Package {agriPAM}

agriPAM: Agricultural Policy Analysis Matrix Toolkit

Description

Build, inspect, and stress-test Policy Analysis Matrices (PAMs) for agricultural production systems. The package implements private, social, and transfer accounts and standard competitiveness, comparative-advantage, and policy-protection indicators.

Details

Create a matrix with pam() or pam_from_budget(), inspect it with pam_matrix(), pam_transfers(), and pam_indicators(), and then examine uncertainty with pam_sensitivity(), switching_value(), or pam_monte_carlo().

References

Monke, E. A. and Pearson, S. R. (1989). The Policy Analysis Matrix for Agricultural Development. Cornell University Press. ISBN 0801419530.

Food and Agriculture Organization of the United Nations (2007). Agricultural Trade Policy and Food Security in the Caribbean. ISBN 9789251057476.

See Also

pam, pam_from_budget, pam_monte_carlo

Illustrative Agricultural PAM Data

Description

Return an itemised, synthetic crop budget or the corresponding Policy Analysis Matrix. Values are for examples and teaching, not policy inference.

Usage

agri_pam_example(type = c("pam", "budget"))

Arguments

Value

An agri_pam object for type = "pam"; otherwise a data frame with 28 itemised budget rows.

Examples

x <- agri_pam_example()
pam_indicators(x)
head(agri_pam_example("budget"))

Test for an agriPAM Object

Description

Test whether an object inherits from class agri_pam.

Usage

is_agri_pam(x)

Arguments

Value

A single logical value.

Examples

is_agri_pam(agri_pam_example())
is_agri_pam(data.frame())

Construct a Policy Analysis Matrix

Description

Construct one or more agricultural Policy Analysis Matrices from private and social revenues and costs, directly or from columns in a data frame.

Usage

pam(
  private_revenue,
  private_tradable_inputs,
  private_domestic_factors,
  social_revenue,
  social_tradable_inputs,
  social_domestic_factors,
  id = NULL,
  unit = NULL,
  currency = NULL
)

pam_from_data(
  data,
  private_revenue,
  private_tradable_inputs,
  private_domestic_factors,
  social_revenue,
  social_tradable_inputs,
  social_domestic_factors,
  id = NULL,
  unit = NULL,
  currency = NULL
)

Arguments

Details

Every component vector must be non-negative and finite. Scalars are recycled to the common length. Private and social profits are revenue minus tradable-input cost minus domestic-factor cost.

Value

An object of class agri_pam.

Examples

x <- pam(
  private_revenue = 150000,
  private_tradable_inputs = 42000,
  private_domestic_factors = 61000,
  social_revenue = 140000,
  social_tradable_inputs = 46000,
  social_domestic_factors = 55000,
  id = "Paddy", unit = "ha", currency = "INR"
)
x
pam_matrix(x)

dat <- data.frame(
  crop = c("Paddy", "Wheat"),
  pr = c(150000, 110000), pt = c(42000, 31000), pd = c(61000, 44000),
  sr = c(140000, 116000), st = c(46000, 34000), sd = c(55000, 46000)
)
pam_from_data(dat, "pr", "pt", "pd", "sr", "st", "sd", id = "crop")

Aggregate Policy Analysis Matrices

Description

Sum the six additive PAM accounts within groups and recalculate profits, transfers, and ratios from the totals.

Usage

pam_aggregate(x, group = NULL, id = "All")

Arguments

Value

An agri_pam object with one row per group.

Examples

x <- agri_pam_example()
pam_aggregate(x)
pam_aggregate(x, c("Cereal", "Cereal", "Pulse", "Oilseed"))

Build a PAM from an Itemised Agricultural Budget

Description

Convert item quantities and private/social prices into revenue, tradable-input, and domestic-factor accounts, optionally for several production systems.

Usage

pam_from_budget(
  data,
  quantity,
  private_price,
  social_price,
  category,
  id = NULL,
  unit = NULL,
  currency = NULL
)

Arguments

Details

Categories are normalised without regard to case, spaces, or hyphens. Accepted categories are output, tradable_input, and domestic_factor; documented aliases are also accepted. Values are quantity multiplied by price and summed within production system and category.

Value

An agri_pam object.

Examples

budget <- agri_pam_example("budget")
x <- pam_from_budget(
  budget, "quantity", "private_price", "social_price", "category",
  id = "crop", unit = "ha", currency = "INR"
)
pam_matrix(x, "Paddy")

Monte Carlo Uncertainty Analysis for a PAM

Description

Simulate uncertainty in the six additive PAM components using correlated lognormal or non-negative normal draws, then summarise policy indicators and decision probabilities.

Usage

pam_monte_carlo(
  x,
  cv = 0.1,
  n = 10000L,
  distribution = c("lognormal", "normal"),
  correlation = NULL,
  seed = NULL,
  index = 1L,
  probs = c(0.025, 0.5, 0.975)
)

## S3 method for class 'agri_pam_mc'
print(x, ...)

## S3 method for class 'agri_pam_mc'
summary(object, ...)

## S3 method for class 'agri_pam_mc'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

## S3 method for class 'agri_pam_mc'
plot(x, metric = "drc", breaks = "FD", ...)

Arguments

Value

pam_monte_carlo() returns an agri_pam_mc object containing draws, summary, and probabilities data frames. summary() returns the latter two tables, as.data.frame() returns the draws, and the other methods return x invisibly.

Examples

x <- agri_pam_example()
mc <- pam_monte_carlo(
  x, cv = 0.10, n = 500, seed = 123, index = "Paddy"
)
mc
summary(mc)
plot(mc, metric = "drc")

cvs <- c(social_revenue = 0.15, social_tradable_inputs = 0.08)
pam_monte_carlo(x, cv = cvs, n = 250, seed = 42, index = "Wheat")

Deterministic Sensitivity Analysis for a PAM

Description

Vary one PAM component by specified proportional changes and recalculate all profits, transfers, and policy indicators.

Usage

pam_sensitivity(
  x,
  parameter,
  changes = seq(-0.2, 0.2, by = 0.05),
  index = 1L
)

## S3 method for class 'agri_pam_sensitivity'
print(x, ...)

## S3 method for class 'agri_pam_sensitivity'
plot(x, metric = "drc", ...)

Arguments

Value

pam_sensitivity() returns an object of class agri_pam_sensitivity containing a results data frame. Methods return x invisibly.

Examples

x <- agri_pam_example()
s <- pam_sensitivity(
  x, "social_revenue", changes = seq(-0.2, 0.2, by = 0.1),
  index = "Paddy"
)
s
plot(s, metric = "drc")

Extract Policy Analysis Matrix Results

Description

Expose value accounts, policy transfers, standard indicators, qualitative classifications, or the conventional three-by-four PAM table.

Usage

pam_values(x)

pam_transfers(x)

pam_indicators(x)

pam_classify(x)

pam_matrix(x, index = 1L)

Arguments

Details

For the conventional matrix, private values are denoted by A–D, social values by E–H, and transfers by I–L:

D=A-B-C, \quad H=E-F-G, \quad L=D-H=I-J-K.

The indicator columns are:

• npco: nominal protection coefficient on output, A/E;

• npci: nominal protection coefficient on tradable inputs, B/F;

• epc: effective protection coefficient, (A-B)/(E-F);

• pcr: private cost ratio, C/(A-B);

• drc: domestic resource cost ratio, G/(E-F);

• pc: profitability coefficient, D/H;

• srp: subsidy ratio to producers, L/E;

• scb: social cost-benefit ratio, (F+G)/E.

A ratio is NA when its denominator is zero.

Value

pam_values(), pam_transfers(), pam_indicators(), and pam_classify() return data frames. pam_matrix() returns one numeric matrix, or a named list of matrices if multiple analyses are selected.

Examples

x <- agri_pam_example()
pam_values(x)
pam_transfers(x)
pam_indicators(x)
pam_classify(x)
pam_matrix(x, "Paddy")

Calculate an Import- or Export-Parity Price

Description

Calculate import- or export-parity prices for social valuation.

Usage

parity_price(
  border_price,
  exchange_rate = 1,
  transport = 0,
  handling = 0,
  other_costs = 0,
  quality_adjustment = 1,
  direction = c("import", "export")
)

Arguments

Details

Import parity adds domestic transfer costs to the quality-adjusted border value. Export parity subtracts them. Taxes and subsidies should not be included when constructing an undistorted social price.

Value

A numeric vector of parity prices in domestic currency per unit.

Examples

parity_price(300, exchange_rate = 83, transport = 1200,
             handling = 300, direction = "import")
parity_price(300, exchange_rate = 83, transport = 1200,
             handling = 300, direction = "export")

Methods for agriPAM Objects

Description

Print, summarise, plot, or coerce Policy Analysis Matrices.

Usage

## S3 method for class 'agri_pam'
print(x, ...)

## S3 method for class 'agri_pam'
summary(object, ...)

## S3 method for class 'summary_agri_pam'
print(x, ...)

## S3 method for class 'agri_pam'
plot(
  x,
  type = c("profits", "coefficients", "transfers"),
  index = NULL,
  main = NULL,
  ...
)

## S3 method for class 'agri_pam'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

Value

Print and plot methods return x invisibly. summary() returns a summary_agri_pam object containing an overview data frame. as.data.frame() returns a combined results data frame.

Examples

x <- agri_pam_example()
print(x)
summary(x)
as.data.frame(x)
plot(x, type = "coefficients")

Find a PAM Switching Value

Description

Find the nearest proportional change in one PAM component that makes a selected profit, transfer, or ratio reach a target.

Usage

switching_value(
  x,
  parameter,
  metric = "drc",
  target = NULL,
  interval = c(-0.95, 3),
  index = 1L,
  tol = 1e-08
)

## S3 method for class 'agri_pam_switch'
print(x, ...)

Arguments

Value

A one-row data frame of class agri_pam_switch. The found column is FALSE when the target is not reached inside interval.

Examples

x <- agri_pam_example()
switching_value(x, "social_revenue", metric = "drc", index = "Paddy")
switching_value(
  x, "private_revenue", metric = "private_profit", index = "Paddy"
)