Bachelor Thesis
BSc Economics and Business Economics Faculty of Economics and Business
Liquidity Constrained Households and their MPC: Empirical Analysis and Theoretical Considerations
Name: Daniella Sorrosa Student Number: 11719087 Supervisor: Stefan Wöhrmüller Date of Submission: 24.06.2020
Statement of Originality
This document is written by Student Daniella Sorrosa Lange who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Abstract
Liquidity constrained households are likely to have a high Marginal Propensity to Consume (MPC), hence having a higher sensitivity to transitory income changes. This paper implements the Kaplan, Violante & Weidner (2014) methodology, which categorizes these households into Wealthy-Hand-to-Mouth and Poor-Hand-to-Mouth to answer the questions; How did the share of liquidity constrained households evolve since the financial crisis? And, how does the methodology of Kaplan, Violante, Weidner (2014) reacts to a robustness analysis? Data from the United States Survey of Consumer Finances (SFC) throughout the period 1989 – 2016 is implemented to answer the aforementioned questions. This empirical analysis leads to the finding that the share of liquidity constrained households has remained at a steady rate over the observed period and that the Great Recession had an impact on the asset composition and share of the Wealthy-Hand-to-Mouth.
JEL classification: E21 D14
Table of Contents
Introduction ... 3
i. Literature review ... 5
ii. Method and data ... 8
LIQUID WEALTH ... 9
ILLIQUID WEALTH ... 9
INCOME ... 9
CATEGORIZATION OF HOUSEHOLDS ... 10
iii. Results ... 12
iv. Observations of results ... 15
v. Discussion ... 16
vi. Robustness analysis ... 19
vii. Conclusion ... 21
Introduction
It is a well-known fact that consumer response can have a significant impact on the effectiveness of an expansionary fiscal policy, such as tax cuts and increased government spending, aimed at increasing aggregate demand. This can be attributed to the fiscal multiplier, which in very general terms, is the ratio of change in income that arises from changes in government spending (Mishkin, Kent, & Giuliodori, 2013, p. 469). A significant component of the fiscal multiplier is the Marginal Propensity to Consume (MPC), a concept that measures the proportion of an aggregate raise in disposable income that, in this case, the recipients of stimulus packages consume in goods and services (Mishkin et al., 2013, p. 465).
In 1936, economist John Maynard Keynes explored the concept of MPC, stating that “Men are disposed (…) to increase their consumption as their income increases, but not by as much as the increase in their income” (2018). This assertion implies that MPC is homogeneous across societies, however, this is not likely the case as different demographic groups can be expected to display very different responses to an increase in income depending on their characteristics regarding self-control, planning, and patience, or circumstances that they face (Gelman, 2020, p. 2).
Economists have been studying MPC and the various factors shaping this concept for decades. In doing so, some have run into liquidity constrained households, which are more likely to have a higher MPC out of transitory income changes (Kaplan, Weidner, & Violante, 2014, p. 78). In theory, given the high consumption sensitivity of these households a precise knowledge of the share and demographics of this type of consumer would allow governments in need of fast economic stimuli to target these households when implementing fiscal stimuli, to maximize the effectiveness of their policies (Kaplan & Violante, 2014, p. 1234).
This leads to the questions; how did the share of liquidity constrained households evolve since the financial crisis? And how does the methodology proposed by Kaplan, Violante, Weidner (2014) reacts to a robustness analysis? These will be answered through an empirical analysis of the share of liquidity constrained households in the United States throughout the 1989 – 2016 period, where different variable measurements are considered, and a study of existing research related to this field is also provided, taking into consideration the main dissimilarities when compared to the approach presented in this paper.
This paper is structured as follows, the subsequent section (I) offers a literature review on consumption theory, and other empirical researches which objective was to measure the share of liquidity constrained households or to study the behavior of household consumption, this includes the work of Kaplan, Weidner, & Violante (2014). Afterward, in section (II), the method and data used in this study are presented, and the variables needed to determine the categorization of households which are liquid assets, illiquid assets, income, credit limit, pay-frequency, among others described, as well as the thresholds used to distinguish among the three types of households. Section (III) displays the results in a series of graphs which deliver the share of liquidity constrained households under different approaches and the asset composition of the Wealthy-Hand-to-Mouth. Section (IV) highlights certain results observations. Section (V) offers then a discussion about the results, relating them to the literature discussed in section (II) and to events that hit the United States economy during the period being studied. Section (VI) offers a robustness analysis of some of the variables used in this study. Finally, this paper ends with section (VII), which summarizes the main findings, adds a conclusion, and provides recommendations for future research.
I. Literature Review
Homogeneous consumer models such as the Life-Cycle Permanent-Income Hypothesis state that consumers wish to avoid fluctuations in their consumption; as a result, they resort to savings to transfer income, and hence purchasing power into the future (Ando & Modigliani, 1963; Friedman, 1957). Departing from this idea of homogeneous agents, models that allow for differences in liquidity constraints and preferences indicate instead that different types of consumers will react differently to changes in income, meaning that their MPC’s will variate.
For instance, the models proposed by Deaton (1991) and Carrol & Kimball (1996) incorporate the line of thinking of Ando & Modigliani (1963) and Friedman (1957) briefly described above, with liquidity constraints and impatient consumers to produce Buffer-Stock models. These models feature impatient households that are willing to consume out of savings; however, their usage of this resource is limited by their need to self-insure against income risks creating the so-called “Buffers”. Additionally, these models propose an MPC out of cash that is strictly decreasing on wealth, which in turn, translates to the argument that predictable changes in income should not alter consumption. However, this last assertion is at odds with empirical research affirming that consumption is highly sensitive to expected income fluctuations (Souleles, 1999, p. 956).
Nevertheless, models that use a measure of net worth to determine whether or not a household is liquidity-constrained usually provide evidence for the claim that MPC is lower for wealthy households. It is estimated that the share of liquidity constrained households is at around 14 percent of the total population (Kaplan, Weidner, & Violante, 2014, p. 103). The work of Di Maggio, Kermani, & Majlesi (2020) coincides with this conclusion by focusing on the consumption response of changes in stock market returns. To be precise, they find an MPC of 3 percent for the households at the highest of the wealth distribution and 23 percent for those
at the bottom half of the distribution. On the other hand, Jappelli & Pistaferri (2014) try to estimate the magnitude of MPC by looking at the effects of fiscal stimulus on consumption. They find an average MPC of 48 percent, which is higher than the results of the net-income approach. However, in doing so, they also assert that households with low cash have a higher MPC. These results can be associated with those of the models that use net worth to determine the share of liquidity constrained households, given that they point to an MPC that is decreasing on wealth.
Furthermore, the research of Garbinti, Lamarche, Lecanu, & Frédérique (2020), provides essential findings on cross-country heterogeneity. They used panel data on Spain, Germany, Italy, Cyprus, and Belgium to observe the effects of the sovereign debt crisis and found that a higher MPC for lower-income households was the trend that occurred across all of their selected countries. Alternatively, Krueger, Mitman, & Perri (2016) also implemented data on household net worth during the 2006 – 2010 period in the United States, to study the extent to which wealth distribution has an effect on the business cycle’s aggregate output, investment and our subject of interest; consumption. They found that due to the elevated unemployment risk that these households face when it comes to a recession, there must be some wealth poor households who do not act in a liquidity constrained manner and instead keep precautionary savings.
Moving on, it is also possible to relate income shocks and their effect on consumption to provide estimates on the MPC, and accordingly, arrive to findings regarding the share of liquidity constrained households. For example, various researchers have analyzed data from the 2001 and 2008 recessions and their effects on consumption of the tax rebates and stimulus programs granted by the United States government. One of these is the study of Kaplan & Violante (2014), which indicated that about 25 percent of tax refunds are spent on non-durable
consumption during the same period as the grant, this evidence points to a large proportion of Hand-to-Mouth households in society. In addition, Parker, Souleles, Johnson, & NcClelland (2013) used this period to measure the variation of spending provoked by these stimulus payments. They found that around 12 and 30 percent of this endowment was used for non-durable expenditure within three months from the payment, which indicates a larger response than that suggested by the life-cycle permanent-income hypothesis (p. 2550). They also found a greater response for lower-income households, in other words, an MPC decreasing on wealth.
Another approach to measure this proportion or distinguish these consumers is to focus on the composition of the household’s liabilities. For instance, Cloyne and Surico (2014) indicated that income changes caused by a fiscal policy are likely to have a more substantial effect on the consumption response of mortgage owners despite their level of assets. Dynan (2012) provided insight into this process by examining the “debt overhang”, which is a term used to describe the high level of household leverage, and how it might decrease consumer spending in the face of a recession, when wealth starts to decrease. They pointed to financial institutions refusing to provide credit to highly leveraged households, thus decreasing their access to liquidity. They found evidence for this in the fact that during the great recession, highly leveraged households, despite having endured smaller hits to their wealth, presented a bigger decrease in spending compared to ones with lower levels of debt during the crisis. Moreover, the study of Baker (2014) looks into the sensitivity of income changes depending on the levels of household debt and finds that one standard deviation increase from the normalized mean of the debt to asset ratio increases consumption elasticity by about 25 percent, meaning that households with high levels of debt are more reactive to changes in income.
Alternatively, the measure for liquidity constrained households of Kaplan et al. (2014) takes into account asset composition in the balance sheet to develop their two-asset model.
Distinguishing between liquid and illiquid assets allows them to determine whether households are Wealthy or Poor Hand-to-Mouth. Wealthy-Hand-to-Mouth households being those that own positive amounts of illiquid households, but no liquid assets and Poor-Hand-to-Mouth those that have no liquid or illiquid assets; This model results in a share of liquidity constrained households estimated almost at a constant rate of 30 percent. Their estimation is larger than other approaches because models that focus solely on net worth ignore the Wealthy-Hand-to-Mouth, who despite having positive amounts of net worth, choose to face income shocks instead of accessing their illiquid assets to smooth out consumption, which in this study is assumed to happen at a constant rate. After all, the latter option would entail bypassing returns and having to pay transaction costs or interest rates (Kaplan & Violante, 2014). Besides this, the work of Kaplan et al. (2014) also presents a relation between leverage ratio and Hand-to-Mouth status, in which this condition doubles from 20 to 40 percent as the ratio of leverage goes to one
II. Method and Data
Following the methodology of Kaplan, Violante, Weidner (2014), this paper uses data from the United States Survey of consumer finances (SFC) from years 1989 through 2016. The monetary values are deflated to 2016 terms using data from the 2016 Consumer Price Index Research Using Current Methods (CPI-U-RS). The SFC survey provides cross-sectional data on household balance sheets, income, credit limit, and demographic characteristics. This information enables the calculation of variables like income, net liquid wealth, liquid debt and liquid assets, net illiquid wealth, and net illiquid wealth, measurements that are necessary to categorize households into Non-Hand-to-Mouth, Wealthy-Hand-to-Mouth, and Poor-Hand-to-Mouth. The calculations of these variables are described below. –
Net Liquid Wealth
Net liquid wealth is calculated by subtracting liquid debt from liquid assets. Liquid assets are defined as those that have a low transaction cost. Thus they are determined by adding up all stocks, bonds, directly-held investment funds, and all call, interest-bearing, money market, and savings accounts. These last four mentioned reports (call, interest-bearing, money market, and savings accounts) are inflated by the 2010 ratio of cash holdings calculated by Kaplan et al. (2014) with the 2010 Survey of Consumer Payment Choice. This is done to account for the lack of information on cash holdings presented by the SFC. The calculation of liquid debt simply encompasses adding up all credit card balances after their most recent payment.
Net Illiquid Wealth
Net illiquid wealth is computed by adding up the value of housing, real estate - net of mortgages, home equity loan, private retirement accounts, insurance policies, saving bonds, and deposit certificates. An Alternative measurement for this variable is also considered in which vehicles are taken into account.
Income
The measure for income includes all recurrent earnings such as wages, government benefits, and self-employment salary. It excludes all infrequent sources of income like dividends. However, households whose earnings originate solely from self-employment are dropped from the calculations. Considering results from the Consumer Expenditure Survey (1990 – 2010), the amount of time between paydays is set to two weeks for all households, given that in this model the pay-period is set as two times the number of times per month, the variable pay
frequency is set to 4. However, to provide alternative characterizations, the possibility of a one month pay period is also explored. This research only takes into account households whose head is within the 22 – 79 age range.
Categorization of Households
Household consumption is limited by the intertemporal budget constraint at each period. A period starts when income is received and ends right before the next salary is obtained. This generates kinks, in other words, points at which households have consumed all of their available resources for that period. In this case, it means that their cash in hand is either zero hence zero liquid wealth, or a negative amount equal to their credit limit, this being one month’s worth of income.
Hand-to-Mouth households are expected to end their consumption each period, at one of these two kinks found on the household budget constraint. For the variables previously calculated, being at the zero-liquid-wealth kink means that the measure for liquidity is above zero, but below the amount of cash that the household receives at each payday. Furthermore, being at the credit limit means that its measure for liquidity is below the amount of cash that the household receives at each payday minus the household's credit limit.
Households are categorized as Non-Hand-to-Mouth otherwise. The distinction between Poor and Wealthy Hand-to-Mouth is done with the measure of illiquid assets and is straight-forward. If a Hand-to-Mouth household has an illiquid wealth equal to zero, then it is considered to be Poor-Hand-to-Mouth. If its measure for illiquid wealth is above zero, then it is deemed to be Wealthy-Hand-to-Mouth.
In summary. –
1. Wealthy-Hand-to-Mouth
At the credit limit: 𝑎!" > 0 & 𝑚!" ≤#!"
$%− 𝑚
At zero liquid wealth: 𝑎!" > 0 & 0 ≤ 𝑚!" ≤ #$%!"
2. Poor-Hand-to-Mouth
At the credit limit: 𝑎!" = 0 & 𝑚!" ≤#!"
$%− 𝑚
At zero liquid wealth: 𝑎!" = 0 & 0 ≤ 𝑚!" ≤ #!"
$%
Where 𝑎!" are illiquid assets; 𝑚!" are liquid assets; 𝑚 is the credit limit; 𝑦!" is income per period and 𝑝𝑓 is pay frequency (Kaplan, Weidner, & Violante, The Wealthy Hand-to-Mouth, 2014).
III. Results
Figure 1: Time-series of Fraction of HtM Households in the U.S. Source: Figure based on data from the U.S. SCF. (1989 – 2016)
0 .1 .2 .3 .4 .5 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 W−HtM P−HtM 0 .1 .2 .3 .4 .5 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 Other illiquid but no housing wealth
Only housing wealth
Both other and housing wealth
(a) Share of Total, Wealthy, and Poor HtM
Figure 1.2: Share of Wealthy-Hand-to-Mouth in Hand-to-Mouth Source: Figure based on data from the U.S. SCF. (1989 – 2016)
Figure 2: Share of HtM Households Among Homeowners by Leverage Ratio. Source: Figure based on data from the U.S. SCF. (1989 – 2016)
0 .1 .2 .3 .4 .5 .6 .7
Share of HtM among Homeowners
=0 0−.1 .1−.2 .2−.3 .3−.4 .4−.5 .5−.6 .6−.7 .7−.8 .8−.9 .9−1 >1 Leverage Ratio W−HtM P−HtM .55 .6 .65 Share of Wealthy in HtM 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 Year
Figure 3: Time Series of Fraction of HtM Households in the U.S., Alternate Definitions Source: Figure based on data from the U.S. SCF. (1989 – 2016)
0 .1 .2 .3 .4 .5 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 Time series of fraction HtM: 1 month credit limit
W−HtM P−HtM 0 .1 .2 .3 .4 .5 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 Monthly pay period
0 .1 .2 .3 .4 .5 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 0 .1 .2 .3 .4 .5 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016
(a) Income-weighted Share of HtM (b) Pay-period of 1 Month
IV. Observations of Results
Figure (1a) shows that the share of liquidity constrained households remained at a steady rate over the period at hand, with an average of 31 percent. This considerable proportion of liquidity constrained households goes in line with the findings of Kaplan & Violante (2014) and Parker, Souleles, Johnson, & NcClelland (2013) mentioned in sections above.
Figure (1b) shows that the number of Wealthy-Hand-to-Mouth households with both hosing wealth and other types of assets is the largest component of the group, at an average of 50 percent of all Wealthy-Hand-to-Mouth-Households. In fact, most of the variation in the share of Wealthy-Hand-to-Mouth households comes from this group, the most prominent fluctuations occur in 1995 a year that experienced a growth rate of 13 percent since the previous observed year, and on 2013, year in which a decline of -11 percent was faced. Moreover, the share of this group in the population of Wealthy-Hand-to-Mouth has been at a continuous decline since 2010, reaching its lowest point in 2016, when it made up only about 44 percent of the wealthy liquidity constraint households.
Moving on, the other two groups which make up the type of asset composition of the Wealthy-Hand-to-Mouth are those who only own housing wealth or “other” types of wealth, these groups presented an average of 22 and 21 percent of the Wealthy-Hand-to-Mouth respectively over the observed period, presenting levels of fluctuation considerably lower than the other group. Figure (1b), also shows that the group of Wealthy-Hand-to-Mouth households that owns both types of assets presented its sharpest increase in year 2016, representing 4 percent of the population, a level that can only be found in years previous to 2007. Besides this, another observation from this figure is that the group made up out of those that only own other types of illiquid wealth besides housing seems to be the most stable of the three.
Figure (2) presents the share of Hand-to-Mouth households depending on their leverage ratio, and conditioning on homeownership. Since the term Poor-Hand-to-Mouth represents all households with almost no liquid assets, by construction, there will be an intense concentration of them in the bins with the highest leverage; in our results, this means that this group presents a liquidity ratio above 0.9. The addition of years 2013 and 2016 did not trigger any significant change in this distribution; as mentioned in section (II), we can still see that the fraction of Hand-to-Mouth households increases by 20 percentage points as the leverage ratio approaches one. According to Kaplan, Weidner, & Violante (2014), this means that leverage ratio can be a reliable forecaster of the liquidity constraint condition, given that households that commit a larger fraction of their income to mortgage payments are more prone to be Hand-to-Mouth. This finding goes along with the work of Baker (2014), whose findings indicate that households with illiquid assets and high leverage ratios seem to be particularly responsive to income changes.
V. Discussion
In figure (1), the highest observed share of liquidity constrained households occurs in year 2010, with a value of 34 percent, and the lowest can be found in 2016, at 26 percent. The fact that year 2010 represents a peak in the results can be expected, since at this time the American economy was going through what has been described as a “credit squeeze” in other words, a decrease in the loan supply for banks, entrepreneurs and consumers (Mizen, 2008, pp. 531 - 532). This decrease in the loan availability results in a decrease in the credit limit of some households, thus reducing their access to liquidity, and making them liquidity constrained households. Moreover, during 2010, the share of Poor-Hand-to-Mouth households also
presented a growth rate of 20 percent, the largest increase seen in the observed period. This finding does not align entirely with those of Krueger, Mitman, & Perri (2016) briefly mentioned in section (II), which state that some wealth poor households should not be expected to act in a liquidity constrained matter during a recession.
This same year also presents a decrease of 4 percent in the share of Wealthy-Hand-to-Mouth households. Both of these events could be attributed to the burst of the housing market bubble in December 2008, which provoked a sharp decline in house prices (Mizen, 2008, p. 564). Given that housing is one of the most important components of illiquid assets, this event could have provoked negative house equities for those households whose assets ended up being valued below their mortgages. It is essential to mention that in this study, households with negative home equity are counted as Poor-Hand-to-Mouth because they are unable to get cash from their illiquid asset even if they were willing to do so (Kaplan, Weidner, & Violante, 2014, p. 89). As figure (1b) shows, there is a sizable number of Wealthy-Hand-to-Mouth households whose illiquid wealth is solely made up out of housing wealth, this group makes up on average 22 percent of the Wealthy-Hand-to-Mouth, and its lowest point in the observed period as a percentage of the total Hand-to-Mouth population occurs in 2007 when it decreased by about two percentage points from the previous year. Therefore, it would not be implausible to think that the shock in the housing market turned some of the members of this group, who before the crisis presented a positive amount of illiquid assets into Poor-Hand-to-Mouth.
Figure (1) shows that the biggest drops in the share of liquidity constrained households occur in years 2013 and 2016. These were variations of -9 and -14 percent respectively, meaning that the share of Hand-to-Mouth households had been declining since the Great Recession, feasibly due to the steady recovery of credit supplies, which according to The Federal Reserve Bank of New York, began to grow rather quickly after the 2008 crisis
(McAndrews, 2015). Moreover, while in year 2013, this change was mostly a result of a decrease in the share of Wealthy-Hand-to-Mouth, as it can be seen that the share of Poor-Hand-to-Mouth remained almost stable. The opposite can be seen happening in 2016 when the share of Poor-Hand-to-Mouth experienced a sharp decline, and instead, the share of Wealthy-Hand-to-Mouth barely changed in comparison.
Taking a closer look at figure (1b) to see the composition of the assets of the Wealthy-Hand-to-Mouth during these two years allows us to explain some of these trends. In year 2013, it is possible to see that while the share of households whose illiquid wealth was solely made up of housing wealth increased, the other two groups decreased. Therefore, this decrease in Wealthy-Hand-to-Mouth households could be explained by an alteration to the valuation or demand of the non-housing components of the variable illiquid wealth. In year 2016, we observe instead a sharp increase in the share of households that possessed only housing wealth; this is not a surprise since, in 2016, the United States housing market experienced a peak in house sales since 2007 (Irwin, 2016). Moving on, this was accompanied by a rise in the share of households who own only “other” types of assets and a decrease in the share of households that had both.
As figure (1.2) shows, the segment of Wealthy-Hand-to-Mouth households consistently makes up more than half of liquidity constrained households at an average of 61 percent. This share hits its lowest point in year 1989 at 55 percent and peaks in 2007, at 65 percent. This peak in the proportion of Wealthy-Hand-to-Mouth could be explained by the events leading up to the Great Recession. During this time, the United States economy witnessed an extreme growth of the subprime mortgage market, creditors offering loans for the entire value of a property, and the development of new assets backed by these mortgages, offering high returns with low-risk ratings (Mizen, 2008, p. 532).
In figure (1a) it can be seen that the proportion of Wealthy-Hand-to-Moth has been experiencing a steady decline since the 2008 crisis; however, figure (1.2) shows that, although the share of this group took a dive in years following the crisis, in 2016 their share of households within those with Hand-to-Mouth status started to increase, perhaps due to a recovery in asset prices. Despite these heavy shocks to the economy and the long-lasting effects of the recession, the average share of Hand-to-Mouth and Wealthy-Hand-to-Mouth did not endure any substantial changes with the extension of the data. Nevertheless, it is possible to observe more variation in the share of Hand-to-Mouth households than in previous years.
VI. Robustness analysis
Figure (3) explores alternative definitions for some of the variables used to categorize households. The first panel, subfigure (3a), presents our results, weighting the share of Hand-to-Mouth households by income. This approach causes the share of these households to decrease to an average of 19 percent of total income. This effect is to be expected from this approach since Hand-to-Mouth households make up about 20 percent of the total income in the United States (Kaplan, Weidner, & Violante, 2014, p. 110). However, we can see that most of this change comes from the low weight of Poor-Hand-to-Mouth households whose income represents only 4 percent of total income. This shrinkage can be attributed to the fact that the median annual income of Poor-Hand-to-Mouth households in the United States is below the national average at about 20 thousand (2010) US dollars (Kaplan, Weidner, & Violante, 2014, p. 110).
On the other hand, the share of Wealthy-Hand-to-Mouth was instead almost unaffected by this approach, only presenting a small decline of 4 percentage points. This disparity in the
effect that this method had on these two groups is because the Wealthy-Hand-to-Mouth can be considered high-income households, presenting earnings similar to those of Non-Hand-to-Mouth households, and are therefore able to reach a peak life-cycle median income of 70 thousand (2010) US dollars (Kaplan, Weidner, & Violante, 2014, p. 110). These results are in agreeance with the assertion that net worth studies like the ones done by Deaton (1991) and Carrol & Kimball (1996) find a proportion of Hand-to-Mouth households that only account for the Poor-Hand-to-Mouth because the wealth and income of the Wealth-Hand-to-Mouth are too similar to the Non-Hand-to-Mouth to be picked up by their methodologies.
The second panel, subfigure (3b) shows the effects of setting the pay period to one month instead of two weeks. This methodology increases the average share of Hand-to-Mouth-Households to almost 40 percent, meaning an increase of about ten percentage points from the approach presented in figure (1). However, the fraction of Wealthy-Hand-to-Mouth within the group of liquidity constrained households only increased by three percentage points, from 61 percent (figure 1) to 64 percent. This change means that extending the pay period allowed both groups to increase their share of total households almost by the same rate (the increase of the Wealthy-Hand-to-Mouth being the slightly higher one).
The third panel, subfigure (3c), proposes the use of the credit limit reported by households, instead of our one-month worth of income proxy. This change in the credit limit provokes a small drop in the share of Hand-to-Mouth households, resulting from an almost proportional decrease in the share of Wealthy-Hand-to-Mouth households. This response could mean that the reported credit limit is on average above our proxy, as it increased the measure of liquidity for some households, making them Non-House-to-Mouth. Nevertheless, the most exciting change produced by this approach might be from year 2007, which is the only year in which a change in the direction of the growth rate can be seen. This shift can be accredited to
the excessive access to credit that households experienced in 2007 during the events leading up to the Great Recession, making them inflate their level of liquidity.
Finally, subfigure (3d) shows the effects of considering vehicles as illiquid wealth. This method does not affect the total share of Hand-to-Mouth households, given that it does not alter the holdings of liquid assets. It did, however, increase the illiquid wealth of many car owners, which made the share of Wealthy-Hand-to-Mouth to increase by 22 percentage points from the original approach, to 83 percent of liquidity constrained households. The reason for this is that Poor-Hand-to-Mouth households that were previously considered to have no illiquid wealth display a positive value of it under this method.
VII. Conclusion
This paper provided an empirical analysis of the share of liquidity constrained households and their asset composition in the United States. Our results show that the average share of liquidity constrained households does not change with the addition of years 2013 and 2016 to the original 1989 – 2010 period examined by Kaplan, Weidner, & Violante (2014). However, the two added years do present substantially higher variations in the shares.
Some of the results obtained were related to the events of the 2008 financial crisis and its aftermath, the elevated access to housing credit prior to the recession was aligned with the 2007 peak in the share of the Wealthy-Hand-to-Mouth, and the subsequent drop in the housing market to the sharp decline in the share of this same group in 2010. This phenomenon was also associated with the observed asset composition of the Wealthy-Hand-to-Mouth through the years. For instance, the high performance of the 2016 housing market, aligns with a peak in the share of Hand-to-Mouth households whose illiquid assets is only made out of housing assets in
that year. Therefore, although it was shown that the share of Hand-to-Mouth households is constant throughout the years, it is likely that income fluctuation, credit availability, and even the markets of certain assets play a significant role in it determining the behavior of these consumers. This sensitivity is especially true for the division between Wealthy and Poor Hand-to-Mouth of these households.
This paper also provides a robustness analysis for some of the variables that determine the categorization of the households were also explored, these changes placed the average share of Hand-to-Mouth households in the total population or income at a low of 20 percent and a high of 40 percent, these values could serve bounds or thresholds for the actual share of liquidity constrained households in the society. Moreover, these different methodologies also enabled the visualization of some of the differences between the two groups making up Hand-to-Mouth households. By weighting the share depending on their income weight it was possible to perceive the big difference in income that exist between the Poor-Hand-to-Mouth households and Non-Hand-to-Mouth ones, and on the contrary, the similarities between this last-mentioned group and the Wealthy-Hand-to-Mouth, which is probably the reason why this group is often overlooked by other methodologies.
Research aimed to find aspects that precisely distinguish the Wealthy-Hand-to-Mouth from the Non-Hand-to-Mouth could be very useful for the previously stated purpose. Furthermore, although the data used to calculate these results was of high quality, it would be ideal to have closer intervals between the measurements and more precise information regarding consumption habits, cash holdings, and other measurements for which assumptions were made, for future research in this matter.
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