gemLand_Labor: Some Examples of Market Clearing Paths Involving Land and...

In GE: General Equilibrium Modeling

Some Examples of Market Clearing Paths Involving Land and Labor

Description

Some examples of market clearing paths involving land and labor. The labor supply may increase from the fifth period.

Usage

gemLand_Labor(...)

Arguments

...	arguments to be passed to the function sdm2.

Examples

#### a 3-by-3 economy
f <- function(GRLabor = 0,
              es.land.labor = 1) {
  dst.firm <- node_new("output",
                       type = "SCES",
                       es = es.land.labor, alpha = 1,
                       beta = c(0.5, 0.5),
                       "land", "lab"
  )

  dst.land.owner <- node_new(
    "util",
    type = "Leontief", a = 1,
    "prod"
  )

  dst.laborer <- Clone(dst.land.owner)

  dstl <- list(dst.firm, dst.land.owner, dst.laborer)

  ge <- sdm2(
    A = dstl,
    B = diag(c(1, 0, 0)),
    S0Exg = matrix(c(
      NA, NA, NA,
      NA, 100, NA,
      NA, NA, 100
    ), 3, 3, TRUE),
    names.commodity = c("prod", "land", "lab"),
    names.agent = c("firm", "land.owner", "laborer"),
    maxIteration = 1,
    numberOfPeriods = 30,
    numeraire = "lab",
    ts = TRUE,
    policy = list(
      function(time, state) {
        if (time >= 5) {
          state$S[3, 3] <- 100 * (1 + GRLabor)^(time - 4)
        }
        state
      },
      policyMarketClearingPrice
    ),
    z0 = c(200, 100, 100),
    p0 = c(1, 1, 1)
  )

  par(mfrow = c(1, 2))
  matplot(growth_rate(ge$ts.p), type = "o", pch = 20)
  matplot(growth_rate(ge$ts.z), type = "o", pch = 20)

  ge
}

ge <- f()
ge$p
ge$z


f(GRLabor = 0.03)
f(GRLabor = -0.03)
f(GRLabor = 0.03, es.land.labor = 0.5)
f(GRLabor = 0.03, es.land.labor = 1.5)

#### a 4-by-3 economy
GRLabor <- 0.03

dst.agri <- node_new("agri",
                     type = "SCES", es = 0.5, alpha = 1,
                     beta = c(0.75, 0.25),
                     "land", "lab"
)

dst.manu <- node_new("manu",
                     type = "SCES", es = 0.5, alpha = 1,
                     beta = c(0.25, 0.75),
                     "land", "lab"
)

dst.consumer <- node_new("util",
                         type = "SCES", es = 0.5, alpha = 1,
                         beta = c(0.5, 0.5),
                         "agri", "manu"
)

dstl <- list(dst.agri, dst.manu, dst.consumer)

ge <- sdm2(
  A = dstl,
  B = matrix(c(
    1, 0, 0,
    0, 1, 0,
    0, 0, 0,
    0, 0, 0
  ), 4, 3, TRUE),
  S0Exg = {
    S0Exg <- matrix(NA, 4, 3)
    S0Exg[3:4, 3] <- 100
    S0Exg
  },
  names.commodity = c("agri", "manu", "land", "lab"),
  names.agent = c("agri", "manu", "consumer"),
  numeraire = c("manu"),
  ts = TRUE,
  policy = list(
    function(time, state) {
      if (time >= 5) {
        state$S[4, 3] <- 100 * (1 + GRLabor)^(time - 4)
      }
      state
    },
    policyMarketClearingPrice
  ),
  numberOfPeriods = 40,
  maxIteration = 1,
  z0 = c(100, 100, 200),
  p0 = c(1, 1, 1, 1)
)

matplot(ge$ts.z, type = "o", pch = 20)
matplot(growth_rate(ge$ts.z), type = "o", pch = 20)
matplot(growth_rate(ge$ts.p), type = "o", pch = 20)

GE documentation built on Nov. 8, 2023, 9:07 a.m.