# Some General Equilibrium Models with Skill (i.e. Human Capital) Some general equilibrium models with skill (i.e. human capital). ```r gemSkill(...) ``` ## Arguments - `...`: arguments to be passed to the function sdm2. ## Examples ```r depreciation.rate <- 0.05 # the depreciation rate of skill skill.density <- 10 relative.efficiency.coef <- 2 efficiency.coef <- skill.density * relative.efficiency.coef dst.efficiency.unit <- node_new("efficiency unit", type = "Leontief", a = 1 / efficiency.coef, "complex labor" ) dst.firm <- node_new( "product", type = "SCES", alpha = 1, beta = c(0.4, 0.6), es = 0.5, "product", "labor" ) node_set(dst.firm, "labor", type = "SCES", alpha = 1, beta = c(0.5, 0.5), es = 1.5, "simple labor", dst.efficiency.unit ) dst.school <- node_new( "skill", type = "Leontief", a = c(0.1, 1, 0.1), "product", "simple labor", dst.efficiency.unit ) dst.complex.laborer <- node_new( "complex labor", type = "Leontief", a = c(skill.density, 1), "skill", "simple labor" ) dst.simple.laborer <- node_new( "util", type = "Leontief", a = 1, "product" ) ge <- sdm2( A = list(dst.firm, dst.school, dst.complex.laborer, dst.simple.laborer), B = matrix(c( 1, 0, 0, 0, 0, 1, skill.density * (1 - depreciation.rate), 0, 0, 0, 1, 0, 0, 0, 0, 0 ), 4, 4, TRUE), S0Exg = { tmp <- matrix(NA, 4, 4) tmp[4, 4] <- 100 tmp }, names.commodity = c("product", "skill", "complex labor", "simple labor"), names.agent = c("firm", "school", "complex laborer", "simple laborer"), numeraire = "simple labor", policy = makePolicyMeanValue(50), priceAdjustmentVelocity = 0.05, maxIteration = 1, numberOfPeriods = 1000, ts = TRUE ) ge$p ge$z addmargins(ge$D, 2) addmargins(ge$S, 2) matplot(ge$ts.p, type = "l") #### Assumed that the amount of education and training purchased by laborers is ## determined primarily by their preferences rather than their investment motives. depreciation.rate <- 0.05 skill.density <- 10 relative.efficiency.coef <- 2 efficiency.coef <- skill.density * relative.efficiency.coef dst.efficiency.unit <- node_new("efficiency unit", type = "Leontief", a = c(skill.density / efficiency.coef, 1 / efficiency.coef), "skill service", "simple labor" ) dst.firm <- node_new( "product", type = "SCES", alpha = 1, beta = c(0.4, 0.6), es = 0.5, "product", "labor" ) node_set(dst.firm, "labor", type = "SCES", alpha = 1, beta = c(0.5, 0.5), es = 1.5, "simple labor", dst.efficiency.unit ) dst.school <- node_new( "skill", type = "Leontief", a = c(0.1, 1, 0.1), "product", "simple labor", dst.efficiency.unit ) dst.laborer <- node_new( "util", type = "CD", alpha = 1, beta = c(0.7887, 0.2113), # beta <- c(0.9, 0.1), # beta <- c(0.6, 0.4), "product", "skill", skill.stock = 0 ) ge <- sdm2( A = list(dst.firm, dst.school, dst.laborer), B = matrix(c( 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ), 4, 3, TRUE), S0Exg = { tmp <- matrix(NA, 4, 3) tmp[4, 3] <- 100 tmp }, names.commodity = c("product", "skill", "skill service", "simple labor"), names.agent = c("firm", "school", "laborer"), numeraire = "simple labor", policy = function(A, state) { last.D <- state$last.A %*% dg(state$last.z) new.skill <- last.D[2, 3] state$S[3, 3] <- A[[3]]$skill.stock <- A[[3]]$skill.stock * (1 - depreciation.rate) + new.skill state }, priceAdjustmentVelocity = 0.05, maxIteration = 1, numberOfPeriods = 1000, ts = TRUE ) ge$p ge$z addmargins(ge$D, 2) addmargins(ge$S, 2) matplot(log(ge$ts.p), type = "l") ```

gemSkill function