Trade determinants and opportunities for Indian rice: a dynamic panel gravity model perspective

Figures & data

Figure 1. Leading importing countries of rice (in term of quantity) from India (2021).

Figure 2. Leading trading partners for Indian rice (2021).

Table 1. Description and expected sign of empirical models’ independent variables.

Variable	Description & Units of measurement	Source	References	Expected sign (±)
$X_{\mathit{\text{INDjt}}}$	Quantity export of rice from India (‘$\mathit{\text{IND}}$’) to trading partners (‘j’) in period ‘t’ (tonnes)	www.fao.org	Gashi et al. (2016), Kahouli (2016), Nguyen (2010), Sokvibol et al. (2019)
$X_{\mathit{\text{INDj}},t-1}$	Dynamic (lagged) quantity export of rice from India (‘$\mathit{\text{IND}}$’) to trading partners (‘j’) (tonnes)	www.fao.org	Gashi et al. (2016), Kahouli (2016), Nguyen (2010), Sokvibol et al. (2019)	+
${\mathit{\text{GDP}}}_{\mathit{\text{INDt}}}.{\mathit{\text{GDP}}}_{\mathit{\text{jt}}}$	Product of GDPs of India (‘$\mathit{\text{IND}}$’) and trading partners (‘j’) in period ‘t’ (US$)	www.fao.org	Narayan and Nguyen (2016)	+
${\mathit{\text{PCI}}}_{\mathit{\text{IDt}}}.{\mathit{\text{PCI}}}_{\mathit{\text{jt}}}$	Product of per capita income of India (‘$\mathit{\text{IND}}$’) and trading partners (‘j’) in period ‘t’ (US$)	www.fao.org	Popova and Rasoulinezhad (2016)	+
${\Delta \mathit{\text{PCI}}}_{\mathit{\text{INDjt}}}$	Absolute difference between per capita income of India (‘$\mathit{\text{IND}}$’) and trading partners (‘j’) in period ‘t’	www.fao.org	Irshad et al. (2018b), Rasoulinezhad and Kang (2016)	±
${\mathit{\text{GFCF}}}_{\mathit{\text{jt}}}$	Gross Fixed Capital Formation in trading partners (‘j’) in period ‘t’ (US$)	www.fao.org	–	−
${\mathit{\text{TIR}}}_{\mathit{\text{jt}}}$	Total Imports of Rice in trading partners (‘j’) in period ‘t’ (tonnes)	www.fao.org	Wang (2016)	−
${\mathit{\text{AREA}}}_{\mathit{\text{jt}}}$	Area under rice in trading partners (‘j’) in period ‘t’ (Ha)	www.fao.org^1	Thuong (2018), Wang (2016)	−
${\mathit{\text{PROD}}}_{\mathit{\text{jt}}}$	Production of rice in trading partners (‘j’) in period ‘t’ (tonnes)	www.fao.org	–	−
${\mathit{\text{EXCRT}}}_{\mathit{\text{jt}}}$	Bilateral exchange rate of ‘Rupee’ of India in relation to trading partners ‘j’ in period ‘t’	www.fao.org	Irshad et al. (2018a), Rasoulinezhad and Wei (2017)	+
${\mathit{\text{POP}}}_{\mathit{\text{jt}}}$	Population in trading partners ‘j’ in period ‘t’ (Million)	www.fao.org	–	+
${\mathit{\text{DIST}}}_{\mathit{\text{jt}}}$	Distance between capital of India and capital of trading partners ‘j’ in period ‘t’ (kms)	Distance calculator of www.timeanddate.com	Marti et al. (2014)	−
${\mathit{\text{COB}}}_{\mathit{\text{jt}}}$	Border dummy; 1: country ‘j’ share land border to India in period ‘t’, 0: otherwise	http://www.cepii.fr^2	Fan et al. (2016), Zhou et al. (2019)	±
${\mathit{\text{COLONY}}}_{\mathit{\text{jt}}}$	Colony dummy; 1: country ‘j’ is colonized in period ‘t’, 0: otherwise	http://www.cepii.fr	Lee and Pyun (2018), Santos Silva and Tenreyro (2006)	±
${\mathit{\text{LL}}}_{\mathit{\text{jt}}}$	Land Locked dummy; 1: country ‘j’ is a landlocked country in period ‘t’, 0: otherwise	http://www.cepii.fr	Carrère (2006), Martínez-Zarzoso and Johannsen (2017)	−
${\mathit{\text{WTOmem}}}_{\mathit{\text{jt}}}$	WTO membership dummy; 1: country ‘j’ is a WTO-member in period ‘t’, 0: otherwise	www.wto.org^3	Lien and Lo (2017), Rasoulinezhad and Popova (2017)	±
${\mathit{\text{ASEAN}}}_{\mathit{\text{jt}}}$	WTO membership dummy; 1: country ‘j’ is a member of ASEAN in period ‘t’, 0: otherwise	https://asean.org4	Li et al. (2019), Soeng and Cuyvers (2018)	±
${\mathit{\text{AFICA}}}_{\mathit{\text{jt}}}$	WTO membership dummy; 1: country ‘j’ is in African continent in period ‘t’, 0: otherwise	https://au.int/en/memberstates/countryprofiles2	Bui and Chen (2017)	±
${\mathit{\text{REC}}}_{2008j}$	2008 economic recession dummy; 1: country ‘j’ experienced recession between 2008 to 2016, 0: otherwise	https://bigeconomics.org	Kahouli (2016)	−

Note. 1 - Food and Agriculture Organization;.

2 - CEPII: Centre for Prospective Studies and International Information;.

3 - World Trade Organization.

4 - The Association of Southeast Asian Nations.

Table 2. Descriptive statistics of selected variables.

Variable	Mean	S.D	Min	Max
log($X_{\mathit{\text{INDjt}}}$)	4.25	1.86	0.00	6.39
log(${\mathit{\text{GDP}}}_{\mathit{\text{INDt}}}.{\mathit{\text{GDP}}}_{\mathit{\text{jt}}}$)	10.93	1.03	8.94	13.75
log(${\mathit{\text{PCI}}}_{\mathit{\text{INDt}}}.{\mathit{\text{PCI}}}_{\mathit{\text{jt}}}$)	6.18	0.69	4.89	7.73
log(${\mathrm{\Delta }\mathit{\text{PCI}}}_{\mathit{\text{INDjt}}}$)	2.75	0.79	0.47	4.36
log ${\mathit{\text{GFCF}}}_{\mathit{\text{jt}}}$	4.22	0.99	1.98	6.87
log ${\mathit{\text{TIR}}}_{\mathit{\text{jt}}}$	5.57	0.94	0.30	6.69
log ${\mathit{\text{AREA}}}_{\mathit{\text{jt}}}$	5.45	1.79	0.04	7.50
log ${\mathit{\text{PROD}}}_{\mathit{\text{jt}}}$	5.96	1.93	0.32	8.33
log ${\mathit{\text{EXCRT}}}_{\mathit{\text{jt}}}$	2.47	1.15	0.57	4.62
log ${\mathit{\text{POP}}}_{\mathit{\text{jt}}}$	4.67	0.61	3.78	6.15
log ${\mathit{\text{DIST}}}_{\mathit{\text{jt}}}$	3.45	0.51	2.18	4.08
${\mathit{\text{COB}}}_{\mathit{\text{jt}}}$	0.30	0.46	0	1
${\mathit{\text{WTOmem}}}_{\mathit{\text{jt}}}$	0.60	0.49	0	1
${\mathit{\text{COLONY}}}_{\mathit{\text{jt}}}$	0.10	0.30	0	1
${\mathit{\text{LL}}}_{\mathit{\text{jt}}}$	0.10	0.30	0	1
${\mathit{\text{ASEAN}}}_{\mathit{\text{jt}}}$	0.10	0.30	0	1
${\mathit{\text{AFRICA}}}_{\mathit{\text{jt}}}$	0.20	0.40	0	1
${\mathit{\text{REC}}}_{2008j}$	0.70	0.46	0	1

Table 3. Results of Panel CD test.

Test	Statistic	Prob
Pesaran	4.361**	0.000
Friedman	59.562**	0.000
Frees	3.991**	0.179^#

Note. **Significant at 1% level.

^#Critical value from Frees’ Q distribution at 1% level.

Table 4. India’s Export, Import and Trade Intensity Indices of rice among top 10 leading trade partners (TE 2021).

Indices	Quantity	Value
Export Intensity Index (EII)	1.490	2.008
Import Intensity Index (III)	0.205	0.185
Trade Intensity Index (TII)	1.375	1.891

Table 5. Determinants for bilateral trade between India and trading partners (Selection equation from Heckman model).

Variable	Coefficient	S.E	Prob
log(${\mathit{\text{GDP}}}_{\mathit{\text{INDt}}}.{\mathit{\text{GDP}}}_{\mathit{\text{jt}}}$)	12.518**	3.125	0.000
log(${\mathit{\text{PCI}}}_{\mathit{\text{INDt}}}.{\mathit{\text{PCI}}}_{\mathit{\text{jt}}}$)	1.375**	0.355	0.000
log(${\mathrm{\Delta }\mathit{\text{PCI}}}_{\mathit{\text{INDjt}}}$)	−1.010**	0.342	0.003
log ${\mathit{\text{TIR}}}_{\mathit{\text{jt}}}$	5.476*	2.430	0.024
log ${\mathit{\text{GFCF}}}_{\mathit{\text{jt}}}$	−0.972**	0.289	0.000
log ${\mathit{\text{EXCRT}}}_{\mathit{\text{jt}}}$	7.567**	2.735	0.006
log ${\mathit{\text{AREA}}}_{\mathit{\text{jt}}}$	−0.196	0.376	0.602
log ${\mathit{\text{PROD}}}_{\mathit{\text{jt}}}$	−1.663**	0.623	0.008
log ${\mathit{\text{DIST}}}_{\mathit{\text{jt}}}$	−1.378**	0.381	0.001
log ${\mathit{\text{POP}}}_{\mathit{\text{jt}}}$	1.018	0.649	0.117
Cons	39.196**	10.136	0.000
Wald chi^2	292.57** (0.000)
Mills lambda (λ)	0.5215157
Rho (ρ)	0.59477
Sigma (σ)	0.8768384

**, * indicates significant at 1% and 5%, respectively.

Table 6. Estimation of Gravity model through GLS, PPML and Heckman approaches.

Variables	GLS model			PPML model			Heckman model
	Coefficient	S.E	Prob	Coefficient	S.E	Prob	Coefficient	S.E	Prob
log($X_{\mathit{\text{INDj}}.t-1}$)	0.316**	0.057	0.000	0.577**	0.122	0.000	0.145**	0.032	0.000
log(${\mathit{\text{GDP}}}_{\mathit{\text{INDt}}}.{\mathit{\text{GDP}}}_{\mathit{\text{jt}}}$)	7.204**	1.920	0.000	7.148**	2.563	0.005	7.026**	2.665	0.008
log(${\mathit{\text{PCI}}}_{\mathit{\text{INDt}}}.{\mathit{\text{PCI}}}_{\mathit{\text{jt}}}$)	7.426**	2.328	0.001	7.924**	2.903	0.006	7.900**	1.278	0.000
log(${\mathrm{\Delta }\mathit{\text{PCI}}}_{\mathit{\text{INDjt}}}$)	−0.519*	0.235	0.027	−0.262*	0.123	0.033	−0.254**	0.082	0.002
log ${\mathit{\text{GFCF}}}_{\mathit{\text{jt}}}$	−0.831	0.637	0.192	−0.584**	0.153	0.000	0.131**	0.025	0.000
log ${\mathit{\text{TIR}}}_{\mathit{\text{jt}}}$	0.194	0.155	0.211	1.945**	0.433	0.000	1.111**	0.240	0.000
log ${\mathit{\text{AREA}}}_{\mathit{\text{jt}}}$	−0.522	0.458	0.254	−0.121	0.099	0.222	−0.189	0.378	0.617
log ${\mathit{\text{PROD}}}_{\mathit{\text{jt}}}$	−0.316	0.266	0.235	−0.236**	0.091	0.009	−0.250*	0.105	0.017
log ${\mathit{\text{EXCRT}}}_{\mathit{\text{jt}}}$	0.148**	0.046	0.001	0.631*	0.269	0.019	1.791**	0.229	0.000
log ${\mathit{\text{POP}}}_{\mathit{\text{jt}}}$	3.956	2.223	0.075	0.905	2.641	0.732	18.044	13.270	0.174
log ${\mathit{\text{DIST}}}_{\mathit{\text{jt}}}$	−3.688**	1.303	0.005	−5.038**	1.282	0.000	−6.213**	2.020	0.002
${\mathit{\text{COB}}}_{\mathit{\text{jt}}}$	5.929	4.711	0.208	3.154**	1.066	0.003	3.128**	1.199	0.009
${\mathit{\text{WTOmem}}}_{\mathit{\text{jt}}}$	1.370*	0.586	0.019	2.611*	1.255	0.038	1.800**	0.296	0.000
${\mathit{\text{COLONY}}}_{\mathit{\text{jt}}}$	−5.110	7.474	0.494	−9.721	6.245	0.119	−8.815	6.626	0.184
${\mathit{\text{LL}}}_{\mathit{\text{jt}}}$	−8.233	9.583	0.390	−5.666	3.036	0.062	−4.513	9.176	0.623
${\mathit{\text{ASEAN}}}_{\mathit{\text{jt}}}$	−2.476	1.607	0.123	−2.136	1.182	0.071	−1.224	3.856	0.751
${\mathit{\text{AFRICA}}}_{\mathit{\text{jt}}}$	−0.193	1.316	0.883	−0.935	0.606	0.123	−0.851	0.667	0.202
${\mathit{\text{REC}}}_{2008j}$	−1.480	2.880	0.607	−3.218*	1.175	0.006	−1.812	0.967	0.061
Cons	27.319**	9.406	0.004	57.727**	16.042	0.000	12.210**	4.248	0.004
Observations	260			260			260
Wald chi^2	454.69** (0.000)			597.95** (0.000)			311.41** (0.000)
Log likelihood	−399.678			Log pseudolikelihood −16894480.27
				Pseudo R^2	0.76 (0.000)

Note. **, * indicates significant at 1% and 5%, respectively.

Table 7. Gravity model estimates: one-way and two-way fixed effects model (n = 270).

Variables	One-way fixed effect model			Two-way fixed effect model
	Coefficient	S.E	Prob	Coefficient	S.E	Prob
log(${\mathit{\text{GDP}}}_{\mathit{\text{it}}}.{\mathit{\text{GDP}}}_{\mathit{\text{jt}}}$)	0.568**	0.156	0.000	7.431**	2.782	0.008
log(${\mathit{\text{PCI}}}_{\mathit{\text{it}}}.{\mathit{\text{PCI}}}_{\mathit{\text{jt}}}$)	−0.789*	0.335	0.019	−6.088*	3.009	0.043
log ${\mathrm{\Delta }\mathit{\text{PCI}}}_{\mathit{\text{ijt}}}$	0.490**	0.140	0.000	0.471**	0.124	0.000
log ${\mathit{\text{AREA}}}_{\mathit{\text{jt}}}$	−0.763	0.492	0.121	−0.596	0.476	0.211
log ${\mathit{\text{PROD}}}_{\mathit{\text{jt}}}$	−0.399	0.271	0.141	−0.221*	0.098	0.024
log ${\mathit{\text{POP}}}_{\mathit{\text{jt}}}$	8.143	7.393	0.271	4.600	7.232	0.525
log ${\mathit{\text{TIR}}}_{\mathit{\text{jt}}}$	0.338**	0.116	0.004	0.195**	0.036	0.000
log ${\mathit{\text{EXCRT}}}_{\mathit{\text{jt}}}$	0.120*	0.050	0.016	0.511*	0.231	0.027
Cons	34.217**	12.204	0.005	59.198**	20.134	0.003
F test	F(8,252)=15.93**			F(34,226) = 6.64**
Hausman test	52.41** (0.000)			49.61** (0.000)
R^2	0.81			0.81

Note. **, * indicates significant at 1% and 5%, respectively.

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Data availability statement

The data presented in this study are available on request from the authors.