WeChat ID bashusongonfinance Intro “金融读书会”是由在金融界不同领域工作的百余名专业人士共同义务编辑的金融专业领域公众平台。以“聚焦金融政策研究、促进金融专业交流”为宗旨，定位于打造具有国际视野的金融专业平台。 本文是美联储系列研究文章。作者使用消费者财务状况调查数据探讨了“攀比现象”，具体来讲，作者考察了不同债务类型的支付收入比率在不同收入阶层的变化。结果表明，本文认为富裕阶层收入上升的一个后果是导致其他阶层收入降低，为了维持生活，其他阶层会采取增加借贷的行为。其中，中上层收入家庭在房贷收入比等指标大于富裕阶层，因此承担更多不动产债务，而低层收入家庭则拥有更多非住房抵押贷款。敬请阅读。       Rising levels of income inequality have long been recognized by researchers in the US and other wealthy countries (Morelli, Smeeding, and Thompson, 2015). High-level policymakers are increasingly acknowledging the widening of the distribution of income as an area of concern. Indeed, in 2014 the head of the International Monetary Fund1 and the Chair of the Federal Reserve Board2 each gave important addresses on the subject of income inequality (and equality of opportunity), and in December 2013 President Obama identified income inequality and inadequate mobility as the “defining challenge of our time.” This new attention by policymakers is partly a result of inequality continuing to rise, and partly due to other changes in the conversation around inequality. Commentary and research on the topic are increasingly asking about the potential consequences of inequality (Thompson and Leight, 2013). Instead of simply representing a distributional outcome which might be considered “unfair,” rising income inequality itself may actually be producing potentially harmful outcomes. There is already a well-established, if unsettled, literature on the effects of inequality on overall levels of economic growth, and other potential consequences are also being explored.   One of those questions concerns the consequences of rising top income inequality on consumption and debt of households lower down the distribution. As the share of income held by households at the very top of the distribution has risen to the highest levels in generations, household borrowing has also climbed to historic high levels, and earnings across the broad middle and bottom of the distribution have experienced little growth. Several recent papers explore the link between inequality and consumption and borrowing.   This paper extends the budding literature on this question; it uses data from the Survey of Consumer Finances (SCF) to explore how changes in the income levels at the 95th and 99th percentiles of the distribution at the state-level have impacted borrowing and debt payments of households further down the income distribution. The contributions this paper makes to the literature include using superior data, as well improved outcome and inequality measures. Borrowing and debt payments are arguably better outcome measures than consumption in capturing an unsustainable household response to rising inequality. Changes in high-income levels, particularly at the 99th percentile, are also a better measure of the inequality signal that might influence households at various parts of the distribution than other measures such as the P90/P10 ratio or the Gini coefficient.   The results from this paper indicate that household borrowing and debt payment does respond to changes in top-income levels, and that this response is primarily concentrated in housing-related debt payments and among households in the upper-middle and middle portions of the income distribution. These households are going into greater housing-related debt in places where top incomes are rising faster for reasons than cannot simply be explained by home prices; the results condition on MSA-level variation in quality-adjusted rent and elasticity of the housing supply as well as time-varying MSA-level measures of changes in average home prices, as well as length of household tenure. The findings also confirm that rising top incomes are associated with decreases in non-mortgage borrowing and payments. The paper proceeds in the next section by discussing different mechanisms by which income inequality could lead to increased consumption and debt among non-affluent households and several of the recent papers exploring this topic. Section three highlights the contribution made by this paper, the data used, and the empirical strategy. Section four discusses the findings, and section five concludes.   There are multiple channels through which increasing inequality in the distribution of income could lead to higher consumption and greater levels of household debt. Broadly, the influence of inequality could work through the supply of credit or the demand for credit. Financial institutions could use increasing inequality of income within a region as information to help them target credit (Coibion et al, 2014). Alternatively, there are a variety of ways rising inequality could affect the demand for credit (Bertrand and Morse, 2013). If households value  their consumption relative to peer groups (including aspirational benchmark groups with somewhat higher incomes), rising incomes at the top of the distribution could lead to “expenditure cascades” where households further down the distribution increase their spending to maintain their relative status (Levine, Frank, and Dijk, 2010). Alternatively, rising top incomes could lead to a rising supply of “rich” goods in a market or rising prices for supplyconstrained good and services, both of which could result in higher levels of consumption and debt among households across the rest of the distribution.   One recent paper addresses the way households signal status to their neighbors, and explores how changing inequality might influence signaling consumption. Bricker, Ramcharan, and Krimmel (2014) (BRK) argue that increased dispersion of incomes within a community increases the importance of using consumption to signal status to ones neighbors. They use data from the SCF and Census-tract level measures of income to explore the relationship between luxury car-buying and local income inequality. They find that census tracts with greater inequality do experience higher levels of luxury car-buying and household debt.   There are two important limitations of the analysis by BRK for understanding the implications of the rising levels of inequality in recent decades. The first is their use of a measure of inequality (Gini) that does not distinguish between changes in income at the top or bottom of the distribution. The Gini coefficient is a widely available distribution statistics, and one of the only measures available at the tract level, but it is relatively insensitive to changes in income at the top of the distribution. Compared to other distribution statistics, the Gini coefficient reveals the lowest levels of change in inequality in recent decades (Figure 3). The Gini coefficient does have a number of strengths, but it does a poor job of capturing the aspects of changing income rising concentration at the top of the distribution – that has captured the public imagination in recent years.   The second limitation of BKR is their use of very small area geography, focusing on Census-tract level income in their analysis. One artifact of the way in which households sort themselves residentially, however, is that high-income communities experience the lowest levels of within-county inequality, and have seen the least change in within-county inequality over time. Figure 4 uses county-level data from the 2000 Decennial Census and shows a strong negative relationship between county-level median household income and the county-level Gini coefficient (Figure 4A). Between 1990 and 2006-10 counties with higher median incomes also experienced much smaller changes in their Gini coefficient, while lower-income counties had much larger increases and decreases in their Gini coefficients (Figure 4B). The geographic pattern of inequality depicted in Figure 4 suggests that the relatively high-income (within tract) households living in high inequality tracts that BRK find engaged in high levels of luxury carbuying actually overwhelmingly reside in low-income communities. The inequality they are exploring reflects distributional issues that are largely distinct from the dramatic increases in the top income shares seen in recent years.   Additional ways rising inequality could influence household consumption include the possibilities that consumption of high-income households influences a social standard or benchmark level that other household aspire to – regardless of neighbor status signaling – and also that the consumption behavior of high-income households could influence the prices and range of goods available to other households. Bertrand and Morse (2013) explore these mechanisms, using household income and consumption data from the Consumer Expenditure Survey (CEX) and income inequality measures from the Current Population Survey (CPS). They find that rising income at the 90th percentile of the distribution, at the state-level, does lead to higher levels of consumption, conditional on income, among households further down the distribution.   The findings of Bertrand and Morse (2013) are only an obvious concern if the higher levels of consumption they identify are not supported by higher current or future levels of household income. One important limitation of their analysis is that the CEX is a weak foundation on which to “hold income constant.” The CEX has serious problems with underreporting of income at the bottom of the distribution, in addition to its problems reporting income and consumption at the top (Sabelhaus et al, 2012, Sabelhaus and Groen, 2000). An additional potential limitations of the Bertrand and Morse’s (2013) findings are that, while they report rising levels of certain types of consumption, rising inequality could also be related to changes in the composition of consumption, leading them to overstate (or understate) the extent of the change in consumption.   Finally, the 90th percentile of the distribution is substantially lower than the income levels most Americans regard as “rich” and may be insufficient to capture the aspects of changes in the distribution that are capable of shaping the consumption behavior across the distribution. 5 In 2014 household taxable income at the 90th percentile was $121,000, equivalent to the family income of a married couple where one partner is a police officer ($60,000 average annual earnings) and the other is a secondary-level special education teacher ($61,000).6 At the 99th percentile taxable income was $423,000 (Saez, 2015).   An entirely different mechanism through which rising inequality might influence consumption and debt is through the supply side of the credit market. If creditors use information on income levels and local distributions of income to identify credit risk, then rising inequality might result in less credit being made available to lower-income households in high inequality areas.   Coibion et al (2014) propose this outcome and test it using data from the FRBNY Consumer Credit Panel/ Equifax Data. They find that low-income households in high-inequality areas accumulated less debt and had lower credit limits than their low-income counterparts in areas with lower inequality. Coibion et al (2014) interpret these findings as a rejection of the “keeping up with the Joneses,” “trickle-down consumption” story, but this conclusion warrants additional caveats. Their paper focuses primarily on household in the bottom fifth of the income distribution, but low-income families are not the only – or even the primary – group presumed to be impacted by the potential consequences of rising top-end inequality. Thompson and Leight (2012) find a negative correlation between state-level top-income shares and average income levels at the middle of the distribution, but no relationship at the bottom. Bertrand and Morse (2013) only find any consumption response among households above the bottom quintile of the distribution.   Another limitation of Coibion et al (2014) is the fact that the consumer credit panel data does not include income; they have information on household borrowing, credit scores, and location but not their incomes. Instead they predict household income based on the relationship between assets, debt, and income observed in the SCF. Ultimately the income variable used to identify a households location in the distribution as well as the area-level distribution statistic (P90/P10) are all based on predicted income. Biases and any other problems in these predictions could be driving the relationships identified by Coibion et al (2014).（完）   文章来源：美联储官网2016年5月2日（本文仅代表作者观点） 本篇编辑：郑子文 【漫步华尔街】专栏往期回顾： 第736期：银行更偏爱高利率？ 第737期：美国银行间一般抵押品回购市场的历史演进与发展 第738期：中美股票做空模式比较研究 第739期：Daniel K. Tarullo：保险公司与美联储职能 第740期：BCG:全球资本市场中的价值转移 第741期：LEI发展之路：全球金融危机后的监管重建 第742期：Morgan Stanley：银行业区块链 第743期：美元升值对新兴市场的抑制效应 第744期：美联储现金再流通政策 第745期：美国商业周期中的生产力分散现象 第746期：宏观审慎政策有效性的国际证据 第747期：杰罗姆•鲍威尔：美国经济发展的“供给侧视角”与货币政策展望 第748期：美国货币政策框架转型研究 第749期：美国量化宽松货币政策效果分析 第750期：全球金融危机后美国经济复苏的特点、动因及启示   查找公众号bashusongonfinance或扫描下方二维码关注本平台，查看往期文章。   温馨提示：现微信最新版本“订阅号”已实现公众号置顶功能，广大读者可点开“金融读书会”公众号，点“置顶公众号”键，即可将“金融读书会”置顶，方便查阅。 关注巴曙松教授“百度百家”专栏（网址：http://bashusong.baijia.baidu.com），请点击底部“阅读原文”链接。 Scan QR Code via WeChat to follow Official Account