Intermediate tariffs and intraregional intermediate exports: Implications for regional value chains in ECOWAS

Figures & data

Table 1. Summary of PCA for quality of institution and infrastructure

	Quality of Institution	Quality of Infrastructure
KMO measure of adequacy	0.85	0.54
KMO range/label	0.80–0.89 (meritorious)	0.50–0.59 (miserable)
PCA Decision	Accept	Do not accept

Note: Authors’ design based on PCA result.

Table 2. A priori expectations

	Dependent Variable: Log of intermediate export$ln(IMDE{X}_{ijt})$
Independent Variable	Partial Derivative and ceteris paribus	Coefficient from Partial Derivative	A priori Expectation
Log of relative factor endowment	$\delta ln(RL{F}_{ijt})$	${\phi }_1$	-
Log of GDP similarity	$\delta ln(GDPSI{M}_{ijt})$	${\phi }_2$	+
Log of intermediate tariff	$\delta ln(IMDTA{R}_{it}.IMDTA{R}_{jt})$	${\phi }_3$	-
Quality of institution (index)	$\delta (INS{T}_{it}.INS{T}_{jt})$	${\phi }_4\ast 100$	+
Contiguity(dummy)	$\delta CONTI{G}_{ij}$	$e^{{\phi }_5}-1$	+
Common Language(dummy)	$\delta COM{L}_{ij}$	$e^{{\phi }_6}-1$	+
Log of distance	$\delta ln(DI{S}_{ij})$	${\phi }_7$	-
Log of trade cost	$\delta ln(TCOS{T}_{ijt})$	${\phi }_8$	-
Log of agriculture contribution to GDP	$\delta ln(AtGD{P}_{it}.AtGD{P}_{jt})$	${\phi }_9$	+
Both WAEMU (dummy)	$\delta WAEM{U}_{ijt}$	$e^{{\phi }_{10}}-1$	+
Both WAMZ (dummy)	$\delta WAM{Z}_{ijt}$	$e^{{\phi }_{11}}-1$	+
Log of the quality of infrastructure	$\delta ln(INF{R}_{it}.IN{F}_{jt})$	${\phi }_{12}$	+
Log of FDI inflows	$\delta ln(FD{I}_{it}.FD{I}_{jt})$	${\phi }_{13}$	+

Note: Authors’ design based on hypothesised relationships.

Table 3. Summary statistics

Variable	Obs.	Mean	Std. Dev.	Min	Max
LRAWEX	1382	−.8,821,404	3.176,919	−13.81,551	7.726,012
LIMDEX	1575	−.8,816,576	3.134,887	−11.8696	6.689,053
LCONEX	1748	.0665514	3.360,297	−13.81,551	8.410,486
LALLEX	1912	.9,653,036	3.390,742	−13.81,551	8.432,508
LRAWTAR	2912	.5,191,202	1.425,259	0	5.993,961
LIMDTAR	2912	.949,8941	.896,535	0	6.879,187
LCONTAR	2911	1.289,009	2.16,961	0	7.517,931
LALLTAR	2912	1.234,375	2.007694	0	7.169,353
LRLF	2521	8.807,427	7.226,629	−5.115,104	23.31,332
LGDPSIM	2912	−.4,720,949	1.611,785	−16.84,518	.6,916,826
LTCOST	2094	5.220,028	.5,323,538	3.611,696	6.840,292
LAtGDP	2912	6.564,251	.664,1774	.615,5898	.339,304
CONTIG	2912	.2,527,473	.4,346,619	0	1
COML	2912	.4,179,258	.4,933,025	0	1
WAEMU	2912	.2,304,258	.4,211,777	0	1
WAMZ	2912	.1,102,335	.3,132,344	0	1
LDIS	2912	7.048401	.6,044,909	5.238,067	8.119,218
LFDI	2912	10.16,885	2.552,782	1.907,016	17.18,281
LINFR	2912	4.198,252	3.474,976	−6.952,686	8.811,519
INST	2912	−.3,133,891	4.185,296	−21.34,308	15.5987

Table 4. Panel unit root tests

Variable	CIPS	Fisher ADF	Fisher PP	Levin-Lin-Chu (LLC)	AIC-Selectedlag length	Hadri
LIMDEX^9	−1.52*(0.06)	−2.62***(0.01)	−2.27***(0.01)	−4.63***(0.00)	1	7.36***(0.00)
LRLF	−2.21***(0.01)	−3.34**(0.00)	−4.43***(0.00)	−6.99***(0.00)	2	9.19***(0.00)
LGDPSIM	−1.91**(0.02)	−1.85**(0.03)	1.40 (0.92)	−3.27***(0.00)	2	4.79***(0.00)
LIMDTAR	−2.53**(0.04)	−6.04***(0.00)	−5.27***(0.00)	−8.72***(0.00)	0	3.07***(0.00
LAtGDP	5.54***(0.00)	12.62***(0.00)	9.93***(0.00)	−9.62***(0.00)	1	44.07***(0.00)
LTCOST	−5.23***(0.00)	−11.45***(0.00)	−8.83***(0.00)	−7.54***(0.00)	1	39.09***(0.00)
INST	−4.94***(0.00)	−4.43***(0.00)	−7.50***(0.00)	−7.00***(0.00)	2	11.03***(0.00)
LFDI	−0.76(0.22)	−0.89(0.21)	−0.93(0.18)	−1.77**(0.04)	2	6.11***(0.00)

Note: The null (H₀) for IPS is that there is unit root while for Hadri the null is stationarity.

CIPS is the cross-sectional augmented test of Im, Perasan and Shin (IPS).

***, ** and * denotes a stationary series at 1%, 5% and 10% level, respectively

p values are in parentheses.

AIC and BIC used to select the lag length in case of Fisher ADF, Fisher PP, IPS and LLC. For Hadri tests, Newey-West bandwidth selecting using Bartlett kernel is employed to choose maximum lags.

The results are similar when considering the intercept only and the intercept plus trend cases.

Table 5. Exogeneity test for WAEMU and WAMZ

Dependent Variable: Log(IMDEX)
Regressor	Heckman Two-step	FGLS
LRLF	−0.0839	−0.0639
	(0.077)	(0.059)
LGDPSIM	0.9946***	1.0423***
	(0.1434)	(0.108)
LIMDT	−0.0633	0.0271
	(0.078)	(0.036)
INST	0.0876***	0.0875***
	(0.030)	(0.023)
CONTIG	1.5177***	1.9395***
	(0.289)	(0.219)
COML	−0.2317	−0.4670
	(0.472)	(0.375)
LDIS	−0.8565***	−0.5674***
	(0.211)	(0.163)
LTCOST^11	-	-
LAtGDP	2.2049*	2.2374***
	(1.164)	(0.714)
WAEMU	0.9151	1.5874***
	(0.578)	(0.409)
WAMZ	0.5955	1.0251***
	(0.493)	(0.361)
WAEMU + 5	−0.1567	−0.0411
	(0.238)	(0.200)
WAMZ + 5	−0.1694	−0.1599
	(0.347)	(0.272)
WAEMU—5	0.1009	0.1234
	(0.235)	(0.191)
WAMZ—5	−0.1708	0.0934
	(0.370)	(0.293)

Note: WAEMU+5 and WAMZ+5 denote the fifth lead while WAEMU-5 and WAMZ—5 denote the fifth lag of each of WAEMU and WAMZ.

Table 6. Empirical results for intermediate tariffs-exports nexus using Heckman two-step, PPML, FGLS, LSDV and Pooled OLS

	Heckman		FGLS PPML		LSDV	Pooled OLS
Dep. Var. LIMDEX	Selection	Two-Step
Regressors	(1)	(2)	(3)	(4)	(5)	(6)
LRLF	0.0262***	−0.0535	−0.0024	−0.0107	−0.1819	−0.0517
	(0.005)	(0.054)	(0.044)	(0.058)	(0.113)	(0.0636)
LGDPSIM	−0.0209	0.74,362***	1.1238***	1.1212***	1.2071***	1.1410***
	(0.024)	(0.070)	(0.072)	(0.090)	(0.115)	(0.106)
LIMDT	7719.69	−0.0196	0.0201	0.0561**	0.0602	0.0206
	-	(0.053)	(0.024)	(0.028)	(0.044)	(0.036)
INST	0.0084	0.0666***	0.0714***	0.0113	0.0621**	0.0682***
	(0.010)	(0.019)	(0.015)	(0.016)	(0.026)	(0.025)
CONTIG	0.3953***	1.1063***	1.47,856***	0.9932***	1.0867***	1.1450***
	(0.121)	(0.189)	(0.135)	(0.155)	(0.248)	(0.238)
COML	−0.1237	−0.5174*	−0.76,204***	−0.86,398***	−0.8826**	−0.5423
	(0.105)	(0.272)	(0.203)	(0.388)	(0.386)	(0.376)
LDIS	−0.0863	−1.0459***	−0.59,607***	−0.47,408***	−1.0477***	−1.059***
	(0.083)	(0.138)	(0.103)	(0.122)	(0.178)	(0.167)
LTCOST^14
LAtGDP	−0.1451**	1.4570*	1.5830***	0.6604	1.4404	1.4720*
	(0.068)	(0.868)	(0.562)	(0.645)	(1.544)	(0.819)
WAEMU	1.1284***	1.0139***	1.3166***	2.0980***	1.4473***	1.1739***
	(0.140)	(0.338)	(0.253)	(0.473)	(0.396)	(0.400)
WAMZ	0.5194***	0.7597**	0.7029***	0.3864	0.9490**	0.8313**
	(0.130)	(0.320)	(0.268)	(0.553)	(0.392)	(0.392)
CONSTANT	0.9236	−3.4149	−4.5094	1.3832	0.9886	−4.9372
	(0.869)	(6.570)	(4.334)	(4.877)	(10.307)	(7.061)
Inverse Mills Ratio		−0.3034	-
		(0.336)
Exporter-year FEs	No	Yes	Yes	Yes	Yes	Yes
Importer-year FEs	No	Yes	Yes	Yes	Yes	Yes
Observations	—	1841	1121	1841	1124	1124
No. of countries	14	14	14	14	14	14
Wald chi-squared(F-Stat)	—	2640.09	5787.49	45,003.77	–	–
R-squared	—	-	—	0.82	0.77	0.73
Chi-squared [p-value]	—	[0.0000]	[0.0000]	[0.0000]	-	-

Note: For PPML, the dependent variable is IMDEX and not its log.

Robust standard errors, consistent in the presence of any pattern of heteroskedasticity and autocorrelation within panels.

are reported in curly brackets.

p-values are presented in square brackets.

*, ** and ***, respectively, indicate significance at 10%, 5% and 1% levels.

Table 7. Auxiliary regression

	ALL EXPORTS Dep. Variable: LALLEX				RAW EXPORTS Dep. Variable: LRAWEX				CONSUMER EXPORTS Dep. Variable: LCONEX
	Heckman Two-step	FGLS	PPML	LSDV	Heckman	FGLS	PPML	LSDV	Heckman	FGLS	PPML	LSDV
LRLF	0.0001	−0.0139	−0.0814**	−0.0066	−0.0576	−0.0232	0.0266	−0.0254	0.0565*	0.0330	−0.1790***	0.0812**
	(0.025)	(0.020)	(0.040)	(0.031)	(0.059)	(0.039)	(0.053)	(0.058)	(0.029)	(0.024)	(0.050)	(0.041)
LGDPSIM	0.0195	−0.0131	−0.0151	0.0301	0.0704	0.1225**	0.2476***	0.1015	−0.0027	−0.0220	−0.0508	−0.0521
	(0.050)	(0.030)	(0.053)	(0.045)	(0.090)	(0.059)	(0.083)	(0.084)	(0.057)	(0.036)	(0.073)	(0.057)
LALLTAR/LRAWTAR/LCONTAR	−0.0485	0.0299	−0.3218***	0.0154	−0.0032	−0.0475	−0.3258***	−0.0025	0.0725*	0.0566***	0.0436	0.0195
	(0.039)	(0.023)	(0.0369)	(0.030)	(0.067)	(0.037)	(0.059)	(0.054)	(0.042)	(0.022)	(0.054)	(0.327)
INST	0.0019	0.0040	−0.0015	0.0107	0.0171	0.0170	0.0180	0.0251	0.0146	0.0086	0.0206	0.0116
	(0.011)	(0.006)	(0.010)	(0.009)	(0.021)	(0.137)	(0.019)	(0.020)	(0.124)	(0.008)	(0.013)	(0.008)
CONTIG	0.0748	0.0047	0.1324	0.0658	0.0346	−0.0174	0.1341	−0.0355	0.0387	−0.0125	0.6344***	−0.0499
	(0.109)	(0.062)	(0.108)	(0.089)	(0.208)	(0.121)	(0.215)	(0.192)	(0.123)	(0.075)	(0.126)	(0.112)
COML	0.2030	0.0497	0.0655	0.2177*	0.3009	0.3179*	0.8893***	0.4809*	0.1096	0.2172**	−0.0230	0.3127*
	(0.153)	(0.084)	(0.180)	(0.129)	(0.326)	(0.183)	(0.274)	(0.281)	(0.178)	(0.103)	(0.192)	(0.165)
LDIS	0.0425	−0.0086	−0.020	−0.0348	−0.0720	−0.1735*	0.1975	−0.2341	0.1649*	0.0131	0.2466**	0.0086
	(0.084)	(0.050)	(0.086)	(0.070)	(0.166)	(0.097)	(0.149)	(0.150)	(0.094)	(0.060)	(0.105)	(0.083)
LTCOST	−4.0199***	−3.5498***	−2.8624***	−2.8997***	-	-	-	-	-	-	-	-
	(0.096)	(0.090)	(0.128)	(0.199)
LAtGDP	0.1164	−0.2270	0.5375	0.8161	−2.2127***	−1.3428	−4.2223***	2.5067	0.7656	0.3675	1.4508***	1.5551*
	(0.427)	(0.372)	(0.548)	(0.759)	(0.848)	(0.930)	(1.299)	(1.747)	(0.474)	(0.497)	(0.411)	(0.850)
WAEMU	−0.1450	−0.0657	−0.2251	−0.2557*	0.0945	−0.3951**	−1.2844***	−0.6169**	−0.1797	−0.2625**	−01388	−0.3746*
	(0.210)	(0.089)	(0.204)	(0.136)	(0.445)	(0.188)	(0.337)	(0.282)	(0.256)	(0.109)	(0.202)	(0.169)
WAMZ	−0.2443	−0.1777	0.2947	−0.3100*	−0.3350	−0.7071**	0.5387*	0.3764	−0.1277	−0.2820*	−0.1774	−0.1463
	(0.167)	(0.119)	(0.251)	(0.162)	(0.367)	(0.300)	(0.325)	(0.428)	(0.192)	(0.144)	(0.273)	(0.218)
CONSTANT	20.5469***	21.5089***	13.8339***	10.405**	38.4255***	28.7686***	41.6782***	−4.8271	13.6612***	14.4970***	5.9480*	2.1742
	(3.221)	(2.776)	(4.061)	(5.248)	(6.637)	(6.933)	(9.478)	(11.633)	(5.579)	(3.685)	3.246	(5.882)
No of obs.	1841	1457	1841	1465	1841	1147	1841	1156	1841	1381	1841	1389
Wald chi2	7654.23	6476.28	-	-	2018.55	2190.77	-	-	5967.76	6640.69	-	-
[p-value]	[0.0000]	[0.0000]	-	-	[0.0000]	[0.0000]	-	-	[0.0000]	[0.0000]	-	-
R-squared	-	-	0.90	0.93	-	-	0.96	0.84	-	-	0.92	0.92

Table B. Correlation statistics for WAEMU

	Waemu	Waemu_f	Waemu_f2	Waemu_f3	Waemu_f4	Waemu_f5	Waemu_1	Waemu_2	Waemu_3	Waemu_4	Waemu_5
Waemu	1.000
Waemu_f	0.2277	1.000
Waemu_f2	0.2800	0.2205	1.000
Waemu_f3	0.2677	0.2790	0.2403	1.000
Waemu_f4	0.2541	0.2500	0.2673	0.2597	1.000
Waemu_f5	0.2252	0.2515	0.2689	0.2818	0.2525	1.000
Waemu_1	0.2444	0.2911	0.2677	0.2602	0.2311	0.2735	1.000
Waemu_2	0.2757	0.2460	0.2685	0.2196	0.2830	0.2639	0.2320	1.000
Waemu_3	0.2376	0.2487	0.2457	0.2843	0.2910	0.2614	0.2501	0.1981	1.000
Waemu_4	0.2233	0.2432	0.2546	0.2532	0.2752	0.2605	0.2231	0.1765	0.2213	1.000
Waemu_5	0.2855	0.2347	0.2989	0.2599	0.2771	0.2630	0.2673	0.1943	0.2227	0.2211	1.000

Table C. Correlation statistics for WAMZ

	Wamz	Wamz_f	Wamz_f2	Wamz_f3	Wamz_f4	Wamz_f5	Wamz_1	Wamz_2	Wamz_3	Wamz_4	Wamz_5
Waemu	1.000
Wamz_f	0.1818	1.000
Wamz_f2	0.1469	0.1903	1.000
Wamz_f3	0.2002	0.1228	0.1738	1.000
Wamz_f4	0.2193	0.2193	0.1152	0.2124	1.000
Wamz_f5	0.1950	0.2297	0.2211	0.1165	0.2071	1.000
Wamz_1	0.1465	0.1658	0.1913	0.2012	0.1945	0.1781	1.000
Wamz_2	0.1695	0.1620	0.1605	0.1697	0.1817	0.1651	0.1684	1.000
Wamz_3	0.1473	0.1403	0.1656	0.1936	0.1871	0.2442	0.1640	0.1497	1.000
Wamz_4	0.1543	0.1532	0.1243	0.1812	0.1754	0.1823	0.1643	0.1345	0.1532	1.000
Wamz_5	0.1899	0.2394	0.1706	0.2091	0.2027	0.2433	0.1474	0.1316	0.1359	0.1345	1.000

Table A. Weighted average differences (AHS) of effectively applied tariffs on intermediate goods

Benin	B/Faso	Cape Verde	Cote d’Ivoire	Gambia	Ghana	Guinea	Liberia	Mali	Niger	Nigeria	Senegal	S/Leone	Togo
−0.364	−1.287	0.896	−2.049	−0.474	1.881	2.711	2.338	−1.399	−0.132	−1.586	−3.456	2.609	−0.940