Agricultural Policy Analysis Matrix Toolkit

Builds and analyses Policy Analysis Matrices ('PAMs') for agricultural production systems. Computes private and social profitability, policy transfers, the domestic resource cost ratio, nominal protection coefficients for outputs and inputs, the effective protection coefficient, the private cost ratio, the profitability coefficient, subsidy ratios, and social cost-benefit ratios. Supports itemised farm budgets, parity prices, grouped analysis, deterministic sensitivity analysis, switching values, and correlated Monte Carlo simulation. Methods follow Monke and Pearson (1989, ISBN:0801419530) and the Food and Agriculture Organization of the United Nations (2007, ISBN:9789251057476).

agriPAM

agriPAM is a dependency-light R toolkit for Agricultural Policy Analysis Matrices. It turns private and social farm budgets into the conventional PAM, calculates competitiveness and protection indicators, and evaluates how the conclusions respond to deterministic and stochastic uncertainty.

The package is authored by Chiranjit Mazumder, Himadri Sekhar Roy, Utkarsh Tiwari, Pramit Pandit, and Bikramjeet Ghose.

What it calculates

For private accounts (A), (B), (C) and social accounts (E), (F), (G), agriPAM calculates:

Installation

From a built source archive:

install.packages("agriPAM_0.1.0.tar.gz", repos = NULL, type = "source")

During development, install from the package directory with:

install.packages(c("testthat", "knitr", "rmarkdown"))
devtools::install("agriPAM")

Basic workflow

library(agriPAM)

pam_result <- 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"
)

pam_matrix(pam_result)
pam_transfers(pam_result)
pam_indicators(pam_result)
pam_classify(pam_result)

An itemised budget can be converted directly:

budget <- agri_pam_example("budget")

pam_result <- pam_from_budget(
  budget,
  quantity = "quantity",
  private_price = "private_price",
  social_price = "social_price",
  category = "category",
  id = "crop",
  unit = "ha",
  currency = "INR"
)

Sensitivity and uncertainty

sensitivity <- pam_sensitivity(
  pam_result,
  parameter = "social_revenue",
  changes = seq(-0.20, 0.20, by = 0.05),
  index = "Paddy"
)
plot(sensitivity, metric = "drc")

switching_value(
  pam_result,
  parameter = "social_revenue",
  metric = "drc",
  index = "Paddy"
)

simulation <- pam_monte_carlo(
  pam_result,
  cv = c(social_revenue = 0.15, social_tradable_inputs = 0.08),
  n = 5000,
  seed = 2026,
  index = "Paddy"
)
simulation$summary
simulation$probabilities

agri_pam_example() contains synthetic teaching data. It is deliberately not presented as an official estimate and should not be used for policy inference.

Methodological 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.

License

MIT © 2026 Chiranjit Mazumder, Himadri Sekhar Roy, Utkarsh Tiwari, Pramit Pandit, and Bikramjeet Ghose.