{'fraction_non_alphanumeric': 0.01762114537444934, 'fraction_numerical': 0.0, 'mean_word_length': 5.418079096045198, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Endogenous Matching Functions: an Agent-Based Computational Approach

The matching function has become a popular tool in labor economics. It relates job creation (a flow variable) to two stock variables: vacancies and job searchers. In most studies the matching function is considered to be exogenous and assumed to have certain properties. The present study, instead, looks at the properties of an endogenous matching function. For this purpose we have programmed an agent-based computational labor market model with endogenous job creation and endogenous job search behavior. Our~simulations suggest that the endogenous matching technology is subject to decreasing returns to scale. The Beveridge curve reveals substitutability of job searchers and vacancies for a small range of inputs, but is flat for relatively high numbers of job searchers and vertical for relatively high numbers of vacancies. Moreover, the matching technology changes with labor market policies. This raises concerns about the validity of labor market policy evaluations conducted with flow models of the labor market that employ exogenous matching functions.