Learning-by-Exporting? Firm-Level Evidence for UK Manufacturing and Services Sectors

Table 4: Long-run ‘learning-by-exporting’ effect for certain UK industries, 1996-2004

	______AGF	FBT	TCL	WPP	CRR	MET	ENG	OMF
IV model (GMM)
EXP_entry _t+1	0.061	-0.013	0.066	0.678^*	0.242	-0.004	0.112	-0.094
EXP_entry t	-0.012	_sksk 0.117^**	0.173	-0.228	0.030	-0.043	0.278^***	0.272^***
EXP_entry t-1	-0.066	-0.019	-0.094	-0.238^**	0.068	-0.052	_ _ _sksk -0.215^**	_ _ _ * -0.084^*
EXP_exit t+1	0.084	-0.126^*	-0.284^*	0.107	-0.186^*	-0.264^***	0.098	-0.068
EXP_exit t	0.133	-0.054	-0.072	0.118	_ _ .sksksk -0.692^***	0.120	-0.091	0.094
EXP_exit t-1	-0.447	-0.049	0.157	-0.236	0.399^*	_ _ _sksksk 0.237^***	-0.114^*	0.055
EXP_both t+1	0.689^***	-0.021	-0.086	0.076	-0.025	-0.072	-0.048	-0.045
EXP_both t	0.042	0.211^**	-0.153	-0.013	-0.004	0.071	0.036	0.042
EXP_both t-1	0.260	sk -0.104^*	0.110	0.057	-0.017	-0.008	0.009	-0.055
No. of Obs.	1702	3065	2223	6903	4629	7075	9596	3731
No. of groups	508	741	530	1798	1107	1719	2252	948
Control function
EXP_entry t+1	0.074	-0.014	0.013	0.743^**	0.256	-0.013	0.094	-0.102
EXP_entry t	-0.061	0.271^**	0.298	0.390	-0.041	0.039	0.410^*	0.353
EXP_entry t-1	-0.043	-0.013	-0.157	-0.329^***	0.056	-0.065	-0.200^*	-0.063
EXP_exit t+1	0.086	-0.108^*	-0.272	0.127	sksk -0.181^**	_ .sksksk -0.198^***	0.043	-0.068
EXP_exit t	0.117	-0.257	-0.029	-0.433	-0.476	-0.001	-0.281	-0.066
EXP_exit t-1	-0.383	-0.015	0.174	-0.272^**	0.295	_sksksk 0.215^***	_ _ * -0.102^*	0.070
EXP_both _t+1	0.693^***	-0.054	-0.115	0.056	0.006	-0.053	-0.051	-0.051
EXP_both t	0.018	0.369^**	-0.117	0.463^**	-0.065	0.118	0.222	0.092
EXP_both t-1	0.283	-0.158^*	0.103	0.035	0.001	-0.010	0.025	-0.045
λ1	0.009	-0.014	0.009	_ _ _ sksksk -0.201^***	0.109	-0.040	0.012	0.017
λ0	0.058	-0.240^***	-0.014	0.352	0.015	0.007	-0.122^*	-0.121
No. of Obs.	1702	3065	2223	6903	4629	7075	9596	3731
No. of groups	508	741	530	1798	1107	1719	2252	948
Matched sample
EXP_entry t+1	0.048	0.020	-0.043	__.sksk 0.533^**	* 0.241^*	0.008	0.115	-0.063
EXP_entry t	0.009	0.093^**	- _ - * 0.340^*	-0.113	-0.001	-0.065	_ _ .sksksk 0.276^***	0.278^***
EXP_entry t-1	0.025	0.001	-0.031	-0.246^***	0.082	-0.022	-0.201^*	-0.136^*
EXP_exit t+1	0.036	-0.092^*	-0.349^*	0.097	-0.192^*	-0.281^***	0.092	-0.067
EXP_exit _t	-0.006	-0.077	0.039	0.120	___.sksksk -0.729^***	__ .sk 0.176^*	-0.075	0.108
EXP_exit t-1	-0.415	-0.042	0.200	-0.254^*	0.436^*	.sksksk 0.212^***	-0.106^*	0.052
EXP_both t+1	0.694	-0.040	-0.091	0.086	-0.006	-0.072	-0.039	-0.058
EXP_both t	0.026	_ _sksk 0.185^**	-0.057	-0.002	0.001	0.070	0.035	0.038
EXP_both t-1	_ _ -SkSkSk 0.309^***	-0.063	0.205	0.064	-0.012	-0.010	-0.006	-0.088
No. of Obs.	682	2564	2100	5178	4525	6386	3731	3443
No. of groups_________	261	685	509	1526	1089	1610	948	890

Notes: Refer to Table 2 for details of industry group codes. The 2-step GMM system estimator in STATA 9 is used
using FAME data in conjunction with weights; the instrument set includes lagged values of the RHS variables
in the model as well as ln Age_it and Intang_it . Standard errors are obtained using the ‘delta’ method. See

Table 3 for details of estimation of other variables. ^***/**/* significant at the 1%/5%/10% level.

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Learning-by-Exporting? Firm-Level Evidence for UK Manufacturing and Services Sectors

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