MAgPIE - An Open Source land-use modeling framework

4.6.4

created with goxygen 1.3.2

Employment (36_employment)

Description

This module is used to calculate hourly labor costs in agriculutre and the number of people employed in crop and livestock production. Hourly labor costs are calculated based on a regression with GDP pc from 09_drivers. They can be increased by an externally set global minimum wage. Wages as well as productivity gain from higher wages are provided to 38_factor_costs, 70_livestock, and 57_maccs to scale total labor costs accordingly. The calculation of employment then uses labor costs for crop and livestock production and mitigation coming from 38_factor_costs, 70_livestock, and 57_maccs.

Interfaces

Input

Output

Realizations

(A) exo_may22

This realization calculates agricultural employment based on it’s relation to total labor costs. It therefore depends on the labor costs calculated in the modules 38_factor_costs, 57_maccs and 70_livestock. Resulting total costs and production patterns can be affected if an external wage scenario is used, which increases labor costs for crop and livestock production in 38_factor_costs and 70_livestock, and for mitigation in 57_maccs.

Employment is calculated as total labor costs divided by hourly labor costs and average hours worked per employed person per year. Employment for crop and livestock production and employment for mitigation of GHG emissions are calclated in separate equaations. For the former, total labor costs include labor costs from crop production (see 38_factor_costs), livestock production (see 70_livestock), and some labor costs for crop and livestock production which are not covered by MAgPIE. For employment from mitigation of GHG emissions labor costs come from the marginal abatement cost curves (MACCs) (see 57_maccs).

\[\begin{multline*} v36\_employment(i2) = \left(vm\_cost\_prod\_crop(i2,"labor") + vm\_cost\_prod\_livst(i2,"labor") + \sum_{ct}p36\_nonmagpie\_labor\_costs(ct,i2)\right) \cdot \left(\frac{1 }{ \sum_{ct}\left(f36\_weekly\_hours(ct,i2) \cdot s36\_weeks\_in\_year \cdot pm\_hourly\_costs(ct,i2,"scenario")\right)}\right) \end{multline*}\]

\[\begin{multline*} v36\_employment\_maccs(i2) = vm\_maccs\_costs(i2,"labor") \cdot \left(\frac{1 }{ \sum_{ct}\left(f36\_weekly\_hours(ct,i2) \cdot s36\_weeks\_in\_year \cdot pm\_hourly\_costs(ct,i2,"scenario")\right)}\right) \end{multline*}\]

Hourly labor costs are projected into the future by using a linear regression with GDPpcMER, which is calibrated such that historic values of agricultural employment are met. A threshold is used in the regression to avoid too low or negative hourly labor costs.

p36_hourly_costs_iso(t_all,iso,"baseline") = max((im_gdp_pc_mer_iso(t_all,iso) * f36_regr_hourly_costs("slope") +
                                                  f36_regr_hourly_costs("intercept") + p36_calibration_hourly_costs(iso)),
                                                 f36_regr_hourly_costs("threshold"));

In case of a scenario with an external global minimum wage we add a linear term to the baseline hourly labor costs, starting from 0 zero in 2020 and increasing such that resulting hourly labor costs will match the minimum wage in 2050. After 2050 the additional term is decreased again, reaching 0 in 2100 (where baseline hourly labor costs and new hourly labor costs will match again). In case this means that hourly labor costs would decrease again below the minimum wage after 2050, we keep them at minimum wage. If baseline hourly labor costs are already high enough to meet the minimum wage in 2050, they are not changed.

A scenario that increases wages can either be fully related to productivity increase (leading to lower employment for the same total labor costs), or keep productivity constant (leading to the same employment for higher total labor costs), or show a mixture between productivity increase and total labor cost increase. The scalar s36_scale_productivity_with_wage describes how high the labor productivity gain should be relative to the increase in hourly labor costs and is used to calculate pm_productivity_gain_from_wages, which is applied to labor costs for crop production (38_factor_costs), livestock production (70_livestock), mitigation costs (57_maccs) and the non-MAgPIE labor costs. If s36_scale_productivity_with_wage = 1 the productivity gain and wage increase cancel out, leading to the same total labor costs as without wage scenario. For s36_scale_productivity_with_wage = 0 the total labor costs scale proportional to the hourly labor costs. For other values, the total labor costs show a non-linear realtionship with s36_scale_productivity_with_wage.

pm_productivity_gain_from_wages(t,i) = s36_scale_productivity_with_wage * (pm_hourly_costs(t,i,"scenario") / pm_hourly_costs(t,i,"baseline")) + (1 - s36_scale_productivity_with_wage);

Non-MAgPIE labor costs consist of the labor cost share of subsidies and from livestock categories not covered by MAgPIE (i.e. wool, beeswax, honey, silk-worms), which are both kept constant for future years.

p36_nonmagpie_labor_costs(t,i) = (f36_unspecified_subsidies(t,i) + f36_nonmagpie_factor_costs(t,i)) *
                                     (1-p36_cost_share(t,i)) * (1/pm_productivity_gain_from_wages(t,i)) *
                                     (pm_hourly_costs(t,i,"scenario") / pm_hourly_costs(t,i,"baseline"));

Limitations Labor availability is not seen as a limiting factor for agricultural production, as the number of people employed is directly linked to labor costs which scale with the amount of production. Furthermore, hourly labor costs are projected into the future based on a regression with GDP pc, while factor requirements for crops (i.e. labor + capital costs per production unit) are kept constant. This means, that the increase in the baseline wages over time is assumed to be matched by a corresponding increase in labor productivity. For external wage scenarios that lead to higher wages the additional wage increase can be either related to productivity increase or higher total labor costs.

Definitions

Objects

Sets

Authors

Debbora Leip

See Also

09_drivers, 38_factor_costs, 57_maccs, 70_livestock

References