The impact of tourism on poverty in South Africa

Figures & data

Table 1: Percentage spending by tourists (domestic and foreign) on leisure/holiday, 2001

Province	Transport	Accommodation	Food	Entertainment	Gifts	R million	Weights
Eastern Cape	34	30	20	13	3	809	12.5%
Free State	48	24	21	6	1	106	1.6%
Gauteng	37	23	26	10	4	490	7.6%
KwaZulu-Natal	26	32	26	10	6	2130	32.9%
Mpumalanga	33	30	29	5	3	358	5.5%
Northern Cape	32	32	24	7	5	91	1.4%
Northern Province	31	40	25	3	1	272	4.2%
North-West	66	18	10	4	2	251	3.9%
Western Cape	32	29	26	9	4	1976	30.5%
RSA	32.2	29.7	24.6	9.2	4.2	6483	100%
Source of data: Rule et al. (2001).

Table 2: Estimated share of tourist expenditure in the South African SAM

Sectors	Industry type	Share based on domestic household expenditure	Tourist spending on major items					Final estimated share of tourist expenditure
			Transport (32.2%)	Accommodation (29.7%)	Food (24.6%)	Entertainment (9.2%)	Gifts (4.2%)
Agricultural products	Food	3.32%			1			2.17%
Meat products		7.72%			1			5.03%
Fish products		0.89%			1			0.58%
Fruit and vegetable products		1.41%			1			0.92%
Oils and fats products		1.14%			1			0.74%
Dairy products		2.43%			1			1.59%
Grain mill products		3.39%			1			2.21%
Bakery products		2.01%			1			1.31%
Sugar products		0.77%			1			0.50%
Confectionery products		0.78%			1			0.51%
Other food products		1.35%			1			0.88%
Beverages and tobacco products		12.60%			1			8.21%
Wearing apparel	Hardware	6.35%					1	2.54%
Handbags		0.15%					1	0.06%
Footwear		2.08%					1	0.83%
Published and printed products		1.15%					1	0.46%
Recorded media products		0.18%					1	0.07%
Petroleum products	Chemicals	5.63%	1					9.14%
Pharmaceutical products		3.15%		1				4.27%
Soap products		3.67%		1				4.98%
Rubber tyres	Spares	0.84%	1					1.36%
General hardware products		0.31%					1	0.12%
Motor vehicle parts		1.14%	1					1.84%
Jewellery		0.37%					1	0.15%
Accommodation	Services	4.62%		1				6.26%
Transport services		6.87%	1					11.15%
Communications		5.39%	1					8.76%
Financial services indirectly measured (FSIM) + insurance services		10.47%		1				14.20%
Other services / activities		9.80%				1		9.16%
Sum of household expenditure		299 539	19.86%	21.91%	37.83%	9.80%	10.60%	100.00%
Ratio adjusted			1.62	1.36	0.65	0.93	0.40
Source of data: Authors' calculations based on Rule et al. (2001) and Stats SA (2004).

Table 3: Differences between scenarios 1, 2, and 3

Scenarios	Model	Policy interventions incorporated	Data used	Conclusions	Closure	Variable(s) shocked
Scenario 1	UPGEM03, based on UPGEM02; Van Heerden et.al. (2006)	Simulation experiment to evaluate the economy-wide impacts on South Africa of a 10% inbound tourism expansion with fixed supplies of all primary factors	Simulation experiments and alternative tourism expenditure scenarios; based on tourism statistics of the South African Tourism Strategic Research Unit	A 10% inbound tourism expansion leads to a 0.15% increase in real GDP, a 0.3% increase in total employment, a positive trade balance, and negligible changes in productivity	Modified DPSV standard short run	f4q(‘agriculture,… , CommunServs’) = shocks applied based on expenditure patterns and distribution in Section 4.2, to simulate a 10% inbound tourism expansion
Scenario 2	UPGEM03, based on UPGEM02; Van Heerden et.al. (2006)	Simulation experiment to evaluate the economy-wide impacts on South Africa of a 20% inbound tourism expansion with fixed supplies of all primary factors	Simulation experiments and alternative tourism expenditure scenarios; no data required	A 20% inbound tourism expansion leads to a 0.29% increase in real GDP, increased employment, a positive trade balance, and higher levels of inflation	Modified DPSV standard short run	f4q(‘agriculture,… , CommunServs’) = shocks applied based on expenditure patterns and distribution in Section 4.2, to simulate a 20% inbound tourism expansion
Scenario 3	UPGEM03, based on UPGEM02; Van Heerden et.al. (2006)	Simulation experiment to evaluate the economy-wide impacts on South Africa of a 10% inbound tourism reduction with fixed supplies of all primary factors	Simulation experiments and alternative tourism expenditure scenarios; no data required	A 10% inbound tourism reduction leads to a 0.15% decrease in real GDP, decreased employment, a negative trade balance, and lower levels of inflation	Modified DPSV standard short run	f4q(‘agriculture,… , CommunServs’) = shocks applied based on expenditure patterns and distribution in Section 4.2, to simulate a 10% inbound tourism reduction
Source: Framework adapted from Shoven & Whalley (1992:88–90).

Table 4: Impact on macro-economic variables (% change relative to the base case)

Annualised % change	Scenario 1	Scenario 2	Scenario 3
% Δ in real GDP^a (x0gdpexp)	0.15	0.29	–0.15
% Δ in aggregate employment (employ_iop)	0.30	0.60	–0.30
% Δ in GDP price index (p0gdpexp)	0.95	1.84	–1.00
% Δ in consumer prices (p3tot)	0.87	1.69	–0.93
% Δ in average nominal wage (p1lab_iop)	0.87	1.69	–0.93
% Δ in exports price index (p4tot)	0.60	1.17	–0.62
% Δ in total supplies of imported goods (x0imp_c)	0.88	1.72	–0.93
% Δ in export volume (x4tot)	1.23	2.41	–1.29
% Δ in import volume CIF (x0cif_c)	0.88	1.72	–0.93
% Δ in competitiveness (p0realdev)	–0.94	–1.81	1.02
% Δ in contribution of balance of trade to real GDP (contBOT)	0.15	0.29	–0.15
% Δ in the terms of trade (p0toft)	0.60	1.17	–0.62
Source: Authors' UPGEM model results
^aReal GDP from expenditure side.

Table 5: Sector results for scenario 1 (structural effects)

Sector		Value added		Exports		Imports		Employment
		Volume (x1tot)	Price (p1tot)	Volume (x4tot)	Price (p4tot)	Volume (x0imp)	Price (p0imp)	Volume (employ_op)	Price (p1lab_op)
1	Agriculture	0.15	1.70	2.89	1.68	3.04	0.00	0.54	0.87
2	Gold mining	–0.46	0.09	–0.47	0.12	0.78	0.00	–0.70	0.87
3	Other mining	–0.19	0.00	–0.11	0.03	–0.44	0.00	–0.47	0.87
4	Food processing	0.33	1.14	5.11	1.14	1.87	0.00	0.64	0.87
5	Beverages	–0.04	0.78	6.38	0.84	2.17	0.00	–0.17	0.87
6	Tobacco	–0.05	0.77	–2.59	0.66	1.71	0.00	–0.20	0.87
7	Textiles	–0.79	0.65	–2.51	0.64	0.90	0.00	–0.98	0.87
8	Clothing	0.84	0.75	6.73	0.76	2.26	0.00	0.96	0.87
9	Leather	–1.92	0.61	–2.37	0.60	0.86	0.00	–3.01	0.87
10	Footwear	–0.01	0.61	7.35	0.61	1.54	0.00	–0.02	0.87
11	Wood	–1.25	0.75	–2.85	0.73	1.09	0.00	–1.64	0.87
12	Paper	–0.73	0.54	–2.09	0.53	0.51	0.00	–1.54	0.87
13	Printing and publishing	0.12	0.77	6.61	0.79	0.90	0.00	0.20	0.87
14	Chemicals	–0.51	0.41	–1.53	0.39	0.34	0.00	–1.30	0.87
15	Rubber	0.99	0.99	5.72	1.00	1.25	0.00	2.03	0.87
16	Plastic	–0.49	0.63	–2.34	0.59	0.67	0.00	–0.62	0.87
17	Non-metallic minerals	–0.56	0.51	–1.96	0.50	0.78	0.00	–1.11	0.87
18	Basic metal products	–0.88	0.32	–1.31	0.33	0.61	0.00	–1.92	0.87
19	Fabricated metal products	–0.52	0.56	–2.15	0.54	1.05	0.00	–0.84	0.87
20	Machinery	2.50	0.96	5.85	0.97	0.53	0.00	3.71	0.87
21	Electrical machinery	–0.72	0.41	–1.61	0.41	0.26	0.00	–1.25	0.87
22	Transport equipment	1.85	0.81	6.51	0.81	1.74	0.00	2.80	0.87
23	Other manufacturing	0.80	1.52	3.57	1.52	2.15	0.00	2.84	0.87
24	Electricity	–0.07	0.67	0.00	0.00	0.00	0.00	–0.22	0.87
25	Building	0.02	0.63	0.00	0.00	0.00	0.00	0.03	0.87
26	Civil engineering	–0.15	0.65	–2.55	0.65	0.00	0.00	–0.27	0.87
27	Trade	0.19	0.93	–3.63	0.93	0.00	0.00	0.35	0.87
28	Accommodation & catering	0.46	1.66	2.71	1.73	1.47	0.00	1.89	0.87
29	Transport	0.08	0.83	6.41	0.83	1.03	0.00	0.18	0.87
30	Communication	0.11	0.87	6.28	0.86	0.00	0.00	0.25	0.87
31	Financial services	0.26	1.17	5.03	1.16	0.00	0.00	0.79	0.87
32	Communication services	0.53	0.88	6.17	0.89	1.29	0.00	0.63	0.87
	Industry average	0.00	0.77	1.66	0.73	0.95	0.00	0.05	0.87
Source: Authors' UPGEM model results.

Table 6: Sector results for scenario 2 (structural effects)

Sector		Value added		Exports		Imports		Employment
		Volume (x1tot)	Price (p1tot)	Volume (x4tot)	Price (p4tot)	Volume (x0imp)	Price (p0imp)	Volume (employ_op)	Price (p1lab_op)
1	Agriculture	0.29	3.30	5.52	3.27	5.93	0.00	1.03	1.69
2	Gold mining	–0.91	0.18	–0.91	0.23	1.50	0.00	–1.35	1.69
3	Other mining	–0.37	0.00	–0.22	0.05	–0.85	0.00	–0.92	1.69
4	Food processing	0.64	2.22	9.92	2.22	3.63	0.00	1.25	1.69
5	Beverages	–0.08	1.51	12.47	1.63	4.23	0.00	–0.33	1.69
6	Tobacco	–0.10	1.49	–4.95	1.28	3.33	0.00	–0.38	1.69
7	Textiles	–1.52	1.27	–4.81	1.24	1.75	0.00	–1.88	1.69
8	Clothing	1.65	1.46	13.18	1.47	4.40	0.00	1.89	1.69
9	Leather	–3.67	1.18	–4.54	1.17	1.67	0.00	–5.74	1.69
10	Footwear	–0.01	1.19	14.45	1.19	3.00	0.00	–0.02	1.69
11	Wood	–2.39	1.46	–5.44	1.41	2.12	0.00	–3.13	1.69
12	Paper	–1.39	1.05	–4.01	1.03	1.00	0.00	–2.95	1.69
13	Printing and publishing	0.24	1.51	12.93	1.53	1.74	0.00	0.39	1.69
14	Chemicals	–0.99	0.80	–2.95	0.75	0.66	0.00	–2.50	1.69
15	Rubber	1.93	1.94	11.11	1.94	2.43	0.00	3.96	1.69
16	Plastic	–0.94	1.23	–4.48	1.15	1.30	0.00	–1.18	1.69
17	Non–metallic minerals	–1.09	0.98	–3.77	0.97	1.52	0.00	–2.14	1.69
18	Basic metal products	–1.70	0.63	–2.53	0.64	1.20	0.00	–3.67	1.69
19	Fabricated metal products	–0.99	1.08	–4.12	1.06	2.05	0.00	–1.60	1.69
20	Machinery	4.88	1.87	11.39	1.88	1.02	0.00	7.27	1.69
21	Electrical machinery	–1.39	0.80	–3.09	0.79	0.50	0.00	–2.41	1.69
22	Transport equipment	3.64	1.58	12.72	1.58	3.39	0.00	5.51	1.69
23	Other manufacturing	1.53	2.95	6.86	2.94	4.17	0.00	5.50	1.69
24	Electricity	–0.12	1.29	0.00	0.00	0.00	0.00	–0.41	1.69
25	Building	0.04	1.22	0.00	0.00	0.00	0.00	0.06	1.69
26	Civil engineering	–0.29	1.27	–4.88	1.26	0.00	0.00	–0.51	1.69
27	Trade	0.37	1.81	–6.91	1.81	0.00	0.00	0.70	1.69
28	Accommodation & catering	0.88	3.22	5.18	3.35	2.85	0.00	3.64	1.69
29	Transport	0.17	1.62	12.52	1.62	2.00	0.00	0.37	1.69
30	Communication	0.21	1.69	12.25	1.68	0.00	0.00	0.49	1.69
31	Financial services	0.50	2.28	9.73	2.26	0.00	0.00	1.53	1.69
32	Communication services	1.04	1.72	12.04	1.73	2.52	0.00	1.24	1.69
	Industry average	0.00	1.49	3.27	1.41	1.85	0.00	0.12	1.69
Source: Authors' UPGEM model results

Table 7: Sector results for scenario 3 (structural effects)

Sector		Value added		Exports		Imports		Employment
		Volume ( x1tot )	Price ( p1tot )	Volume ( x4tot )	Price ( p4tot )	Volume ( x0imp )	Price ( p0imp )	Volume ( employ_op )	Price (p1lab_op )
1	Agriculture	–0.17	–1.82	–3.21	–1.80	–3.21	0.00	–0.60	–0.93
2	Gold mining	0.49	–0.09	0.49	–0.12	–0.84	0.00	0.74	–0.93
3	Other mining	0.20	0.00	0.11	–0.03	0.49	0.00	0.51	–0.93
4	Food processing	–0.35	–1.22	–5.46	–1.22	–1.99	0.00	–0.67	–0.93
5	Beverages	0.05	–0.83	–6.70	–0.90	–2.28	0.00	0.19	–0.93
6	Tobacco	0.06	–0.82	2.86	–0.70	–1.81	0.00	0.22	–0.93
7	Textiles	0.88	–0.70	2.77	–0.68	–0.94	0.00	1.09	–0.93
8	Clothing	–0.87	–0.80	–7.04	–0.81	–2.38	0.00	–1.00	–0.93
9	Leather	2.12	–0.65	2.62	–0.64	–0.91	0.00	3.35	–0.93
10	Footwear	0.03	–0.65	–7.61	–0.65	–1.64	0.00	0.04	–0.93
11	Wood	1.38	–0.81	3.16	–0.77	–1.16	0.00	1.81	–0.93
12	Paper	0.80	–0.57	2.28	–0.56	–0.53	0.00	1.70	–0.93
13	Printing and publishing	–0.12	–0.82	–6.93	–0.84	–0.95	0.00	–0.20	–0.93
14	Chemicals	0.56	–0.44	1.66	–0.41	–0.35	0.00	1.42	–0.93
15	Rubber	–1.05	–1.06	–6.08	–1.06	–1.32	0.00	–2.13	–0.93
16	Plastic	0.55	–0.67	2.57	–0.63	–0.70	0.00	0.68	–0.93
17	Non-metallic minerals	0.61	–0.54	2.15	–0.53	–0.83	0.00	1.22	–0.93
18	Basic metal products	0.96	–0.35	1.42	–0.35	–0.63	0.00	2.11	–0.93
19	Fabricated metal products	0.57	–0.59	2.35	–0.58	–1.11	0.00	0.92	–0.93
20	Machinery	–2.63	–1.02	–6.21	–1.03	–0.56	0.00	–3.86	–0.93
21	Electrical machinery	0.79	–0.44	1.75	–0.43	–0.27	0.00	1.37	–0.93
22	Transport equipment	–1.93	–0.86	–6.84	–0.86	–1.84	0.00	–2.89	–0.93
23	Other manufacturing	–0.88	–1.63	–3.91	–1.62	–2.29	0.00	–3.06	–0.93
24	Electricity	0.07	–0.71	0.00	0.00	0.00	0.00	0.24	–0.93
25	Building	–0.02	–0.67	0.00	0.00	0.00	0.00	–0.03	–0.93
26	Civil engineering	0.17	–0.69	2.80	–0.69	0.00	0.00	0.30	–0.93
27	Trade	–0.19	–0.99	4.04	–0.98	0.00	0.00	–0.35	–0.93
28	Accommodation & catering	–0.50	–1.78	–3.00	–1.86	–1.58	0.00	–2.06	–0.93
29	Transport	–0.08	–0.88	–6.74	–0.89	–1.09	0.00	–0.18	–0.93
30	Communication	–0.11	–0.92	–6.62	–0.92	0.00	0.00	–0.26	–0.93
31	Financial services	–0.27	–1.24	–5.42	–1.23	0.00	0.00	–0.84	–0.93
32	Communication services	–0.56	–0.94	–6.51	–0.95	–1.36	0.00	–0.66	–0.93
	Industry average	0.02	–0.82	–1.73	–0.77	–1.00	0.00	–0.03	–0.93
Source: Authors' UPGEM model results.

Table 8: Decomposition of demand for locally produced goods (percentage change)

Scenarios	Scn 1	Scn2	Scn 3	Scn 1	Scn2	Scn 3	Scn 1	Scn2	Scn 3	Scn 1	Scn2	Scn 3
Fandecomp	Local market			Domestic share			Export			Total
TranspEquip	0.27	0.53	–0.24	–0.93	–1.80	0.88	2.50	4.89	–0.58	1.85	3.62	0.06
Rubber	0.18	0.36	–0.01	–0.40	–0.79	0.21	1.22	2.37	–0.18	1.00	1.94	0.02
Clothing	0.06	0.12	–0.03	–0.55	–1.07	0.07	1.28	2.51	–0.12	0.79	1.55	–0.08
AccommCater	–0.08	–0.15	–0.17	–0.60	–1.15	0.40	1.19	2.27	–0.33	0.51	0.97	–0.11
CommunServs	0.01	0.02	–0.09	–0.01	–0.02	0.00	0.44	0.87	–0.02	0.45	0.87	–0.11
FoodPrcsing	–0.01	–0.02	–0.01	–0.27	–0.53	0.00	0.62	1.20	–0.26	0.33	0.65	–0.27
FinancServs	0.01	0.03	0.01	0.00	0.00	0.29	0.24	0.47	–0.66	0.26	0.50	–0.35
Source: Authors' UPGEM model results.

Table 9: Percentage change in real household consumption by population group

Households (x3tot_h)	Scenario 1	Scenario 2	Scenario 3
White	0.02	0.05	–0.03
Coloured	0.10	0.19	–0.11
Asian	0.07	0.13	–0.07
Black	–0.05	–0.10	0.05
Source: Authors' UPGEM model results.

Table 10: Percentage change in real household consumption by household type and population group (1)

x3totx (quintiles)	White	Coloured	Asian	Black
q1	0.05	0.09	0.06	–0.05
q2	0.05	0.09	0.06	–0.05
q3	0.03	0.10	0.07	–0.05
q4	0.02	0.11	0.07	–0.05
d9	0.02	0.10	0.07	–0.05
d10	0.02	0.08	0.06	–0.08
Source: Authors' UPGEM model results.

Table 11: Percentage change in household consumption price indices (Scenarios 1–3)

p3totx (quintiles)	White			Coloured			Asian			Black
	Scn 1	Scn 2	Scn 3	Scn 1	Scn 2	Scn 3	Scn 1	Scn 2	Scn 3	Scn 1	Scn 2	Scn 3
q1	0.85	1.66	–0.91	0.88	1.70	–0.94	0.88	1.72	–0.94	0.86	1.66	–0.92
q2	0.86	1.67	–0.92	0.87	1.70	–0.93	0.88	1.72	–0.94	0.86	1.66	–0.91
q3	0.88	1.70	–0.93	0.86	1.67	–0.92	0.88	1.71	–0.94	0.86	1.66	–0.92
q4	0.88	1.72	–0.94	0.86	1.67	–0.92	0.88	1.71	–0.94	0.86	1.66	–0.92
d9	0.89	1.72	–0.95	0.87	1.69	–0.93	0.88	1.71	–0.94	0.86	1.67	–0.92
d10	0.89	1.72	–0.95	0.88	1.71	–0.94	0.89	1.72	–0.94	0.88	1.71	–0.94
Source: Authors' UPGEM model results.

Rule , S , Struwig , J , Langa , Z , Viljoen , J and Bouare , O . 2001 . “ South African Domestic Tourism Survey: Marketing the provinces. Unpublished report compiled by the HSRC (Human Sciences Research Council) for SAT (South African Tourism) and DEAT (Department of Environmental Affairs and Tourism), ” . Pretoria

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Shoven , J B and Whalley , J . 1992 . Applying General Equilibrium , Cambridge : Cambridge University Press .

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