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--- input_allocation [2020/02/25 09:43] – [Input allocation for labour] matsz
+++ input_allocation [2020/03/31 08:12] – [Input allocation for feed] matsz
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 Wide supports for the Gross Value Added of the fodder activities mirror the problem of finding good internal prices but also the dubious data quality both of fodder output as reported in statistics and the value attached to it in the EAA. The wide supports allow for negative Gross Value Added, which may certainly occur in certain years depending on realised yields. In order to exclude such estimation outcomes as far as possible an additional constraint is introduced:
-FIXME
 \begin{equation}
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-This part of the objective functions tries to minimize the difference between the requirements calculated from the feed input coefficients (v_animReq) and the expected (mean) requirements (p_animReq) coming from literature. Due to the weighting with number of animals (v_actLevl) and expected requirements (p_animReq) the optimal solution tends to distribute over or under supply of nutrients relatively even over all activities and regions. It has been decided to attach an exponent smaller one to these weights which strongly pulls them towards unity (see: [...] FIXME (doppelstern) .1). This tends to give more weight to “less important” animal types compared with untransformed weights.
+This part of the objective functions tries to minimize the difference between the requirements calculated from the feed input coefficients (v_animReq) and the expected (mean) requirements (p_animReq) coming from literature. Due to the weighting with number of animals (v_actLevl) and expected requirements (p_animReq) the optimal solution tends to distribute over or under supply of nutrients relatively even over all activities and regions. It has been decided to attach an exponent smaller one to these weights which strongly pulls them towards unity (see: [...] FIXME (section? .1). This tends to give more weight to “less important” animal types compared with untransformed weights.
 __Deviation of sub regional total feed intake from regional average__
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 As well as the reconciliation process, two other procedures have to be carried out. The first results from the fact that a number of activities don’t have labour input coefficient estimates. In order to estimate them, the revenue shares for the relevant activities are used as a proxy for the amount of labour they require.  Labour input for the different activities is then calculated based on these shares. The second procedure is due to the presence of infeasibilities in this model. In order to try and eliminate them, a number of courses of action can be followed from excluding outlying estimates to dropping regional estimates.
+It should be noted that the reconciliation process has to be divided into these two steps because it is highly computationally burdensome. For the model to run properly (or even at all), it is necessary to divide it into two parts, with the one part obtaining plausible elements and the other implementing the final reconciliation.
+**Table 20: Total labour input coefficients from different econometric estimations and steps in reconciliation procedure (selected regions and crops)**
+|  Region  |  crop or aggregate  |  Econometric estimation  |||  HPD solution including  |||
+|:::| |  regional  |  national- \\ including yield  |  national - \\ without yield  |  regional, \\ national, crop \\ aggregates  |  + expert assumption  |  + regional \\ labour supply  |
+|Belgium (BL24)|Soft wheat|	31.49|	31.26|	31.49|	24.99|	32.73|	53.88|
+|:::|Sugar beet	|  76.25|	77.39|	76.25|	62.19|	48.27|	68.36|
+|:::|Cereals	|  28.23|	32.89|	28.23|	32.78|	28.16|	32.66|
+|:::|Root crops	|  58.75|	65.43|	58.75|	58.8|	64.52|	105.89|
+|Germany (DEA1)|Soft wheat|	36.78|	35.32|	36.78|	36.98|	38.62|	34.46|
+|:::|Sugar beet	|  82.01|	58.99|	82.01|	55.06|	39.61|	43.58|
+|:::|Cereals	|  40.13|	32.63|	40.13|	39.94|	41.65|	35.12|
+|:::|Root crops	|  28.83|	14.23|	28.83|	38.32|	41.26|	0.01|
+|France (FR24)	|Soft wheat|	14.65|	23.3|	23.68|	14.71|	16.5|	13.22|
+|:::|Sugar beet	|  -7.42|	2.24|	-1.68|	11.08|	19.72|	18.5|
+|:::|Cereals	|  10.48|	35.9|	22.7|	15.61|	15.43|	12.7|
+|:::|Root crops	|  11.68|	29.78|	19.42|	17.05|	24.64|	18.43| \\ Source: CAPRI Modelling System
+The Table visualizes the adjustments regarding an implausible labour input coefficient for sugar beet in a French region. The econometric estimation come up with very low or negative values. The HPD solution combining crop specific estimates with corresponding averages of crop aggregates corrects this untrustworthy value to 11.08 h/ha. This value is in an acceptable range but it strikes that in opposite to many other regions the labour input for sugar beet is still less than for soft wheat. After adding equations in the reconciliation procedure that ensure that the relation of labour input coefficients among crops follows an similar “European” pattern the labour input is supposed to be 19.72 h/ha. There is up to now no theoretical or empirical evidence for this similar pattern regarding relation of input coefficients but the results seem to be more plausible when checked with expert knowledge. In the last column bounds on regional labour supply derived from FADN are added which “scales” the regional value. This final result is and is now part of the CAPRI model.
+===Projecting Labour Use===
+For typical applications of CAPRI, regional projections of labour use are needed. Such projections have been prepared as well in the CAPSTRAT project, using a cohort analysis to separate 2 components of changes over time: (1) an autonomous component, which comprises structural changes due to demographic factors such as ageing, death, disability and early retirement, and (2) a non-autonomous component, which incorporates all other factors that influence changes in farm structure and has been analysed econometrically.
+The results of this analysis are loaded in the context of CAPRI task “Generate trend projection” in file baseline\labour_ageline.gms, but only to serve as one type of bounds for labour use in the contrained trends for European regions. Other bounds are derived from engineering knowledge (or assumptions) on plausible labur use per activity which is based on the initial estimation of labour allocation by activity.