because of either an increase in the economy's size or the citizens' purchasing power.The weakness of the developing countries encourages foreign firms to take advantage of the existing level of imperceptible resources including knowhow, registered marks, and managerial expertise.Foreign firms may also invest in export sectors increasing exports of largely non-artificial resources and prime commodities with some level of backward and forward linkage integration into labour-exhaustive low know-how.

Discussion of control variables
The positive and statistically significant coefficient of AGTO implies that trade enhances NOFDI.Aside from accessing new markets with new products at the start of internationalisation, home, and foreign affiliates engage in the trade of raw materials and finished and semi-finished products.Where divestments occur, the markets could be filled with products from foreign affiliates of the home country.In all these, the constituents of trade openness, imports and exports take place, hence, this is a positive sign.Djokoto (2021a) found a negative sign of the estimates of trade that were statistically insignificant for both developing and the combined Small States except for developed Small States.Djokoto (2021a) did not provide reasons for the results on the trade variable.
The negative sign of the coefficient of EXRATE is consistent with that of Djokoto (2021a), there is a departure regarding the statistical significance.The coefficient of INFLA is also negative but statistically insignificant.Our finding is contrary to that of Djokoto (2021a) who found positive and statistically significant coefficients for developing Small States as well as the combined sample, but negative and statistically significant coefficients for developed Small States.As in the case of the other control variables, Djokoto (2021a) did not provide reasons for the significant coefficients.
Regarding the control variables, in summary, agricultural trade openness enhances agricultural net foreign direct investment whilst the exchange rate has the opposite effect.Inflation does not affect agricultural net foreign direct investment.

Conclusions and recommendations
We employed data from 1991 to 2021 for 55 developing countries to empirically investigate the IDP for agriculture in developing countries.Our analytical approach indirectly accounts for interactions among countries regarding crossborder resource transfers.Aside from providing knowledge on testing the IDP by inferential statistics, the information would be relevant for policy.Further, the stage of the IDP reflects the cross-border attractiveness of agriculture and tortuously of agricultural businesses going abroad from the agricultural sector and gives guideposts for apt economic strategy.
We found agricultural production in developing countries in IDP's stages I and II.Broadly, agricultural production requires policies that would increase both OFDI and IFDI.Agricultural multinationals in developing countries must develop capacity by learning from foreign multinationals.This would enable them to go abroad.Such a move would lead to an increase in OFDI.As this would have resulted from increased GDP per capita, it will lead to movement from the existing stage to higher ones.At the macroeconomic level, the government must support the building of strong institutions for FDI and improving the macroeconomic environment as these drive both OFDI and IFDI.Promoting trade in agricultural commodities is essential in this regard.
Our study is limited to developing countries.Further studies can also explore the role of the IDP in transition and or developed countries.As noted in the introduction that sectors of the economy possess net foreign direct investment and the gross domestic product per capita, and that the IDP can be established for sectors, a sector such as manufacturing can also be examined in further research.
The work takes into account interactions among countries regarding cross-border resource transfers.
The stage of the IDP reflects the cross-border attractiveness of agriculture and tortuously of agricultural businesses going abroad from the agricultural sector and gives guideposts for apt economic strategy.

Is the work clearly and accurately presented and does it cite the current literature? Yes
Is the study design appropriate and is the work technically sound?Yes

If applicable, is the statistical analysis and its interpretation appropriate? Yes
Are all the source data underlying the results available to ensure full reproducibility?Yes

Bienmali Kombate
School of Accounting, Guangzhou College of Technology and Business, Guangzhou, Guangdong, China The work looks well structured and all the sections discussed the most appropriate concepts.The study has a sound novelty given its cross-sectional framework, data sources and the statistics models applied.I only have minor suggestions I suggest that the corresponding author to consider changing the heading of the section three named "Data, Models and Modelling" to "Data and Method." The study design subsection should be extended to add value.Try to explain why the choice of this method and what is it advantages and disadvantage grounding of the previous work.It is suggested to consider discussing the data and data sources under the subsection of "Research design."The subsection heading named "Model" is suggested to change to be renamed "Methodology" and spare this subsection into two which discuss "the research model specification" and "the research variables".Finally, prove the theoretical and practical implication of this work and the study limitations given the cross-sectional framework.

Is the work clearly and accurately presented and does it cite the current literature? Yes
Is the study design appropriate and is the work technically sound?Yes

Are sufficient details of methods and analysis provided to allow replication by others? Yes
If applicable, is the statistical analysis and its interpretation appropriate?
Yes Are all the source data underlying the results available to ensure full reproducibility?Yes Are the conclusions drawn adequately supported by the results?Yes Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Financial Risk Management
I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Is the work clearly and accurately presented and does it cite the current literature? Yes
Is the study design appropriate and is the work technically sound?Yes

Are sufficient details of methods and analysis provided to allow replication by others? Yes
If applicable, is the statistical analysis and its interpretation appropriate?Yes Are all the source data underlying the results available to ensure full reproducibility?version 3. We create a last paragraph to the results section of version 3 as follows: "We acknowledge the role of multicollinearity especially when using powers of the same variable as explanation variables.An important consequence of multicollinearity is the inflation of the standard errors leading to the invalidation of the hypothesis tests.Our hypotheses tests are not appreciably invalidated, moreover, the estimates are robust and consistent with theory, thus, we did not consider the impairment of our results due to multicollinearity." Regarding the lags, first, the dependent variable is not inward FDI only.It is outward FDI minus inward FDI.Thus, we are not modelling the location factor of inward FDI for which lags of the location factors may be relevant.Secondly, more than 100 published literature on the IDP does introduce lags explicitly.We do not intend to depart from that.Lags may, however, be relevant in cases where GMM estimation is required.

Comment
It is also suggested to provide a justification for studies analyzing the IDP in specific sectors and their validity beyond the entirety of the national economy.These are important points that could enhance the quality and validity of the study.Or at least the authors can mention them in the limitations section.

Response
In our response to the reviewer's comment on version 1, we justified the plausibility of the sectoral (agriculture) IDP and updated the second paragraph of the introduction in version 2. We now do a further update of version 2, so the beginning of the second paragraph of version 3 reads as follows: "The investment development path is established by the interaction of net foreign direct investment and the gross domestic product per capita.Sectors of the economy possess net foreign direct investment and the gross domestic product per capita, so IDP can be established for sectors.Additionally, as the levels of these indicators can change, the IDP for each sector can change in ways that the IDP of the total economy can change.Further, Gorynia, Nowak and Wolniak (2009) demonstrated some sectoral perspectives on the IDP, including manufacturing.Hence, as in the case of the total economy, the IDP could apply to the agriculture sector which continues to be important for many developing countries.Indeed, as the agricultural sector has been studied across economies in the world, a similar study of the IDP in the agricultural sector is in place.The case of agriculture is interesting because the United Nations Food and Agricultural Organisation (FAO) has a dedicated website and database for agriculture with data including foreign direct investment and agricultural gross domestic product.Please see https://www.fao.org/faostat/en/#data/FDI"Following the justification, we do not consider the sectoral analysis a limitation.

Comment
Anyway, it was a pleasure to review it and we think that it is an interesting article.

Response
We are most grateful, and we will be glad if you can make time to review version 3.
in the introduction, finding the gap.

2.
The IDP figure should indicate whether it is from the authors, they have taken it from another article, or it is based on "X" article.

3.
Regarding the methodology.While the authors employ rigorous statistical methods, enhancing transparency in the methodology section by providing more detailed explanations of the analytical approach, model specifications, and data sources would improve the reproducibility and credibility of the study.For example: -The NOFDIPC variable must be better justified.Why is it divided by the population?Why aren't logarithms applied?
-Why are the control variables chosen and not others?And how do each of them influence each other specifically?Only a few are mentioned.
-Doesn't the fact of introducing 3 variables that are combinations interacting with each other generate multicollinearity?The correlation table should appear, justifying its impact on the model (VIF), as well as considering the possibility of centering the interactions with respect to the means.
-How are developing countries selected?Explain the criterion, because this can change and there are countries that over a period, if it is GDP/capita, can change their status.You should set the criteria and source.Are all the countries "developing" in such a long period of time according to one standard source?
-The R 2 of the models is very low, which may be a sign that many other things are not being considered in those models.Maybe it would be good to explain it and put in limitations.
-When an investment attraction variable is estimated, its explanatory factors (generally location factors) are usually delayed by at least one period, given that it is understood that what motivates the decision precedes the subsequent result.The models do not mention that they are delayed, it does not appear in the t-1 subscripts, nor is it explained why it is not done.Either it is justified, or it is done, or it is put in limitations.
-At the end of Table 4 a number 3 appears at the bottom, which we do not know what it is.
-In the notes of the tables you mention "parenthesis", should it be "parentheses" once they are two?
4.About the contribution of the work and discussion/limitations.
In the end, the conclusion is that developing countries, specifically in the agriculture sector, are located in stages I and II of development, something that would explain the theory and would not be adding anything new.The authors should "sell" the implications or singularities of the work better.
While the study acknowledges its focus on developing countries, further discussion on the limitations and generalizability of the findings would strengthen the article.Addressing potential confounding variables or factors not accounted for in the analysis could enhance the robustness of the conclusions.We appreciate having been the reviewers of this article.We found the article interesting, it is well written, we have also learned and we believe there is room for improvement.

Response
We acknowledge your summary and general impressions of the paper.However, we note that the title of the paper is "The Investment Development Path Theory: Evidence from developing countries' agricultural sector".
Below are a series of comments to improve the work: Specific Comments 1.In the introduction it is said that there are works that analyze the IDP in a country (its path) or groups of countries, but they are not rigorous (they do not control for other variables) and do not focus on agriculture.This is not totally true because there are works where the PDI per se is analyzed or it is used with many control variables (in fact more and different than those used by the authors Response Not every country had data over the period, hence we used an unbalanced panel.This is common with FDI data, especially, the outward FDI.Further, the lags resulted in some data attrition leading to differences in the number of observations listed in the model diagnostics section of some tables.

Comment
-The R2 of the models is very low, which may be a sign that many other things are not being considered in those models.Maybe it would be good to explain it and put in limitations.

Comment
In the econometric literature, low R2 are a concern if the model will be used for forecasting.However, as we used the model for hypothesis testing of the coefficients, the low R2 is not a limitation of the model.

Comment
-When an investment attraction variable is estimated, its explanatory factors (generally location factors) are usually delayed by at least one period, given that it is understood that what motivates the decision precedes the subsequent result.The models do not mention that they are delayed, it does not appear in the t-1 subscripts, nor is it explained why it is not done.Either it is justified, or it is done, or it is put in limitations.

Response
In IDP studies, the lag is not necessarily the case.Where GMM is used this takes account of the lags.Hence, we avoided the lags.

Comment
-At the end of Table 4 a number 3 appears at the bottom, which we do not know what it is.

Response
The '3' was left after the footnote was edited.This is now deleted.
Comment -In the notes of the tables you mention "parenthesis", should it be "parentheses" once they are two?Responses Noted and correction is done.
Comment 4.About the contribution of the work and discussion/limitations.In the end, the conclusion is that developing countries, specifically in the agriculture sector, are located in stages I and II of development, something that would explain the theory and would not be adding anything new.The authors should "sell" the implications or singularities of the work better.

Response
The primary goal of any IDP paper is to establish the stage of development based on the theory.That is our primary objective as well.After stating that, we noted the policy recommendations, as required in an applied economics paper.

Comments
While the study acknowledges its focus on developing countries, further discussion on the limitations and generalizability of the findings would strengthen the article.Addressing potential confounding variables or factors not accounted for in the analysis could enhance the robustness of the conclusions.

Response
We employed variables that have been used in IDP studies.Indeed, some did not use these at all.We have added a sentence (long one though) to account for this in the last paragraph of the paper.Addressing these points would contribute to making the article scientifically sound and enhance its impact on both academic research and policymaking in the agricultural sector.

Figure 1 .
Figure 1.Investment development path.Note: Not drawn to scale -For illustrative purposes only.

Figure 2 .
Figure 2. Chart of investment development path for developing countries' agriculture.

Comment 5 .
Future Research Directions.Including a section on future research directions could add value to the article by highlighting avenues for further exploration, such as examining the role of specific policy interventions or conducting comparative analyzes across different regions or country groups.

Table 1 .
Summary of the IDP literature.
Regression, FE.No controlsThe position of the CEECs is at stage I or II of the IDP.CEECs diverged from EU15 in terms of NOIP per capita but converged in terms of GDP per capita.Less developed CEECs are converging with more developed CEECs in terms of outward investment position but not in terms of GDP per capita.Brazil: Stage IV.Russia: stage III & IV.China: stage II.USA, France, Germany, Japan, Singapore, Australia and Canada: Stage V. > Half of the Small States are in stages I and II.< half are in stage III.Estonia and Malta on stage.Small States together in stage IV.A new approach to IDP using factor analysis.Test of the power of structural variables to explain inward and outward FDI.Reformulation of the fourth stage.Fonseca et al. (2016) Developed countries,Portugal, 1990-2011.Regression, FE.Contrasts between the theory and evidence of IDP in several cases.

Table 1 .
Continued Table 2 from 1991 to 2021 based on United Nations (2022).The data include a total of 885 observations.Data to construct NOFDIPC and GDPPC were obtained from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) (2023a, b) whilst data for EXRATE, HC and INFLA were obtained from the World Development Index of the World Bank (2023a

Table 2 .
List of developing countries in the data.

Table 4 .
Selection of order of the polynomial.

Table 6 .
Random effects estimations of IDP and robustness of estimates to control variables.

Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests:
Future Research Directions.Including a section on future research directions could add value to the article by highlighting avenues for further exploration, such as examining the role of specific policy interventions or conducting comparative analyzes across different regions or country groups.Addressing these points would contribute to making the article scientifically sound and enhance its impact on both academic research and policymaking in the agricultural sector.No competing interests were disclosed.

We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. Responses to Review by J. Fleta-Asin Comments: Background
The document titled "Impact of Agricultural Trade Openness on the Investment Development Path for Agriculture in Developing Countries" explores the relationship between agricultural trade openness and the Investment Development Path (IDP) for agriculture in developing countries.The authors use data from 1991 to 2021 for 55 developing countries to empirically investigate the IDP, which indirectly considers interactions among countries regarding cross-border resource transfers.Through inferential statistics, the study aims to provide insights relevant to policymaking and economic strategy in the agricultural sector.The authors employ an analytical approach, incorporating random effects estimates and robustness checks with control variables to ascertain the impact of agricultural trade openness on agricultural net foreign direct investment (NOFDIPC).Their findings suggest that agricultural trade openness enhances NOFDIPC, while variables such as the exchange rate and GDP per capita exhibit opposite effects.Additionally, the study provides insights into the stages of the IDP, indicating that agricultural production in developing countries aligns with stages I and II of the IDP.Policy recommendations include fostering policies to increase both outward and inward foreign direct investment (OFDI and IFDI), developing institutional capacity for FDI, and promoting trade in agricultural commodities.
ResponseThe reference: Ramirez-Aleson, M., & Fleta-Asin, J.(2016).Is the importance of location factors different depending on the degree of development of the country?Journal of InternationalManagement, 22(1), 29-43, addressed the location factors of FDI.The dependent variable is FDI and not NOFDIPC as in our paper.Although the countries analysed were claimed by Ramirez-Aleson and Fleta-Asin to be based on the IDP, this was not the case.The authors used GDP per capita only to categorise countries into stages of development.This is at variance with the IDP methodology developed by JH Dunning, which employed the interaction of the net FDI (outward FDI -inward FDI) and GDP per capita.Therefore, Ramirez-Aleson and Fleta-Asin (2016) is not an IDP study in the true sense of the IDP methodology.Although many control variables were used, that is irrelevant to the study under review.Therefore, our failure to acknowledge Ramirez-Aleson and Fleta-Asin (2016) is in place.Hence, the assertions of the authors of the study under review, are 'totally' true.Comment-Doesn't the fact of introducing 3 variables that are combinations interacting with each other generate multicollinearity?The correlation table should appear, justifying its impact on the model (VIF), as well as considering the possibility of centering the interactions with respect to the means.ResponseWe noted the VIF during our analysis.An important consequence of multicollinearity is the inflation of the standard errors leading to the invalidation of the hypothesis tests.Our hypothesis tests are not appreciably invalidated, moreover, the estimates are robust and consistent with theory, thus, we discounted the role of multicollinearity.Comments-How are developing countries selected?Explain the criterion, because this can change and there are countries that over a period, if it is GDP/capita, can change their status.You should set the criteria and source.Are all the countries "developing" in such a long period of time according to one standard source?Response The developing countries were chosen based on the United Nations classification in the statistical annexe of World Economic Situation and Prospects 2022 ( https://www.un.org/development/desa/dpad/wpcontent/uploads/sites/45/WESP2022_ANNEX.pdf).The classification has not changed throughout the study.This classification is not based on only GDP per capita, as in Ramirez-Aleson and Fleta-Asin (2016), but on other economic properties.All countries are developing according to the source, from 1991 to 2021.