Impact of participatory guarantee systems on sustainability outcomes: the case of vegetable farming in Vietnam

Figures & data

Figure 1. Map of the study area in Vietnam.

Table 1. Summary statistics of the outcome variables used in the analysis.

		Farms without PGS certification (n = 301)		Farms with PGS certification (n = 119)
Description of outcome variables	Short name of outcomes	Mean	SD	Mean	SD
Gross margin (USD/ha)	gm	6,036.25	4,192.27	11,812.57	7,278.75
Revenues (Million USD/ha)^a		8,135.04	4,138.51	13,589.91	7,654.42
Variable input costs (USD/ha)^a		2,082.69	1,209.01	1,765.34	1,451.44
Return to labour (USD/personday)	returnlab	22.27	20.20	15.60	12.69
Own labour (persondays/ha)^a		430.54	417.66	1,179.23	953.85
Crop yield (t/ha)	yield	34.72	14.00	21.60	11.43
Agroecology performance (0–1 score)	tape5	0.31	0.08	0.47	0.07
Awareness of AE practices (# 1–11)	aware	6.95	2.91	9.06	1.34
Use of AE practices (# of 1–11)	use	4.02	2.34	6.90	1.57
Confidence of using AE practices (0–1 score)	conf	0.46	0.24	0.62	0.13
Knowledge (# of key words mentioned, 1–9)	orgknow	2.05	2.02	4.12	2.42
Choice of marketing channel (1 = yes)	choice	0.60	0.49	0.83	0.38

Note: AE = agroecological; gross margin, return to labour and yield are crop-specific (white cabbage), other variables are farm-specific.

^a These variables were used in calculations but were not included in the analysis as outcome variables.

Figure 2. Descriptive group comparison for the individual components of aggregate environmental outcome variables ‘use’ (graph A) and ‘tape5’ (graph B).

Table 2. Description of agroecological practices used as outcomes.

Practice	Description
B1 Intercropping	Different crops are grown together on one plot, e.g. lettuce together with cabbage, cauliflowers, beans in a mosaic pattern or vegetables grown in-between early-stage fruit crops
B2 Rotation with legumes	Commercial legume crops, such as long bean, soybean, green beans, are rotated with vegetable crops according to cropping seasons.
B3 Mulching	Dry crop residues, such as rice, maize or legume husks) are used to cover the soil beds when growing vegetables.
B4 Cover crops	Fallow plots are covered by wild legumes or grasses, which farmers consider improves soil quality before the subsequent cropping season
B5 Microbial composting	Molasses and microorganism products are used for improving the composting process of manure and crop residues.
B6 Mineral fertilizer microdosing	Application of small quantities of mineral fertiliser at planting time or as top dressing after planting
B7 Biopesticides or natural predator release	Application of biological pesticides, such as bacillus thuringiensis and pyrethroid, which are purchased in the markets or of self-produced biological pesticides. Release of biological control products sold in the market, which contain predators, such as spiders or ladybugs.
B8 Push or pull crops (trap/ barrier crops)	Use of plants on the edge of the plot to either repel or trap insects, such as chrysanthemum, pot marigold, or garlic.
B9 Pest pheromone traps	Use of yellow or green sticky traps or molasses fly trap.
B10. Net houses for pest protection	Vegetable tunnels or greenhouses that are fully covered by plastics or tight nets to protect vegetables from insects and other animals.
B11. Pest resistant varieties	Use of pest resistant vegetable varieties, such as hybrid tomato F1 VNS390 or Japanese cucumber Gaga F1.

Table 3. Summary statistics of control variables in the analysis.

Description of covariates	Short name of covariates	Farms without PGS certification (n = 301)		Farms with PGS certification (n = 199)
		Mean	SD	Mean	SD
Sex (1 = female)	sex	0.74	0.44	0.85	0.36
Age of farm manager (years)	age	58.02	9.03	55.70	8.68
Education of farm manager (years)	edu	7.47	2.20	7.83	2.03
Vegetable growing experience of farm manager (years)	vegexp	27.70	12.49	14.51	8.17
Household size (# of persons sharing a meal)	hhsize	4.30	2.07	4.72	1.83
Non-farm income (1 = very low, 2 = low, 3 = med, 4 = high)	nfinc	2.87	1.16	3.18	1.01
Loan (1 = yes)	loan	0.13	0.34	0.13	0.33
Farm size (ha)	fsize	0.42	0.21	0.42	0.23
Proportion of rented farmland (%)	rentp	0.38	0.17	0.26	0.17
Size of vegetable area (ha)	vegsize	0.16	0.11	0.15	0.19
Proportion of vegetable area with title (%)	titlep	0.80	0.42	0.83	0.69
Extension visit in the last five years (1 = yes)	ext	0.06	0.24	0.10	0.30
Interaction with neighbours (1 = no, 2 = some, 3 = regular)	neighb	2.40	0.74	2.73	0.62
Membership in farmer organization (1 = yes)	forg	0.49	0.50	0.56	0.49

Table 4. Estimated treatment effects.

		Entropy weighted regression adjustment						Inverse probability weighted regression adjustment
Outcomes	Unit	ATT	Sign	SE	POM	ATT (%)	f	ATT	Sign	SE	POM	ATT (%)
gm	USD/ha	6,377.96	***	(1,607.38)	5,434.61	117.4		5,671.48	***	(1,538.220)	6,141.08	92.4
returnlab	USD/day	−355.08	ns	(254.060)	1915.45	−18.54		−440.10	ns	(324.55)	2000.34	−21.9
yield	t/ha	−8.71	***	(2.563)	30.24	−28.8		−13.38	***	(2.541)	34.92	−38.3
tape5	0–1 score	0.14	***	(0.015)	0.33	40.6		0.15	***	(0.016)	0.32	46.2
aware	# practices	2.15	***	(0.247)	6.90	31.2		2.54	***	(0.225)	6.52	39.0
use	# practices	2.79	***	(0.493)	4.11	67.8		2.70	***	(0.517)	4.19	64.5
conf	0–1 score	0.15	***	(0.041)	0.47	32.6		0.19	***	(0.028)	0.43	45.0
orgknow	# keywords	1.59	***	(0.388)	2.52	63.1		2.10	***	(0.222)	2.02	103.7
choice	1 = yes	0.16	*	(0.090)	0.67	23.3		0.21	***	(0.076)	0.62	33.2

Note: See Table 2 for full names of outcome variables.

ATT, average treatment effect on the treated; ATT (%) is calculated as ATT/POM. POM, potential outcome mean; SE, standard error; Sign., significance.

***Significance level: 0.01.

**Significance level: 0.05.

*Significance level: 0.1.

Figure 3. Sensitivity analysis with breakdown points (rxbar) for different correlation levels (cbar).

Table A. Balancing of covariates for both weighting approaches.

		Entropy weights					Inverse Probability Weights
Covariates		cFarms without PGS certification (n = 301)		Farms with PGS certification (n = 199)			Farms without PGS certification (n = 301)		Farms with PGS certification (n = 199)
Name	Unit	Mean	SD	Mean	SD	C	Mean	SD	Mean	SD
sex	(1 = female)	0.863	0.119	0.863	0.119		0.863	0.119	0.889	0.099
age	(years)	56.140	77.270	56.140	77.270		56.137	77.268	56.158	116.904
edu	(years)	7.600	3.626	7.600	3.626		7.600	3.626	7.655	4.562
vegexp	(years)	15.010	74.690	15.010	74.690		15.011	74.691	13.080	105.978
hhsize	(persons)	4.811	3.283	4.811	3.283		4.811	3.283	5.145	4.287
nfinc	(1-4)	3.358	0.594	3.358	0.594		3.358	0.594	3.467	0.497
loan	(1 = yes)	0.084	0.078	0.084	0.078		0.084	0.078	0.138	0.120
fsize	(ha)	0.422	0.053	0.422	0.053		0.422	0.053	0.392	0.046
rentp	(%)	0.272	0.028	0.272	0.028		0.272	0.028	0.274	0.028
vegsize	(ha)	0.155	0.037	0.155	0.037		0.154	0.037	0.135	0.010
titlep	(%)	0.847	0.555	0.847	0.555		0.847	0.555	0.786	0.139
ext	(1 = yes)	0.105	0.095	0.105	0.095		0.105	0.095	0.070	0.065
neighb	(1-3)	2.716	0.397	2.716	0.397		2.716	0.397	2.621	0.339
forg	(1 = yes)	0.558	0.249	0.558	0.248		0.558	0.249	0.556	0.248

Note: Full variable names are provided in Table 3.

Table B. Exact results of sensitivity analysis with breakdown points for different correlation levels.

	rxbar (Breakdown)
Outcome	cbar = 0.00	cbar = 0.25	cbar = 0.50	cbar = 0.75	cbar = 1.00
gm	65.9%	58.1%	55.1%	55.0%	55.0%
returnlab	28.4%	27.3%	27.3%	27.3%	27.3%
yield	64.9%	57.4%	54.5%	54.5%	54.5%
tape5	120.3%	94.8%	82.0%	76.9%	76.9%
aware	64.2%	56.9%	54.1%	54.1%	54.0%
use	103.6%	84.4%	74.8%	71.9%	71.9%
conf	55.2%	49.9%	48.3%	48.3%	48.3%
orgknow	77.9%	67.0%	62.1%	61.5%	61.5%
choice	49.5%	45.4%	44.4%	44.4%	44.4%