Kathrin Happe
email: happe@iamo.de

Alfons Balmann
email: balmann@iamo.de

Konrad Kellermann
email: kellermann@iamo.de

Christoph Sahrbacher
email: sahrbacher@iamo.de

Does structure matter? The impact of switching the agricultural policy regime on farm structures

Keywords: JEL Classification codes: C15, Q12, Q18 Simulation, agent-based modeling, structural change, policy analysis, agriculture

This paper investigates the relationship between structural change in agriculture and a policy regime switch at the regional level. Using the agent-based spatial and dynamic simulation model AgriPoliS, we simulate structural change for two different farm structures in response to a policy change. Results show that structural adjustment differs depending on the initial structure.

Introduction

Many people associate two things with European agriculture. One is that farming is very diverse across regions in terms of farm size, structure, and specialization. For example, farm structures range from mostly family-operated small-scale farms, such as in France or southern Germany, to large-scale agricultural holdings with many employees and thousands of hectares in Germany's 'Neue Länder', as well as in many of the New Member States of the European Union (EU). The second association is the magnitude of government support programs for farming as implemented in the European Union's Common Agricultural Policy (CAP). In 2003, subsidies accounted for 33% of agricultural gross-production (OECD 2005b). In the United States and Australia, the same figure was 18% and 4%, respectively. This corresponds to 775 € per hectare of utilized agricultural land in the EU (100 € per hectare in the U.S.). Since the early 1990s, several reforms have introduced a major shift in the way the EU provides income support to farmers. Guaranteed prices have been reduced and replaced with compensatory payments. The most recent reform, in 2003, took this development further by providing farmers with decoupled payments based on historical entitlements that replace the per hectare compensatory payments (EU Commission 2003). The new policy regime is referred to as decoupled because payments are independent of production decisions and should not exert an influence on production decisions. 1 This paper is motivated by the 2003 reform of the direct payment system for decoupled direct 1 In fact, agricultural economists have repeatedly advocated decoupled direct payments (e.g., [START_REF] Swinbank | Proposal for the future of direct payments under the CAP[END_REF][START_REF] Baffes | Experience with decoupling agricultural support[END_REF][START_REF] Beard | Decoupled payments to facilitate CAP reform[END_REF][START_REF] Dewbre | The transfer efficiency and trade effects of direct payments[END_REF][START_REF] Lewis | Eliminating price support: A political economy perspective[END_REF], OECD 2001).

A c c e p t e d M a n u s c r i p t 2 payments. Moreover, governments in many EU countries introduced similar decoupled direct payment schemes despite great variations between regions and farm structures. In view of that, this paper investigates the extent to which differing farm structures determine the impact of a policy change and subsequent structural change. Specifically, the study considers how a policy regime switch influences structural change in two distinct regional farm structures in Germany. The first region, Hohenlohe, located in the state of Baden-Württemberg in the western part of Germany, is characterized by family-farming and intensive livestock production. The second region, in the state of Saxony, is an arable farming region in which producer cooperatives, limited liability firms, and partnership farms dominate production.

Studies have investigated the effect of decoupling direct payments on production responses and land allocation (e.g., [START_REF] Moro | Modelling the CAP arable crop regime in Italy: Degree of decoupling and impact of agenda 2000[END_REF], Burfisher and Hopkins 2003[START_REF] Young | How decoupled is US agricultural support for major crops?[END_REF]; some authors put a special focus on risk and uncertainty (e.g., Hennessy 1998, Sckokai and[START_REF] Sckokai | Modeling the reforms of the common agricultural policy for arable crops under uncertainty[END_REF]. Other studies focus on the impact on land prices, the capitalization of payments and investments (e.g., [START_REF] Goodwin | What's wrong with our models of agricultural land values?[END_REF][START_REF] Lagerkvist | Agricultural policy uncertainty and farm level adjustments -The case of direct payments and incentives for farmland investments[END_REF], OECD 2005a[START_REF] Roberts | The incidence of government program payments on agricultural land rents: The challenges of identification[END_REF][START_REF] Turvey | Measuring the capitalization of income transfer programs into agricultural asset values[END_REF][START_REF] Weersink | The effect of agricultural policy on farmland values[END_REF] as well as on the degree of decoupling (e.g., OECD 2004, Swinbank and[START_REF] Swinbank | Decoupling EU farm support: Does the new single payment scheme fit within the green box? The Estey Centre[END_REF].

However, issues such as the policy impact on farm structures and structural change, as well as distributional impacts, have been neglected. One explanation for this lack may be shortcomings of traditional agricultural policy models. These models are typically of four types: econometric models, single farm and regional models based on mathematical programming, general equilibrium models, and partial equilibrium sector models. Of these, regional and sector models based on mathematical programming are a popular means of analyzing the impact of decoupled payments and other EU policies. 2 The EU's switch from price support policies to more direct means of granting support have made econometric approaches less suitable [START_REF] Heckelei | Models based on positive mathematical programming: State of the art and further extensions[END_REF]. Many of the new policies such as decoupled direct payments have no historical precedent and they induce a structural break in the system. Regional and sector programming models aggregate farms into representative groups of farms of a specific type (e.g., Heckelei andBritz 2001, Offermann et al. 2005). However, these approaches are mostly comparatively static and they use a sample of representative or average farms.

In this paper we use the agent-based model AgriPoliS (Agricultural Policy Simulator) [START_REF] Happe | Agent-based analysis of agricultural policies: An illustration of the agricultural policy simulator AgriPoliS, its adaptation and behavior[END_REF][START_REF] Happe | Agricultural policies and farm structures -agent-based modelling and application to EU-policy reform[END_REF]) to simulate the impact of a policy regime switch on structural change. AgriPoliS is a normative spatial and dynamic model of agricultural structures and explicitly takes account of actions and interactions (e.g. rental activities, investments, and continuation of farming) of a large number of individually acting farm agents. Accordingly, AgriPoliS allows for endogenous structural change and is thus particularly suited to analyzing structural, allocative, and distributive effects of policy changes on a region's agricultural structure. In contrast to many other policy models in agriculture such as single farm and regional mathematical programming models, the agent-based approach is suitable for explicitly modeling the land market to reflect the local interactions of farms, which is crucial in regard to distributional as well as the dynamic effects of agricultural policies. Compared to agent-based models in economics, AgriPoliS is very detailed in its model parameters. The model is initialized with an empirical-based farming structure that provides a close approximation to observed regional characteristics such as the share of arable land use. Individual farm agents are specified based on individual farm data on farm organization and production.

Agent-based models have increasingly been used to model agricultural and resource problems, but with a focus on the interactions between socio-economic and environmental 2 For an overview of the state of the art of modeling agricultural policies, please see Arfini (2005).

A c c e p t e d M a n u s c r i p t 3 systems and land use problems [START_REF] Balmann | Farm-based modelling of regional structural change: A cellular automata approach[END_REF][START_REF] Barreteau | SHADOC: A multi-agent model to tackle viability of irrigated systems[END_REF][START_REF] Berger | Agent-based spatial models applied to agriculture. A simulation tool for technology diffusion, resource use changes, and policy analysis[END_REF][START_REF] Bousquet | CORMAS: Common-pool resources and multi-agent systems[END_REF][START_REF] Evans | Multiscale analysis of household level agent-based model of land-cover change[END_REF][START_REF] Deffuant | Agent based simulation of organic farming conversion in Allier Département[END_REF][START_REF] Parker | Measuring pattern outcomes in an agent-based model of edge-effect externalities using spatial metrics[END_REF]. AgriPoliS has been previously used to analyze the impact of different policy schemes. Happe et al. (2005a) analyze the impact of decoupling schemes on structural change in the Hohenlohe region in southwest Germany, and Happe et al. (2005b) investigate the impact of policies intended to raise the efficiency of agricultural structures. In this paper we apply the same approach, but focus on comparing structural change in two fundamentally different regions.

The remainder of the paper is structured as follows. In Section 2, we show key characteristics of farm structures in two regions in Germany. In Section 3, we briefly present the AgriPoliS model, followed by the empirical adaptation to the two case study regions in Section 4. In Section 5, we define policy scenarios and show simulation results. Section 6 concludes with a summary and discussion.

Characteristics of farm structures in Germany

To provide a better notion of the differences in farm structures in Germany, we will present some key characteristics of farm structures in the Eastern and Western parts of Germany (Table 1). We have provided some key indicators for Saxony and Baden-Württemberg, the two German states in which the case study regions are located and that clearly exemplify the structural differences.

-TABLE 1 HERE -In 2003, the average size of a holding in Saxony was 200 hectares, while in the Western parts of Germany this was only 37.1 hectares [START_REF] Bmvel | Bundesministerium für Verbraucherschutz, Ernährung und Landwirtschaft[END_REF]. In addition, whereas in West Germany, 5% of all farms have more than 100 hectares of land managing 27% of West Germany's utilized agricultural area, in East Germany, 32% of all farms have more than 100 hectares and manage 92% of the area (Statistisches Bundesamt 2004).

The simulation model

The concept

The agent-based model AgriPoliS is a spatial and dynamic agent-based model that simulates endogenous structural change in agriculture. 3 The core idea of AgriPoliS is the understanding and modeling of the agricultural system as an agent-based system in which farms are interpreted as individual agents. Therefore, heterogeneous agents are situated in an environment in which they interact, co-operate, and exchange information with other agents who have possibly conflicting aims. What can be observed at a higher level of scale (region, sector) is the result of actions and interactions at the individual farm level. In this way, it is possible to develop a stylized picture of a system such as the agricultural sector in a specific region in a one-to-one manner as a computer model, which then can be used to simulate changes in the composition of the systems we are modeling. AgriPoliS maps the key components of regional agricultural structures: heterogeneous farm enterprises and 3

Here we only provide a brief overview of the model. A detailed documentation is provided in [START_REF] Happe | Agent-based analysis of agricultural policies: An illustration of the agricultural policy simulator AgriPoliS, its adaptation and behavior[END_REF]. AgriPoliS has been written in C++. The reader interested in the program code may directly contact the authors.

A c c e p t e d M a n u s c r i p t 4 households, space, markets for products and production factors. These are embedded in the general technical and political environment.

Farm agents

Farm agents are key entities in AgriPoliS. 4 The main element of a farm agent is its behavioral model that determines the decision making process and is therefore responsible for selecting a suitable action out of the available action space, according to the current state of the farm and the state of the farms' environment. A farm agent's decision-making is based on the assumption that farm agents maximize household income. This is a reasonable assumption regarding the behavior of many farming enterprises and farm households in Western Europe. 5To derive the farms' actions, a mathematical programming approach is used as a means of combining various farm production activities and investment choices given the farm's resource constraints (cf. [START_REF] Hazell | Mathematical Programming for Economic Analysis in Agriculture[END_REF]Norton 1986, Schreinemachers and[START_REF] Schreinemachers | Land use decisions in developing countries and their representation in multi-agent systems[END_REF]. Even though we assume a normative behavioral foundation, the farms agents' decision-making can be characterized as myopic or boundedly rational. This assumption holds for two reasons.

First, expectations about future prices rely on an adaptive expectation formulation process, even though we assume that farm agents anticipate the impact of major policy changes one period in advance. Second, a farm agent does not act strategically. Whereas this assumption is normally unproblematic on markets where farms are price-takers (which holds generally for agricultural product markets), this is not necessarily true for local factor markets such as the land market, which plays an important role in agricultural structural change. Therefore, land is treated separately, as described later on.

Farm agents can carry out a range of production activities. In order to produce, farm agents utilize different production factors (land, buildings, machinery, liquid capital, labor) of different types and capacities. Regarding the latter, we implement economies of size.

AgriPoliS also aims to mimic the effect of technological progress. More specifically, we assume that with every new investment, production costs of the product produced with this investment decrease by a certain percentage. New investments affect production capacities for the operating lifetime of the investment; the salvage value of fixed assets is zero. Hence, investment costs are sunk costs.

In addition to the standard production activities, there are a number of auxiliary activities. Farms can engage in rental activities for land, production quotas, and manure disposal rights.

Labor can be hired on a fixed or per-hour basis; conversely, farm family labor can be offered for off-farm employment. To finance farm activities and to balance short-term liquidity shortages, farm agents can take on long-term and/or short-term credit. Liquid assets not used on the farm can be invested in a bank.

As a result of its actions, the farm agent's internal state changes from one period to the next. A farm agent's internal state is organized as a balance sheet that keeps track of factor endowments, the operator's age, and price expectations, along with a number of financial indicators. These indicators determine whether a farm agent exits or remains in the sector. In Handing over the farm to the next generation also affects the opportunity costs of labor because we assume that a successor takes over the farm operation only if the expected income generated on the farm would be at least as high as the comparable industry salary.

Agent interaction via factor and product markets

In AgriPoliS, farm agents interact indirectly via markets for the production factors land, labor and capital, and on product markets. Markets for products, capital and labor are coordinated via a simple price function with an exogenously given price elasticity and a price trend for each product.

The land market is central to AgriPoliS. The land market as implemented in AgriPoliS gave close consideration to the conditions of German agriculture. These are most notably given by the great importance of land rental as compared to land purchase as a means of farm growth and the direct dependence of livestock production on land to dispose of manure and to produce feedstuff. Accordingly, agents in AgriPoliS extend their hectare base exclusively by renting additional land. 6

At the beginning of each production period, each agent faces a situation where there are a number of free plots in the region. Various land owners offer these plots to farm agents using a common platform. To mimic this situation, the land market in AgriPoliS is modeled as a sequential first-price auction in which a market agent allocates free plots of farmland to farm agents wishing to rent these plots. In addition, the sequential auction shows itself to be a simple and efficient way of allocating plots.

In brief, the land allocation process works as follows. First, each farm agent produces a bid for a particular plot of land. A reasonable strategy in a sequential auction is using the marginal valuation for a good, in this case land, to derive the bid. Taking the non-convex production function into account, we compute the shadow prices for one additional plot of land and for a fixed number of additional plots. The bid equals the maximum of these two values, less transportation costs between the farmstead and the plot 7 . Second, the auctioneer collects all bids, compares them, and allocates the free plot to the highest bidder. The auction process alternates between arable land and grassland to consider complementarities between different land qualities. If all land has been allocated or bids are zero, the auction terminates.

Space

AgriPoliS models spatial characteristics in a stylistic way. Spatial relationships are given by transport costs that depend on the distance between farmsteads and plots. Space is represented by a kind of cellular automaton consisting of a grid of equally sized cells/plots. Each cell 6

There is, however, a difference between East and West Germany that is due to historical reasons. In the west, the proportion of rented land to owned land has been steadily increasing over the past decades. The opposite is true for the so-called 'Neue Länder', where a large share of land rented out to farmers after German Unification is put up for sale after rental contracts have terminated.

7

One critique of this kind of combination between mathematical programming and behavioral rules is that there is often no sound economic or empirical foundation. To overcome this problem, we isolated the land market and used an adaptive learning mechanism based on genetic algorithms to create a benchmark strategy that enables us to compare alternative behavioral rules such as the one we chose above (cf. [START_REF] Kellermann | How smart should farms be modeled? Behavioral foundation of bidding strategies in agent-based land market models[END_REF]. One basic finding is that a reasonable bidding strategy should at least anticipate the size effects of the own production function that we could achieve with the chosen bidding rule.

A c c e p t e d M a n u s c r i p t 6 represents a plot of land of a certain size. Cells can either be farm land (i.e., arable land or grassland) or non-agricultural land (e.g. forests or roads). Farm land is either owned by farm agents or non-farm land-owners. In the latter case, land is rented out to be used by farm agents.

Technological, economic and political framework conditions

We assume that farm agents operate using assets of different vintages and technological standards. Production technology is assumed to undergo a steady technological progress that is created in the up-stream sector but not on the farms themselves. Farm agents can benefit from technological progress by adopting new technologies, reducing production costs. When adopting new technologies of a type, labor requirements per unit produced decrease with the size of the investment. The political environment is given by the predominant agricultural policy setting. Agricultural (and environmental) policies affect the farm in various ways, for example, prices, stocking density, and direct payments.

Model initialization and course of events

Before the simulation start, the initial endowments with production factors (family labor, machinery, buildings, production facilities, land, production quota, liquid assets, and borrowed capital) are specified. During the following periods these variables are changed as a result of production, rent, and investment activities carried out by the farm agents. Figure 1 provides an overview of the model dynamics and the course of events during one simulation period.

-FIGURE 1 HERE -

Empirical application

We adjust AgriPoliS to the two regions by creating an input data set that represents a virtual farm structure and provides a close approximation to the observed real farm structure in the reference year 2001. 8 The Saxony region comprises about 479,000 hectares; Hohenlohe about 73,500 hectares. Hohenlohe's farm structure is characterized by small-scale, family-operated farms. On the Hohenlohe plains, many farms specialize in pig and poultry production, while in the valleys there are mostly dairy farms. Because of the excellent soil quality, many farms in Saxony specialize in crop production with livestock production playing only a minor role. Large-scale farms dominate agricultural production in Saxony.

The input data set contains information on the structure and information on 12 to 30 individual farms that one could typically find in the study region. These typical farms represent the structural characteristics of the region. We calibrated the input data set to reflect the production capacities and key economic figures of the chosen typical farms. In order to reflect the regional heterogeneity of the farms, we derived typical farms from farm accountancy data of the European Farm Accountancy Data Network (FADN) as well as general data on prices, technical coefficients, and costs. Although accountancy data provided a very good data source to describe the specialization and economic situation of individual typical farms, data on the kinds and vintage of fixed farm assets are lacking. Based on expert information from regional agricultural authorities, we therefore assume that farm agents in A c c e p t e d M a n u s c r i p t 7 AgriPoliS operate with buildings, machinery and facilities that are considered to be typical for the region.

For Hohenlohe, we identified a set of 24 typical farms; for Saxony, 30 typical farms were specified. For both regions, the chosen farms correspond to the variety of farm types in the region. To create the initial farm structure of AgriPoliS, we assign weights to each typical farm. The weights are derived in a calibration procedure that minimizes the quadratic deviation between characteristics of the virtual farm structure and a series of observed characteristics of the real region such as the total number of farms, production capacities, or the total utilized agricultural area. For the purpose of AgriPoliS, the weights represent the number of times a typical farm must be located in the region such that the agricultural structure of the region is best represented. Accordingly, we initialize AgriPoliS with 2,857 farms for Hohenlohe and 1,835 farms in Saxony. Thus, the average farm size at the outset of AgriPoliS is 26 hectares for Hohenlohe and 261 hectares for Saxony. 9 Figure 2 compares the distribution of farm types in reality with the distribution initialized in AgriPoliS.

-FIGURE 2 HERE -5 Policy experiments

Policy scenarios

We carried out simulation experiments with three different policies, one baseline policy and two alternative decoupling policies (Table 2). The baseline, Agenda 2000, represents the business as usual scenario. The scenario assumes that the policy prevalent in the year 2001 remains valid throughout the simulation. Under Agenda 2000, there are different area payments per hectare, depending on what is planted. The total subsidy increases with the area produced of a specific type of crop. Moreover, certain livestock (suckler cows, beef cattle) receive headage payments. Because of these properties, payments under Agenda 2000 generate an incentive to produce either specific livestock or to increase the acreage of specific crops.

-

TABLE 2 HERE -

The alternative policies represent two distinct ways of decoupling support. They are inspired by the decoupling options of the most recent reform of the CAP, yet they do not exactly reproduce the new policy. 10 The first alternative scenario implements a so-called single area payment scheme (SAP), which is close to the regional model as adopted by Germany. Under the SAP scheme, farmers can claim a fixed area payment for each plot they manage. We assume a uniform payment for arable land and grassland. The payment is derived from the 9

Previous analyses showed that it proves to be technically and computationally very demanding to analyze data from 2,800 farms simulated over 25 periods. We therefore simulated a fraction of 20 percent of the Hohenlohe region and a fraction of 33 percent of Saxony while keeping the relative composition of the whole sample. This means that 572 farms are initialized and simulated in Hohenlohe and 612 farms in Saxony. Robustness of the results has been tested through repeated simulations with varying seeds speeds (see [START_REF] Happe | Agricultural policies and farm structures -agent-based modelling and application to EU-policy reform[END_REF].

10

In fact, the recent reform is more complex since it allows member states to keep some partially coupled payments or to introduce a dynamic component to the introduction of payments (see EU Commission 2003)

A c c e p t e d M a n u s c r i p t 8 total quantity of payments in the region averaged over a period of three years prior to the reform divided by the total number of hectares. The second policy introduces a fully decoupled idealized single farm payment (iSFP). Under this scheme, the owner of the farm receives a payment independent of any farming activity. The amount is based on the historical average payment the farm has received during the three periods prior to the policy change.

There is, however, an important difference between this scenario and the single farm payment introduced as part of the actual reform of the CAP. In our scenario, it is unnecessary to continue farming, and owning land is not necessary to receive payments. In reality, payments are only receivable if the corresponding amount of land is also used and farmers stay in business. Both decoupling policies are conditional on keeping farmland in good agricultural condition.

We simulate each policy scenario for 25 time periods. During the first four simulation periods, the baseline policy Agenda 2000 sets the political framework condition before a policy change to one of the decoupled policies sets in. In the simulations, we assume that all farm agents face the same output prices. Unit production costs, however, vary between farms depending on their managerial ability and technical change due to farm investment activity and farm size. Prices for intensive livestock and crops follow a downward trend; labor costs follow an upward trend.

AgriPoliS generates a large output dataset containing information on the farm agents' economic performance, production, and land use at the regional level and the individual farm level. With regard to analyzing structural change, we focus on the analysis of four indicators: farm size, allocative efficiency, rental rates, and profits. We observe the development of farm sizes in the two regions. In Germany, farm growth most often takes place via farm size growth to exploit economies of size. Regarding efficiency, for both regions we observe the development of the average economic land rent, which is a measure of the residual utilization of land after all other production factors (labor, capital) have been paid for. Furthermore, in the introduction we investigate the policy impact on land rental rates. Profits generated through farming activity are the final indicator observed in this analysis. The reader may note that due to space restrictions, some indicators that support the following arguments are not reported here, but they are available upon request.

Results

The following figures show the evolution of the indicators farm size, rental rates, and profits and should provide an impression about the speed of structural change under the defined policy conditions. Increasing average farm sizes indicates that structural change takes place in all policy scenarios. This reflects some of AgriPoliS's assumptions about economies of size and the effect of technological change.

In both regions, the policy induces a change in the average farm size (Figure 3) and a change in the distribution of farm size classes (Figure 4). In Hohenlohe, a gradual increase in average farm size can be detected for scenarios Agenda 2000 and SAP. Most obviously, the introduction of a decoupled single farm payment (iSFP) represents a structural break. In Hohenlohe, only one half of the farms with fewer than 30 hectares continue after the policy switch (Figure 4). The fact that the single farm payment is granted independently of the farming activity provides an explanation for this. Accordingly, for smaller farms it appears to be more profitable to take the payment and exit farming. A policy switch to the SAP scheme has only a minor effect on the distribution of farm size classes, yet this small effect is due to a redistribution of payments occurring at the introduction of the SAP. Depending on their specialization before the reform, some farms receive fewer payments under the SAP-scheme, which induces a few farms to leave the sector.

A c c e p t e d M a n u s c r i p t

-FIGURE 3 HERE --FIGURE 4 HERE -

The overall picture is somewhat similar in Saxony, although there are some significant differences. First of all, a gradual increase in farm size can be observed in both Saxony and Hohenlohe for the Agenda 2000 and SAP scenarios, yet the Saxony rate is lower than in Hohenlohe. Second, size classes with farms above 50 hectares hardly change in response to a policy switch. Before policy change, farms below 50 hectares accounted for 33% of all farms, but they farmed only 4% of the total agricultural land. Hence, the sharp decrease in the number of farms in small farm size classes does not offer much growth potential for the remaining farms.

Regarding the capitalization of support in farmland values, Figure 5 shows the differing policy impacts on rental rates. On the one hand, under fully decoupled single farm payments, lower shadow prices for land transfer into lower rental rates. In Hohenlohe, where the rental rates level is generally higher, the fall in rental rates takes place immediately after the policy change. It is remarkable that under the SAP scheme, rental rates for arable land in Hohenlohe increase. Thereby, the mentioned redistribution effect induces more farms to leave under the SAP scheme than under Agenda 2000. On average, the farms leaving are those with relatively lower shadow prices for land. Accordingly, remaining farms with higher shadow prices compete for free arable land and push rental rates upwards. The decoupled single area payment, on the other hand, also transfers into higher rental rates for grassland. The reason is that before policy change, only certain products using grassland, such as suckler cows or beef cattle, received payments. Under the new SAP scheme it is the grassland itself that receives the payment. The impact is particularly strong in Saxony, where rental rates for grassland are three times higher than before the policy switch. This means that around one-third of the area payment of 329 € per hectare is transferred into higher rental rates for grassland.

-FIGURE 5 HERE -

The specific policy impact on rental rates is also reflected in the development of farm profits.

Figure 6 shows the evolution of mean profits of identical farms surviving, regardless of the policy scheme, as well as the mean profit of all farms in each period in the baseline scenario. 11 It appears that the farms with the best starting position to survive in all scenarios are farms with significantly higher profits than the average at any point in time. Moreover, under the iSFP scheme, profits of surviving farms increase relative to the baseline, but decrease in case of single area payments. This effect is particularly pronounced in Saxony, and it can be explained by the increase in rental rates for grassland. It is important to consider that in Saxony, the proportion of rented land of all farm land is some 90 % at the outset of the simulation. Hence, higher rental rates transfer into lower average profits. In the SAP scheme, farms survive even if they experience an income loss on average due to the policy change.

The effect is less evident in Hohenlohe, where around one-third of the land is rented at the outset of the simulation.

11

We considered only surviving farms to avoid the sample effect generated by the exit of farms. Structural change in both regions is accompanied by a change in the economic land rent per hectare of utilized agricultural area (Figure 7). Efficiency growth, however, differs greatly between Hohenlohe and Saxony. Efficiency increases with time in Hohenlohe, irrespective of policy. This is much less pronounced in Saxony; in the case of a single farm payment, land rents even decrease in later simulation periods because of less intensive production and an increasing amount of grassland that is no longer rented by farms. The various origins of labor and the divergent labor productivity explain most of the different efficiency changes between Saxony and Hohenlohe. Due to investments in larger, labor-saving technology, the costs of farm family labor per unit in Hohenlohe decreased in each period by 20 € per hectare, on average. 12 In addition, efficiency increases as a result of policy-independent structural change because particularly small family farms exit and make way for the expansion of other farms.

In Saxony, the efficiency increase is less pronounced. Due to the significantly lower share of farm-family labor, adjustment regarding labor takes place by laying off hired labor first. Moreover, cereal production is less labor-intensive than intensive livestock production, which is the dominant form of production in Hohenlohe.

-FIGURE 7 HERE -As for the impact of decoupling, a single farm payment significantly improves factor allocation as measured by the economic land rent per hectare of land in the region (Figure 7). This is despite only a small effect on profits.

Discussion and conclusions

If payments are no longer attached to production, but to land use only through a single area payment (SAP), the agricultural structure is hardly affected compared to the reference. Increasing rental rates for grassland and a redistribution of payments between farms tend to offset profit gains and efficiency increases. Fully decoupled direct payments (iSFP), granted independently of agricultural production, are shown to have landslide effects in small-scale farm structures. Shadow prices for production factors such as land fall dramatically due to policy. Thus, farm agents spend less on leasing land and look for alternative uses of the complementary factors labor and capital. This accelerates structural change.

Considering that farm agents maximize household income, both unprofitable farms and farms with growth potential benefit from a fully decoupled single farm payment. Unprofitable farms benefit because farm agents are rewarded for leaving the sector, despite significantly lower opportunity costs of land. On aggregate, this takes away some strain on the land market, particularly in regions dominated by small farms, as more land is available for lease. The remaining farms have the opportunity to lease land at lower prices and to realize size effects more easily. As these farms' share of leased land is already higher at the outset of the simulation, farms remaining in the sector earn additional income from lower rental rates. In a region dominated by large-scale farms, economies of size are already exploited at the outset such that structural change has only a small effect on efficiency, with the exception being that 12

We have carried out alternative simulation runs in which we turned off the impact of technological change and managerial ability. The results gave us the pure impact of labor-saving technological change. Here also, the efficiency increase was 17 € per hectare.

A c c e p t e d M a n u s c r i p t 11 farms with better managerial ability replace those with lower managerial ability. Farmers with a high share of rented land benefit from fully decoupled single farm payments due to lower rental rates. Land owners would suffer because lower rental rates are reflected in lower land values. This redistribution of rent from land owners to land operators has, however, consequences for the use of land as a security, particularly for capital-intensive livestock production in regions such as Hohenlohe. Thus, lower land values limit the growth potential of smaller farms through new investments.

The results obtained with AgriPoliS are subject to a number of assumptions that influence the behavior and interactions of farm agents and, hence, the model results. In particular, it should be stressed that we assume labor-saving technical change in addition to the assumptions on technological change and managerial ability. In this paper, we analyzed the consequences of structural adjustment from the farms' point of view and, in only a very basic way, from the societal point of view. However, the process of structural change is multi-dimensional and includes the whole supply and value chain, including food production, processing and distribution industries, and changing societal needs [START_REF] Balmann | On the dynamics of structural change in agriculture: Internal frictions, policy threats, and vertical integration[END_REF][START_REF] Boehlje | Structural changes in the agricultural industries: How do we measure, analyze and understand them?[END_REF][START_REF] Swinnen | When the market comes to you -or not. The dynamics of vertical coordination in agri-food chains in transition[END_REF]). Based on this, one could envision refocusing the analysis of structural change beginning with supply and value chains rather than firms and markets. Food quality and environmental aspects that form another important part of current agricultural policy-making have also been left out. Moreover, farms face equal opportunities with regard to seeking offfarm labor. Nevertheless, the majority of the findings are plausible from a theoretical and empirical point of view. Modeling agents' behavior and interactions represents a starting point for further developments and research. In the process of understanding the behavior of economic agents, behavioral norms other than income maximization and optimization could be explored. As many applications in the field of sociology and communication sciences have shown, it is furthermore possible to introduce explicitly different, not necessarily economic, behavioral patterns into agent-based models.

4A

  farm agent corresponds to the entity of a farm including the farm manager as the decision-making unit and, in the case of family-farms, farm-family members (farm household).

  exit decision is determined by the level of equity capital and opportunity costs of farm-owned production factors. Accordingly, a farm agent leaves the sector if equity capital is zero or if the opportunity costs of farm-owned production factors are not covered.

Fig. 1 :Fig. 2 :

 12 Abbreviation Scenario name Scenario description and conditions REF Agenda 2000 -Full implementation of Agenda 2000 at the end of 2002 -No cross-compliance SAP Decoupled single area payment -Payment of 310 € per hectare in Hohenlohe, and 329 € per hectare in Saxony -Conditional on managing land -Cross-compliance: all farmland has to be kept in good agricultural condition (cut at least once a year) iSFP Idealized decoupled single farm payment -Historical payment (3 year average) paid to the farm operator -The payment is independent on the continuation of farming -Cross-compliance: all farmland has to be kept in good agricultural condition (cut at least once a year)

Fig. 4 :

 4 Fig. 4: Development of six farm size classes over 25 iterations for the Hohenlohe and Saxony regions.

Fig. 5 :

 5 Fig. 5: Development of the average rental rates for arable land and grassland the Hohenlohe and Saxony regions under alternative policy scenarios.

Fig. 6 :

 6 Fig. 6: Development of average profits of farms surviving throughout 25 iterations under all policy schemes.

Using a normative behavioral foundation enables us to derive farm agents' behavior from well-known concepts such as mathematical programming. We are aware that by doing so, we neglect properties such as adaptive behavior of agents over time. However, given the model's detail, it is impractical to include such concepts directly into the model. Instead we follow the approach of isolating specific aspects of farms' decision-making from AgriPoliS and define alternative behavioral models later to support the decisionmaking of agents in AgriPoliS (cf.Kellermann and Balmann 2006, Balmann and[START_REF] Balmann | Applying parallel genetic algorithms to economic problems: The case of agricultural land market[END_REF]). An example is given in the section about the land market.

For details on the adjustment methodology, refer to[START_REF] Sahrbacher | Adaptation of the agent-based model AgriPoliS to 11 study regions in the enlarged European Union[END_REF] and[START_REF] Happe | Agent-based analysis of agricultural policies: An illustration of the agricultural policy simulator AgriPoliS, its adaptation and behavior[END_REF].

A c c e p t e d M a n u s c r i p t


Michael Neugart
email: neugart@wz-berlin.de

Labor Market Policy Evaluation with ACE

Keywords: Labor market policy evaluation, agent-based computational model, endogenous matching function, job displacement JEL Classification: C63, J61, J68

I develop an agent-based computational economics (ACE) model with which I evaluate the aggregate impact of labor market policies. The findings are that government-financed training measures increase the outflow rate from unemployment to employment. Although the overall effect is positive, this effect is achieved by reducing the outflow rate for those who do not receive subsidies. Furthermore, the outflow rate would have been downward-biased had one supposed a matching function that is exogenous to policies.

A c c e p t e d M a n u s c r i p t 1 Introduction

The objective of labor market policies is at least twofold: to provide benefits to those people who are unemployed in order to cushion their income loss, and to improve the allocation of workers to job openings in the labor market.

The former usually goes under the heading of passive labor market policy whereas measures that follow the second aim are usually referred to as active labor market policies. As table 1 shows, a considerable amount of money is spent on labor market policies in OECD countries each year. The interest in the effectiveness of those policies follows quite naturally.

Nowadays, there is considerable microeconometric evidence on the effects of training measures for individuals, mainly about whether it improves an individual's chance of finding a job, about how it has an impact on wages, and sometimes also about its stabilizing role on lifetime employment and income (see, e.g., [START_REF] Heckman | The economics and econometrics of active labor market programs[END_REF]. However, studies based on microdata cannot tell us what the aggregate effects of those labor market policies look like. As has been proposed by [START_REF] Garfinkel | Micro experiments and macro effects[END_REF], [START_REF] Calmfors | Active labour market policy and unemploymenta framework for the analysis of crucial design features[END_REF], [START_REF] Schmid | International Handbook of Labour Market Policy and Evaluation[END_REF], and the OECD (2005), the aggregate impact of labor market policies might be smaller than what evaluations on the individual level suggest because deadweight losses and substitution and displacement effects of labor market policies are not taken into account. This is why studies based on micro-data should be complemented by aggregate impact studies of labor market policies in order to arrive at sound public policy recommendations.

In this article I link individual and aggregate impact studies with an agent-based model of the labor market. In particular, I address three issues: a) I evaluate the aggregate impact of government training subsidies, which, as table 1 reveals, make up a considerable share of active policies; b) quantify how a policy that improves an individual's chances of finding a job harms 2 A c c e p t e d M a n u s c r i p t those who do not receive government transfers; and c) point out that evaluating labor market policies within flow models that depend on matching functions that are exogenous to the labor market policies under investigation may result in biased results.

Following up on the first issue, I intend to add to an existing literature that studies the aggregate impact of training policies an alternative methodology (as suggested by [START_REF] Freeman | Labour market institutions without blinders: the debate over flexibility and labour market performance[END_REF], among others for policy evaluation) that complements the use of flow models and the estimation of aggregate matching functions (see, e.g., [START_REF] Mortensen | New developments in models of search in the labour market[END_REF]Bellmann and Jackman, 1996, respectively). The second topic is, in my estimation, a potentially interesting contribution that agent-based models can make in linking micro-and macro-level evaluations. Micro-evidence is not informative when it comes to judging the macroeconomic consequences of labor market policies.

In addition, a mere look at aggregate variables does not allow one to make (1977) and [START_REF] Hall | A theory of the natural unemployment rate and the duration of employment[END_REF] were among the first to provide a microfoundation of the matching function on the basis of a coordination failure argument exemplified by the so-called urn-ball model. From that point of departure many roads have been taken to add more structure to either the demand side or the supply side of the market, or to introduce models of wage determination in order to study the properties of the endogenous matching function. 2 Agent-based approaches to matching functions were made by [START_REF] Tesfatsion | Preferential partner selection in evolutionary labor markets: a study in agent-based computational economics[END_REF], [START_REF] Richiardi | A search model of unemployment and firm dynamics[END_REF][START_REF] Fagiolo | Matching, bargaining, and wage setting in an evolutionary model of labor market and output dynamics[END_REF]. In [START_REF] Neugart | Endogenous matching functions: an agent-based computational approach[END_REF] I showed within an agent-based computational economics (ACE) framework what properties for an endogenous matching function arise if there is endogenous vacancy creation, endogenous search intensity, and a wage formation such that heterogenous workers are paid their reservation wages. I also hinted towards the possibility of policies affecting the properties of the matching function, an issue that has been pointed out by [START_REF] Lagos | An alternative approach to search frictions[END_REF] before.

Butters

The model with which I address the three issues is an agent-based computational model of a labor market with different sectors. 3 Firms in those sectors have sector-specific skill requirements. Sectors are hit by exogenous 1 Examples of evaluations of labor market policies with an exogenous matching function can be found in [START_REF] Mortensen | Reducing supply-side disincentives to job creation[END_REF], [START_REF] Pissarides | The impact of employment tax cuts on unemployment and wages; the role of unemployment benefits and tax structure[END_REF], and [START_REF] Fredriksson | Optimal unemployment insurance in search equilibrium[END_REF], who look at the effects of unemployment benefit systems. Job protection legislation, active labor market policies, and a negative income tax system were analyzed by [START_REF] Petrongolo | Looking into the black box: a survey of the matching function[END_REF], Mortensen (1996), and [START_REF] Coe | Policy complementarities: the case for fundamental labor market reform[END_REF], respectively.

2 Work in this direction has been done by [START_REF] Cao | Coordination, matching, and wages[END_REF], [START_REF] Julien | Bidding for labor[END_REF], [START_REF] Burdett | Pricing and matching with frictions[END_REF], [START_REF] Albrecht | Matching with multiple applications[END_REF]Smith and[START_REF] Smith | A discrete-time stochastic model of job matching[END_REF]. A comprehensive survey of the matching function is provided by [START_REF] Petrongolo | Looking into the black box: a survey of the matching function[END_REF].

3 For an introduction to agent-based modeling, see, for example, Tesfatsion (2006).

A c c e p t e d M a n u s c r i p t

shocks. In such an event firms close down, dismissing their employees. As sectors are differentiated by skill requirements, unemployed workers have to make an upfront investment in their human capital in order to qualify for vacancies opening up in other sectors. The government subsidizes the unemployed workers' human capital investment, which is financed by a tax on the employed.

The findings are that subsidizing training increases the outflow rate from unemployment to employment and reduces the unemployment rate. Furthermore, dividing the group of jobseekers into a treatment and a non-treatment group shows that a higher outflow rate of the treated jobseekers comes at the cost of a lower outflow rate of the non-treated. Finally, had I supposed an exogenous matching function, the aggregate impact of the government policy on the outflow rate from unemployment would have been underestimated.

In the following section I describe the model. Section 3 presents the results, and the last section summarizes my findings.

The model

There shall be numSectors sectors in the economy, allocated on a circle (see figure 1). The number of firms in the labor market is numF irms, with numF irms > numSectors. Firms in sector i have different skill requirements from firms in sector j, with i = j. Skill differentiation varies with the distance between sectors. Assume numSectors = 10; then from the perspective of the sector i = 0, sectors numbered 1 and 9 would be closest in terms of the skill requirements of the firms, whereas sectors 2 and 8 would require skills more distinct from what is needed in sectors 1 and 9, and so on. Each firm posts one vacancy. If the firm can fill its vacancy, it produces output.

A c c e p t e d M a n u s c r i p t

The returns from production are fully paid out to the workers in terms of a fixed wage wage.

Initially Firms that have a vacancy on the market and received applications randomly choose one applicant, to whom they make an offer. The worker always accepts the first offer that he gets. The order in which firms are allowed to make offers is random, approximating the simultaneous actions of the firms.

Thus, it may happen that a firm that received multiple applications cannot fill the vacancy because all applicants were hired by other firms, or that a vacancy is not filled because no worker applied. The number of workers in the market equals the number of firms. Thus, 4 Sectors hit by a shock, however, may be populated by firms again in the future.

6

A c c e p t e d M a n u s c r i p t in principle there could be full employment. Workers are heterogenous in terms of their skill endowments, their initial skills determined by the random allocation to a sector. Unemployed workers can make an upfront human capital investment. If they decide not to invest in their human capital, they can only apply for vacancies that are posted in the sector in which they worked.

However, if they make an upfront investment in their human capital of the size of one unit, they would also qualify for vacancies posted in sectors 1 and 9, assuming that they are currently located in i = 0 and numSectors = 10.

A human capital investment of the size of two units would, in addition, qualify the worker for jobs in sectors 2 and 8. The human capital investment is costly. Acquiring skills that qualify the worker for the adjacent two sectors implies costs humCapInvCost. An investment in the worker's human capital that would qualify him for the closest additional four sectors incurs costs 2 • humCapInvCost, and so on. Workers may want to make upfront human capital investments up to the point at which they qualify for all sectors (0 ≤ numHumCapInv ≤ numSectors/2). 5 A worker shall send applications to all firms that have vacancies with skill requirements that match the worker's skills. An unemployed worker who makes an upfront human capital investment, but does not find a job in the current period, will have to invest in his human capital again in order to qualify for job openings outside his past field of work.

Workers learn how much they should invest in their human capital whenever they become unemployed or do not find a job. I model this as a process of individual reinforcement learning. 6 In the initial stage, a worker chooses the amount of human capital investment from the strategy set with equal 5 I assume that numSectors is an even number.

6 Another option to model learning on the individual level is genetic algorithms; see [START_REF] Mcfadzean | A C++ plaform for the evolution of trade networks[END_REF] or [START_REF] Dawid | Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economic Models[END_REF]. [START_REF] Brenner | Agent learning representation: Advice on modelling economic learning[END_REF] extensively discusses the pros and cons of various learning models.

A c c e p t e d M a n u s c r i p t probabilities. Then, workers keep track of the payoffs that accrued to them after the choice of a distinct human capital investment strategy. As time evolves each worker experiences from market outcomes that some strategies work better than others. The performance measure is the average of payoffs of each strategy accruing in periods in which a worker had to look for a new job (whether successful or not). Let numHumCapInv denote a human capital investment strategy; then an unemployed worker k will choose a strategy

h = numHumCapInv with probability p(k, h) = e λ•payOf f Ave(k,h) numSectors/2 s=0 e λ•payOf f Ave(k,s) , ( 1
)
where λ > 0 is a learning parameter, reflecting the speed of learning, and payOf f Ave(k, h) is a worker k's average payoff for a strategy h. A learning mechanism like the one proposed in equation ( 1) has the characteristic that application strategies that led to relatively high payoffs are more likely to be chosen. 7

The role of the government is to subsidize training. This implies that in order to acquire the skills most distinct from the current endowment, the worker would have to invest 10 units, given that there are 20 sectors. In this case, human capital investment costs would amount to 150% of the quarterly wage. The learning parameter is λ = 10.

A single iteration involves the steps summarized in the pseudocode (see figure 2). An iteration is repeated 1000 times, which makes up a run. After each run new initial conditions are set and a new run begins. What is reported are the values of the last iteration of each of the 5000 runs that were conducted.

In figures 3, 4, 5, and 6 the histograms for the number of vacancies posted, the number of jobseekers, outflows from unemployment to employment, and the unemployment rate are plotted. The histograms refer to the case without government training policies. Summing up the observations that are plotted into the histograms yields the number of runs conducted, namely 5000.

On average the unemployment rate is 10.7% (see first row in table 3). The average inflow rate and the quarterly calibration imply an average duration of jobs of five years. The average outflow rate implies an unemployment A c c e p t e d M a n u s c r i p t duration of slightly less than a year. 8 Each vacancy receives on average 2.6 applications, which could be considered too small given casual evidence on employers being inundated with applications, but note that by assumption workers send applications only to vacancies for which they qualify. The sum of human capital investment translates into 5% if measured as a share of total output, or put differently, an unemployed worker on average invests into almost three units of human capital. Tightness shows that on average there are 0.66 vacancies for an unemployed worker. Compared with crosscountry evidence this ratio seems to be too high (see OECD, 2001). Overall, however, the properties of the labor market model appear to be reasonable.

Table 3 summarizes the findings of the labor market policy evaluation.

Increasing the subsidy for the training costs for the unemployed workers from zero to 50% in steps of 10 percentage points lowers the unemployment rate from 10.7% to 8.2%. The decline in the unemployment rate is driven by an increase in the outflow rate from unemployment to employment given a constant inflow rate from employment to unemployment. As it becomes less costly for the unemployed to invest in training, more training is undertaken.

The sum of human capital investments increases from 29.5 units to 49.8 units. Additional training qualifies the unemployed for jobs with skills that are more distinct from those of the jobs they currently hold. Consequently, they will apply for job openings in sectors that are more distant in terms of skills from those sectors in which they currently work. This is reflected in the increase in the average number of applications per unemployed person,

8 While it might be tempting to compare the outcomes of the model also with flow data on labor markets (see, e.g., Key Indicators of the Labor Market (KILM), International Labour Office Geneva, 2007), one should be aware of the sensitiveness of the simulation results with respect to the calibration time of the underlying model. In the future, less stylized models may investigate perhaps more realistic approaches where firms and workers are more flexible with respect to the timing of their decisions as the labor market unfolds.

10

A c c e p t e d M a n u s c r i p t as shown in the last column of table 3. Thus, a government subsidizing training reduces unemployment. Unemployment is reduced because lower training costs encourage unemployed workers to invest more in their human capital. As a consequence they qualify for job openings in sectors for which they otherwise would not have been qualified. 9 In short, the training policy reduces frictional unemployment due to skill mismatch.

Quite importantly, this result is not sensitive to the magnitude of the costs for the human capital investment. Increasing humCapInvCost reflects again the driving mechanism of the model, frictional unemployment due to a skill mismatch. The results are not shown in the table. However, as the costs for human capital investments increase, unemployment goes up driven by a lower outflow rate from unemployment. The decrease of the outflow rate is caused by a smaller amount of human capital investment that lowers the average applications sent out by a worker.

Whereas we have empirical knowledge on the size of most of our parameters listed in table 2, the choice of the learning parameter is debatable. Reducing the learning parameter to half of the size of the baseline model yields a decrease of the unemployment rate for all government policy parameters. The lower unemployment rates are driven by higher average outflow rates.

Behind the higher outflow rates are increased human capital investments and, consequently, a higher average number of applications per person. However, although a less strong feedback mechanism distorts the levels of the endogenous variables, the effects are still small enough to argue that the magnitudes of the endogenous variables are reasonable. Doubling the size of the learning 9 While the subsidies reduce the costs for training that raises transitions to employment, employment may become less attractive from the perspective of an unemployed worker as the employed finance the training policy. However, this effect is comparably small as the tax on the income of employed persons amounts to 4% on average when the subsidy is 50%.

A c c e p t e d M a n u s c r i p t

parameter with respect to the baseline model results in an increase of the unemployment rate for all government policy parameters. The outflow rates are somewhat lower, as are human capital investments and applications per vacancy, but again, and more important, quite considerably increasing the size of the learning parameter with respect to the baseline model, leaves the qualitative results of the baseline simulation unaffected. It is still the case that as the government takes over a higher share of the training costs, the unemployment rate decreases. Thus, with respect to the learning parameter, evaluation results of the training policy appear to be robust. Moreover, for given learning parameters, increasing the number of time steps from 1000 to 2000 does not distort the results shown in table 3 that suggest that the system has converged.

Endogenous matching function

A matching function relates the two stock variables, vacancies and jobseekers, to the flow variable outflows from unemployment. In policy evaluations this relationship is assumed to be exogenous to the policies under investigation.

A policy that is targeted to change the behavior of agents is assumed not to affect the properties of the matching function. For the same inputs the matching function is supposed to deliver the same number of outflows from unemployment. However, what happens if the policy changes the properties of the matching function? Will not taking into account that policies might change the relationship between jobseekers and vacancies on the one hand and outflows from unemployment on the other hand lead to biased results in policy evaluations? If so, one would have a situation in which the policy alters the properties of the matching function, leading to a situation in which for equal inputs, outflows differ.

A c c e p t e d M a n u s c r i p t

The agent-based model developed here does not make use of an exogenous matching function, so it becomes possible to shed some light on whether there is an issue of biased evaluation results. Whether policies change the properties of the matching process in the labor market can easily be checked by comparing the outflow rates in the model with a training policy with the outflow rates in the model without a training policy for (almost) equal inputs. The simulation generated 5000 observations for the treatment and each non-treatment case. From those observations I extract triples of jobseekers, vacancies, and outflow rates, where the two inputs to the matching function, jobseekers and vacancies, match between the treatment and the non-treatment case. I say that the inputs match if the absolute deviation of the inputs between the treatment and the non-treatment case is smaller than two. Table 4 illustrates this procedure. After the comparable cases had been extracted, a Wilcoxon-test was applied in order to check for the null hypothesis of equal distributions of outflow rates.

In all five treatments, the average outflow rate is higher than in the nontreatment case where there is no government policy (see table 5). Thus, the policy improves the matching process in the labor market. The shift in the distribution of outflow rates is, moreover, statistically significant. Again, I checked for the robustness of the results when the learning parameter is doubled and reduced to half of the size of the baseline case. 10 A matching function endogenous to labor market policies still seems to be an issue.

Job displacement effects

The model also can be used to shed light on the relationship between treatment effects on the individual level and the macroeconomic outcome. Al-10 Those results are not given in this article.

13

A c c e p t e d M a n u s c r i p t though a training policy might increase the chances of those jobseekers who receive subsidies, it might do so by reducing the job-finding rates of those who are not covered by the program. Job displacement effects in the sense that the treated jobseekers crowd out other jobseekers who are not benefiting from a labor market policy program might reduce the overall success of a policy. I attempt to quantify the effect within the agent-based model by dividing the workers into equally sized groups, one of treated and one of nontreated workers. If a worker from the treated group becomes unemployed, he will receive government subsidies for his human capital investment. The workers from the non-treated group do not receive transfers. The following experiment was then conducted (see also figure 7): the number of iterations is increased to 2000, whereas all other parameters are identical to the baseline simulation. In the first 1000 iterations, no subsidies are given for jobseekers' human capital investments, neither for the treatment nor for the non-treatment group. The outflow rates at iteration 1000 are noted for both groups. From iteration 1001 to 2000, jobseekers who belong to the treatment group receive a transfer from the government of 50% of their human capital investment costs. Finally, the outflow rates at iteration 2000 for both groups are stored again. The experiment was repeated 5000 times.

Table 6 summarizes the mean values for the outflow rates for the treated and the non-treated group before and after the government policy was implemented. First of all, it can be seen that jobseekers of the treatment and the non-treatment group have equal outflow rates before the policy was introduced. We cannot reject the null hypothesis of equal outflow rates, as shown in the last row of the first column. A comparison of the mean outflow rates for the treatment group before and after the government policy was introduced reveals that on average the policy increases the job-finding rate 14 A c c e p t e d M a n u s c r i p t from 0.257 to 0.374. Testing against the null hypothesis of equal outflow rates, the difference turns out to be strongly statistically significant. Also in economic terms the effect is strong: the outflow rate of the treated group of jobseekers increases by 45%. If there were no substitution effect, the mean values of the outflow rates of the non-treatment group should be equal before and after the policy was introduced. This is, however, not the case. Again, one can clearly reject the null hypothesis of equal outflow rates. The substitution effect is also economically relevant. The outflow rate of the jobseekers who do not receive government subsidies is reduced by 12%.11 A sensitivity analysis with respect to the choice of the learning parameter does not change the qualitative nature of the results.12 What is the interpretation of the job displacement effect? In this model, it is driven by a reduction of the treated jobseekers' human capital investment costs. As the costs are reduced, workers in the treatment group invest more, allowing them to apply for jobs that are more distant in terms of skills from their current human capital resources. In those sectors where the treated workers would not have applied without government transfers, they compete for jobs with workers who did not receive transfers. Those workers might not get a job offer because a treated worker was given that offer. Thus, the job-finding rate of the non-treated group declines.

One may wonder how other policy measures fare with respect to the displacement effect. In an extension to the described model the effect of unemployment benefit policies on the transition probability of an unemployed worker into a job was analyzed. In order not to compare apples with pears the following approach was taken. With the parameters as in the previous In many respects this study employed strong modeling assumptions. However, the approach of evaluating labor market policies with agent-based models allows for a range of extensions that could alleviate some of the assumptions made in this work. For example, one could analyze the role of market power on the side of the firms or workers (c.f. Tesfatsion, 2001)

  inferences about to what extent the success of a program of a treated group of individuals comes at a cost for the non-treated. By its very construction, an agent-based labor market model allows the extraction of information on the individual level and the aggregate level. Thus, job displacement effects of labor market policies can be studied. The third result questions the use of matching functions as a key building block in flow models of the labor market. Matching functions relate two stock variables, jobseekers and vacancies, to outflows from unemployment (a flow variable). The properties usually attributed to the matching function are outflows as an increasing and concave function of the inputs to the matching function, and constant returns to scale. Moreover, those properties are usually seen as exogenous to policies. Here I raise the concern that if one takes a micro-foundation of the matching function seriously, namely, that policies target the agents' choices A c c e p t e d M a n u s c r i p t and that the properties are driven by firms' and workers' search decisions, then one should also expect that properties of the matching function change under the auspices of different policies. A failure to take into account that the matching function is endogenous to policies might lead to biased results in aggregate impact studies of labor market policies. 1

Figure 1 :

 1 Figure 1: Allocation of sectors in the labor market

  s c r i p t experiment, one can calculate the average budget of the government needed to finance the training policy, and this average budget was taken as exogenous to the unemployment benefit program. Then, the taxes for the employed and the transfers to the unemployed were determined endogenously given the number of unemployed in the economy. One finds that an unemployment benefits policy that has the same budget as the training policy does not affect the transition rates. The reason for this finding is straightforward. Contrary to the training policy, unemployment benefits do not give an incentive to invest in one's human capital. Thus, labor market frictions due to skill mismatch are not reduced. 134 ConclusionsFor the purpose of an aggregate impact study of government-sponsored training policies that rank prominently among active labor market policies in OECD countries, I developed an agent-based labor market model with sectorspecific skill requirements. In the model, firms are hit by asymmetric shocks, and workers becoming unemployed have to invest in their human capital in order to qualify for job openings. The government steps in and subsidizes the workers' training costs. First, I show that the subsidizing of training increases the outflow rate from unemployment and reduces the unemployment rate on the aggregate level. Second, there is evidence that the matching technology is not exogenous to policies. This has consequences for aggregate impact studies of labor market policies. In my case, not taking into account that policies change the matching properties of the labor market would have led to an underestimation of the impact of the training policy on the outflow rate 13 More detailed results are available from the author.Page 17 of 26 A c c e p t e d M a n u s c r i p t from unemployment. Third, I show how an agent-based labor market model can be used to elaborate potential job displacement effects of labor market policy programs, an evaluation exercise that closed-form analytical models are less apt to achieve. An agent-based approach is especially promising here, because information about the program effects on the individual level and the aggregate level is available. Exploiting this property of an agentbased approach, I find that the training policy reduces the job-finding rate of those who do not benefit from the program to an economically significant extent. In other words, although those individuals receiving transfers have a higher probability of leaving unemployment and although the overall impact of the program on outflow rates is positive, there is a crowding-out of non-participants of the program.

  and its impact on labor market performance. Different wage-setting institutions should be analyzed, and although the current study focuses on training policies, it might also be of interest to compare those policies to alternative measures.Various other unemployment benefit schemes or the effectiveness of job protection legislation are only two examples of social policies that deserve further attention. Another unexplored link is the effect of labor market policies on the distribution of firms and employment across sectors, with its repercussions for employment dynamics.

Figure

  Figure 2: Pseudocode

Table 1 :

 1 Labor market policy expenditures in OECD countries in 2002

	Country	Total	Active LMP	Training a	Training
		in % of GDP in % of GDP in % of GDP in % of active LMP
	Austria	1.78	0.53	0.19	35.8
	Belgium	3.65	1.25	0.19	15.2
	Czech Republic	0.45	0.17	0.02	11.8
	Finland	3.07	1.01	0.27	26.7
	France	3.06	1.25	0.21	16.8
	Germany	3.31	1.18	0.32	27.1
	Hungary	0.90	0.52	0.06	11.5
	Italy	1.20	0.57	-	-
	Japan	0.76	0.29	-	-
	Korea	0.41	0.27	0.04	14.8
	Netherlands	3.56	1.85	0.52	28.1
	New Zealand	1.52	0.52	0.14	26.9
	Norway	1.41	0.87	0.05	5.7
	Slovak Republic	0.94	0.46	0.04	8.7
	Spain	2.42	0.87	0.12	13.8
	Sweden	2.45	1.4	0.28	20.0
	Switzerland	1.30	0.53	0.12	22.6
	United Kingdom	0.75	0.37	0.01	2.7
	United States	0.71	0.14	0.03	21.4
	a: Training includes course costs and subsistence allowances for unemployed adults
	and those at risk. Special training programs for youths and disabled persons are
	excluded.

Source: OECD labor market statistics, http : //stats.oecd.org.

Table 2

 2

	: Parameters
	Parameter	Value
	Number of Sectors	numSectors = 20
	Number of Workers	numW orkers = 100
	Number of Firms	numF irms = 100
	Probability for Random Shock to Sector	r = 0.05
	Wage	wage = 1
	Cost for One Unit of Human Capital	humCapInvCost = 0.15
	Learning	λ = 10
	22

Table 3 :

 3 Average labor market effects of government-sponsored training

				Baseline model: λ = 10
	workerPolicy unRate inflowRate outflowRate humCapInvSum appliPerVac tightness appliAve
	0	0.107	0.054	0.260	29.5	2.61	0.66	1.63
	10	0.101	0.054	0.275	32.5	2.95	0.64	1.78
	20	0.094	0.055	0.288	35.9	3.38	0.61	1.93
	30	0.091	0.053	0.287	39.8	3.83	0.57	2.01
	40	0.087	0.053	0.305	44.4	4.45	0.54	2.22
	50	0.082	0.053	0.316	49.8	5.20	0.51	2.45
				Sensitivity analysis: λ = 5
	workerPolicy unRate inflowRate outflowRate humCapInvSum appliPerVac tightness appliAve
	0	0.095	0.054	0.289	35.6	3.33	0.61	1.88
	10	0.092	0.054	0.300	38.6	3.70	0.59	2.01
	20	0.089	0.055	0.304	41.8	4.10	0.57	2.14
	30	0.085	0.053	0.312	44.6	4.47	0.55	2.21
	40	0.085	0.055	0.323	49.9	5.17	0.52	2.45
	50	0.081	0.055	0.326	53.2	5.68	0.50	2.57
				Sensitivity analysis: λ = 20
	workerPolicy unRate inflowRate outflowRate humCapInvSum appliPerVac tightness appliAve
	0	0.116	0.053	0.229	25.8	2.30	0.66	1.47
	10	0.109	0.051	0.240	28.9	2.59	0.64	1.58
	20	0.101	0.055	0.260	33.2	3.14	0.61	1.85
	30	0.097	0.054	0.273	37.8	3.70	0.57	2.01
	40	0.089	0.054	0.289	42.7	4.29	0.53	2.23
	50	0.086	0.056	0.303	50.9	5.44	0.50	2.60

Table 4 :

 4 Outflow rates comparing treatment and non-treatment cases with workerP olicy = 50

	N o.		workerPolicy = 0			workerPolicy = 50
	of observation jobSearchers vacSum outflow rate jobSearchers vacSum outflow rate
	1	...	...	...	...	...	...
	...	...	...	...	...	...	...
	13	5	3	0	6	4	0.17
	14	15	7	0.27	14	7	0.50
	15	5	5	0.20	4	4	0
	16	5	2	0.20	5	1	0.20
	17	14	11	0.36	13	11	0.54
	18	16	16	0.50	16	16	0.94
	...	...	...	...	...	...	...
	N	...	...	...	...	...	...

Note: Parameters were set as in baseline model.

In more technical terms, one would claim that the stable unit treatment value assumption (SUTVA) is violated; see[START_REF] Rubin | Estimating causal effects of treatments in randomized and nonrandomized studies[END_REF] for an early discussion of causal effects in experiments.

 12 Again, those tables are not included in this article.

Acknowledgements

I would like to thank the participants at the research seminar of the Evolutionary Economics Group at the Max Planck Institute for Economic Systems Research, Jena; at the research seminar of the Department of Economics at Technische Universität Darmstadt; and at the ACEPOL05 Workshop on Agent-Based Models For Economic Policy Design at the Center for Interdisciplinary Research (ZIF) at the Universität Bielefeld, as well as Günther Schmid and two anonymous referees for their suggestions. Of course, I am responsible for all errors.

A c c e p t e d M a n u s c r i p t ---( 2006): "Agent-based computational economics: A constructive approach to economic theory," in Handbook of Computational Economics, ed. by K. Judd and L. Tesfatsion, Amsterdam: North-Holland, vol. 2, Agent-Based Computational Economics, 831-880.

Appendix

The model is programmed in RePast. The code is available from the author (neugart@wz-berlin.de). 0 6 1 2 1 8 2 4 3 0 3 6 4 2 4 8 5 4 6 0 6 6 7 2 7 8 8 4 9 0 9