Wealth accumulation and factors accounting for success
财富积累与成功的影响因素AI 解读 ↓
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AI 根据所提供的论文材料归类:摘要明确使用七年月度家庭面板,并以家庭固定效应分析资产回报和财富积累。
财富积累与成功的影响因素AI 解读 ↓
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We use detailed income, balance sheet, and cash flow statements constructed for households in a long monthly panel in an emerging market economy, and some recent contributions in economic theory, to document and better understand the factors underlying success in achieving upward mobility in the distribution of net worth. Wealth inequality is decreasing over time, and many households work their way out of poverty and lower wealth over the seven year period. The accounts establish that, mechanically, this is largely due to savings rather than incoming gifts and remittances. In turn, the growth of net worth can be decomposed household by household into the savings rate and how productively that savings is used, the return on assets (ROA). The latter plays the larger role. ROA is, in turn, positively correlated with higher education of household members, younger age of the head, and with a higher debt/asset ratio and lower initial wealth, so it seems from cross-sections that the financial system is imperfectly channeling resources to productive and poor households. Household fixed effects account for the larger part of ROA, and this success is largely persistent, undercutting the story that successful entrepreneurs are those that simply get lucky. Persistence does vary across households, and in at least one province with much change and increasing opportunities, ROA changes as households move over time to higher-return occupations. But for those households with high and persistent ROA, the savings rate is higher, consistent with some micro founded macro models with imperfect credit markets. Indeed, high ROA households save by investing in their own enterprises and adopt consistent financial strategies for smoothing fluctuations. More generally growth of wealth, savings levels and/or rates are correlated with TFP and the household fixed effects that are the larger part of ROA.
I study randomization inference for dynamic causal effects in panels with many units and few periods when treatment is endogenous and an instrument is as good as randomly assigned only through the innovations of a first-order autoregressive assignment mechanism with unknown persistence. The persistence is a nuisance parameter in two senses: it determines which weighted average of heterogeneous causal effects the instrumented local projection estimand measures, and it governs…
动态面板多项Logit模型
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短时间维度异质性面板中的平均效应估计
The commonly used two-way fixed effects estimator is biased under correlated heterogeneity and can lead to misleading inference. The mean group estimator proposed by \cite{PesaranSmith1995} is robust to correlated heterogeneity but requires the individual estimators to have second-order moments that could fail if the number of estimated coefficients ($k$) is close to the time dimension ($T$) of the panel. This paper focuses on panels where $T-k$ is mall (including $T=k$),…