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Unemployment Expectations in an Agent-Based Model with Education

Part of the Lecture Notes in Computer Science book series (LNAI,volume 10978)

Abstract

Why are unemployment expectations of the “man in the street” markedly different from professional forecasts? We present an agent-based model to explain this deep disconnection using boundedly rational agents with different levels of education. A good fit of empirical data is obtained under the assumptions that there is staggered update of information, agents update episodically their estimate and there is a fraction of households who always and stubbornly forecast that the unemployment is going to raise. The model also sheds light on the role of education and suggests that more educated agents update their information more often and less obstinately fixate on the worst possible forecast.

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Notes

  1. 1.

    By professional forecasts in this paper we refer to the figures made public by OECD every quarter. The use of a specific set of “professional” forecasts is not affecting the results sensibly as other forecasts, say produced by different research centers or governmental offices, are typically very similar and strongly correlated with the OECD data.

  2. 2.

    There are several micro-level empirical studies on this topic, for example Lusardi and Mitchell [11], Souleles [12] and Easaw et al. [13].

  3. 3.

    “Remain the same” and “do not know” are both encoded as zero.

  4. 4.

    A “balance index” is constructed as the difference between the percentages of respondents giving positive and negative replies.

  5. 5.

    From 2003, OECD releases forecasts also for each quarter, while for the period from 1995 to 2002 OECD releases forecasts only for each semester. Therefore until 2002 we estimate the missing quarters forecasts through interpolation of the biannual forecasts.

  6. 6.

    2000 individuals are interviewed by ISTAT per month (6000 individuals per quarter). About \(\frac{1}{2}\) of the Italian population belongs to the lths group, about \(\frac{1}{3}\) to the hs group and about \(\frac{1}{6}\) to the col group. Hence, the baseline simulation has 3000 agents per quarter with lths, 2000 with hs and 1000 with col.

  7. 7.

    The code can be downloaded at http://virgo.unive.it/paolop/epidemics-istat-paams.nlogo.

  8. 8.

    In the interpretation of the results, it has to be remembered that some of the individuals (on average, a fraction \(\lambda ^{edu}\cdot \mu ^{edu}\)) receive the signal \(o_t\) but still provide a stubbornly pessimistic answer. This implies that the fraction of individuals who receive the signal \(o_t\) and really incorporate it into the answer \(ans_{it}\) is approximately \(\lambda ^{edu}\cdot (1-\mu ^{edu})\), that is 0.059 for lths, 0.070 for hs and 0.083 for col.

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Correspondence to Luca Gerotto .

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Gerotto, L., Pellizzari, P. (2018). Unemployment Expectations in an Agent-Based Model with Education. In: Demazeau, Y., An, B., Bajo, J., Fernández-Caballero, A. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. PAAMS 2018. Lecture Notes in Computer Science(), vol 10978. Springer, Cham. https://doi.org/10.1007/978-3-319-94580-4_14

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  • DOI: https://doi.org/10.1007/978-3-319-94580-4_14

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