Guatemala

Notes:

  • 1 Int’l $ = 4.01 GTQ (Quetzal) using 2020 World Bank PPP conversion rates (1 Int’l $ = 1 USD)
  • Focus crop = bean and potato
  • All costs are reported per hectare, except for labeling and packaging costs
  • For beans these costs are per bag of 3 pounds or 1.36 kg
  • For potato these costs are per crate of 22.68 kg
  • Other marketing costs are assumed also per hectare
  • Inspection and certification fees are per farm (total fees for a single season)
  • Actual sales are assumed for the planted area (not by hectare)
  • Sales prices are assumed by bag or crate (not by kg)
  • Some farmers grow multiple crops

Survey Recodes

xrate <- 4.01

# Load respondent data
hh <- fread("../data/gtm/hh.csv")
group <- fread("../data/gtm/group.csv")

There are 21 variables and 39 observations in this set. A summary is shown below.

print(dfSummary(hh), max.tbl.height=500)
Variable Stats / Values Freqs (% of Valid) Valid Missing
Group
[character]		 1. ACORDI
2. ADAT, AXOLA, ADIPY
3. PAQUIX
4. SAN BARTOLO		 10 (25.6%)
9 (23.1%)
10 (25.6%)
10 (25.6%)		 39
(100.0%)		 0
(0.0%)
Province
[character]		 1. CAPELLANIA
2. PAQUIX
3. REGADILLOS, CHIANTLA
4. SAN MARTIN CUCHUMATAN		 10 (25.6%)
10 (25.6%)
10 (25.6%)
9 (23.1%)		 39
(100.0%)		 0
(0.0%)
Code
[character]		 1. Agricultor 8
2. Agricultor 9
3. Agricultor 10
4. Agricultor 1
5. Agricultor 2
6. Agricultor 3
7. Agricultor 4
8. Agricultor 5
9. Agricultor 6
10. Agricultor 7
[ 21 others ]		 4 (10.3%)
4 (10.3%)
3 ( 7.7%)
1 ( 2.6%)
1 ( 2.6%)
1 ( 2.6%)
1 ( 2.6%)
1 ( 2.6%)
1 ( 2.6%)
1 ( 2.6%)
21 (53.8%)		 39
(100.0%)		 0
(0.0%)
Age
[character]		 1. 15-29
2. 30		 4 (10.3%)
35 (89.7%)		 39
(100.0%)		 0
(0.0%)
Sex
[character]		 1. Female
2. Male		 5 (12.8%)
34 (87.2%)		 39
(100.0%)		 0
(0.0%)
Cost seed per ha (LCU)
[numeric]		 Mean (sd) : 7776.9 (7059.6)
min < med < max:
690 < 13225 < 15794.1
IQR (CV) : 13819.5 (0.9)		 690.00 : 19 (48.7%)
13225.00 : 10 (25.6%)
15794.10 : 10 (25.6%)		 39
(100.0%)		 0
(0.0%)
Cost of fertilizer per ha (LCU)
[numeric]		 Min : 3685.3
Mean : 3689.8
Max : 3703		 3685.29 : 29 (74.4%)
3703.00 : 10 (25.6%)		 39
(100.0%)		 0
(0.0%)
Cost of pesticide per ha (LCU)
[numeric]		 Mean (sd) : 9182.7 (591.6)
min < med < max:
8583.8 < 9728.2 < 9775
IQR (CV) : 1167.9 (0.1)		 8583.75 : 19 (48.7%)
9728.25 : 10 (25.6%)
9775.00 : 10 (25.6%)		 39
(100.0%)		 0
(0.0%)
Cost of transport per ha (LCU)
[numeric]		 Mean (sd) : 2322.6 (1163.3)
min < med < max:
1144.5 < 3433.5 < 3450
IQR (CV) : 2297.2 (0.5)		 1144.50 : 19 (48.7%)
3433.50 : 10 (25.6%)
3450.00 : 10 (25.6%)		 39
(100.0%)		 0
(0.0%)
Labor cost (LCU)
[numeric]		 Mean (sd) : 18187 (4397.1)
min < med < max:
13734 < 22363.5 < 22471
IQR (CV) : 8683.3 (0.2)		 13734.00 : 19 (48.7%)
22363.53 : 10 (25.6%)
22471.00 : 10 (25.6%)		 39
(100.0%)		 0
(0.0%)
Inspection / certification Fees (LCU)
[numeric]		 Min : 0
Mean : 12.2
Max : 23.8		 0.00 : 19 (48.7%)
23.82 : 20 (51.3%)		 39
(100.0%)		 0
(0.0%)
Labelling costs per kg (LCU)
[numeric]		 Mean (sd) : 77.7 (77.5)
min < med < max:
16.5 < 40.6 < 324.5
IQR (CV) : 91.2 (1)		 25 distinct values 39
(100.0%)		 0
(0.0%)
Packaging costs per kg (LCU)
[numeric]		 Mean (sd) : 3194.7 (4107.4)
min < med < max:
49.6 < 1947.1 < 15576.7
IQR (CV) : 5202.3 (1.3)		 25 distinct values 39
(100.0%)		 0
(0.0%)
Other marketing costs? (LCU)
[numeric]		 Mean (sd) : 381.4 (322.8)
min < med < max:
113.4 < 113.4 < 1050
IQR (CV) : 374.1 (0.8)		 12 distinct values 39
(100.0%)		 0
(0.0%)
Estimated Yield (kg/ha)
[numeric]		 Mean (sd) : 66157.6 (86000.7)
min < med < max:
2105.9 < 42117.6 < 336940.8
IQR (CV) : 103390.5 (1.3)		 13 distinct values 39
(100.0%)		 0
(0.0%)
Selling price of seed per kg (LCU)
[numeric]		 Mean (sd) : 7473.5 (7745.6)
min < med < max:
1386 < 3992.8 < 31942.4
IQR (CV) : 9248.3 (1)		 25 distinct values 39
(100.0%)		 0
(0.0%)
How many kg were sold in the season?
[integer]		 Mean (sd) : 3049.9 (3857.5)
min < med < max:
90 < 1840 < 14720
IQR (CV) : 4868.5 (1.3)		 25 distinct values 39
(100.0%)		 0
(0.0%)
What was your expected gross margin?
[numeric]		 Mean (sd) : 2837.2 (3382.9)
min < med < max:
170 < 1566.8 < 12534.4
IQR (CV) : 4406.7 (1.2)		 26 distinct values 39
(100.0%)		 0
(0.0%)
How long have you been a member of this group?
[integer]		 Mean (sd) : 3.9 (2.6)
min < med < max:
1 < 3 < 15
IQR (CV) : 3 (0.7)		 1 : 3 ( 7.7%)
2 : 8 (20.5%)
3 : 10 (25.6%)
4 : 7 (17.9%)
5 : 7 (17.9%)
6 : 1 ( 2.6%)
8 : 1 ( 2.6%)
10 : 1 ( 2.6%)
15 : 1 ( 2.6%)		 39
(100.0%)		 0
(0.0%)
Area (ha)
[numeric]		 Mean (sd) : 0.1 (0.1)
min < med < max:
0 < 0.1 < 0.4
IQR (CV) : 0.1 (0.7)		 0.04!: 15 (38.5%)
0.09!: 14 (35.9%)
0.13!: 5 (12.8%)
0.18!: 2 ( 5.1%)
0.26!: 1 ( 2.6%)
0.31!: 1 ( 2.6%)
0.35!: 1 ( 2.6%)
! rounded		 39
(100.0%)		 0
(0.0%)
Crop
[character]		 1. bean
2. potato		 19 (48.7%)
20 (51.3%)		 39
(100.0%)		 0
(0.0%)

Recode variable names.

setnames(hh, lbl$label, lbl$code, skip_absent=T)

Additional recodes for categorical variables.

setorder(hh, adm1_nm, group, gender, crop)

hh[, `:=`(
  hhid = paste("GTM", gsub(" ", "0", format(1:.N, width=3)), sep=""),
  iso3 = "GTM",
  crop = factor(crop),
  adm1_nm = factor(adm1_nm),
  group = factor(group, levels=c(
    "ACORDI",
    "PAQUIX",
    "SAN BARTOLO",
    "ADAT, AXOLA, ADIPY"
  ), labels=c(
    "Acordi",
    "Paquix",
    "San Bartolo",
    "Adat Axola Adipy"    
  )),
  gender = factor(gender, levels=c("Male", "Female")),
  age = factor(age, levels=c("15-29", "30"), labels=c("< 30", "≥ 30")),
  years = factor(member_years >= 5, levels=c(F, T), labels=c("< 5", "≥ 5"))  
)]

Constructed Variables

Farmers report both expected yields yield_ha_kg and actual sales in the last season sales_ha_kg, so we can construct both expected and realized costs in monetary terms costs_exp_ha_lcu and costs_real_ha_lcu. Note that we then use realized sales to calculate profitability metrics. We assume that in Guatemala marketing costs for both bean and potato are reported for the entire farm (not by kg as in Vietnam).

hh[, `:=`(
  tran_ha_lcu = as.numeric(tran_ha_lcu),
  # Reported sales assumed by planted area
  sales_ha_kg = sales_ha_kg/area_ha,
  # Reported sales price assumed by bag/crate
  sales_kg_lcu = fcase(
    crop=="bean", 1/1.36,
    crop=="potato", 1/22.68) * sales_kg_lcu
)][, 
  tran_ha_lcu := fifelse(is.na(tran_ha_lcu), 0, tran_ha_lcu)
][, `:=`(
  # Expected costs
  costs_exp_ha_lcu = 
    # Per ha costs
    seed_ha_lcu + fert_ha_lcu + pest_ha_lcu + tran_ha_lcu + labor_ha_lcu + 
    cert_lcu + mark_kg_lcu +
    # Per kg costs
    fcase(
      crop=="bean", 1/1.36,
      crop=="potato", 1/22.68) * yield_ha_kg * (labl_kg_lcu + pckg_kg_lcu),  
  
  # Realized costs
  costs_real_ha_lcu = 
    # Per ha costs
    seed_ha_lcu + fert_ha_lcu + pest_ha_lcu + tran_ha_lcu + labor_ha_lcu + 
    cert_lcu + mark_kg_lcu +
    # Per kg costs
    fcase(
      crop=="bean", 1/1.36,
      crop=="potato", 1/22.68) * sales_ha_kg * (labl_kg_lcu + pckg_kg_lcu)
)]

hh[, summary(costs_exp_ha_lcu)]
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##    130758    141622   3746290  25100149  25565442 236288594
hh[, summary(costs_real_ha_lcu)]
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##   127592   149713  3709358  6396050 10804345 29307914

Using realized costs and sales, we construct gross margin per ha margin_ha_lcu, total sales sales_ha_sh and profit margin margin_ha_sh per unit of (variable) input costs, and costs_ha_ppp, sales_ha_ppp and margin_ha_ppp in PPP terms to allow for comparisons across groups and countries.

We also construct a measure of total factor productivity tfp as expected output per unit of (expected) input costs. Strictly speaking it is only “partial factor productivity” here because we don’t include the rental cost of land, land preparation costs, irrigation costs, and the costs of animal and mechanical implements.

hh[, `:=`(
  sales_exp_ha_lcu = yield_ha_kg * sales_kg_lcu,
  sales_real_ha_lcu = sales_ha_kg * sales_kg_lcu
)][, `:=`(
  margin_ha_lcu = sales_real_ha_lcu - costs_real_ha_lcu
)][, `:=`(
  # Shares
  sales_ha_sh = sales_real_ha_lcu / costs_real_ha_lcu,
  margin_ha_sh = margin_ha_lcu / costs_real_ha_lcu,
  # PPP$
  costs_ha_ppp = costs_real_ha_lcu / xrate,
  sales_ha_ppp = sales_real_ha_lcu / xrate,
  margin_ha_ppp = margin_ha_lcu / xrate
)][, `:=`(
  tfp = yield_ha_kg / (costs_exp_ha_lcu / xrate)
)]

Note that expected seed yields and actual sales look somewhat inconsistent, especially for potato. Packaging costs for potato seems also too high.

ggplot(hh, aes(x=hhid, color=group)) +
  geom_linerange(aes(ymin=yield_ha_kg, ymax=sales_ha_kg), size=.6) +
  geom_point(aes(y=yield_ha_kg), shape=20, size=1.4) +
  geom_point(aes(y=sales_ha_kg, fill=group, shape=sales_ha_kg-yield_ha_kg>0), size=1.4) +
  scale_y_continuous(labels=comma) +
  scale_shape_manual(values=25:24) +
  facet_wrap(~crop, scales="free") +
  guides(x="none", shape="none") +
  labs(x=NULL, y=NULL, color="", fill="",
    title="Profit Margin (PPP$ / ha) - Guatemala",
    subtitle="Each bar is a respondent's gross profit margin") +
  theme_def(
    legend.position="right",
    panel.grid.major.x=element_blank()
  )

Verify unit price (per kg) in PPP$ terms.

hh[crop=="bean", summary(sales_kg_lcu / xrate)]
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   254.1   259.8   279.6   374.7   519.6   593.0
hh[crop=="potato", summary(sales_kg_lcu / xrate)]
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   43.90   87.81  118.54  138.90  161.89  351.22

Below we append some of the information that was recorded at the group level.

kbl(group, align="llc")
Group Region District Location Market Access Irrigation Seasons Transboundary trade Members Established Notes
ADAT, AXOLA, ADIPY Huehuetenango Aguacatan Climentoro – No 1 No 13 1974
  • Aplicación de abono orgánico (Compost).
  • No existe acceso a riego.
  • Un ciclo de cultivo por año.
  • Manejo integrado de plagas.
  • Rotación de cultivos.
  • Practicas de conservación de suelo Barreras vivas, pastos)
PAQUIX Huehuetenango Chiantla Paquix – No 1 No 50 1998
  • Aplicación de abono orgánico (Compost).
  • No existe acceso a riego.
  • Un ciclo de cultivo por año.
  • Manejo integrado de plagas.
  • Rotación de cultivos.
  • Practicas de conservación de suelo Barreras vivas, pastos)
    • ACORDI Huehuetenango Chiantla Capellania – No 1 No 29 1998
    • Aplicación de abono orgánico (Compost).
  • No existe acceso a riego.
  • Un ciclo de cultivo por año.
  • Manejo integrado de plagas.
  • Rotación de cultivos.
  • Practicas de conservación de suelo Barreras vivas, pastos)
    • SAN BARTOLO Huehuetenango Chiantla Regadillos – Yes 2 No 55 2016 -Incorporación de abono orgánico. -Dos ciclos de producción (primer ciclo los meses de enero, febrero y marzo; segundo ciclo meses de noviembre y diciembre).
  • Rotación de cultivos.
  • Manejo integrado de plagas.
  • sistema de riego por aspersión. 
    • # Same recodes in the group-level dataset
group[, Group := factor(Group, levels=c(
  "ACORDI",
  "PAQUIX",
  "SAN BARTOLO",
  "ADAT, AXOLA, ADIPY"
), labels=c(
  "Acordi",
  "Paquix",
  "San Bartolo",
  "Adat Axola Adipy"
))]

# Merge
hh[group, on=.(group=Group), `:=`(
  group_year = `Established`,
  group_size = `Members`,
  seasons = `Seasons`,
  irrigated = `Irrigation`,
  market_access = `Market Access`,
  ttrade = `Transboundary trade`
)]

    Finally we normalize all farmer cost line items into a “long” table hh_prod_cost for charting.

    # Normalize production cost table per ha
hh_prod_cost <- hh[, .(hhid,
  Seeds = seed_ha_lcu, 
  Fertilizer = fert_ha_lcu, 
  Pesticides = pest_ha_lcu, 
  Labor = labor_ha_lcu, 
  Transport = tran_ha_lcu, 
  Certification = cert_lcu,
  Labeling = fcase(
    crop=="bean", 1/1.36,
    crop=="potato", 1/22.68) * sales_ha_kg * labl_kg_lcu,
  Packaging = fcase(
    crop=="bean", 1/1.36,
    crop=="potato", 1/22.68) * sales_ha_kg * pckg_kg_lcu,
  Marketing = mark_kg_lcu
)]

hh_prod_cost <- melt(hh_prod_cost, id.vars=1, value.name="lcu", variable.name="type")

    And we lump all seed marketing costs into a single category.

    levels(hh_prod_cost$type) <- levels(hh_prod_cost$type)[c(1,2,3,4,9,9,9,9,9)]

hh_prod_cost <- hh_prod_cost[, .(
  lcu = sum(lcu, na.rm=T)
), by=.(hhid, type)
][, `:=`(
  # Add cost shares and PPP terms
  share = lcu/sum(lcu, na.rm=T),
  ppp = lcu/xrate
), by=.(hhid)
][hh, on=.(hhid), `:=`(
  # Add classes
  group = group,
  gender = gender,
  age = age,
  years = years,
  crop = crop
)]

    Descriptive Statistics

    Respondent Characteristics

    Breakdown by categorical variables.

    ggplot(
  hh[, .N, by=.(group, age, gender, crop, years)],
  aes(axis1=crop, axis2=gender, axis3=age, axis4=years, y=N)) +
  geom_alluvium(aes(fill=group), width=1/4, alpha=.7, color="white") +
  geom_stratum(width=1/4) +
  geom_text(stat="stratum", aes(label=after_stat(stratum)), angle=90, size=2.2) +
  scale_x_discrete(limits=c("Crop", "Gender", "Age", "Years in Seed Club")) +
  labs(y=NULL, fill="Seed Club",
    title = "Categories of Survey Respondents - Guatemala",
    subtitle = "Stratified by seed club") +
  theme_def(axis.text=element_text(face="bold"))

    Showing contingency table between each pair of categorical variables (seed club group, gender, age age, and years in seed club years).

    ttt_ftable(hh, vars=c("group", "gender", "years"))
    Contingency Table (% of respondents)
    group gender < 5 ≥ 5 Sum

    N = 39 | Mantel-Haenszel chi-squared = 5.44 | p-value = 0.1424
    Acordi Male 10.3 7.7 17.9
    Female 2.6 5.1 7.7
    Sum 12.8 12.8 25.6
    Paquix Male 17.9 7.7 25.6
    Sum 17.9 7.7 25.6
    San Bartolo Male 20.5 0 20.5
    Female 5.1 0 5.1
    Sum 25.6 0 25.6
    Adat Axola Adipy Male 15.4 7.7 23.1
    Sum 15.4 7.7 23.1
    Sum Male 64.1 23.1 87.2
    Female 7.7 5.1 12.8
    Sum 71.8 28.2 100

    Seed Production Costs

    General breakdown and distribution of input costs across seed clubs, gender, years in seed club, and input type.

    Note that for potato input costs are too high because expected yields yield_ha_kg are too high (potato yields should be around 25 mt/ha). This needs checking.

    ttt(costs_ha_ppp ~ group+crop | gender+years, data=hh, render=fmt,
  caption="Total Input Costs in Absolute Terms (PPP$ / ha) - Guatemala")
    Total Input Costs in Absolute Terms (PPP$ / ha) - Guatemala
    group crop Statistic < 5 ≥ 5
    Male Female Male Female
    Acordi potato mean 3,324,027 2,749,359 5,337,739 1,381,555
    median 2,530,891 2,749,359 5,484,968 1,381,555
    sd 2,819,561 NA 1,139,867 644,789
    Paquix potato mean 2,699,210 NA 2,544,545 NA
    median 2,639,341 NA 1,836,896 NA
    sd 976,226 NA 1,836,403 NA
    San Bartolo bean mean 44,824 45,372 NA NA
    median 36,439 45,372 NA NA
    sd 15,640 19,168 NA NA
    Adat Axola Adipy bean mean 42,659 NA 50,262 NA
    median 35,552 NA 58,926 NA
    sd 13,130 NA 15,006 NA 
    ttt(yield_ha_kg ~ group+crop | gender+years, data=hh, render=fmt,
  caption="Expected Yield (kg / ha) - Guatemala")
    Expected Yield (kg / ha) - Guatemala
    group crop Statistic < 5 ≥ 5
    Male Female Male Female
    Acordi potato mean 147,412 126,353 238,666 63,176
    median 105,294 126,353 252,706 63,176
    sd 130,949 NA 64,336 29,782
    Paquix potato mean 96,731 NA 98,747 NA
    median 84,640 NA 84,640 NA
    sd 31,991 NA 64,645 NA
    San Bartolo bean mean 2,896 3,159 NA NA
    median 2,106 3,159 NA NA
    sd 1,090 1,489 NA NA
    Adat Axola Adipy bean mean 2,106 NA 2,106 NA
    median 2,106 NA 2,106 NA
    sd 0 NA 0 NA

    Boxplots with mean comparison p-value and significance levels. When more than two levels, each level is compared to the group mean.

    (ns : p > 0.05, * : p ≤ 0.05, ** : p ≤ 0.01, *** = p ≤ 0.001, **** = p ≤ 0.0001)

    ggBoxTest(hh, 
  aes(crop, costs_ha_ppp, color=gender, fill=gender), 
  grp.c=aes(group=crop), grp.s=aes(group=gender)) +
  scale_y_continuous(labels=comma) +
  labs(x="", y="", fill="", color="",
    title="Total Input Costs (PPP$ / ha) - Guatemala",
    subtitle="Stratified by crop and gender") +
  theme_def(legend.position="top")

ggBoxTest(hh, 
  aes(crop, costs_ha_ppp, color=group, fill=group),
    grp.c=aes(group=crop), grp.s=aes(group=group)) +
  scale_y_continuous(labels=comma) + 
  labs(x="", y="", fill="", color="",
    title="Total Input Costs (PPP$ / ha) - Guatemala",
    subtitle="Stratified by crop") +
  theme_def(legend.position="top")

    Breakdown across categories of farm input.

    ttt(ppp ~ type | gender+crop, data=hh_prod_cost, render=fmt,
  caption="Input Costs in Absolute Terms by Gender (PPP$ / ha) - Guatemala")
    Input Costs in Absolute Terms by Gender (PPP$ / ha) - Guatemala
    type Statistic bean potato
    Male Female Male Female
    Seeds mean 172 172 3,562 3,939
    median 172 172 3,298 3,939
    sd 0 0 325 0
    Fertilizer mean 919 919 922 919
    median 919 919 923 919
    sd 0 0 2 0
    Pesticides mean 2,141 2,141 2,433 2,426
    median 2,141 2,141 2,438 2,426
    sd 0 0 6 0
    Labor mean 3,425 3,425 5,593 5,577
    median 3,425 3,425 5,604 5,577
    sd 0 0 14 0
    Marketing mean 38,363 38,715 3,272,046 1,824,629
    median 30,374 38,715 3,211,431 1,824,629
    sd 14,011 19,168 1,864,099 911,869 
    tbl <- hh_prod_cost[, .(
  ppp = mean(ppp, na.rm=T)
), keyby=.(gender, crop, type)]

ggplot(tbl, aes(gender, ppp, fill=type)) +
  geom_bar(stat="identity", position="fill", alpha=.7, width=.6, color="white") +
  scale_y_continuous(labels=percent) +
  facet_wrap(~crop) +
  labs(y="", x="",
    title="Breakdown of Input Costs by Category - Guatemala",
    subtitle="Stratified by crop and gender") +
  theme_def(legend.position="right")

    ttt(ppp ~ type | years+crop, data=hh_prod_cost, render=fmt,
  caption="Input Costs in Absolute Terms by Years in Seed Group (PPP$ / ha) - Guatemala")
    Input Costs in Absolute Terms by Years in Seed Group (PPP$ / ha) - Guatemala
    type Statistic bean potato
    < 5 ≥ 5 < 5 ≥ 5
    Seeds mean 172 172 3,565 3,698
    median 172 172 3,298 3,939
    sd 0 0 330 332
    Fertilizer mean 919 919 922 921
    median 919 919 923 919
    sd 0 0 2 2
    Pesticides mean 2,141 2,141 2,433 2,430
    median 2,141 2,141 2,438 2,426
    sd 0 0 6 6
    Labor mean 3,425 3,425 5,593 5,587
    median 3,425 3,425 5,604 5,577
    sd 0 0 14 14
    Marketing mean 37,424 43,605 2,899,150 3,288,609
    median 29,477 52,269 2,681,789 2,971,592
    sd 14,051 15,006 1,667,567 2,114,119 
    tbl <- hh_prod_cost[, .(
  ppp = mean(ppp, na.rm=T)
), keyby=.(years, crop, type)]

ggplot(tbl, aes(years, ppp, fill=type)) +
  geom_bar(stat="identity", position="fill", alpha=.7, width=.6, color="white") +
  scale_y_continuous(labels=percent) +
  facet_wrap(~crop) +
  labs(y="", x="",
    title="Breakdown of Input Costs by Category - Guatemala",
    subtitle="Stratified by crop and years in seed club") +
  theme_def(legend.position="right")

    ttt(ppp ~ type | group+crop, data=hh_prod_cost, render=fmt,
  caption="Input Costs in Absolute Terms by Seed Group (PPP$ / ha) - Guatemala")
    Input Costs in Absolute Terms by Seed Group (PPP$ / ha) - Guatemala
    type Statistic bean potato
    San Bartolo Adat Axola Adipy Acordi Paquix
    Seeds mean 172 172 3,939 3,298
    median 172 172 3,939 3,298
    sd 0 0 0 0
    Fertilizer mean 919 919 919 923
    median 919 919 919 923
    sd 0 0 0 0
    Pesticides mean 2,141 2,141 2,426 2,438
    median 2,141 2,141 2,426 2,438
    sd 0 0 0 0
    Labor mean 3,425 3,425 5,577 5,604
    median 3,425 3,425 5,577 5,604
    sd 0 0 0 0
    Marketing mean 38,277 38,537 3,469,319 2,640,548
    median 29,782 30,374 2,973,965 2,468,071
    sd 15,203 13,360 2,276,833 1,179,128 
    tbl <- hh_prod_cost[, .(
  ppp = mean(ppp, na.rm=T)
), keyby=.(group, crop, type)]

ggplot(tbl, aes(group, ppp, fill=type)) +
  geom_bar(stat="identity", position="fill", alpha=.7, width=.6, color="white") +
  scale_y_continuous(labels=percent) +
  facet_wrap(~crop, scales="free_x") +
  labs(y="", x="",
    title="Breakdown of Input Costs by Category - Guatemala",
    subtitle="Stratified by crop and seed club") +
  theme_def(legend.position="right")

    Are there significant differences across groups? We first compare input cost shares across gender, then across seed clubs.

    ggBoxTest(hh_prod_cost, 
  aes(type, share, color=gender, fill=gender),
  grp.c=aes(group=type), grp.s=aes(group=gender)) +
  scale_y_continuous(labels=percent) +
  facet_wrap(~crop) +  
  labs(x="", y="", fill="", color="",
    title="Input Costs by Category (Percent of Total Costs by Ha) - Guatemala",
    subtitle="Stratified by crop and gender") +
  theme_def(legend.position="top")

    ggBoxTest(hh_prod_cost, 
  aes(type, share, color=years, fill=years),
  grp.c=aes(group=type), grp.s=aes(group=years)) +
  scale_y_continuous(labels=percent) +
  facet_wrap(~crop) +  
  labs(x="", y="", fill="", color="",
    title="Input Costs by Category (Percent of Total Costs by Ha) - Guatemala",
    subtitle="Stratified by crop and years in seed club") +
  theme_def(legend.position="top")

    ggBoxTest(hh_prod_cost, 
  aes(type, share, color=group, fill=group),
  grp.c=aes(group=type), grp.s=aes(group=group)) +
  scale_y_continuous(labels=percent) +
  facet_wrap(~crop) +  
  labs(x="", y="", fill="", color="",
    title="Input Costs by Category (PPP$ by Hectare) - Guatemala",
    subtitle="Stratified by crop and seed club") +
  theme_def(legend.position="top")

    Differences between the 2 crops.

    ggBoxTest(hh_prod_cost, 
  aes(type, share, color=crop, fill=crop),
  grp.c=aes(group=type), grp.s=aes(group=crop)) +
  scale_y_continuous(labels=percent) +
  labs(x="", y="", fill="", color="",
    title="Input Costs by Category (PPP$ by Hectare) - Guatemala",
    subtitle="Stratified by crop") +
  theme_def(legend.position="top")

    Efficiency

    Differences in productivity measures (expected seed yields and actual sales) across groups.

    ttt(yield_ha_kg ~ group | gender+crop, data=hh, render=fmt,
  caption="Expected Seed Yield (kg / ha) - Guatemala")
    Expected Seed Yield (kg / ha) - Guatemala
    group Statistic bean potato
    Male Female Male Female
    Acordi mean NA NA 186,521 84,235
    median NA NA 168,470 84,235
    sd NA NA 111,053 42,118
    Paquix mean NA NA 97,336 NA
    median NA NA 84,640 NA
    sd NA NA 40,148 NA
    San Bartolo mean 2,896 3,159 NA NA
    median 2,106 3,159 NA NA
    sd 1,090 1,489 NA NA
    Adat Axola Adipy mean 2,106 NA NA NA
    median 2,106 NA NA NA
    sd 0 NA NA NA 
    ttt(sales_ha_kg ~ group | gender+crop, data=hh, render=fmt,
  caption="Realized Seed Sales (kg / ha) - Guatemala")
    Realized Seed Sales (kg / ha) - Guatemala
    group Statistic bean potato
    Male Female Male Female
    Acordi mean NA NA 42,593 41,723
    median NA NA 41,723 41,723
    sd NA NA 1,506 0
    Paquix mean NA NA 46,382 NA
    median NA NA 46,939 NA
    sd NA NA 2,669 NA
    San Bartolo mean 2,143 2,063 NA NA
    median 2,086 2,063 NA NA
    sd 125 32 NA NA
    Adat Axola Adipy mean 2,111 NA NA NA
    median 2,086 NA NA NA
    sd 53 NA NA NA 
    ttt(yield_ha_kg ~ group | years+crop, data=hh, render=fmt,
  caption="Realized Seed Sales (kg / ha) - Guatemala")
    Realized Seed Sales (kg / ha) - Guatemala
    group Statistic bean potato
    < 5 ≥ 5 < 5 ≥ 5
    Acordi mean NA NA 143,200 168,470
    median NA NA 126,353 168,470
    sd NA NA 113,795 107,379
    Paquix mean NA NA 96,731 98,747
    median NA NA 84,640 84,640
    sd NA NA 31,991 64,645
    San Bartolo mean 2,948 NA NA NA
    median 2,106 NA NA NA
    sd 1,087 NA NA NA
    Adat Axola Adipy mean 2,106 2,106 NA NA
    median 2,106 2,106 NA NA
    sd 0 0 NA NA 
    ttt(sales_ha_kg ~ group | years+crop, data=hh, render=fmt,
  caption="Realized Seed Sales (kg / ha) - Guatemala")
    Realized Seed Sales (kg / ha) - Guatemala
    group Statistic bean potato
    < 5 ≥ 5 < 5 ≥ 5
    Acordi mean NA NA 42,419 42,245
    median NA NA 41,723 41,723
    sd NA NA 1,555 1,166
    Paquix mean NA NA 46,889 45,200
    median NA NA 46,939 46,939
    sd NA NA 2,582 3,011
    San Bartolo mean 2,127 NA NA NA
    median 2,086 NA NA NA
    sd 116 NA NA NA
    Adat Axola Adipy mean 2,124 2,086 NA NA
    median 2,098 2,086 NA NA
    sd 62 0 NA NA

    Differences in efficiency measures across gender with mean comparison (Wilcoxon) p-value.

    ggBoxTest(hh, aes(gender, yield_ha_kg, color=gender, fill=gender), cp=list(1:2)) +
  scale_y_continuous(labels=comma) +
  facet_wrap(~crop, scales="free_y") +  
  labs(x="", y="", fill="",
    title="Expected Seed Yield (kg / ha) - Guatemala",
    subtitle="Stratified by crop and gender") +
  theme_def(legend.position="none")

ggBoxTest(hh, aes(gender, sales_ha_ppp, color=gender, fill=gender), cp=list(1:2)) +
  scale_y_continuous(labels=comma) +
  facet_wrap(~crop, scales="free_y") +  
  labs(x="", y="", fill="",
    title="Total Seed Sales (PPP$ / ha) - Guatemala",
    subtitle="Stratified by gender") +
  theme_def(legend.position="none")

    Differences in efficiency measures by years in seed club with mean comparison (Wilcoxon) p-value.

    ggBoxTest(hh, aes(years, yield_ha_kg, color=years, fill=years), cp=list(1:2)) +
  scale_y_continuous(labels=comma) +
  facet_wrap(~crop, scales="free_y") +  
  labs(x="", y="", fill="",
    title="Expected Seed Yield (kg / ha) - Guatemala",
    subtitle="Stratified crop and by years in seed club") +
  theme_def(legend.position="none")

ggBoxTest(hh, aes(years, sales_ha_ppp, color=years, fill=years), cp=list(1:2)) +
  scale_y_continuous(labels=comma) +
  facet_wrap(~crop, scales="free_y") +  
  labs(x="", y="", fill="",
    title="Total Seed Sales (PPP$ / ha) - Guatemala",
    subtitle="Stratified crop and by years in seed club") +
  theme_def(legend.position="none")

    Differences in efficiency measures across seed clubs with global ANOVA p-value.

    ggBoxTest(hh, aes(group, yield_ha_kg, color=group, fill=group)) +
  scale_x_discrete(labels=label_wrap(5)) +
  scale_y_continuous(labels=comma) +
  facet_wrap(~crop, scales="free_y") +
  labs(x="", y="", fill="",
    title="Expected Seed Yield (Kg / ha) - Guatemala",
    subtitle="Stratified by crop and seed club") +
  theme_def(legend.position="none")

ggBoxTest(hh, aes(group, sales_ha_ppp, color=group, fill=group)) +
  scale_x_discrete(labels=label_wrap(5)) +
  scale_y_continuous(labels=comma) +
  facet_wrap(~crop, scales="free_y") +
  labs(x="", y="", fill="",
    title="Total Seed Sales (PPP$ / ha) - Guatemala",
    subtitle="Stratified crop and by seed club") +
  theme_def(legend.position="none")

    Looking at production frontiers (units of output vs. units of input). We expect S-shape curves with farmers at different levels of technical efficiency along the curve.

    Note that in the approximated curves below we remove outliers with total input costs over PPP$ 6,000,000 per ha.

    outlier <- hh[costs_ha_ppp > median(costs_ha_ppp) + 2*sd(costs_ha_ppp), hhid]

kbl(
  caption="Farmers with total input costs > median + 2*sd",
  hh[hhid %in% outlier, .(hhid, group, crop, yield_ha_kg, costs_ha_ppp)],
  format.args=list(big.mark=","))
    Tab. 5: Farmers with total input costs > median + 2*sd
    hhid group crop yield_ha_kg costs_ha_ppp
    GTM004 Acordi potato 294,823.2 6,396,837
    GTM005 Acordi potato 336,940.8 7,308,707
    GTM007 Acordi potato 252,705.6 5,484,968 
    ggplot(hh[!hhid %in% outlier], aes(costs_ha_ppp, yield_ha_kg)) +
  geom_smooth(size=.8) +
  geom_point(alpha=.7, shape=20, color=1) +
  scale_x_continuous(labels=comma) +
  scale_y_continuous(labels=comma) +
  facet_wrap(~crop, scales="free") +
  labs(x="", y="",
    title="Production Frontier (Output vs. Input) - Guatemala",
    subtitle="Each point is a respondent. Shade shows 90% CI (kg vs. PPP$ / ha)") +
  theme_def(legend.position="none")

    Profitability

    Farmers’ gross profit margins by gender and years in seed club.

    ttt(margin_ha_ppp ~ group+crop | gender+years, data=hh, render=fmt,
  caption="Mean Gross Profit Margin in Absolute Terms (PPP$ / ha) - Guatemala")
    Mean Gross Profit Margin in Absolute Terms (PPP$ / ha) - Guatemala
    group crop Statistic < 5 ≥ 5
    Male Female Male Female
    Acordi potato mean 3,325,655 2,745,931 5,357,088 1,366,090
    median 2,525,539 2,745,931 5,505,612 1,366,090
    sd 2,844,373 NA 1,149,897 650,463
    Paquix potato mean 2,696,532 NA 2,540,506 NA
    median 2,636,137 NA 1,826,630 NA
    sd 984,817 NA 1,852,563 NA
    San Bartolo bean mean 745,174 755,928 NA NA
    median 578,783 755,928 NA NA
    sd 310,640 380,543 NA NA
    Adat Axola Adipy bean mean 702,153 NA 853,020 NA
    median 561,133 NA 1,025,013 NA
    sd 260,632 NA 297,900 NA 
    ttt(margin_ha_sh ~ group+crop | gender+years, data=hh, render=fmt_pct,
  caption="Mean Gross Profit Margin in Relative Terms (% of variable input costs) - Guatemala")
    Mean Gross Profit Margin in Relative Terms (% of variable input costs) - Guatemala
    group crop Statistic < 5 ≥ 5
    Male Female Male Female
    Acordi potato mean 99% 100% 100% 99%
    median 100% 100% 100% 99%
    sd 1% NA 0% 1%
    Paquix potato mean 100% NA 99% NA
    median 100% NA 99% NA
    sd 1% NA 1% NA
    San Bartolo bean mean 1 632% 1 635% NA NA
    median 1 588% 1 635% NA NA
    sd 100% 148% NA NA
    Adat Axola Adipy bean mean 1 622% NA 1 675% NA
    median 1 578% NA 1 740% NA
    sd 95% NA 112% NA

    Note that 0 respondents show negative margins.

    kbl(caption="Respondents with negative gross margins.",
  hh[margin_ha_ppp < 0, 
    .(hhid, code, group, crop, costs_ha_ppp, yield_ha_kg, sales_ha_kg, margin_ha_ppp)])
    Tab. 6: Respondents with negative gross margins.
    hhid code group crop costs_ha_ppp yield_ha_kg sales_ha_kg margin_ha_ppp 
    outlier <- 
kbl(caption="Respondents with gross margins > median + 3*sd",
  hh[margin_ha_ppp > median(margin_ha_ppp) + 3*sd(margin_ha_ppp), 
    .(hhid, code, group, crop, costs_ha_ppp, yield_ha_kg, sales_ha_kg, margin_ha_ppp)],
  format.args=list(big.mark=",", digits=1))
    ggplot(hh, aes(x=hhid, color=group)) +
  geom_hline(aes(yintercept=0), color=1) +
  geom_linerange(aes(ymin=0, ymax=margin_ha_ppp), size=.6) +
  geom_point(aes(y=0), shape=20, size=1.4) +
  geom_point(aes(y=margin_ha_ppp, shape=margin_ha_ppp < 0, fill=group), size=1.4) +
  scale_y_continuous(labels=comma) +
  scale_shape_manual(values=24:25) +
  guides(x="none", shape="none") +
  labs(x=NULL, y=NULL, color="", fill="",
    title="Profit Margin (PPP$ / ha) - Guatemala",
    subtitle="Each bar is a respondent's gross profit margin") +
  theme_def(
    legend.position="right",
    panel.grid.major.x=element_blank()
  )

    Farmers’ gross profit margins by gender and across groups in both absolute terms and in relative terms as percentage of total costs per hectare.

    ggBoxTest(hh[!hhid %in% outlier], 
  aes(gender, margin_ha_ppp, color=gender, fill=gender), cp=list(1:2)) +
  scale_y_continuous(labels=comma) +
  facet_wrap(~crop, scales="free_y") +
  labs(x="", y="", fill="", color="",
    title="Gross Profit Margin in Absolute Terms - Guatemala",
    subtitle="Stratified by gender (PPP$ / ha)") +
  theme_def(legend.position="none")

ggBoxTest(hh[!hhid %in% outlier], 
  aes(gender, margin_ha_sh, color=gender, fill=gender), cp=list(1:2)) +
  scale_y_continuous(labels=percent) +
  facet_wrap(~crop, scales="free_y") +
  labs(x="", y="", fill="", color="",
    title="Gross Profit Margin in Relative Terms - Guatemala",
    subtitle="Stratified by gender (% of total costs)") +
  theme_def(legend.position="none")

    ggBoxTest(hh[!hhid %in% outlier], 
  aes(years, margin_ha_ppp, color=years, fill=years), cp=list(1:2)) +
  scale_y_continuous(labels=comma) +
  facet_wrap(~crop, scales="free_y") +
  labs(x="", y="", fill="",
    title="Gross Profit Margin in Absolute Terms - Guatemala",
    subtitle="Stratified by years in seed club (PPP$ / ha)") +
  theme_def(legend.position="none")

ggBoxTest(hh[!hhid %in% outlier], 
  aes(years, margin_ha_sh, color=years, fill=years), cp=list(1:2)) +
  scale_y_continuous(labels=percent) +
  facet_wrap(~crop, scales="free_y") +
  labs(x="", y="", fill="",
    title="Gross Profit Margin in Relative Terms - Guatemala",
    subtitle="Stratified by years in seed club (% of total costs)") +
  theme_def(legend.position="none")

    ggBoxTest(hh[!hhid %in% outlier], 
  aes(group, margin_ha_ppp, color=group, fill=group)) +
  scale_x_discrete(labels=label_wrap(5)) +
  scale_y_continuous(labels=comma) + 
  facet_wrap(~crop, scales="free_y") +
  labs(x="", y="",
    title="Gross Profit Margin in Absolute Terms - Guatemala",
    subtitle="Stratified by seed club (PPP$ / ha)") +
  theme_def(legend.position="none")

ggBoxTest(hh[!hhid %in% outlier], 
  aes(group, margin_ha_sh, color=group, fill=group)) +
  scale_x_discrete(labels=label_wrap(5)) +  
  scale_y_continuous(labels=percent) +  
  facet_wrap(~crop, scales="free_y") +
  labs(x="", y="",
    title="Gross Profit Margin in Relative Terms - Guatemala",
    subtitle="Stratified by seed club (% of total costs)") +
  theme_def(legend.position="none")

    ggplot(hh[!hhid %in% outlier], aes(member_years, margin_ha_ppp)) +
  geom_smooth(size=.8) +
  geom_point(alpha=.7, shape=20) +
  scale_x_continuous(limits=c(0, 22)) +
  scale_y_continuous(labels=comma) +
  labs(x="", y="", color="",
    title="Gross Profit Margin in Absolute Terms vs. Years in Seed Club - Guatemala",
    subtitle="Each point is a respondent (years vs. PPP$)") +
  theme_def(legend.position="top")

    Correlation

    Significant pairwise associations.

    ggpairs(
  hh[, .(`seed club`=group, `years in club`=member_years,
    `costs PPP$`=costs_ha_ppp, `seed yield kg/ha`=yield_ha_kg,
    `margin PPP$`=margin_ha_ppp, `margin %`=margin_ha_sh)],
  upper = list(
    continuous=wrap("cor", size=4), 
    combo=wrap("summarise_by", color=pal[1:4], size=2)),
  lower = list(
    continuous=wrap("smooth", shape=NA), 
    combo=wrap("box_no_facet", fill=pal[1:4], alpha=.8)),
  diag = list(
    continuous=wrap("densityDiag", fill=NA),
    discrete=wrap("barDiag", fill=pal[1:4], alpha=.8)),
  title="Correlogram stratified by seed club - Guatemala"
) + 
  theme_def(
    strip.text=element_text(hjust=.5),
    axis.text.x=element_text(angle=-45),
    panel.grid.major=element_blank()
  )

    ggpairs(
  hh[, .(gender, `years in club`=member_years,
    `costs PPP$`=costs_ha_ppp, `seed yield kg/ha`=yield_ha_kg, 
    `margin PPP$`=margin_ha_ppp, `margin %`=margin_ha_sh)],
  upper = list(
    continuous=wrap("cor", size=4), 
    combo=wrap("summarise_by", color=pal[1:2], size=2)),
  lower = list(
    continuous=wrap("smooth", shape=NA, color=hh[, pal[gender]]), 
    combo=wrap("box_no_facet", fill=pal[1:2], alpha=.8)),
  diag = list(
    continuous=wrap("densityDiag", fill=NA),
    discrete=wrap("barDiag", fill=pal[1:2], alpha=.8)),
  title="Correlogram stratified by gender - Guatemala"
) +   
  theme_def(
    strip.text=element_text(hjust=.5),
    panel.grid.major=element_blank()
  )

    ggpairs(
  hh[, .(`years in club`=years,
    `costs PPP$`=costs_ha_ppp, `seed yield kg/ha`=yield_ha_kg, 
    `margin PPP$`=margin_ha_ppp, `margin %`=margin_ha_sh)],
  upper = list(
    continuous=wrap("cor", size=4), 
    combo=wrap("summarise_by", color=pal[1:2], size=2)),
  lower = list(
    continuous=wrap("smooth", shape=NA, color=hh[, pal[gender]]), 
    combo=wrap("box_no_facet", fill=pal[1:2], alpha=.8)),
  diag = list(
    continuous=wrap("densityDiag", fill=NA),
    discrete=wrap("barDiag", fill=pal[1:2], alpha=.8)),
  title="Correlogram stratified by years in seed club - Guatemala"
) +   
  theme_def(
    strip.text=element_text(hjust=.5),
    panel.grid.major=element_blank()
  )

    saveRDS(hh, "../tmp/data_gtm.rds")