Consumption and the permanent income of households

Consumption and the permanent income of households | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Consumption and the permanent income of households Roni Frish This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4204612/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the household consumption utilizing data from the Israeli Consumer Expenditure Surveys and longitudinal administrative income records for 2004–2016. Three key findings challenge the predictions of the Permanent Income Hypothesis (PIH) and the Life-Cycle Hypothesis (LCH): First, households with higher income growth (2004–2016) exhibited lower consumption levels in the earlier period (2004–2007) and higher consumption in the later period (2013–2016). Second, households tend to consume a significant portion of transitory incomes immediately. Third, households without a pension plan show a marked decrease in consumption upon retirement. Consumption Empirical Analysis Permanent Income Life-Cycle Income 1. Introduction The Permanent Income Hypothesis (PIH) and the Life-Cycle Hypothesis (LCH) provide foundational frameworks for analyzing consumption behavior, positing that individuals smooth consumption over time. The standard empirical tests used to assess the validity of the PIH require a consumption survey that tracks a household's spending behavior over multiple periods, a survey which is not available for most countries. However, in various countries, including Israel, it is possible to combine each household's Consumer Expenditure Survey (CES) data with its longitudinal administrative income records from the Tax Authority. The PIH posits that consumption growth should be uncorrelated with income growth. This research examines the validity of this hypothesis by comparing households with markedly different income trajectories. The study employs a linear regression approach to determine the income growth rate for each household, measured by the slope of the income trend line over time. Households are then divided based on whether their income growth rate surpasses the median within their age cohort, assigning a binary value. It was found that households with steeper income growth exhibit lower consumption in earlier years (2004–2007) and higher consumption later on (2013–2016) relative to the other group, with moderate income growth. This suggesting that the timing of income receipt affects consumption patterns. Furthermore, the study assesses the propensity to consume out of income received before versus after the CE survey. A ratio of these propensities near unity would corroborate the PIH. However, the findings indicate a significantly lower ratio, ranging from 0.3 to 0.5, suggesting a striking deviation from the hypothesis. These findings do not necessarily contradict the PIH. It is possible that households' risk aversion is so high, and the variability in future household income is so great, that households prefer to wait and consume the majority of their additional future income only after it has been securely received. However, in reality, the propensity to consume out of future income is very low. The study further explores the consumption response to temporary deviations from a household's long-term income average. Contrary to the PIH, which predicts a moderate consumption response, the findings indicate that households consume 23% of exceptional one-time income in the year it is received, underscoring a predilection for immediate consumption. Additionally, the research examines consumption patterns in the context of retirement, uncovering that households with a pension plan exhibit stable consumption following retirement, while those lacking a pension plan experience a 21% decrease in consumption. 2. Literature review The PIH is a central theory in understanding household consumption behavior, with an extensive body of literature examining its theoretical underpinnings and empirical validity. The PIH posits that rational households anticipate future events and aim to maximize their utility over multiple periods, subject to a budget constraint that spans these periods. The multi-period utility is conceptualized as the sum of discounted utilities - 'u' - across periods - 't', where each period's utility is a function of consumption - u(c t ) - which is discounted by a rate of time preference. Assuming negative second derivative of utility with respect to consumption and identical interest rates ('r') for borrowing and lending, Euler's equation emerges, guiding intertemporal consumption decisions. $$\text{u}`\left({\text{c}}_{\text{t}}\right)={(1+{\delta })}^{-\text{s}}{\text{E}}_{\text{t}}\left[\right(1+{\text{r}}_{\text{t}+\text{s}})\text{u}`\left({\text{c}}_{\text{t}+\text{s}}\right)]$$ 1. While - s - is the number of periods between period 't' and another period. Delta represents the rate of time preference. When assuming a quadratic utility function, specific consumption trajectories can be derived, leading to predictable patterns of consumption smoothing. $${\text{c}}_{\text{i},\text{t}}={(1+{\delta })}^{-\text{s}}{\text{E}}_{\text{t}}(1+{\text{r}}_{\text{t}+\text{s}}){\text{E}}_{\text{t}}{(\text{c}}_{\text{i},\text{t}+\text{s}})$$ 2. Alternatively, if income is certain consumption is expected to grow at a constant rate. However, assuming income uncertainty and precautionary savings motive, characterized by a negative third derivative of the utility function with respect to consumption, suggests that households may consume less in earlier periods due to income uncertainty, leading to asset accumulation as a buffer against potential income declines, as suggested by Deaton ( 1992 ). The orthogonality test of the PIH testes whether changes in consumption are independent of predicted changes in income. Hall and Mishkin ( 1980 ) provided mixed support for the hypothesis, while Altonji and Siow ( 1987 ) corrected for measurement errors in the Panel Study of Income Dynamics (PSID) data and found stronger evidence for the PIH. The Excess Sensitivity test examines the response of consumption to transitory income shocks. Parker et al. (2013), Parker ( 1999 ), Agarwal & Souleles (2007), and Souleles ( 1999 , 2002 ) found that fiscal stimulus measures and tax cuts led to immediate increases in consumption, a pattern that is not consistent with the PIH. Crawley and Kuchler ( 2023 ) observed the same pattern in Denmark, while households with substantial liquid assets are the only exceptions. Hsieh ( 2003 ), however, posited that the response to income changes is contingent on their frequency and predictability, with households more likely to follow the PIH for regular, substantial income variations. Additional phenomenon that contradicts the PIH and the LCH known as the `retirement consumption puzzle` refers to the unexpected decline in household spending upon retirement. Studies such as Banks et al. ( 1998 ) observed a marked decrease in consumption post-retirement in the UK. Bernheim et al. ( 2001 ) used PSID data to investigate various factors that might explain the puzzle, yet found little evidence supporting the LCH. However, Hurst (2008) shows that the standard model of lifecycle consumption augmented with home production and uncertain health shocks does well in explaining the retirement consumption puzzle. Aguiar and Hurst ( 2005 ) suggested that retirees might spend less on food not because of insufficient savings, but due to increased time for shopping and cooking, which lead to more efficient spending without reducing food intake. In genera the literature reveals several factors that lead to deviations from the LCH consumption patterns such as liquidity constraints (Zeldes, 1989 ; Carroll, 1997) and financial friction (Laibson et al., 2003). Beyond these traditional analyses, behavioral economists have challenged the assumption of rational behavior inherent in the PIH. Thaler (1990) and Tversky and Kahneman (1991) have explored the impact of mental accounting and loss aversion on spending decisions, suggesting that cognitive biases can lead to deviations from the consumption patterns predicted by the PIH and LCH. 3. Data The study uses data from each of the 13 annual Consumer Expenditure (CE) surveys conducted from 2004 to 2016. These cross-sectional surveys provide a representative sample of households, with a new sample being selected each year. The dataset includes detailed records of household consumption expenditures and demographic attributes of household members, such as education and age. This information is complemented by matched net wage income data from the Tax Authority's administrative records for the years 2002–2016, which pertains to the heads of households and their spouses as identified in the CE surveys. For most households, longitudinal wage data are available over a 15-year span, while consumption data are captured at a single point in time. 4. Methodology The PIH suggests that individuals plan their consumption based on an expectation of their lifetime income, rather than their current income alone. To evaluate this hypothesis, the study employs a multi-faceted approach that considers future income expectations, one-time transitory income shocks, and the effects of reaching retirement age. 4.1. Income Growth and Consumption Patterns: The analysis begins by categorizing households of each age group into two distinct groups based on the rate of their income growth. This is achieved by calculating the slope of a linear regression line for each household's income against a time trend. Households are then classified according to whether their income growth rate exceeds the median within their age cohort. The study calculates the differences in consumption between these two groups across various years and then examines whether these differences are consistent over time, as predicted by the PIH. 4.2. Future Income and Consumption Propensity: The study investigates how the expectation of future income influences household consumption decisions. The analysis pays special attention to the timing of income changes, examining those that occurring after the Consumer Expenditure (CE) survey as opposed to those occur before it. It estimates the propensity to spend additional future income and compares it to the propensity to spend additional past income. A ratio near one, when comparing the propensity to consume out of additional future income with that of additional past income, would corroborate the PIH. 4.3. The study conducts a simple version of the excess sensitivity test, using transitory income as the independent variable and consumption as the dependent variable—instead of the change in consumption between two time periods as is generally done. It models consumption as a function of transitory income and multi-year average income to account for permanent income. According to the PIH, the coefficient on transitory income is expected to be relatively small, suggesting that households do not significantly adjust their consumption in response to temporary changes in income. 4.4. Post-Retirement Consumption Behavior: The research examines whether households maintain their pre-retirement consumption levels or experience a shift, which could either confirm or refute the PIH's prediction of consumption smoothing. The study also considers the role of pension plans and whether they influence post-retirement spending behaviors, providing a more nuanced understanding of how financial security affects consumption decisions in later life. The study includes controls for each survey year, thereby isolating any annual effects that may sway consumption patterns. It also accounts for pivotal demographic factors that influence household spending, such as age and family size. The regression models are designed with both linear and quadratic terms for age and family size to describe the observed pattern where consumption initially increases with these variables but then reaches a plateau or even declines. In addition, the study introduces a binary variable to differentiate between the consumption behaviors of homeowners versus renters, enabling a targeted analysis of how homeownership status affects spending habits. 5. Estimation results 5.1. Income steepness The dummy variable 'Income Steepness' classifies households within each age group into two categories based on whether their income growth in the sample period is above or below the median for their age cohort. The regression analysis in Table 1 accounts for 'Income Steepness' and for two interaction terms to examine the stability of its impact on consumption over time: The first interaction term is between 'Income Steepness' and a dummy variable for the early years, which is assigned a value of 1 for the years 2004 to 2007 and 0 for all other years. The second is between 'Income Steepness' and a dummy variable for the later years, which is set to 1 for the years 2013 to 2016 and 0 otherwise. The dependent variable in Table 1 is the natural logarithm of household consumption for households headed by a married couple that have consistently reported a positive combined income from employment each year from 2004 to 2016.[1] The regression also includes the following control variables: household size, size square, homeownership status, the age of the household head and age square, and a dummy variable for each survey year. Table 1: The Effect of 'Income Steepness' on (log) Consumption, by age groups. (1) (2) (3) (4) Age groups 25-65 30-39 40-49 50-59 Dependent variable: Log Consumption Log Consumption Log Consumption Log Consumption Dummy Income steepness -0.0836*** -0.0489 -0.0804** -0.123*** *Dummy for 2004-07 (0.0200) (0.0307) (0.0359) (0.0455) Dummy Income steepness 0.0353* 0.0712** -0.0146 0.0963** *Dummy 2013-16 (0.0182) (0.0298) (0.0323) (0.0393) Dummy Income Steepness 0.235*** 0.251*** 0.280*** 0.142*** (0.0127) (0.0202) (0.0233) (0.0279) Homeownership 0.147*** 0.110*** 0.165*** 0.312*** (Dummy) (0.0126) (0.0169) (0.0251) (0.0387) Household size 0.112*** 0.0309* 0.159*** 0.169*** (0.00971) (0.0165) (0.0185) (0.0212) Household size Squared -0.00824*** -0.00263* -0.0111*** -0.0124*** (0.000871) (0.00149) (0.00148) (0.00214) Age 0.0502*** 0.0340 0.185** -0.123 (0.00430) (0.0621) (0.0848) (0.129) Age Squared -0.00048*** -0.000165 -0.00199** 0.00111 (4.77e-05) (0.000892) (0.000955) (0.00119) Dummy for each survey + + + + Constant 7.726*** 8.178*** 4.550** 12.11*** (0.0875) (1.074) (1.874) (3.509) Number of Observations 12,137 4,052 3,883 2,786 R-squared 0.156 0.158 0.142 0.116 In the parentheses, the standard errors of the estimates are shown. The asterisks indicate the level of significance: *** p<0.01, ** p<0.05, * p<0.1. The analysis includes households headed by a married couple, under the condition that both spouses have a positive (combined) income from work in each of the years - from 2004 to 2016. The results indicate that the timing of income has a significant effect on household consumption. In the early years, households with steeper income growth had significantly lower consumption compared to others, while in the later years, their consumption was significantly higher. This pattern is consistent across different age groups. Consumption and income are in constant 2004 prices. Regression 1 estimates the consumption of households where the age of the household head - at the time of responding to the expenditure survey - ranges from 25 to 65. The coefficient for the interaction between Dummy 'Income Steepness' and the dummy variable for the early years is negative and significant, indicating that households with steeper income growth exhibited lower consumption levels during 2004 to 2007 compared to other households. Conversely, the coefficient for the interaction between 'Income Steepness' and the dummy variable for the later years is positive and significant, suggesting that these households had higher consumption levels during 2013 to 2016. This pattern is consistent across different age groups, as demonstrated by regressions 2, 3, and 4 for the age groups 30 to 39, 40 to 49, and 50 to 59, respectively. These findings imply that households may adjust their consumption not only based on their long-term income expectations but also in response to the timing of actual income receipt. Such a deviation from the smooth consumption pattern indicates the potential influence of factors such as liquidity constraints or changing expectations that the PIH does not account for; as well as income volatility and a precautionary savings motive, which are not addressed in this paper. Table 2: The Effect of Income Steepness during the years 2004-16 on Consumption in the 2004-2010, by age group. (1) 25-65 (2) 30-39 (3) 40-49 (4) 50-59 Dependent variable Log Consumption Log Consumption Log Consumption Log Consumption Dummy Income steepness 0.0847*** 0.102*** 0.112*** -0.00118 (0.0105) (0.0168) (0.0188) (0.0235) Income in Survey Year 1.48e-06*** 1.56e-06*** 1.43e-06*** 1.36e-06*** (3.53e-08) (6.50e-08) (5.82e-08) (6.83e-08) Homeownership 0.0976*** 0.0755*** 0.0934*** 0.247*** (Dummy) (0.0143) (0.0193) (0.0280) (0.0442) Household size 0.102*** 0.0572*** 0.144*** 0.140*** (0.0115) (0.0189) (0.0213) (0.0257) Household size Squared -0.00576*** -0.00279* -0.00838*** -0.00807*** (0.00101) (0.00169) (0.00167) (0.00253) Age 0.00989* -0.0265 0.247** -0.245 (0.00575) (0.0780) (0.108) (0.179) Age Squared -6.34e-05 0.000477 -0.00273** 0.00224 (6.51e-05) (0.00113) (0.00122) (0.00165) Dummy for each survey + + + + Constant 10.94*** 11.69*** 5.549** 17.76*** (0.113) (1.344) (2.399) (4.836) Number of Observations 5,709 2,063 1,790 1,251 R-squared 0.325 0.304 0.341 0.302 In the parentheses, the standard errors of the estimates are shown. The asterisks indicate the level of significance: *** p<0.01, ** p<0.05, * p<0.1. The analysis includes households headed by a married couple, under the condition that both spouses have a positive (combined) income from work in each of the years - from 2004 to 2016. The table presents the results of regression analyses estimating the effect of income steepness on household consumption for different age groups. The dependent variable is the log of household consumption, and the main independent variable of interest is the dummy for income steepness during the years 2004-16. Consumption and income are in constant 2004 prices. Table 2 examines the alternative hypothesis that consumption is uncorrelated with long-term income expectations. The regressions estimate the log of consumption between 2004 and 2010 as a function of household current income in the survey year, the 'Income Steepness' between 2004 and 2016, and a set of control variables (including a dummy variable for each survey year). For the age group 25 to 65 (Regression 1), it was found that income steepness has a significant effect on consumption—households whose income grew more steeply between 2004 and 2016 had a consumption level 8.5% higher between 2004 and 2010 than other households with the same current income, in the survey year, but with more moderate income growth. Among the age group 30 to 39 (Regression 2), a gap of 10% was found, and for those aged 40 to 49, a gap of 11% was observed (Regression 3). The results indicate that future income, as revealed in retrospect, has a significant positive effect on consumption. However, this effect is not significant for the age group 50-59 (Regression 4). 5.2. The Effect of Future Wages on Household Consumption Table 3 examines the effect of future wages on household consumption headed by a married couple. Regression 1 estimates household consumption as a function of the combined wages of the couple in the year of the survey, the difference of this wage over the previous three years (w t -w t-3 ), and the difference over the subsequent three years (w t+3 -w t ), as revealed in retroactively. Additionally, the regression includes the standard control variables (a separate dummy variable for each survey year, household size, size square, homeownership status, age and age square.) It was found that future income has a significant effect on the extent of household consumption expenditure – an addition of one shekel to the combined wages of the couple over the next three years is equivalent in its effect to an addition of 0.44 shekel to their combined wages during the previous three years. (i.e. 0.44 is the ratio between the coefficient of wage increase in the coming years and the coefficient of wage increase in the past years.) A similar result was found in Regression 3, which examined the effect of one shekel received over a time span of five years (instead of three). Regressions 2 and 4 replicate Regressions 1 and 3 with one difference: the dependent variable is the log of consumption (instead of consumption). The ratio between the coefficient of wage increase in the coming years and the coefficient of wage increase in the past three years is 51% (reg. 2) over a time span of three years and 60% (reg. 4) over a time span of five years. Table 3: The effect of past and future earnings on household (log) consumption (1) Consumption (2) Log Consumption (3) Consumption (4) Log Consumption wage Current 0.305*** 1.73e-06*** 0.331*** 1.86e-06*** (0.00594) (3.07e-08) (0.00869) (4.68e-08) Wage increase over 0.0593*** 3.73e-07*** -- -- the next 3 years (0.00820) (4.24e-08) Wage increase over 0.135*** 7.30e-07*** -- -- the past 3 years (0.0107) (5.51e-08) Wage increase over -- -- 0.0642*** 4.29e-07*** the next 5 years (0.00927) (4.99e-08) Wage increase over -- -- 0.138*** 7.13e-07*** the past 5 years (0.0127) (6.84e-08) Homeownership -6,550*** -0.0883*** -12,096*** -0.119*** Dummy (1,968) (0.0102) (2,684) (0.0145) Household size 14,332*** 0.0776*** 11,491*** 0.0616*** (1,740) (0.00898) (2,336) (0.0126) Household size -812.1*** -0.00449*** -629.0*** -0.00334*** Squared (149.7) (0.000773) (200.5) (0.00108) Age 3,324*** 0.0250*** 2,392* 0.0198*** (944.2) (0.00488) (1,278) (0.00688) Age Squared -29.26*** -0.000245*** -20.05 -0.000195** (10.85) (5.60e-05) (14.66) (7.89e-05) Dummy for each survey year + + + + Constant -9,704 10.85*** 21,507 11.03*** (18,995) (0.0981) (25,797) (0.139) Observations 10,756 10,756 5,418 5,418 R-squared 0.257 0.304 0.289 0.319 Ratio future/past 44% 51% 47% 60% In the parentheses, the standard errors of the estimates are shown. The asterisks indicate the level of significance: *** p<0.01, ** p<0.05, * p<0.1. The analysis includes households headed by a married couple, under the condition that both spouses have a positive (combined) income from work in each of the years - from 2004 to 2016; and they survey in the HES between 2005 and 2013. Consumption and income are in constant 2004 prices. Table 4 presents robustness tests for homogenies population groups, using the specified regression model 2 in Table 3. It was found that the propensity to consume out of future income (over the next three years) relative to past income (over the past three years) ranged from 30 to 50 percent (as shown in Column Three of Table 4). A higher consumption ratio was observed in the age group of 40 to 49, compared to the 30 to 39 age group (47% and 41%, respectively). The lowest consumption ratio was noted in the 50 to 59 age group. Among households with high income, a slightly lower consumption ratio was observed - 50% - compared to those with low income - 45%. (The income threshold is set at a combined annual wage of 140 thousand ILS, in 2004 prices.) Finally, non-academics (ages 30 to 60) exhibited a higher consumption ratio compared to academics of the same age group, as detailed in Table 4. Table 4: The ratio of adding one shekel in the future to add one shekel in the past. Each cell summarizes the result of a separate regression for distinctive subgroups. The dependent variable is log consumption. Subgroup Observations With Control Variables Without Control Variables Age 30 to 39 4,018 41% 47% Age 40 to 49 3,407 47% 48% Age 50 to 59 2,466 28% 35% Low Income (age 30-60) 5,455 50% 39% High Income (age 30-60) 5,301 45% 40% Non-academics (age 30-60) 6,829 40% 37% Academics (age 30-60) 3,927 30% 24% Each cell in the two right columns summarizes the result of a separate regression. The explanatory variables: the joint wages of the household heads in the survey year, the growth in it over the previous three years, the growth in it over the next three years, and dummy variables for survey years. The regressions with control variables include the standard set of controls: household size, size square, homeownership status, age and age square. The table presents the ratio between the estimated coefficient for the growth in wages of the household heads over the next three years and that of the previous three years. Consumption and income are in constant 2004 prices. From the estimates in this section, it can be concluded that the propensity to consume from future income is substantially and significantly lower than the propensity to consume from current income. The ratio between these two propensities ranges from 30 to 50 percent. Even among groups typically assumed to have better access to capital market financing, such as academics, high-income earners, and homeowners, the propensity to consume out of future income remains low. Therefore, this phenomenon cannot be solely attributed to a lack of access to convenient loans. However, the results obtained here do not necessarily contradict the PIH. It is possible that the variability in future household income is so large and households' risk aversion is so high that they prefer to wait and consume the majority of additional future income only after it has been securely received. 5.3. Exceptional One-Time annual Income effects on household consumption This subsection examines how an Exceptional One-Time annual Income (EOTI) received in the survey year affects the household consumption level in that year. The EOTI is the current annual income minus the "symmetric income" - the average of the household income over the six years surrounding the survey year (the average of the combined wage income of both spouses received in the three years before and after the survey year). This exceptional one-time income could be either expected or unexpected. Regressions 1 and 2 in Table 5 estimate the household consumption level as a function of the following two income variables: 1. The Exceptional One-Time annual Income. 2. "Symmetric income," which controls for permanent income. It was found that one shekel of EOTI increases the consumption by 0.05 shekels. A similar estimate was obtained in Regression 1, which includes only the dummy variables for the survey years (0.047), and in Regression 2, which includes the set of control variables (0.055). At first glance, these results seem to be consistent with the PIH. However, a different conclusion is drawn from Regression 3, which repeats the same estimate but refers only to households with positive EOTI. (Households whose (real) current income was higher than the (real) symmetric average income.) Regression 3 found that 23% of the exceptional positive one-time income is directed to consumption in the same year the EOTI was received. A similar estimate across different age groups revealed that aged 30-39 spent 18.5% of the positive EOTI on consumption, while those aged 40-49 consumed 21% of it. Regression 4 (in Table 5) estimates the consumption response to a negative EOTI (for age group 30 to 60) – current income lower than the symmetric average income - it was found that decrease of one Shekel in current income relative to the symmetric average income reduces consumption by 0.06 Shekel. The household response to positive exceptional income does not align with the PIH; since a significant portion of it is consumed immediately, whereas the theory predicts it would be spread over the entire life horizon (and have no impact if anticipated). Furthermore, the asymmetric response of households to positive EOTI compared to negative exceptional income might suggests that the utility from consumption in each period depends on consumption in the previous period (habit formation). Table 5: Consumption level as a function of EOTI - Exceptional One Time annual Income (compared to the long-term symmetric income). (1) (2) (3) (4) Consumption Consumption Consumption Consumption Positive EOTI Negative EOTI Exceptional One-Time annual 0.0469*** 0.0545*** 0.233*** -0.0587** income (0.0157) (0.0154) (0.0287) (0.0272) (Average) Symmetric income 0.316*** 0.306*** 0.299*** 0.280*** (0.00588) (0.00596) (0.00877) (0.00930) Homeownership -- 809.3*** 907.9*** 839.7*** ( Dummy ) (197.2) (265.6) (293.0) Household size -- 1,479*** 1,492*** 1,503*** (163.8) (210.1) (259.1) Household size Squared -- -82.50*** -78.96*** -90.18*** (14.56) (18.60) (23.16) Age -- 121.6 77.91 177.2 (77.24) (103.6) (115.4) Age Squared -- -0.682 -0.331 -1.116 (0.861) (1.149) (1.296) Dummy for each survey year + + + + Constant 8,870*** -132.9 527.9 -1,602 (260.8) (1,541) (2,087) (2,283) Observations 8,466 8,466 4,540 3,926 R-squared 0.257 0.285 0.306 0.275 In the parentheses, the standard errors of the estimates are shown. The asterisks indicate the level of significance: *** p<0.01, ** p<0.05, * p<0.1. The analysis includes households headed by a married couple, under the condition that both spouses have a positive (combined) income from work in each of the years - from 2004 to 2016; and they survey in the HES between 2005 and 2013. Consumption and income are in constant 2004 prices. 5.4 household consumption before and after the retirement age According to the PIH, predictable changes such as those caused by reaching retirement age, should not affect household consumption. To investigate this hypothesis, this subsection will analyze the consumption patterns in the years surrounding the legal retirement age, which is 67 for men. It will focus on households of married couples where the husband's age is between 62 and 70. The regression models explain the natural logarithm of household consumption by the following factors: the husband's age (dummy 1 if age is over 68, and 0 otherwise), husband's wage at age 64, the number of persons in the household (individual dummy variables assigned to each household size), and the survey year (individual dummy variables assigned to each survey year). Regression 1 finds that households with male heads aged 68 to 70 exhibit a consumption level that is 8.8% lower than those with male heads aged 62 to 66. Regressions 2 and 3 replicate Regression 1, while Regression 2 including only households with pension plans and Regression 3 including only those without. The findings indicate that consumption remains stable for those with a pension plan, while there is a significant 21% reduction in consumption levels for those without a pension plan. Similar results were obtained when examining the change in consumption, excluding food. Those without a pension plan constituted 34% of the 62-70 sample, and their consumption patterns are inconsistent with the PIH and LCH. Those with a pension plan opt in voluntarily to secure an adequate standard of living after retirement. However, it remains unclear whether their behavior would still align with the LCH even if they did not have the option to join a pension plan. Table 6: Household Consumption after reaching the legal retirement age as a function of participate in pension plans. Male heads aged 62-70. (1) Log (2) Log (3) Log Consumption Consumption Consumption All households With a pension plan Without a pension plan Dummy variable for Male heads -0.0642*** -0.00235 -0.214*** aged 68 to 70 (0.0209) (0.0240) (0.0388) Male heads real wage at 1.66e-06*** 1.44e-06*** 1.92e-06*** age 64 (6.04e-08) (6.88e-08) (1.20e-07) Dummy for each household size + + + Dummy for each survey year + + + Constant 9.165*** 9.288*** 9.032*** (0.0461) (0.0598) (0.0706) Observations 2,759 1,809 950 R-squared 0.230 0.207 0.286 In the parentheses, the standard errors of the estimates are shown. The asterisks indicate the level of significance: *** p<0.01, ** p<0.05, * p<0.1. The analysis includes households headed by a married couple, under the condition that the male heads aged 62-70 and they survey in the HES between 2004 and 2016. Consumption and income are in constant 2004 prices. 6. Conclusion This study investigates whether the behavior of households in Israel aligns with the Permanent Income Hypothesis (PIH) and Life-Cycle Hypothesis (LCH). It assesses the impact of changes in household income on consumption, using data from the Consumer Expenditure Surveys, combined with longitudinal administrative income records. The study examined the relationship between household income growth and household consumption dynamics, using a dummy variable to categorize households by whether their income growth was above or below their age group's median. The analysis included interaction terms for early (2004–2007) and later years (2013–2016) to assess the stability of this impact over time. The results showed that households with a sharper increase in income had lower consumption in the early years, but higher consumption in later years, compared to households with a moderate income growth. This pattern held across various age groups. The findings indicate that households deviating from the PIH, which suggests consumption is based on long-term income expectations. The study analyzed the impact of future wages on household consumption led by married couples, taking into account wages at the time of the survey and wage over the past and subsequent three years. The findings indicate that future income significantly affects household consumption, with one additional shekel to future wages over the next three years having an effect equivalent to 0.3–0.5 of one additional shekel received over the past three years. The results suggest that while future wages do influence consumption, their effect is much smaller than that of past and current wages. The household prefer to wait and consume the majority of additional future income only after it has been securely received. This pattern holds true among groups that have good access to capital market financing, such as academics, high-income earners, and homeowners. This study finds that households prefer to consume a large portion of their Exceptional One-Time Incomes (EOTI) concurrently. EOTI is defined as the current annual income minus the average income over the six years surrounding the survey year. Focusing only on households with positive EOTI showed that 23% of this income is consumed in the same year. This contradicts the PIH, which predicts that such income would be spread out over a lifetime. Additionally, households responded asymmetrically to positive versus negative EOTI, with a one-time exceptional decrease in current income having only a minor effect on consumption. This asymmetric response challenge the PIH, suggesting that factors like habit formation may influence consumption decisions. Finally, the study examined how reaching the legal retirement age affects household consumption. Focusing on married couples with the husband aged 62 to 70, the analysis found that households with husbands aged 68 to 70 consume 8.8% less than those with husbands aged 62 to 66. When dividing the sample into households with pension plans and those without, it was found that consumption remained stable for those with a pension plan but dropped by 21% for those without a pension plan. The latter ones do not align with the PIH and LCH, as they experience a significant reduction in consumption upon reaching the expected legal retirement age. This study did not investigate the effect of uncertainty of future income on current consumption levels among Israeli households, a factor that could explain the discrepancies observed with PIH and LCH. Accurately quantifying the uncertainty of future household income remains a significant challenge that future research needs to address. I declare that the author have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Declarations Author Contribution Roni Frish processed the data and wrote the research including the tables. Data Availability Data that support the findings of this study have been deposited in the Central Bureau of Statistics of Israel, nd their use is conditional upon the approval of the CB of Statistics. Roni References Agarwal, S., Liu, C., & Souleles, N. S. (2007). The reaction of consumer spending and debt to tax rebates—Evidence from consumer credit data. Journal of Political Economy, 115(6), 986–1019. Aguiar, M., & Hurst, E. (2005). Consumption versus expenditure. Journal of Political Economy, 113(5), 919–948. Altonji, J. G., & Siow, A. (1987). Testing the response of consumption to income changes with (noisy) panel data. The Quarterly Journal of Economics, 102(2), 293–328. Banks, J., Blundell, R., & Tanner, S. (1998). Is there a retirement-savings puzzle? American Economic Review, 88(2), 769–788. Bernheim, B. D., Skinner, J., & Weinberg, S. (2001). What accounts for the variation in retirement wealth among U.S. households? American Economic Review, 91(4), 832–857. Browning, M., & Lusardi, A. (1996). Household saving: Micro theories and micro facts. Journal of Economic Literature, 34(4), 1797–1855. Crawley, E., & Kuchler, A. (2023). Consumption heterogeneity: Micro drivers and macro implications. American Economic Journal: Macroeconomics, 15(1), 314–341. Deaton, A. (1992). Understanding consumption . Oxford University Press. Hall, R. E., & Mishkin, F. S. (1980). The sensitivity of consumption to transitory income: Estimates from panel data on households. Econometrica, 48(2), 461–481. Hausman, J. A., & Paquette, L. (1987). Involuntary early retirement and consumption. In S. A. Woodbury & R. A. Triest (Eds.), Work, health, and income among the elderly (pp. 151–175). The Urban Institute Press. Hsieh, C. T. (2003). Do consumers react to anticipated income changes? Evidence from the Alaska Permanent Fund. American Economic Review, 93(1), 397–405. Johnson, D. S., Parker, J. A., & Souleles, N. S. (2006). Household expenditure and the income tax rebates of 2001. American Economic Review, 96(5), 1589–1610. Parker, J. A. (1999). The reaction of household consumption to predictable changes in social security taxes. American Economic Review, 89(4), 959–973. Souleles, N. S. (1999). The response of household consumption to income tax refunds. American Economic Review, 89(4), 947–958. Souleles, N. S. (2002). Consumer response to the Reagan tax cuts. Journal of Public Economics, 85(1), 99–120. Zeldes, S. P. (1989). Consumption and liquidity constraints: An empirical investigation. Journal of Political Economy, 97(2), 305–346. Footnotes This was done as a precautionary measure, since sometimes an annual income of zero results from an error in the administrative file. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4204612","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288032117,"identity":"221b2715-b3fc-4e2d-ac25-ebfe0050ad59","order_by":0,"name":"Roni Frish","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIie3PuwrCMBSA4VMEXQJdD1jaV4gU7CL4KqcInSoIvkA3l4prwcWn0DUiOKlzB5FOzpnEwcF4gYpDqptIfjId8uUCYDL9aAI6wABrawD+nBSVJLqRelQSqrxHbQZk7XKgI8FovBRAB8ebpqdCDsANJjNLe4uz2ZEiQ8b320Ur4+A7eaF/GGLMhXUmxrE/bzIOYYbiAwJEzMvi45cE8rj+IHZSQdiGC1KE51Gg/oI+opXoSSP1pSTqelnvWMhLx0V7tZJSQ+69nIlqhUkVeM8W3wqTyWT6866QzEnw5TyNcwAAAABJRU5ErkJggg==","orcid":"","institution":"Bank of Israel","correspondingAuthor":true,"prefix":"","firstName":"Roni","middleName":"","lastName":"Frish","suffix":""}],"badges":[],"createdAt":"2024-04-02 07:23:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4204612/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4204612/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54404266,"identity":"5b112115-ec45-4116-951c-0480a9ca4816","added_by":"auto","created_at":"2024-04-10 03:14:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":208421,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4204612/v1/97c41528-18fb-4719-b09f-af2c2d4e692c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Consumption and the permanent income of households","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Permanent Income Hypothesis (PIH) and the Life-Cycle Hypothesis (LCH) provide foundational frameworks for analyzing consumption behavior, positing that individuals smooth consumption over time. The standard empirical tests used to assess the validity of the PIH require a consumption survey that tracks a household's spending behavior over multiple periods, a survey which is not available for most countries. However, in various countries, including Israel, it is possible to combine each household's Consumer Expenditure Survey (CES) data with its longitudinal administrative income records from the Tax Authority. The PIH posits that consumption growth should be uncorrelated with income growth. This research examines the validity of this hypothesis by comparing households with markedly different income trajectories. The study employs a linear regression approach to determine the income growth rate for each household, measured by the slope of the income trend line over time. Households are then divided based on whether their income growth rate surpasses the median within their age cohort, assigning a binary value. It was found that households with steeper income growth exhibit lower consumption in earlier years (2004\u0026ndash;2007) and higher consumption later on (2013\u0026ndash;2016) relative to the other group, with moderate income growth. This suggesting that the timing of income receipt affects consumption patterns. Furthermore, the study assesses the propensity to consume out of income received before versus after the CE survey. A ratio of these propensities near unity would corroborate the PIH. However, the findings indicate a significantly lower ratio, ranging from 0.3 to 0.5, suggesting a striking deviation from the hypothesis. These findings do not necessarily contradict the PIH. It is possible that households' risk aversion is so high, and the variability in future household income is so great, that households prefer to wait and consume the majority of their additional future income only after it has been securely received. However, in reality, the propensity to consume out of future income is very low.\u003c/p\u003e \u003cp\u003eThe study further explores the consumption response to temporary deviations from a household's long-term income average. Contrary to the PIH, which predicts a moderate consumption response, the findings indicate that households consume 23% of exceptional one-time income in the year it is received, underscoring a predilection for immediate consumption. Additionally, the research examines consumption patterns in the context of retirement, uncovering that households with a pension plan exhibit stable consumption following retirement, while those lacking a pension plan experience a 21% decrease in consumption.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eThe PIH is a central theory in understanding household consumption behavior, with an extensive body of literature examining its theoretical underpinnings and empirical validity. The PIH posits that rational households anticipate future events and aim to maximize their utility over multiple periods, subject to a budget constraint that spans these periods.\u003c/p\u003e \u003cp\u003eThe multi-period utility is conceptualized as the sum of discounted utilities - 'u' - across periods - 't', where each period's utility is a function of consumption - u(c\u003csub\u003et\u003c/sub\u003e) - which is discounted by a rate of time preference. Assuming negative second derivative of utility with respect to consumption and identical interest rates ('r') for borrowing and lending, Euler's equation emerges, guiding intertemporal consumption decisions.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{u}`\\left({\\text{c}}_{\\text{t}}\\right)={(1+{\\delta })}^{-\\text{s}}{\\text{E}}_{\\text{t}}\\left[\\right(1+{\\text{r}}_{\\text{t}+\\text{s}})\\text{u}`\\left({\\text{c}}_{\\text{t}+\\text{s}}\\right)]$$\u003c/div\u003e\u003c/div\u003e1. \u003c/p\u003e \u003cp\u003eWhile - s - is the number of periods between period 't' and another period. Delta represents the rate of time preference.\u003c/p\u003e \u003cp\u003eWhen assuming a quadratic utility function, specific consumption trajectories can be derived, leading to predictable patterns of consumption smoothing.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${\\text{c}}_{\\text{i},\\text{t}}={(1+{\\delta })}^{-\\text{s}}{\\text{E}}_{\\text{t}}(1+{\\text{r}}_{\\text{t}+\\text{s}}){\\text{E}}_{\\text{t}}{(\\text{c}}_{\\text{i},\\text{t}+\\text{s}})$$\u003c/div\u003e\u003c/div\u003e2. \u003c/p\u003e \u003cp\u003eAlternatively, if income is certain consumption is expected to grow at a constant rate. However, assuming income uncertainty and precautionary savings motive, characterized by a negative third derivative of the utility function with respect to consumption, suggests that households may consume less in earlier periods due to income uncertainty, leading to asset accumulation as a buffer against potential income declines, as suggested by Deaton (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe orthogonality test of the PIH testes whether changes in consumption are independent of predicted changes in income. Hall and Mishkin (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) provided mixed support for the hypothesis, while Altonji and Siow (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1987\u003c/span\u003e) corrected for measurement errors in the Panel Study of Income Dynamics (PSID) data and found stronger evidence for the PIH. The Excess Sensitivity test examines the response of consumption to transitory income shocks. Parker et al. (2013), Parker (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), Agarwal \u0026amp; Souleles (2007), and Souleles (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) found that fiscal stimulus measures and tax cuts led to immediate increases in consumption, a pattern that is not consistent with the PIH. Crawley and Kuchler (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) observed the same pattern in Denmark, while households with substantial liquid assets are the only exceptions. Hsieh (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), however, posited that the response to income changes is contingent on their frequency and predictability, with households more likely to follow the PIH for regular, substantial income variations.\u003c/p\u003e \u003cp\u003eAdditional phenomenon that contradicts the PIH and the LCH known as the `retirement consumption puzzle` refers to the unexpected decline in household spending upon retirement. Studies such as Banks et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) observed a marked decrease in consumption post-retirement in the UK. Bernheim et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) used PSID data to investigate various factors that might explain the puzzle, yet found little evidence supporting the LCH. However, Hurst (2008) shows that the standard model of lifecycle consumption augmented with home production and uncertain health shocks does well in explaining the retirement consumption puzzle. Aguiar and Hurst (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) suggested that retirees might spend less on food not because of insufficient savings, but due to increased time for shopping and cooking, which lead to more efficient spending without reducing food intake.\u003c/p\u003e \u003cp\u003eIn genera the literature reveals several factors that lead to deviations from the LCH consumption patterns such as liquidity constraints (Zeldes, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Carroll, 1997) and financial friction (Laibson et al., 2003). Beyond these traditional analyses, behavioral economists have challenged the assumption of rational behavior inherent in the PIH. Thaler (1990) and Tversky and Kahneman (1991) have explored the impact of mental accounting and loss aversion on spending decisions, suggesting that cognitive biases can lead to deviations from the consumption patterns predicted by the PIH and LCH.\u003c/p\u003e"},{"header":"3. Data","content":"\u003cp\u003eThe study uses data from each of the 13 annual Consumer Expenditure (CE) surveys conducted from 2004 to 2016. These cross-sectional surveys provide a representative sample of households, with a new sample being selected each year. The dataset includes detailed records of household consumption expenditures and demographic attributes of household members, such as education and age. This information is complemented by matched net wage income data from the Tax Authority's administrative records for the years 2002\u0026ndash;2016, which pertains to the heads of households and their spouses as identified in the CE surveys. For most households, longitudinal wage data are available over a 15-year span, while consumption data are captured at a single point in time.\u003c/p\u003e"},{"header":"4. Methodology","content":"\u003cp\u003eThe PIH suggests that individuals plan their consumption based on an expectation of their lifetime income, rather than their current income alone. To evaluate this hypothesis, the study employs a multi-faceted approach that considers future income expectations, one-time transitory income shocks, and the effects of reaching retirement age.\u003c/p\u003e \u003cp\u003e4.1. Income Growth and Consumption Patterns: The analysis begins by categorizing households of each age group into two distinct groups based on the rate of their income growth. This is achieved by calculating the slope of a linear regression line for each household's income against a time trend. Households are then classified according to whether their income growth rate exceeds the median within their age cohort. The study calculates the differences in consumption between these two groups across various years and then examines whether these differences are consistent over time, as predicted by the PIH.\u003c/p\u003e \u003cp\u003e4.2. Future Income and Consumption Propensity: The study investigates how the expectation of future income influences household consumption decisions. The analysis pays special attention to the timing of income changes, examining those that occurring after the Consumer Expenditure (CE) survey as opposed to those occur before it. It estimates the propensity to spend additional future income and compares it to the propensity to spend additional past income. A ratio near one, when comparing the propensity to consume out of additional future income with that of additional past income, would corroborate the PIH.\u003c/p\u003e \u003cp\u003e4.3. The study conducts a simple version of the excess sensitivity test, using transitory income as the independent variable and consumption as the dependent variable\u0026mdash;instead of the change in consumption between two time periods as is generally done. It models consumption as a function of transitory income and multi-year average income to account for permanent income. According to the PIH, the coefficient on transitory income is expected to be relatively small, suggesting that households do not significantly adjust their consumption in response to temporary changes in income.\u003c/p\u003e \u003cp\u003e4.4. Post-Retirement Consumption Behavior: The research examines whether households maintain their pre-retirement consumption levels or experience a shift, which could either confirm or refute the PIH's prediction of consumption smoothing. The study also considers the role of pension plans and whether they influence post-retirement spending behaviors, providing a more nuanced understanding of how financial security affects consumption decisions in later life.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe study includes controls for each survey year, thereby isolating any annual effects that may sway consumption patterns. It also accounts for pivotal demographic factors that influence household spending, such as age and family size. The regression models are designed with both linear and quadratic terms for age and family size to describe the observed pattern where consumption initially increases with these variables but then reaches a plateau or even declines. In addition, the study introduces a binary variable to differentiate between the consumption behaviors of homeowners versus renters, enabling a targeted analysis of how homeownership status affects spending habits.\u003c/p\u003e"},{"header":"5. Estimation results","content":"\u003cp\u003e5.1.\u0026nbsp;Income steepness\u003c/p\u003e\n\u003cp\u003eThe dummy variable \u0026apos;Income Steepness\u0026apos; classifies households within each age group into two categories based on whether their income growth in the sample period is above or below the median for their age cohort. The regression analysis in Table 1 accounts for \u0026apos;Income Steepness\u0026apos; and for two interaction terms to examine the stability of its impact on consumption over time: The first interaction term is between \u0026apos;Income Steepness\u0026apos; and a dummy variable for the early years, which is assigned a value of 1 for the years 2004 to 2007 and 0 for all other years. The second is between \u0026apos;Income Steepness\u0026apos; and a dummy variable for the later years, which is set to 1 for the years 2013 to 2016 and 0 otherwise. The dependent variable in Table 1 is the natural logarithm of household consumption for households headed by a married couple that have consistently reported a positive combined income from employment each year from 2004 to 2016.[1] The regression also includes the following control variables: household size, size square, homeownership status, the age of the household head and age square, and a dummy variable for each survey year.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1: The\u0026nbsp;Effect of \u0026apos;Income Steepness\u0026apos; on (log) Consumption, by age groups.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e(2)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e(3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e(4)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e25-65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e30-39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e40-49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e50-59\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDependent variable:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLog Consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLog Consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLog Consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLog Consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDummy Income steepness\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.0836***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.0489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.0804**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.123***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e*Dummy for 2004-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0307)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0359)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0455)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDummy Income steepness\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0353*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0712**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.0146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0963**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e*Dummy 2013-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0298)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0323)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0393)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDummy Income Steepness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.235***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.251***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.280***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.142***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0202)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0233)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0279)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHomeownership\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.147***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.110***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.165***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.312***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e(Dummy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0169)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0251)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0387)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHousehold size\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.112***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0309*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.159***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.169***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.00971)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0165)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0185)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0212)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHousehold size Squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.00824***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.00263*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.0111***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.0124***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.000871)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.00149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.00148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.00214)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0502***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.185**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.00430)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0621)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0848)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.129)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge Squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.00048***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.000165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.00199**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(4.77e-05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.000892)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.000955)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.00119)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDummy for each survey\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.726***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.178***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.550**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.11***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(0.0875)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.074)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(1.874)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(3.509)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNumber of Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12,137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4,052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,786\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn the parentheses, the standard errors of the estimates are shown. The asterisks indicate the level of significance: *** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1. The analysis includes households headed by a married couple, under the condition that both spouses have a positive (combined) income from work in each of the years - from 2004 to 2016.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results indicate that the timing of income has a significant effect on household consumption. In the early years, households with steeper income growth had significantly lower consumption compared to others, while in the later years, their consumption was significantly higher. This pattern is consistent across different age groups. Consumption and income are in constant 2004 prices.\u003c/p\u003e\n\u003cp\u003eRegression 1 estimates the consumption of households where the age of the household head - at the time of responding to the expenditure survey - ranges from 25 to 65. The coefficient for the interaction between\u0026nbsp;Dummy\u0026nbsp;\u0026apos;Income Steepness\u0026apos; and the dummy variable for the early years is negative and significant, indicating that households with steeper income growth exhibited lower consumption levels during 2004 to 2007 compared to other households. Conversely, the coefficient for the interaction between \u0026apos;Income Steepness\u0026apos; and the dummy variable for the later years is positive and significant, suggesting that these households had higher consumption levels during 2013 to 2016. This pattern is consistent across different age groups, as demonstrated by regressions 2, 3, and 4 for the age groups 30 to 39, 40 to 49, and 50 to 59, respectively. These findings imply that households may adjust their consumption not only based on their long-term income expectations but also in response to the timing of actual income receipt. Such a deviation from the smooth consumption pattern indicates the potential influence of factors such as liquidity constraints or changing expectations that the PIH does not account for; as well as income volatility and a precautionary savings motive, which are not addressed in this paper.\u003c/p\u003e\n\u003cp\u003eTable 2: The Effect of Income Steepness during the years 2004-16 on Consumption in the 2004-2010, by age group.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003cp\u003e25-65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003cp\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\n \u003cp\u003eDependent variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003eLog Consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003eLog Consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003eLog Consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003eLog Consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\n \u003cp\u003eDummy Income steepness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e0.0847***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e0.102***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e0.112***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e-0.00118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(0.0105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(0.0168)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(0.0188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e(0.0235)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\n \u003cp\u003eIncome in Survey Year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e1.48e-06***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e1.56e-06***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e1.43e-06***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e1.36e-06***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(3.53e-08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(6.50e-08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(5.82e-08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e(6.83e-08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\n \u003cp\u003eHomeownership\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e0.0976***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e0.0755***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e0.0934***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e0.247***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\n \u003cp\u003e(Dummy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(0.0143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(0.0193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(0.0280)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e(0.0442)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\n \u003cp\u003eHousehold size\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e0.102***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e0.0572***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e0.144***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e0.140***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(0.0115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(0.0189)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(0.0213)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e(0.0257)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\n \u003cp\u003eHousehold size Squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e-0.00576***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e-0.00279*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e-0.00838***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e-0.00807***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(0.00101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(0.00169)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(0.00167)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e(0.00253)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e0.00989*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e-0.0265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e0.247**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e-0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(0.00575)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(0.0780)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(0.108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e(0.179)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\n \u003cp\u003eAge Squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e-6.34e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e0.000477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e-0.00273**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e0.00224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(6.51e-05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(0.00113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(0.00122)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e(0.00165)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\n \u003cp\u003eDummy for each survey\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e10.94***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e11.69***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e5.549**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e17.76***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(0.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(1.344)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e(2.399)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e(4.836)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\n \u003cp\u003eNumber of Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e5,709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e2,063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e1,790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e1,251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.20517560073937%\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.744916820702404%\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56007393715342%\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn the parentheses, the standard errors of the estimates are shown. The asterisks indicate the level of significance: *** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1. The analysis includes households headed by a married couple, under the condition that both spouses have a positive (combined) income from work in each of the years - from 2004 to 2016.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe table presents the results of regression analyses estimating the effect of income steepness on household consumption for different age groups. The dependent variable is the log of household consumption, and the main independent variable of interest is the dummy for income steepness during the years 2004-16. Consumption and income are in constant 2004 prices.\u003c/p\u003e\n\u003cp\u003eTable 2 examines the alternative hypothesis that consumption is uncorrelated with long-term income expectations. The regressions estimate the log of consumption between 2004 and 2010 as a function of household current income in the survey year, the \u0026apos;Income Steepness\u0026apos; between 2004 and 2016, and a set of control variables (including a dummy variable for each survey year). For the age group 25 to 65 (Regression 1), it was found that income steepness has a significant effect on consumption\u0026mdash;households whose income grew more steeply between 2004 and 2016 had a consumption level 8.5% higher between 2004 and 2010 than other households with the same current income, in the survey year, but with more moderate income growth.\u0026nbsp;Among the age group 30 to 39 (Regression 2), a gap of 10% was found, and for those aged 40 to 49, a gap of 11% was observed (Regression 3).\u0026nbsp;The results indicate that future income, as revealed in retrospect, has a significant positive effect on consumption. However, this effect is not significant for the age group 50-59\u0026nbsp;(Regression 4).\u003c/p\u003e\n\u003cp\u003e5.2. The Effect of Future Wages on Household Consumption\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 examines the effect of future wages on household consumption headed by a married couple. Regression 1 estimates household consumption as a function of the combined wages of the couple in the year of the survey, the difference of this wage over the previous three years (w\u003csub\u003et\u003c/sub\u003e-w\u003csub\u003et-3\u003c/sub\u003e), and the difference over the subsequent three years (w\u003csub\u003et+3\u003c/sub\u003e-w\u003csub\u003et\u003c/sub\u003e), as revealed in retroactively. Additionally, the regression includes the standard control variables (a separate dummy variable for each survey year, household size, size square, homeownership status, age and age square.) It was found that future income has a significant effect on the extent of household consumption expenditure \u0026ndash; an addition of one shekel to the combined wages of the couple over the next three years is equivalent in its effect to an addition of 0.44 shekel to their combined wages during the previous three years. (i.e. 0.44 is the ratio between the coefficient of wage increase in the coming years and the coefficient of wage increase in the past years.) A similar result was found in Regression 3, which examined the effect of one shekel received over a time span of five years (instead of three). Regressions 2 and 4 replicate Regressions 1 and 3 with one difference: the dependent variable is the log of consumption (instead of consumption). The ratio between the coefficient of wage increase in the coming years and the coefficient of wage increase in the past three years is 51% (reg. 2) over a time span of three years and 60% (reg. 4) over a time span of five years.\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"LTR\"\u003eTable 3: The effect of past and future earnings on household (log) consumption\u003c/span\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"535\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003cp\u003eConsumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp\u003e(2)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLog Consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003cp\u003eConsumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp\u003e(4)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLog Consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ewage\u003c/span\u003e \u003cspan dir=\"LTR\"\u003eCurrent\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.305***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1.73e-06***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.331***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1.86e-06***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.00594)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(3.07e-08)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.00869)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(4.68e-08)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eWage increase over\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.0593***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3.73e-07***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e--\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e--\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ethe next 3 years\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.00820)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(4.24e-08)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eWage increase over\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.135***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e7.30e-07***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e--\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e--\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ethe past 3 years\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.0107)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(5.51e-08)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eWage increase over\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e--\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e--\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.0642***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e4.29e-07***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ethe next 5 years\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.00927)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(4.99e-08)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eWage increase over\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e--\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e--\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.138***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e7.13e-07***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ethe past 5 years\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.0127)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(6.84e-08)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eHomeownership\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-6,550***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-0.0883***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-12,096***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-0.119***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eDummy\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(1,968)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.0102)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(2,684)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.0145)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eHousehold size\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e14,332***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.0776***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e11,491***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.0616***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(1,740)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.00898)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(2,336)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.0126)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eHousehold size\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-812.1***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-0.00449***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-629.0***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-0.00334***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eSquared\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(149.7)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.000773)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(200.5)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.00108)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eAge\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3,324***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.0250***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e2,392*\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.0198***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(944.2)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.00488)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(1,278)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.00688)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eAge Squared\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-29.26***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-0.000245***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-20.05\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-0.000195**\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(10.85)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(5.60e-05)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(14.66)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(7.89e-05)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eDummy for each survey year\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e+\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e+\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e+\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e+\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eConstant\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-9,704\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e10.85***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e21,507\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e11.03***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(18,995)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.0981)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(25,797)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.139)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eObservations\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e10,756\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e10,756\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e5,418\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e5,418\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eR-squared\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.257\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.304\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.289\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.319\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.256983240223462%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eRatio future/past\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e44%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"bottom\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e51%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"bottom\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e47%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.435754189944134%\" valign=\"bottom\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e60%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn the parentheses, the standard errors of the estimates are shown. The asterisks indicate the level of significance: *** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1. The analysis includes households headed by a married couple, under the condition that both spouses have a positive (combined) income from work in each of the years - from 2004 to 2016; and they survey in the HES between 2005 and 2013. Consumption and income are in constant 2004 prices.\u003c/p\u003e\n\u003cp\u003eTable 4 presents robustness tests for homogenies population groups, using the specified regression model 2 in Table 3. It was found that the propensity to consume out of future income (over the next three years) relative to past income (over the past three years) ranged from 30 to 50 percent (as shown in Column Three of Table 4). A higher consumption ratio was observed in the age group of 40 to 49, compared to the 30 to 39 age group (47% and 41%, respectively). The lowest consumption ratio was noted in the 50 to 59 age group. Among households with high income, a slightly lower consumption ratio was observed - 50% - compared to those with low income - 45%. (The income threshold is set at a combined annual wage of 140 thousand ILS, in 2004 prices.) Finally, non-academics (ages 30 to 60) exhibited a higher consumption ratio compared to academics of the same age group, as detailed in Table 4.\u003c/p\u003e\n\u003cp\u003eTable 4: The ratio of adding one shekel in the\u0026nbsp;future\u0026nbsp;to add one shekel in\u0026nbsp;the past.\u003c/p\u003e\n\u003cp\u003eEach cell summarizes the result of a separate regression for distinctive subgroups.\u003c/p\u003e\n\u003cp\u003eThe dependent variable is log consumption.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.753222836095766%\"\u003e\n \u003cp\u003eSubgroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.810313075506446%\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.810313075506446%\"\u003e\n \u003cp\u003eWith\u003c/p\u003e\n \u003cp\u003eControl Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.626151012891345%\"\u003e\n \u003cp\u003eWithout\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Control Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.753222836095766%\"\u003e\n \u003cp\u003eAge 30 to 39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.810313075506446%\"\u003e\n \u003cp\u003e4,018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.810313075506446%\"\u003e\n \u003cp\u003e41%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.626151012891345%\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.753222836095766%\"\u003e\n \u003cp\u003eAge 40 to 49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.810313075506446%\"\u003e\n \u003cp\u003e3,407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.810313075506446%\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.626151012891345%\"\u003e\n \u003cp\u003e48%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.753222836095766%\"\u003e\n \u003cp\u003eAge 50 to 59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.810313075506446%\"\u003e\n \u003cp\u003e2,466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.810313075506446%\"\u003e\n \u003cp\u003e28%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.626151012891345%\"\u003e\n \u003cp\u003e35%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.753222836095766%\"\u003e\n \u003cp\u003eLow Income (age 30-60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.810313075506446%\"\u003e\n \u003cp\u003e5,455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.810313075506446%\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.626151012891345%\"\u003e\n \u003cp\u003e39%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.753222836095766%\"\u003e\n \u003cp\u003eHigh Income (age 30-60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.810313075506446%\"\u003e\n \u003cp\u003e5,301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.810313075506446%\"\u003e\n \u003cp\u003e45%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.626151012891345%\"\u003e\n \u003cp\u003e40%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.753222836095766%\"\u003e\n \u003cp\u003eNon-academics (age 30-60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.810313075506446%\"\u003e\n \u003cp\u003e6,829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.810313075506446%\"\u003e\n \u003cp\u003e40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.626151012891345%\"\u003e\n \u003cp\u003e37%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.753222836095766%\"\u003e\n \u003cp\u003eAcademics (age 30-60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.810313075506446%\"\u003e\n \u003cp\u003e3,927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.810313075506446%\"\u003e\n \u003cp\u003e30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.626151012891345%\"\u003e\n \u003cp\u003e24%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eEach cell in the two right columns summarizes the result of a separate regression. The explanatory variables: the joint wages of the household heads in the survey year, the growth in it over the previous three years, the growth in it over the next three years, and dummy variables for survey years. The regressions with control variables include the standard set of controls: household size, size square, homeownership status, age and age square. The table presents the ratio between the estimated coefficient for the growth in wages of the household heads over the next three years and that of the previous three years. Consumption and income are in constant 2004 prices.\u003c/p\u003e\n\u003cp\u003eFrom the estimates in this section, it can be concluded that the propensity to consume from future income is substantially and significantly lower than the propensity to consume from current income. The ratio between these two propensities ranges from 30 to 50 percent. Even among groups typically assumed to have better access to capital market financing, such as academics, high-income earners, and homeowners, the propensity to consume out of future income remains low. Therefore, this phenomenon cannot be solely attributed to a lack of access to convenient loans. However, the results obtained here do not necessarily contradict the PIH. It is possible that the variability in future household income is so large and households\u0026apos; risk aversion is so high that they prefer to wait and consume the majority of additional future income only after it has been securely received.\u003c/p\u003e\n\u003cp\u003e5.3.\u0026nbsp;Exceptional One-Time annual Income effects on household consumption\u003c/p\u003e\n\u003cp\u003eThis subsection examines how an Exceptional One-Time annual Income (EOTI) received in the survey year affects the household consumption level in that year. The EOTI is the current annual income minus the \u0026quot;symmetric income\u0026quot; - the average of the household income over the six years surrounding the survey year (the average of the combined wage income of both spouses received in the three years before and after the survey year). This exceptional one-time income could be either expected or unexpected.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegressions 1 and 2 in Table 5 estimate the household consumption level as a function of the following two income variables: 1. The Exceptional One-Time annual Income. 2. \u0026quot;Symmetric income,\u0026quot; which controls for permanent income. It was found that one shekel of EOTI increases the consumption by 0.05 shekels. A similar estimate was obtained in Regression 1, which includes only the dummy variables for the survey years (0.047), and in Regression 2, which includes the set of control variables (0.055). At first glance, these results seem to be consistent with the PIH. However, a different conclusion is drawn from Regression 3, which repeats the same estimate but refers only to households with positive EOTI. (Households whose (real) current income was higher than the (real) symmetric average income.) Regression 3 found that 23% of the exceptional positive one-time income is directed to consumption in the same year the EOTI was received. A similar estimate across different age groups revealed that aged 30-39 spent 18.5% of the positive EOTI on consumption, while those aged 40-49 consumed 21% of it. Regression 4 (in Table 5) estimates the consumption response to a negative EOTI (for age group 30 to 60) \u0026ndash; current income lower than the symmetric average income - it was found that decrease of one Shekel in current income relative to the symmetric average income reduces consumption by 0.06 Shekel.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe household response to positive exceptional income does not align with the PIH; since a significant portion of it is consumed immediately, whereas the theory predicts it would be spread over the entire life horizon (and have no impact if anticipated). Furthermore, the asymmetric response of households to positive EOTI compared to negative exceptional income might suggests that the utility from consumption in each period depends on consumption in the previous period (habit formation).\u003c/p\u003e\n\u003cp\u003eTable 5: Consumption level as a function of\u0026nbsp;EOTI\u0026nbsp;- Exceptional One Time annual\u0026nbsp;Income\u0026nbsp;(compared to the long-term symmetric income).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(1)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(2)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(3)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(4)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eConsumption\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eConsumption\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eConsumption\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eConsumption\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ePositive\u003c/span\u003e\u003c/p\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eEOTI\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eNegative\u003c/span\u003e\u003c/p\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eEOTI\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExceptional One-Time annual\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.0469***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.0545***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.233***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-0.0587**\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eincome\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.0157)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.0154)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.0287)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.0272)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(Average) Symmetric income\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.316***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.306***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.299***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.280***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.00588)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.00596)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.00877)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.00930)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eHomeownership\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e809.3***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e907.9***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e839.7***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(\u003c/span\u003e\u003cspan dir=\"LTR\"\u003eDummy\u003c/span\u003e\u003cspan dir=\"LTR\"\u003e)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(197.2)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(265.6)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(293.0)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eHousehold size\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1,479***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1,492***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1,503***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(163.8)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(210.1)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(259.1)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eHousehold size Squared\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-82.50***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-78.96***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-90.18***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(14.56)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(18.60)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(23.16)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eAge\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e121.6\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e77.91\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e177.2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(77.24)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(103.6)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(115.4)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eAge Squared\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-0.682\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-0.331\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-1.116\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.861)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(1.149)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(1.296)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eDummy for each survey year\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eConstant\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e8,870***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-132.9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e527.9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-1,602\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(260.8)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(1,541)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(2,087)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(2,283)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eObservations\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e8,466\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e8,466\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e4,540\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e3,926\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eR-squared\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.257\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.285\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.306\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.275\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn the parentheses, the standard errors of the estimates are shown. The asterisks indicate the level of significance: *** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1. The analysis includes households headed by a married couple, under the condition that both spouses have a positive (combined) income from work in each of the years - from 2004 to 2016; and they survey in the HES between 2005 and 2013. Consumption and income are in constant 2004 prices.\u003c/p\u003e\n\u003cp\u003e5.4 household consumption before and after the retirement age\u003c/p\u003e\n\u003cp\u003eAccording to the PIH, predictable changes such as those caused by reaching retirement age, should not affect household consumption. To investigate this hypothesis, this subsection will analyze the consumption patterns in the years surrounding the legal retirement age, which is 67 for men. It will focus on households of married couples where the husband\u0026apos;s age is between 62 and 70. The regression models explain the natural logarithm of household consumption by the following factors: the husband\u0026apos;s age (dummy 1 if age is over 68, and 0 otherwise), husband\u0026apos;s wage at age 64, the number of persons in the household (individual dummy variables assigned to each household size), and the survey year (individual dummy variables assigned to each survey year).\u003c/p\u003e\n\u003cp\u003eRegression 1 finds that households with male heads aged 68 to 70 exhibit a consumption level that is 8.8% lower than those with male heads aged 62 to 66. Regressions 2 and 3 replicate Regression 1, while Regression 2 including only households with pension plans and Regression 3 including only those without. The findings indicate that consumption remains stable for those with a pension plan, while there is a significant 21% reduction in consumption levels for those without a pension plan. Similar results were obtained when examining the change in consumption, excluding food. Those without a pension plan constituted 34% of the 62-70 sample, and their consumption patterns are inconsistent with the PIH and LCH. Those with a pension plan opt in voluntarily to secure an adequate standard of living after retirement. However, it remains unclear whether their behavior would still align with the LCH even if they did not have the option to join a pension plan.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6: Household Consumption after reaching the legal retirement age \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eas a function of participate in pension plans.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMale heads aged 62-70.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(1)\u003c/span\u003e\u003c/p\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eLog\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(2)\u003c/span\u003e\u003c/p\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eLog\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(3)\u003c/span\u003e\u003c/p\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eLog\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eConsumption\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eConsumption\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eConsumption\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAll\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ehouseholds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWith a\u0026nbsp;\u003c/p\u003e\n \u003cp\u003epension plan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWithout a\u0026nbsp;\u003c/p\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003epension plan\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDummy variable for Male heads\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-0.0642***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-0.00235\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-0.214***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eaged 68 to 70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.0209)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.0240)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.0388)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale heads real wage at\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1.66e-06***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1.44e-06***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1.92e-06***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eage 64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(6.04e-08)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(6.88e-08)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(1.20e-07)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDummy for each household size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDummy for each survey year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e9.165***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e9.288***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e9.032***\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.0461)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.0598)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e(0.0706)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e2,759\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1,809\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e950\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.230\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.207\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.286\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn the parentheses, the standard errors of the estimates are shown. The asterisks indicate the level of significance: *** p\u0026lt;0.01, ** p\u0026lt;0.05, * p\u0026lt;0.1. The analysis includes households headed by a married couple, under the condition that the male heads aged 62-70 and they survey in the HES between 2004 and 2016. Consumption and income are in constant 2004 prices.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study investigates whether the behavior of households in Israel aligns with the Permanent Income Hypothesis (PIH) and Life-Cycle Hypothesis (LCH). It assesses the impact of changes in household income on consumption, using data from the Consumer Expenditure Surveys, combined with longitudinal administrative income records.\u003c/p\u003e \u003cp\u003eThe study examined the relationship between household income growth and household consumption dynamics, using a dummy variable to categorize households by whether their income growth was above or below their age group's median. The analysis included interaction terms for early (2004\u0026ndash;2007) and later years (2013\u0026ndash;2016) to assess the stability of this impact over time. The results showed that households with a sharper increase in income had lower consumption in the early years, but higher consumption in later years, compared to households with a moderate income growth. This pattern held across various age groups. The findings indicate that households deviating from the PIH, which suggests consumption is based on long-term income expectations.\u003c/p\u003e \u003cp\u003eThe study analyzed the impact of future wages on household consumption led by married couples, taking into account wages at the time of the survey and wage over the past and subsequent three years. The findings indicate that future income significantly affects household consumption, with one additional shekel to future wages over the next three years having an effect equivalent to 0.3\u0026ndash;0.5 of one additional shekel received over the past three years. The results suggest that while future wages do influence consumption, their effect is much smaller than that of past and current wages. The household prefer to wait and consume the majority of additional future income only after it has been securely received. This pattern holds true among groups that have good access to capital market financing, such as academics, high-income earners, and homeowners.\u003c/p\u003e \u003cp\u003eThis study finds that households prefer to consume a large portion of their Exceptional One-Time Incomes (EOTI) concurrently. EOTI is defined as the current annual income minus the average income over the six years surrounding the survey year. Focusing only on households with positive EOTI showed that 23% of this income is consumed in the same year. This contradicts the PIH, which predicts that such income would be spread out over a lifetime. Additionally, households responded asymmetrically to positive versus negative EOTI, with a one-time exceptional decrease in current income having only a minor effect on consumption. This asymmetric response challenge the PIH, suggesting that factors like habit formation may influence consumption decisions.\u003c/p\u003e \u003cp\u003eFinally, the study examined how reaching the legal retirement age affects household consumption. Focusing on married couples with the husband aged 62 to 70, the analysis found that households with husbands aged 68 to 70 consume 8.8% less than those with husbands aged 62 to 66. When dividing the sample into households with pension plans and those without, it was found that consumption remained stable for those with a pension plan but dropped by 21% for those without a pension plan. The latter ones do not align with the PIH and LCH, as they experience a significant reduction in consumption upon reaching the expected legal retirement age.\u003c/p\u003e \u003cp\u003eThis study did not investigate the effect of uncertainty of future income on current consumption levels among Israeli households, a factor that could explain the discrepancies observed with PIH and LCH. Accurately quantifying the uncertainty of future household income remains a significant challenge that future research needs to address.\u003c/p\u003e \u003cp\u003eI declare that the author have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRoni Frish processed the data and wrote the research including the tables.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData that support the findings of this study have been deposited in the Central Bureau of Statistics of Israel, nd their use is conditional upon the approval of the CB of Statistics. Roni\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgarwal, S., Liu, C., \u0026amp; Souleles, N. S. (2007). The reaction of consumer spending and debt to tax rebates\u0026mdash;Evidence from consumer credit data. Journal of Political Economy, 115(6), 986\u0026ndash;1019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAguiar, M., \u0026amp; Hurst, E. (2005). Consumption versus expenditure. Journal of Political Economy, 113(5), 919\u0026ndash;948.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAltonji, J. G., \u0026amp; Siow, A. (1987). Testing the response of consumption to income changes with (noisy) panel data. The Quarterly Journal of Economics, 102(2), 293\u0026ndash;328.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanks, J., Blundell, R., \u0026amp; Tanner, S. (1998). Is there a retirement-savings puzzle? American Economic Review, 88(2), 769\u0026ndash;788.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBernheim, B. D., Skinner, J., \u0026amp; Weinberg, S. (2001). What accounts for the variation in retirement wealth among U.S. households? American Economic Review, 91(4), 832\u0026ndash;857.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrowning, M., \u0026amp; Lusardi, A. (1996). Household saving: Micro theories and micro facts. Journal of Economic Literature, 34(4), 1797\u0026ndash;1855.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrawley, E., \u0026amp; Kuchler, A. (2023). Consumption heterogeneity: Micro drivers and macro implications. American Economic Journal: Macroeconomics, 15(1), 314\u0026ndash;341.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeaton, A. (1992). \u003cem\u003eUnderstanding consumption\u003c/em\u003e. Oxford University Press.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHall, R. E., \u0026amp; Mishkin, F. S. (1980). The sensitivity of consumption to transitory income: Estimates from panel data on households. Econometrica, 48(2), 461\u0026ndash;481.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHausman, J. A., \u0026amp; Paquette, L. (1987). Involuntary early retirement and consumption. In S. A. Woodbury \u0026amp; R. A. Triest (Eds.), \u003cem\u003eWork, health, and income among the elderly\u003c/em\u003e (pp. 151\u0026ndash;175). The Urban Institute Press.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsieh, C. T. (2003). Do consumers react to anticipated income changes? Evidence from the Alaska Permanent Fund. American Economic Review, 93(1), 397\u0026ndash;405.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson, D. S., Parker, J. A., \u0026amp; Souleles, N. S. (2006). Household expenditure and the income tax rebates of 2001. American Economic Review, 96(5), 1589\u0026ndash;1610.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParker, J. A. (1999). The reaction of household consumption to predictable changes in social security taxes. American Economic Review, 89(4), 959\u0026ndash;973.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSouleles, N. S. (1999). The response of household consumption to income tax refunds. American Economic Review, 89(4), 947\u0026ndash;958.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSouleles, N. S. (2002). Consumer response to the Reagan tax cuts. Journal of Public Economics, 85(1), 99\u0026ndash;120.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeldes, S. P. (1989). Consumption and liquidity constraints: An empirical investigation. Journal of Political Economy, 97(2), 305\u0026ndash;346.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e This was done as a precautionary measure, since sometimes an annual income of zero results from an error in the administrative file.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Consumption, Empirical Analysis, Permanent Income, Life-Cycle, Income","lastPublishedDoi":"10.21203/rs.3.rs-4204612/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4204612/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the household consumption utilizing data from the Israeli Consumer Expenditure Surveys and longitudinal administrative income records for 2004\u0026ndash;2016. Three key findings challenge the predictions of the Permanent Income Hypothesis (PIH) and the Life-Cycle Hypothesis (LCH): First, households with higher income growth (2004\u0026ndash;2016) exhibited lower consumption levels in the earlier period (2004\u0026ndash;2007) and higher consumption in the later period (2013\u0026ndash;2016). Second, households tend to consume a significant portion of transitory incomes immediately. Third, households without a pension plan show a marked decrease in consumption upon retirement.\u003c/p\u003e","manuscriptTitle":"Consumption and the permanent income of households","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-09 20:10:41","doi":"10.21203/rs.3.rs-4204612/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a7d583ce-59fe-477e-b4bb-fb9197f501a1","owner":[],"postedDate":"April 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-10T03:14:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-09 20:10:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4204612","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4204612","identity":"rs-4204612","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}