Impacts Of Fishing On The Rural Household Income; Evidence From Ethiopian Rift-Valley

Figures & data

Figure 1. Map of the study area Source: Arc GIS.

Table 1. Respondent household sampled from each selected Kebele

Location	Fishing households (N = 172)			Non-fishing households (N = 202)			Total sample size
	Total HH	Percent Weight	Sample Size	Total HH	Percent weight	Sample Size
Koka Nagawo Kebele	89	29	50	123	12	24	74
Derar Denbela Kebele	97	31	53	290	30	61	114
Dungugi Bekele	126	40	69	569	58	117	186
Total	312	100	172	982	100	202	374

Source: (Lume District office of Agriculture, 2020)

Figure 2. Sex and Marital status of sample households.

Table 2. Demographic characteristics of sampled respondents

Variables	Fishing HH (N = 172)				Non-fishing HH (N = 202)
	Mean	SD	Min	Max	Mean	SD	Min	Max
Age	37.18	7.56	19	58	40.97	11.76	20	73
Family size	4.7	2.1	1	10.6	4.3	1.7	1	9.8
Educational level	2.38	2.36	0	10	2.97	2.81	0	10
Land size	1.3	0.54	0	4	1.41	0.64	0	4.25
Livestock holding	2.7	2.5	0	8.3	3.64	2.86	0	16.3
Extension contact	0.52	0.49	0	4	0.63	0.54	0	4
Market distance (in a minute)	26.86	15.68	5	90	26.31	11.13	10	120
Distance from Lake (in a minute)	27.47	12.68	10	100	39.55	26.30	10	90

SD = Standard deviation Min = minimum Max = maximum

Source: Field Survey, 2020

Figure 3. Livestock holding by sampled households (multiple responses).

Table 3. Summary of descriptive statistics for categorical variables

Variables	Values	Fishing HH (N = 172)		Non-fishing HH (N = 202)
		N	%	N	%
Access to fishing material	Yes	44	25.6	34	16.8
	No	128	74.4	168	83.2
Participation in off/non-farm activities	Yes	37	21.51	55	27.22
	No	135	78.49	147	72.78
Credit access	Yes	20	11.63	35	17.33
	No	152	88.37	167	82.67

Source: Field Survey, 2020

Sources of Income

Table 4. Households income sources (multiple responses)

Income source	Fishing HH (N = 172)		Non-fishing HH (N = 202)
	Frequency	Percent	Frequency	Percent
Fishing	172	68.80	-	-
Crop production	57	22.80	202	77.10
Livestock production	7	2.80	23	8.78
Livestock product	3	1.20	6	2.29
Off/non-farm activities	11	4.40	31	11.83
Total	250	100.00	262	100.00

Source: Field Survey, 2020

Table 5. Mean annual income generated by different income sources (multiple responses)

Income source		Fishing households		Non-fishing households			T-test
	Freq.	Mean	Std. Dev.	Freq.	Mean	Std. Dev.
Fishing	172	34,382.33	6643.41	-	-	-	-
Crop	57	9051.40	5220.63	202	30,335.52	16,605.14	−9.533***
Livestock	7	3858.33	2990.72	23	8065.00	5661.53	−1.732*
Livestock product	3	4600.00	1979.90	6	11,200.00	5071.49	−1.683
Off/non-farm activities	11	5347.22	3463.73	31	6642.86	4178.60	−1.549

Source: Field Survey, 2020

Table 6. Distribution of estimated propensity score

Group	Obs.	Mean	Std. Dev.	Minimum	Maximum
Total household	374	.459893	.2064071	.0087808	.9020729
Fishing household	172	.5514266	.1697708	.0815016	.9020729
Non-fishing household	202	.3819536	.203141	.0087808	.876152

Obs = Observation, Std. Dev. = Standard Deviation

Source: Field survey 2020

Table 7. Performance of matching estimators

Matching Estimator	Performance criteria
	No. of Var. with insignificance mean the difference	Pseudo-R2	Matched sample size
Nearest Neighbor Matching
NN(1)	12	0.030	356
NN(2)	12	0.028	356
NN(3)	12	0.015	356
NN(4)	12	0.015	356
NN(5)	13	0.018	356
Caliper Matching
caliper(0.01)	13	0.018	317
caliper(0.1)	12	0.030	356
caliper(0.25)	12	0.030	356
caliper(0.5)	12	0.030	356
Kernel Matching
bwidth(0.01)	13	0.010	317
bwidth(0.1)	13	0.006	356
bwidth(0.25)	13	0.013	356
bwidth(0.5)	11	0.049	356

Source: Field Survey, 2020

Figure 4. Distribution of propensity scores of households.

Table 8. Propensity score matching: quality test

Sample	Pseudo R^2	LR chi2	p> chi2	Mean bias
Unmatched	0.139	71.54	0.000	25.8
Matched	0.006	2.82	1.000	4.0

Table 9. Average Treatment Effect on the Treated (ATT)

Outcome var.	Sample	Treated	Controls	ATT	S.E.	T-stat
Annual Income	Unmatched	36,918.26	33,141.96	3776.30	1375.42	2.75**
	Matched	36,914.85	31,768.43	5146.42	1602.67	3.21**

**means significant at 5% probability levels

Table 10. Result of sensitivity analysis using Rosenbaum bounding approach Rbounds delta, gamma (1(0.25)4)

Gamma()	p-critical		Hodges-Lehmann point estimate
	sig+	sig-	t-hat+	t-hat-	CI+	CI-
1	0	0	34,250	34,250	33,050	35,400
1.25	0	0	33,100	35,325	31,950	36,525
1.5	0	0	32,250	36,250	31,000	37,525
1.75	0	0	31,475	37,050	30,250	38,350
2	0	0	30,800	37,750	29,550	39,100
2.25	0	0	30,250	38,350	28,950	39,750
2.5	0	0	29,750	38,900	28,400	40,400
2.75	0	0	29,250	39,425	27,900	41,000
3	0	0	28,850	39,850	27,500	41,500
3.25	0	0	28,500	40,325	27,050	42,000
3.5	0	0	27,750	41,100	26,350	42,900
4	0	0	27,500	41,475	26,000	43,350

gamma-log odds of differential assignment due to unobserved factors

sig+ -upper bound significance level sig- -lower bound significance level

t-hat+ -upper bound Hodges-Lehmann point estimate

CI- -lower bound confidence interval (a = .95)

t-hat- -lower bound Hodges-Lehmann point estimate

CI+ -upper bound confidence interval (a = 0.95)

Lume District office of Agriculture (2020): Performance of Fisheries and Agricultural sector in the district; Office Report, Modjo.

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