Treatment-effect identification without parallel paths: an illustration in the case of Objective 1–Hainaut/Belgium, 1994–2006
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Imagine an impoverished region that becomes eligible for a generous transfer programme (the treatment). Imagine difference-in-differences analysis (DiD)—a before-and-after comparison of the income-level handicap—shows that the handicap has risen. Most observers would conclude to the policy's inefficiency. The point made in this paper is that second thoughts are needed, because DiD rests heavily on the validity of a key assumption: parallel paths in the absence of treatment. What is more, when several pre-treatment periods are available in the data, it can easily be assessed and, if necessary, abandoned in favour of more relevant ones' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["difference-in-differences models","Eu convergence policy","Treatment-effect analysis"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Imagine an impoverished region that becomes eligible for a generous transfer programme (the treatment). Imagine difference-in-differences analysis (DiD)—a before-and-after comparison of the income-level handicap—shows that the handicap has risen. Most observers would conclude to the policy's inefficiency. The point made in this paper is that second thoughts are needed, because DiD rests heavily on the validity of a key assumption: parallel paths in the absence of treatment. What is more, when several pre-treatment periods are available in the data, it can easily be assessed and, if necessary, abandoned in favour of more relevant ones' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Imagine an impoverished region that becomes eligible for a generous transfer programme (the treatment). Imagine difference-in-differences analysis (DiD)—a before-and-after comparison of the income-level handicap—shows that the handicap has risen. Most observers would conclude to the policy's inefficiency. The point made in this paper is that second thoughts are needed, because DiD rests heavily on the validity of a key assumption: parallel paths in the absence of treatment. What is more, when several pre-treatment periods are available in the data, it can easily be assessed and, if necessary, abandoned in favour of more relevant ones
difference-in-differences models; Eu convergence policy; Treatment-effect analysis