GE (version 0.3.5)

gemLand_Labor: 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

# \donttest{
#### 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)
# }