Income uncertainty shocks and precautionary savings

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Income uncertainty shocks and precautionary savings

How can an income uncertainty shock affect Norwegian households saving and consumption

Sindre Fladvad

Master of Philosophy in Economics

Faculty of Social Science Department of Economics UNIVERSITY OF OSLO

June 2020

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Acknowledgements

This thesis represents the end of my studies at the University of Oslo.

I want to thank my supervisors Martin Blomhoff Holm and Lan Lan for help and guidance.

Writing about uncertainty in uncertain times has been both challenging and interesting.

I would also like to thank Elin Halvorsen at Statistics Norway.

Any mistakes or shortages are solely my responsibility.

University of Oslo Oslo, June 2020

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Abstract

This thesis is aimed at studying how income uncertainty shocks can affect household consumption and saving. By using an extended life cycle model combined with proxies of uncertainty the paper attempts to describe the pattern of increased saving in Norway after the Great Recession in the period from 2008 to 2013. Next, by comparing the developments of realized volatility in the income process and developments in proxies of uncertainty, the paper studies how current income uncertainty from the COVID-19 pandemic might affect future consumption and saving. The model is calibrated on Norwegian household data. Based on the calibrated model, a simulation of 100 000 households is performed.

The results from the model shows that by accounting for the observed increased income uncertainty the model predicts a 0.666 percent increase in precautionary savings in the first year after the Great Recession. Assuming that the shock is perceived as permanent and lasts for five years the model yields an increase in financial wealth of 5.6 percent, compared to the baseline estimate without the income uncertainty shock. Further by comparing the developments in proxies uncertainty during the Great Recession with the COVID-19 pandemic, the income shock is of either equal or of a larger proportion depending on two different scenarios of recovery. In the worst case scenario income uncertainty yields an increase in savings of 1 percent in 2020 and a increase in financial wealth of 8.4 percent from 2020 to 2025 compared to the baseline estimate without the income shock.

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Contents iii

List of Figures

2.1 Saving rate . . . 4 2.2 Registered fully unemployed as a share of the labour force. Percent . . . 7 3.1 Household wealth and debt by age of household head. 2011 . . . 9 4.1 Monthly average of VIX and Global Economic Policy Uncertainty Index . 14 5.1 Life-cycle pattern of financial wealth (Fagereng, Gottlieb, and Guiso, 2017) 21 6.1 Income uncertainty of the Great Recession . . . 23 6.2 Income uncertainty shock of the COVID-19 pandemic . . . 25

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List of Tables v

List of Tables

4.1 Unemployment rate estimates from Norges Bank . . . 16 4.2 Proxies of uncertainty (15th of September to end of 2008) . . . 17 4.3 Proxies of uncertainty during global pandemic (28 February to end of April

2020) . . . 17 5.1 Variables in model . . . 22

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1 Introduction

The future will always be uncertain, however, at times it seems more uncertain than before.

In recent years the economic effects of uncertainty has been of particular interest as we have observed increasing financial, economic and political uncertainty. Today the economic effects of uncertainty is especially important following the spread of COVID-19 that resulted in a global pandemic. Currently households find themselves in a situation where the future is very uncertain. The global increase in uncertainty further increases the importance of studying consumption and saving of households to describe and analyze the macroeconomic impacts of uncertainty shocks. Uncertainty is often brought forward as an explanation for the slow consumption growth after the Great Recession. In light of the current pandemic increased uncertainty might delay the economic recovery. Policymakers have argued that uncertainty decreases economic growth, however studies are only suggestive on the effects of uncertainty on growth. The empirical literature finds that uncertainty is countercyclical, increasing in recessions and decreasing in booms. There is a clear downward relationship between increasing uncertainty and GDP growth, however to what extent the relationship is causal is not clear (Bloom, 2014).

After the Great Recession started in 2008, many industrialized countries have seen a steep increase in private household saving (Mody, Ohnsorge, and Sandri, 2012). Norway was one of the countries least affected by the Great Recession. GDP in Norway fell by 1.7 percent in 2009 and the unemployment rate only increased slightly. Despite the relatively quick recovery, increasing saving rates in Norway where observed in the period from 2008 to 2013.

During this period we also observe a decrease in interest rates. This increase in saving does not fit with standard macroeconomic models. In a simple macroeconomic framework a low interest rate will make saving less attractive and encourage more consumption. This did not happen after the financial crisis. A possible explanation for the increase in the household saving rate is increased income uncertainty.

Uncertainty is a broad concept. Knight (1921) defined uncertainty as the inability to forecast the likelihood of events happening. Furthermore, Knight defined risk as agents known probability distribution over known events. Nowadays the term uncertainty is generally used to denote situations where the distributions are known (Fernández-Villaverde

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and Guerrón-Quintana, 2020). This thesis defines income uncertainty as the perceived future income stream being more difficult to predict because of an increase in the standard deviation of the income process. In other words an increase in income uncertainty makes the future outcomes more uncertain/risky by a larger probability of extreme events at the tails of the income distribution, keeping the mean constant. Uncertainty is hard to measure because it is not observable. To approximate uncertainty this thesis combines three different proxies of uncertainty. A measure of implied volatility in the stock market (VIX), a newspaper based measure of global economic policy uncertainty (GEPU) and

the registered unemployment rate in Norway.

This paper is related to studies of income uncertainty and precautionary savings.

Furthermore, it is related to the growing economic literature studying the effects of the COVID-19 pandemic. Baker et al. (2020) estimated the effects of the COVID-19 pandemic in the United States using a model of disaster effects. This paper also uses the VIX and newspaper based measures of economic policy uncertainty to proxy uncertainty.

The studies differ by the model used and scope of analysis. The scope of this thesis is to study the effects of increased income uncertainty, ceteris paribus. Baker et al. (2020) found that the COVID-19 pandemic has created an enormous uncertainty shock, larger than the shock experienced during the Great Recession of 2008. GDP drops 11 percent by the end of 2020 in this model. Their main result implies that around half of the contraction in GDP at the end of 2020 can be attributed to increased uncertainty.

The aim of this paper is two-fold. First, the paper studies how income uncertainty could have affected Norwegian households saving and consumption from 2008 to 2013. Second, the paper studies how income uncertainty of the current global pandemic might affect households saving and consumption in the future. The scope of the paper is theoretical and my motivation is to revisit the literature on precautionary savings and uncertainty.

The paper uses an extended life cycle model calibrated to fit data of Norwegian households to estimate the possible effects of income uncertainty.

The model simulation implies that an increase in income uncertainty, ceteris paribus, increases saving and reduces current consumption. Assuming that the income shock is perceived as permanent, the realized increase in the standard deviation of the income process of the Great Recession yields an increase in saving of 0.666 percent after one year

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in the model. Assuming that the realized increase in the standard deviation is equal to households actual expected income uncertainty the model predicts that consumption is reduced by around 2.34 percent from 2008 to 2013. Financial wealth increases by around 5.6 percent over the same period. Developments of the proxies of uncertainty during the Great Recession and the current developments in these proxies, implies that the COVID-19 pandemic could yield a similar or higher income uncertainty shock, depending on two alternative scenarios of recovery. In the worst case scenario the model predicts that the pandemic induced income uncertainty could increase precautionary wealth by 8.2 percent and reduce consumption by 3.5 percent from 2020 to 2025.

Section 2 of this paper provides an overview of the economic situation following the Great Recession and the current situation in Norway focusing mainly on household consumption and saving. Section 3 consists of a literature review and an overview of economic theory of consumption and saving. Section 4 describes uncertainty and the measures used as proxies for income uncertainty. Based on the theory, the paper presents a life cycle model in Section 5. In Section 6 the paper simulates 100 000 Norwegian households in Matlab based on the model and study the effects of an increase in income uncertainty. Section 7 discuss the results. In Section 8 the paper concludes.

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2 Background

2.1 Background on saving and consumption in Norway

The saving rate is important because in a demand driven economy an increase in the saving rate, ceteris paribus, means lower demand. Saving is the residual between consumption and income. It can also be measured as the change in wealth from one period to the next. The saving rate measures saving as a percentage of disposable income. Figure 2.1 illustrates the development of the saving rate and the saving rate adjusted for dividends measured quarterly from 2002 until the first quarter of 2020.

Figure 2.1: Saving rate

Source: National accounts Statistics Norway

In figure 2.1 we observe that the saving rate has been increasing since the end of 2008 until around the end of 2013. In this period the global economy was experiencing the Great Recession and the European sovereign debt crisis that followed. A large drop is observed around 2005 and 2006. The drop is due to the introduction of a tax on dividends in 2006.

In anticipation of this tax reform, dividends in the preceding years were abnormally high, and subsequently abnormally low in the first years after the reform.

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2.1 Background on saving and consumption in Norway 5

From 2018 and onward the saving rate further increased. The drop in consumption from the first quarter of 2020 yielded an increase in the saving rate of around 3 percentage points to 12,7 percent (Statistics Norway, 2020a). By the end of 2020 they expect the saving rate to be 12,5 percent. Furthermore, in the following years they expect the saving rate to gradually decline.

A large part of the recent increase in saving can be attributed to forced saving. Households may reduce their consumption of certain goods and services because they fear being exposed to COVID-19 or because of lack of supply from producers, and as a result more of their income is saved. However parts of the increase in the saving rate might be attributed to increased income uncertainty.

The Great Recession and the COVID-19 pandemic are different shocks. The underlying factors behind the Great Recession where economic, while the pandemic started as a health crisis. Despite starting as a health crisis, the pandemic has led to a severe and ongoing global recession. Almost all sectors are somewhat negatively affected by the pandemic. However the pandemic does not affect sectors and workers to the same degree.

In Norway workers in the public sector are so far shielded from layoffs. Alstadsæter et al.

(2020) used real-time register data from the period March 1st to April 19th 2020 and studied the layoffs of the pandemic. They found that Norwegian workers with lower education, income, hourly wage and social class background are at higher risk of layoffs.

Despite the large differences between the Great Recession and the COVID-19 pandemic, both situations can be described by heightened levels of uncertainty. The Great Recession was mainly a negative demand shock. Most economists agree that the current situation can be viewed as both a negative supply and demand shock (Eichenbaum, Rebelo, and Trabandt, 2020). It reduces supply because the pandemic exposes workers to the virus.

People react to that risk by reducing their labor supply. It affects demand directly as people are exposed to the virus when purchasing goods and therefore react to that risk by consuming less. Others assume that the pandemic essentially is supply side shock with possible spill-overs to the demand side (Guerrieri et al., 2020). The demand shock can worsen if consumers as a result of increased income uncertainty reduce their purchasing of goods. This is problematic for governments who are designing and implementing monetary and fiscal policies to dampen the effects of the shock because stimulating demand might

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6 2.1 Background on saving and consumption in Norway

not result in higher supply and vice versa. Moreover, interest rates are at historically low levels. For Norway in particular the shock is worse due to the drop in the oil price.

Statistics from Statistics Norway on GDP developments currently point to larger short term effects of the pandemic compared to the Great Recession (Statistics Norway, 2020a).

During the Great recession we observe that Norway’s GDP fell by 1.7 percent in 2009.

According to the National Accounts statistics, mainland GDP for Norway fell by 6.9 percent from February to March this year. Further, mainland GDP fell by 4.7 per cent from March to April and was 11.3 per cent lower than in February.

Housing prices decreased by 10 percent in the last half of 2008 (Boug, 2010). However housing prices quickly recovered and increased in 2009. The pandemic has affected the Norwegian housing market to some extent. We observe a large drop of 1.4 percent adjusted for seasonal variation in March this year according toEiendom Norge (2020), and a drop of 0.2 percent in April. In May housing prices increased by 1.4 adjusted for seasonal variation. The decrease in the key interest rate to zero on the 7th of May by the Norwegian Central Bank likely affected demand.

Card transaction data is available and we observe a large drop in consumption following the COVID-19 pandemic. Recent statistics from Statistics Norway indicate that household consumption has improved somewhat as measures have been gradually reduced (Statistics Norway, 2020a).

Some have argued that the drop in consumption can largely be attributed to the preventative lockdown measures imposed by governments. Andersen et al. (2020) found contradictory results of this view. They studied consumer responses to the COVID-19 pandemic in Denmark and Sweden. Sweden took a much lighter approach in terms of preventative measures while Denmark imposed preventative measures similar to what we have observed in Norway. They used a large sample of bank account transaction data from Danske Bank and found that aggregate card spending dropped 29 percent following the shutdown measures imposed in Denmark and 25 percent in Sweden. This further suggests that changes in consumer behavior can explain large parts of the short term drop in consumption. Whether parts of the short term reduction in consumption can be attributed to increased income uncertainty is debatable. Likely, the self distancing from the virus could explain a large part the reduction in consumption.

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2.1 Background on saving and consumption in Norway 7

As illustrated by figure 2.2, the unemployment rate did not increase to a large extent during the Great Recession. As of this year the registered unemployment rates by the Norwegian Labour and Welfare Administration have reached historic levels. The registered unemployment rate includes workers who are temporarily laid off. Because a large part of the workforce was temporarily laid off from March 12th we observe a high unemployment rate. At the moment presently the registered unemployment rate have decreased as preventive measures are gradually relaxed. While it is likely that a large part of the temporarily laid off workers will return to their job and remain employed, it is also inevitable that some workers will loose their job due to the large economic impact of the pandemic both globally and nationally.

Figure 2.2: Registered fully unemployed as a share of the labour force. Percent

Norwegian Labour and Welfare Administration (2020)

Alstadsæter et al. (2020) used real-time register data to document the magnitude, dynamics and socio-economic characteristics of the pandemic-induced temporary and permanent layoffs in Norway. They found that close to 90 percent of the layoffs are temporary, but this might change as the crisis progresses. Furthermore, they found that there is a strong socio-economic gradient of the layoffs, and the pandemic hit financially vulnerable populations the most.

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8

3 Literature and Theoretical Background

3.1 Life cycle hypothesis and Permanent income hypothesis

A part of influential work on consumption and savings is the Life Cycle Hypothesis (LCH) of Modigliani and Brumberg (1954) and Permanent Income Hypothesis (PIH) of Friedman (1957). Even though the theories at first was proposed as two different theories, they are today commonly used together. The theories differ from Keynes theories of consumption where current income affects current consumption (Keynes, 1936). The LCH/PIH theories argue that it is not current income, but the value of life time income that affects consumption.

The simplest form of the Life Cycle Model of Modigliani and Brumberg (1954) is based on the idea that households have a finite life income and presents the idea of maximizing utility through the lifetime. This simple model assumes no uncertainty over lifetime income and life expectancy is known. The model predicts that agents would want to smooth consumption over their lifetime in order to maximize utility. Agents can achieve this by borrowing during youth and gradually saving a part of income during working life.

In the simplest life cycle model wealth would be shaped as an inverted U. Individual’s level of saving would in the model peak when individuals earn the most during their lifetime and then gradually decline. During retirement individuals would consume saved assets.

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3.1 Life cycle hypothesis and Permanent income hypothesis 9

Figure 3.1: Household wealth and debt by age of household head. 2011

Statistics Norway (2013)

Figure 3.1 illustrates that wealth is higher for older households and peak at normal retirement age which is 67 in Norway. Similarly we can see that younger households are indebted as predicted by the theory. The largest part of wealth is fixed assets. Housing accounts for a large amount of households wealth. This paper excludes housing wealth as the focus is on financial wealth and active saving. However the developments in housing prices and ability to borrow to acquire housing is likely to affect the saving rate.

It is worth noting that figure 3.1 does not illustrate agents behavior over time and might not give a clear picture of actual wealth distribution over the life cycle. To estimate a more accurate life-cycle wealth profile studies follow age cohorts over longer periods and adjust for the known correlation between wealth and longevity. As observed in the figure, mean wealth spikes close to the highest observed age. This is likely related to selection, as richer people tend to live longer.

Whether or not LCH/PIH is actually supported by the data is a longstanding debate.

Many consumption puzzles have been raised in the empirical literature. The main puzzles are that the LCH/PIH theory fails to explain the excess sensitivity, excess smoothness, and excess growth of consumption observed empirically. Flavin (1981) finds that the sensitivity of consumption to changes in current income is far greater than the LCH/PIH predicts.

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10 3.2 Precautionary savings

In other words, the excess sensitivity puzzle is that consumption responds too much to predictable changes in income. The excess smoothness puzzle, on the other hand, refers to consumption responding too little to unpredictable changes in income. The excess growth puzzle refers to the persistent consumption growth even when the real interest rate has been negative. In order to explain these discrepancies between theory and empirical data, different extensions and theories has been made to the life cycle framework. Extensions to the framework such as bequest motive, liquidity constraints, income uncertainty, and heterogeneous preferences have been added to model the choices of households over their life cycle. The next section focuses on the literature of precautionary savings and income uncertainty.

3.2 Precautionary savings

Precautionary saving can be defined as the increased saving that occurs in response to uncertainty about future income. To combat the uncertainty of future income households can either reduce current consumption or work more. By saving more of their income today households can smooth consumption if they anticipate a lower income in the future.

Precautionary behavior and risk aversion are related concepts. Precautionary savings measures how much the economic agent dislikes uncertainty. Kimball (1990) showed that precautionary saving in response to uncertainty is associated with convexity of marginal utility. Mathematically this means that the third derivative of utility is positive. The marginal utility of certain consumption is larger than the marginal utility of uncertain consumption for the same mean.

The presence of liquidity constraints can impact precautionary savings. An individual is liquidity constrained if they cannot borrow as much as they would like. This is usually during early life as income is lower. The life cycle hypothesis assumes that individuals can borrow and save at the same interest rate. In reality this is not the case. The interest rate on credit-cards, car down-payments and other loans tend to be much higher than the interest rate on financial assets that can be used for saving. Further, individuals are constrained on the maximum amount the can borrow, and might not get a loan.

Liquidity constraints can increase savings in two ways. First, it creates a maximum spending limit for the individual so they can not consume as much as they might prefer.

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3.2 Precautionary savings 11

Second, it can increase saving because individuals fear being constrained in the future. It can therefore be argued that liquidity constraints encourage individuals to save in order to insure themselves against the effects of a fall in future income. In this sense liquidity constraints can reinforce the precautionary saving motive (Deaton, 1991).

The empirical literature is mixed on the effects of precautionary savings. Most studies find an effect, but the intensity differs to a large extent. One reason why the studies find mixed effects might be because they tend to differ in the methods used and the proxies they use to measure uncertainty.

Mody, Ohnsorge, and Sandri (2012) studied effects uncertainty and precautionary savings during the Great Recession in 27 countries. They found that more than 40 percent of the increase in savings could be directly related to the increase in unemployment risk and GDP volatility. Chamon, Liu, and Prasad (2013) used variance in income shocks to measure uncertainty and studied panel data from Chinese households from 1989-2009.

They found that an increase in transitory uncertainty could help explain the rising saving rates. Carroll and Samwick (1997) found that the precautionary saving motive was able to explain a sizeable part of wealth holdings by using the Euler equation and income variance. On the other hand Guiso, Jappelli, and Terlizzese (1992) and Skinner (1988) found little or no evidence in favour of a precautionary motive.

The main problem with using income variance as a measure of income risk in cross sections is that it is impossible to separate choice from circumstance. Observing that individuals face different risk may be a result of differing risk aversion and self-selection into jobs. In other words, the agent’s choice of occupation may be correlated with attitudes to risk.

Direct measures of subjective income risk may circumvent such endogeneity problems.

Some studies have access to self-reported measures of uncertainty. The Italian SHIW survey includes questions about self-assessed earnings risk that has been used in several papers. Guiso, Jappelli, and Terlizzese (1992) found that the precautionary motive explained only a small fraction of wealth accumulation, and that subjective earnings risk decreased the demand for risky assets. However, Browning and Lusardi (1996) have pointed out that a problematic feature of such survey data is that they depend on the respondent to understand the question and answer truthfully.

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4 Data

4.1 Uncertainty

The future will always be uncertain, however at times people perceive the future as more uncertain than before. A general definition in the literature is that uncertainty is the conditional volatility of a disturbance that is unforecastable from the perspective of economic agents (Jurado, Ludvigson, and Ng, 2015). However there exists no objective measurement of uncertainty, therefore the empirical literature relies on proxies of uncertainty.

This paper defines income uncertainty as an increase in the variance of future income streams, keeping the mean constant. Bloom (2014) show that wages and income volatility appears to be counter cyclical. Unemployment increases during recessions and the volatility of household incomes also increases during recessions. Less arbitrary is the findings that households who do not loose their job also face a more volatile wage during recessions (Storesletten, Telmer, and Yaron, 2004b).

Bloom (2009) shows that uncertainty appears to dramatically increase after major negative economic and political shocks. Positive shocks on the other hand tends to have little effect on uncertainty. Bloom (2014) argues that increases in uncertainty are both part of the impulse arising from bad news shocks that start recessions and further can amplify recessions by rising further as growth stops. However, the question of whether there is a causality between uncertainty and growth remains unanswered. Does uncertainty decrease growth, or does uncertainty increase because of decreased growth?

4.2 Macroeconomic impacts of uncertainty

Economic theory suggests three main channels through which uncertainty can negatively affect economic growth.

First, uncertainty affects the investment and consumption decisions of firms and consumers.

This revolves around the theory of real options (Bloom, 2014). When uncertainty increases, postponing a decision becomes more attractive. This is particularly the case for choices that

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4.3 Proxies of uncertainty 13

are costly to reverse. For a consumer experiencing high income uncertainty, postponing the decision of a large durable goods purchase can be more attractive than when income uncertainty is low. Similarly, firms hiring and investment decisions follow the same reasoning. Postponing investments such as hiring for longer terms and other large investments that are costly to reverse become more attractive when uncertainty is high.

Uncertainty can therefore reduce levels consumption, investment and hiring. Another aspect of the real options theory is that actors become less sensitive to changes in business conditions. Bertola, Guiso, and Pistaferri (2005) found that higher income uncertainty made consumers expenditures on durable goods less sensitive to demand and prices signals.

This can make monetary and fiscal stimulus less effective during recessions.

Second, uncertainty raises the risk premium. This makes financing more costly. Higher borrowing costs can reduce micro and macroeconomic growth.

Third, higher uncertainty can increase precautionary savings and reduce current consumption given that agents are risk averse and sufficiently prudent. This can lead to a short run contraction of the economy. However the long run effects are less clear. If the country is a small open economy, such as Norway, precautionary saving might reduce growth if parts of the saving flow overseas. Another problem arises if prices are sticky and interest rates does not fall as a result of the increased saving. Then investments would not necessarily increase and output falls. This problem increases when interest rates are close to the zero lower bound as observed today. Despite the theories, there is still much uncertainty around the actual effects of uncertainty on economic growth (Bloom, 2014).

4.3 Proxies of uncertainty

This paper uses three proxies of uncertainty. A measure of implied volatility in the stock market (VIX), a newspaper based measure of global economic policy uncertainty (GEPU) and the registered unemployment rate.

The Chicago Board Options Exchange Volatility index (VIX) represents the market’s expectation of 30-day forward-looking volatility. It is derived from the price inputs of the S&P 500 index options, and provides a measure of market risk and investors expectations.

The VIX is countercyclical. It rises during recession. High values of the VIX index are generally linked to large volatility as a result of increased uncertainty, risk and fear. The

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14 4.3 Proxies of uncertainty

mean value of the VIX is around 20. Alexopoulos and Cohen (2009) argue that what is observed on Wall Street does not necessarily affect households on Main Street. To adjust for this possibility, the paper includes the GEPU and unemployment rate as proxies of uncertainty. Data from daily values of VIX is recieved from VIX Historical price data (2020), GEPU data is recieved from Global Economic Policy Uncertainty Index (2020)

and unemployment rates from Norwegian Labour and Welfare Administration (2020).

The Global Economic Policy Uncertainty Index (GEPU) is a GDP-weighted average of national economic policy uncertainty (EPU) indices for 16 countries that account for two-thirds of global output (Davis, 2016). Each national EPU index reflects the relative frequency of own-country newspaper articles that contain terms related to the economy, uncertainty and policy-related matters. The VIX and GEPU often move together, but are conceptually different. VIX measures implied volatility over a 30-day look-ahead period while GEPU involves no explicit horizon. VIX can be attributed to measure financial uncertainty while the GEPU measures policy uncertainty. This can be why a different pattern after the Great Recession is observed figure 4.1.

Figure 4.1: Monthly average of VIX and Global Economic Policy Uncertainty Index

Figure 4.1 illustrates VIX (values on the right axis) and Global Economic Policy

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Uncertainty index (values on the left axis). We observe that both indexes follow a similar pattern surrounding large uncertainty events such as the fall of Lehman Brothers on the 15th of September and global spread of COVID-19 in March 2020. After the Great Recession, events such as the Eurozone debt crisis, Brexit and trade tensions between the US and China can be observed by small spikes in the VIX, but the events are more largely attributed to global policy uncertainty as illustrated by figure 4.1. The VIX index increases by around the same magnitude following the global pandemic as the peak during the Great Recession. The GEPU on the other hand shows a more upward sloping trend and this might be attributed to a general increase in economic policy uncertainty since the Great Recession.

Unemployment rates are often used as a proxy for income uncertainty. It can be argued that unemployment is the highest income risk faced by households. Mody, Ohnsorge, and Sandri (2012) argue that higher unemployment may lead to higher saving rates not only by increasing labor income uncertainty, but also by reducing expected income.

Unemployment seems to have both short term effects of reduced income while unemployed, and potentially scarring long term effects. Studies have found that unemployment can have potentially scarring permanent effects on life time earnings (Arulampalam, Gregg, and Gregory, 2001). For example, graduating in a recessions can lead to long term effects on life time earnings (Liu, Salvanes, and Sørensen, 2016). They studied the effects of wage differences for Norwegian students who graduated during the economic recession of the 1990s compared to those who graduated during the boom that followed in the late 1990s.

The paper found that a mismatch between graduates skill and skills demand for workers during a recession reduced peoples ability to grow and find jobs that related to their skills.

The impact of income uncertainty measured by unemployment as a proxy might depend on the unemployment insurance scheme. Norway has a generous welfare state and unemployment insurance is more generous in Norway than in for example the US.

Norwegian workers who become unemployed are generally entitled to unemployment benefits equal to 67 percent of their wages before unemployment for a duration of two years. High income earner however have a lower replacement rate. This can indicate that for high income earners the income risk of unemployment might be perceived as higher, than for low income earners.

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Basten, Fagereng, and Telle (2016) used microdata of Norwegian households who experienced a downsizing of their workplace. Their results indicate that households behave precautionary when faced with a higher risk of unemployment. Furthermore, their results indicate that households are able to foresee the upcoming unemployment spell and prepare by increasing their saving and re-balancing their portfolio.

A study of Norwegian households by Odmund Berg and Elin Halvorsen showed that a one percentage point increase in job loss probability leads to an increase in private savings of 9 percent (Berg, 2018). Berg and Halvorsen found that age is important, and younger households with more accumulated debt are more responsive to changes in job loss probability. Debt seems to be an important factor, and the saving response seems to depend more on ability to obtain debt in good times, rather than insure themselves against potential consumption drops in bad times. Juelsrud and Wold (2019) found similar but more modest results, one percentage point increased in job loss risk increases liquid savings by roughly 1.2 - 2.0. percent. They used the oil price collapse in 2014 to study the effects of increased unemployment risk for employees. Reduced job security led to an increase in savings, and this increase in savings could affect the economic downturn in the oil municipalities of Norway.

Using the current registered unemployment rate by the Norwegian Labour and Welfare Administration might overestimate the amount of people who are unemployed in the longer term, because temporarily laid off workers are included in the measure. As observed so far, unemployment rates have decreased to some extent and are likely to decrease from the high level observed in the following years. Therefore this paper uses estimates for the unemployment rate in the following years depicted in table 4.1 from Norges Bank (2020).

Table 4.1: Unemployment rate estimates from Norges Bank

Alternative 1 (Quick recovery) Alternative 2 (Slow recovery)

Year 2020 2021 2022 2023 2020 2021 2022 2023

Unemployment rate in % 5.9 2.8 2.4 2.4 7.2 6.3 5.7 5.1

Norges Bank proposed two recovery scenarios because there is large uncertainty surrounding future economic developments. As we observe from the estimates in table 4.1, even in the best case scenario depicted by alternative 1, the unemployment rate is higher than the registered unemployment rate before the pandemic. It is likely that a large part of

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the temporarily laid off workers will return to their job. However it is inevitable that some workers will loose their job due to the large economic impact of the pandemic both globally and nationally.

Proxies of uncertainty indicate that the COVID-19 pandemic has increased uncertainty substantially from around March to the end of April. Because the GEPU is not updated as frequently, the last observation is from the 30th of April. The VIX reached the highest levels observed ever observed closing at 82.69 on the 16th of March this year. During the great recession the highest observed closing level was 80.86. However the the VIX index has later decreased quicker than what we observed during the Great Recession.

Table 4.2: Proxies of uncertainty (15th of September to end of 2008)

Time period 1.09.2008-31.12.2008 VIX (closing value) GEPU Unemployment rate

Mean value during period 54.89 154.56 1.8

Peak level 80.73 200.9 2

All time historical average value 19.7 120.69 2.71

Percent increase (historical average to peak) 309.8 66.46 -26.2

Table 4.3: Proxies of uncertainty during global pandemic (28 February to end of April 2020)

Time period 28.02.2020-30.04.2020 VIX (closing value) GEPU Unemployment rate

Mean value during period 49.92 313.34 7.5

Peak level 82.69 361.3 10.4

All time historical average value 19.7 120.69 2.71

Percent increase (historical average to peak) 319.75 199.36 283.76

Table 4.2 and 4.3 illustrate the developments of uncertainty during the two crisis. The average values are calculated from the time periods available for the proxies and generally meant to illustrate that we observe a substantial increase in these uncertainty proxies during the Great Recession and the COVID-19 pandemic. In table 4.2 and figure 2.2 we observe that the registered unemployment rate did not increase substantially during the Great Recession. We observe higher unemployment rates during the booming years before the recession. The problem with such aggregated measures is that they do not tell anything about what parts of the population that is affected. The oil prices developments of the Great Recession affected Norway as a whole even though the aggregate unemployment rate did not increase to a large extent. In particular it affected oil municipalities and in these municipalities we observe higher unemployment rates compared with other less

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affected municipalities. Similarly this is observed in the pandemic as the economic shock affects sectors and workers differently (Alstadsæter et al., 2020). The focus in this paper is aggregate effects on consumption and saving, it could be argued that it is beneficial to use aggregated measures. However, in reality we tend to observe that income shocks can be skewed to particular parts of the workforce.

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5 Methodology

5.1 Life cycle model with bequest and risk

In the model we have an economy populated by a continuum of households who live for T periods. They face a life-cycle income profile given by w_t, where w_t is the deterministic income component at age t and face idiosyncratic income risk. The model assumes that markets are incomplete. There is no insurance available against earnings loss. Households can only trade one-period risk-free bonds with real interest rate r and face income risk in form of an uninsurable productivity shock. This shock follows an age-independent Markov chain, with the probability of moving from shock e_j toe_k given by P(e_k|e_j). Households also face a transitory income shock,^T, also following an age-independent Markov chain.

5.2 Preferences and the Households maximization problem

The periodic utility function for household is defined as U(c_t), wherec_t is the household consumption. At periodt, the household solve for the consumption level c_tthat maximizes the corresponding remaining life time utility:

max

{U(ct)}^T₀ T

X

t=0

β^t c^1−γ_t

1−γ +β^Tψ₀ b^1−ψ_T ¹

1−ψ₁ (5.1)

where 0< β <1is the discount factor, ψ₀ and ψ₁ are parameters of the bequest function, r is the return on the risk-free asset. γ is the degree of relative risk aversion. 1/γ is the inter-temporal substitution elasticity between consumption in any two periods. It measures the willingness to substitute consumption between different periods. The smaller γ the more willing is the household to substitute consumption over t.

During working life the household is subject to budget constraints:

c_t=e^y^t +rbt−1−b_t (5.2)

y_t=w_t+p_t+^T_t (5.3)

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20 5.3 Calibration of Model

p_t=ρpt−1+^P_t (5.4)

^P_t ∼N(0, σ_P) (5.5)

^T_t ∼N(0, σ_T) (5.6)

b_t≥0 (5.7)

y is log labor income, pis the persistent component of income, ^T is a transitory income shock, ρis the persistence of income, and ^P is an innovation to permanent income. This framework is similar to many of the common models in the incomplete markets literature.

The households have a constant relative risk aversion (CRRA). This implies similarly to quadratic utility functions that the second derivative is negative. In other words the agents are risk averse. Risk aversion does not necessarily imply precautionary behavior.

Sandmo (1970) showed that precautionary behavior depends on the convexity of marginal utility. The CRRA utility function has a third derivative larger than zero. A positive third derivative makes the marginal utility a convex function of consumption. This leads to the agent disliking uncertainty. The marginal utility of something certain is larger than the marginal utility of something uncertain for the same mean.

The model is annual. Households start life at age 30 with a draw from the ergodic income distribution and end their life when at age 75. The optimization problem is solved by backwards induction.

5.3 Calibration of Model

To calibrate the model to fit with Norwegian households I follow Fagereng, Gottlieb, and Guiso (2017) who study asset market participation over the life cycle of Norwegian households. They focus on financial wealth, similar to this thesis. The focus in this thesis is the income-wealth ratio which is measured by financial wealth divided by income.

Financial wealth includes bank deposits, stocks, bonds, mutual funds and other financial wealth. It excludes housing and other illiquid assets such as cars, cabins and boats. To estimate the wealth-income ratio I use statistics on household wealth and income from Statistics Norway (SSB). I find a mean wealth to income ratio of 1.2277 for households where the household head is 50 years old. This age is chosen because it represents around

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5.3 Calibration of Model 21

the time where most households annual income is around the highest over their life cycle, further it represents an age where most people tend to be employed, and using older households might lead to problems of retired individuals. The life cycle financial wealth profile from Fagereng, Gottlieb, and Guiso (2017) is transformed to depict the wealth to income ratio instead of mean financial wealth.

Figure 5.1: Life-cycle pattern of financial wealth (Fagereng, Gottlieb, and Guiso, 2017)

Figure 5.1 shows the wealth income profile. Calibrating the sharp rise of wealth during early ages is difficult because the model tends to give a convex shape of wealth over the life cycle. In order to calibrate the model to fit the data the discount factor β is adjusted. The discount factor shows how much consumption in future periods are valued compared to today. Calibrating β = 0.90fits the wealth profile best. To further adjust for the slow decumulation of financial assets a high value for the parameter of the bequest motive is chosen. The bequest motive shifter shows how much utility households get from leaving wealth at the end of their life to heirs. The bequest shifter value that fits best is 4.4. It is worth noting that perfectly fitting the observed life cycle income wealth ratio is difficult. By calibrating the model it comes close to illustrating a similar profile as Fagereng, Gottlieb, and Guiso (2017).

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22 5.3 Calibration of Model

Table 5.1: Variables in model

Symbol Type of variable Value

β Discount factor 0.90

ψ₀ Bequest shifter 4.4

ψ₁ Bequest curvature 0

r Risk free interest rate 0.03

γ Degree of relative risk aversion 2

ρ Persistence of income 0.937

σT Transitory standard deviation of income 0.156 σ_P Permanent standard deviation of income 0.095

The value of the degree of relative risk aversion γ is equal to 2, which is similar to many studies in the literature (Domeij and Klein, 2002). Further the risk free rate is equal to 3 percent, close an average real interest rate in Norway over past 25 years.

In order to estimate the income profile over the life cycle the paper uses a life value function of Norwegian households from Ahn and Holm (2020). Similar to observed data, households earn less at young ages and their yearly income peaks around 58 years old.

Retirement is known in the model and all agents retire at age 67 and then get 2/3 of their highest achieved yearly income for the rest of their life. There is no uncertainty of death in the model and agents live until they are 75.

The model simulation is done in Matlab and follows Fernández-Villaverde and Valencia (2018) and Fagereng, Holm, and Natvik (2019). The model simulates 100 000 households to estimate the coefficients. The shock hits at random. Two groups are simulated, a treatment group that receives an expected income shock and a control group that does not receive the income shock. The two groups are approximately the same and only differ in the income shock the treatment group receives.

The income shock perceived as permanent. In other words the shock is an increase in the permanent standard deviation of income (σ_P). It is difficult to estimate how households perceive the permanent income shock, therefore the size of the shock varies between a lower and upper bound. The perceived income shock builds on the uncertainty proxies from the data section. The shock happens in period 0, and lasts for 5 years. It hits at random ages in the population, but the setup requires that age must be 70 or less when the shock happens, since households die at age 75 in the model.

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23

6 Analysis

6.1 Great Recession income uncertainty shock

First, I estimate the effects of a pure uncertainty shock similar to what agents might experience following a financial crisis. It is a stylized simulation. It is a pure income uncertainty shock, in other words the mean of future income remains constant. The model is permuted. This means the effects of the income shock are scaled linearly.

As figure 4.1 and table 4.2 indicates, proxies of uncertainty spiked in the last months of 2008. During the Great Recession the mean closing value of the VIX index was 54.89 in the last 4 months of 2008. The GEPU also increased by a large amount. After the Great Recession, the observed variance of the income process increased from 0.0735 to 0.0835 (Holm, 2015). This corresponds to a 0.1 increase in the standard deviation of future income. Comparing the GEPU index with the VIX index we observe that they both increase, indicating that global economic policy uncertainty also increased when financial market uncertainty increased. The lower bound estimate is half, equal to an increase of 0.05. My middle ground estimate is an increase of 0.075. Because there is uncertainty about how large households expected the shock to be, the estimates have differences in size. Despite the uncertainty of the size of the shock, these estimates are in line with Storesletten, Telmer, and Yaron (2004a), who finds that standard deviation increases from 0.12 to 0.21 as the economy moves from peak to trough.

(a) Percentage change in consumption (b) Percentage change in financial wealth Figure 6.1: Income uncertainty of the Great Recession

Figure 6.2 illustrates the effects of an increase in the standard deviation of income. In the

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24 6.2 COVID-19 income uncertainty shock

model the income uncertainty shock gives a reduction in consumption of 1.34 percent and increase in saving of 0.666 percent, in the upper bound estimate, ceteris paribus, in the first year after the shock.

An increase in income uncertainty, decreases consumption and increases saving in the model simulation. This is in line with the theory of precautionary savings. In the above simulation households perceive that the income shock is permanent and therefore reduce consumption. They increase wealth to have a higher buffer stock to safeguard against the possibility of lower consumption in the future. In the period from 2008 to 2013 the increase in wealth as a result of precautionary saving is between 2.8 and 5.6 percent and consumption is reduced by between 1.17 and 2.34 percent as a result of higher income uncertainty.

Figure 2.1 indicate a small increase in the unemployment rate following the Great Recession.

Compared to other countries during and after the financial crisis the increase is low.

Furthermore, we observe higher unemployment levels during the economic boom before the financial crisis. The registered unemployment rate developments could indicate that the level of income uncertainty was only modest in the period after the Great Recession.

On the other hand the unemployment rate does not necessarily capture all aspects of households expected income variance. A worker could be employed but still have a high variance in their future income stream. Further, the registered unemployed numbers does not account for all people who are unemployed, only the people who are registered as unemployed by the Norwegian Labour and Welfare Administration.

6.2 COVID-19 income uncertainty shock

I estimate the effects of an increase in expected income uncertainty for Norwegian households similar to what they might experience as a result of the COVID-19 pandemic.

Because this is happening in real time, the available evidence on household income is limited.

While it seems reasonable to assume that the pandemic increases income uncertainty for households, it is difficult to precisely estimate how much income uncertainty has increased.

My analysis assumes that the developments in the proxies of uncertainty during the Great Recession and COVID-19 pandemic are comparable and reflects information of how large the income uncertainty shock is perceived by households. However, the long term effects

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6.2 COVID-19 income uncertainty shock 25

are as of this writing largely uncertain. Therefore it is difficult to give precise estimates as short term measures lack in predictability of the long term effects. Because of the large uncertainty surrounding the future developments of the economic recovery I propose two alternative scenarios.

The first scenario assumes that the income shock is of the same magnitude as observed during the Great Recession, and assume that it yields the same increase in the standard deviation of the income process. By observing the developments in the VIX from table 4.3 and 4.2 it can be argued that the shock is of a smaller intensity than during the Great Recession. Even though the VIX peaked at a slightly higher level during the COVID-19 pandemic, the financial markets quickly recovered. The VIX has decreased faster than what we observed in the Great Recession. On the other hand, the GEPU and unemployment rate is pointing to a larger amount of income uncertainty than what was observed during the Great recession. The first scenario assumes that the unemployment rate returns to relatively low levels by 2021, and follow the pattern of alternative 1, illustrated in table 4.1. The effects on consumption and savings will be the same as in figure 6.2.

The second scenario depicts a larger perceived income shock. This scenario assumes that the unemployment rate remains relatively high. It assumes that the unemployment rate follow the pattern of alternative 2, illustrated in table 4.1. Combined with the developments in the GEPU and VIX my back of the envelope calculations yield an increase in the standard deviation of income equal to 0.15. Figure 6.2 illustrates the five year developments after the income uncertainty shock.

(a) Percentage change in consumption (b) Percentage change in financial wealth Figure 6.2: Income uncertainty shock of the COVID-19 pandemic

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26 6.2 COVID-19 income uncertainty shock

The second scenario depicts a larger decrease in consumption due to higher perceived income uncertainty and a yields a larger increase in saving for precautionary motives.

The model predicts that in 2020 consumption will drop between 1-2 percent as a result of increased income uncertainty. As observed in the current short term data, the actual drop in consumption will be larger, partly as a result of households fear of the virus and social distancing measures (Andersen et al., 2020). In order to smooth consumption over their life cycle when faced with increased income risk, the model predicts that financial wealth increases by around 0.5 to 1 percent in 2020. The difference between the upper and lower bound in the estimates is attributed to large uncertainty of how large households perceive the income uncertainty shock. In the model the income shock is perceived as constant and lasts until 2025. Over these 5 years the increase in wealth as a result of precautionary saving is between 4.21 and 8.42 percent and consumption is reduced by between 1.75 and 3.5 percent as a result of higher income uncertainty.

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7 Discussion

The main issue in the literature and in this paper is how to measure perceived income uncertainty. Further, it is especially difficult to measure income uncertainty close to real time. Because it is difficult to measure, the model estimates are adjusted with a large difference between the upper and lower bound. Even though the size and duration of the shock is uncertain, the direction of the effect is certain. An income uncertainty shock in the model yields a reduction in current consumption and increase in saving over the duration of the period.

The goal of this paper was to study how income uncertainty can affect households saving.

It is important to emphasize that households saving is affected by many different factors that are outside the scope of this paper. Furthermore, a sudden uncertainty shock is often related to changes in other factors that can influence household saving. Apart from income uncertainty, Gudmundsson and Reiakvam (2013) explained the increase in the saving rate in Norway after the Great Rescission as a combination of factors. The pension reform of 2011, demographic changes, income uncertainty and tighter credit constraints for households are proposed as explanations for the increase in the saving rate between 2008 and 2013.

Recent empirical research on business cycles find that income uncertainty increases during recessions, but also that the distribution is left skewed (Guvenen, Ozkan, and Song, 2014).

In other words the probability of a large reduction in income becomes more likely than an equally large increase in income during a recession. Salgado, Guvenen, and Bloom (2019) studied the cyclicality of the distribution of the growth rate of firm-level outcomes. They found that the distributions displayed a large left tail of negative growth rates during recessions and a large right tail of positive growth rates during booms. In the model the focus is only on how income uncertainty in itself can affect consumption and saving. In reality it is more likely that there is an increased downside risk, and not necessarily an equal upside.

It could be argued that the pandemic-induced income uncertainty is different from previous income uncertainty shocks in terms of Knightian uncertainty. Knightian uncertainty in this setting refers to the lack of any quantifiable knowledge about households future income.

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28

The current situation could be considered more of a Knightian uncertainty shock compared to the Great Recession. As of this writing there is a lack of quantifiable knowledge of the duration of the pandemic and possibilities of treatment. Further there is uncertainty of possible changes in behavior among economic agents. Previous pandemics such as the Spanish flu in 1918 can give insight, but the current situation is unlike anything we have experienced before. The large amount of Knightian uncertainty of the current situation could be an argument for the current income shock being more persistent. However in the case of comparing present and historical situations one should keep the hindsight bias in mind.

The assumption that the developments in the proxies of uncertainty and observed change in the income process after the Great Recession is comparable with the current developments in proxies of uncertainty can be criticised. As previously stated the shocks are fundamentally different.

Economic policy measures are important to consider when discussing income uncertainty during a crisis. During the pandemic, the role of fiscal policy has been different than in previous crisis (Holden et al., 2020). While the goal in previous crisis usually has been to keep the activity up, this time the purpose has been primarily to screen households and businesses from large reductions in income as the pandemic led to a sharp decline in activity in the economy. The differences in economic policy could affect how households perceive income uncertainty. Holden et al. (2020) analyse the current economic situation and the effects of economic measures put in place to provide economic security in a time of crisis. As of this writing the measures put in place likely has decreased households income uncertainty. However it is important to emphasize that the measures have been introduced mainly to dampen the short term effects of the pandemic. The future of the Norwegian economy largely depend on uncertain factors such as the course of infection, the development in international economics, and the long-term effects of the pandemic on household and business behavior.

A strong assumption of the analysis is that the income uncertainty shock is equally distributed among households at all ages. In reality there is large heterogeneity between how actors are affected by income uncertainty shocks. This is especially observed in the COVID-19 pandemic where young and less educated households are strongly affected by

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29

income uncertainty. It can be argued that workers in the public sector are not affected to an equally large extent by income uncertainty in the current pandemic.

The assumption that the shock is permanent might be more realistic in the current situation compared to the Great Recession. One reason for this is because unemployment will likely be higher as a result of the pandemic compared to the low increase in unemployment observed in Norway during the Great Recession. There is large amounts of empirical evidence on how unemployment can permanently affect households lifetime income and this supports the assumption that the shock is perceived as permanent. Further it could be argued that the possibility of similar pandemics in the future can make households perceive the income shock as more permanent. On the other hand it could be argued that households do not perceive the current shock as permanent, and that their expectations are not aligned with the economic predictions of Norges Bank and Statistics Norway.

The estimates by Norges Bank for the developments in the unemployment rate are subject to large amounts of uncertainty. The future economic developments both globally and nationally are uncertain. If the actual unemployment rate is lower than the modest estimate then the size of the income uncertainty shock will likely be of a smaller magnitude.

It can be argued that using the VIX index as a proxy for Norwegian household uncertainty will not yield clear estimates of the actual perceived income uncertainty of Norwegian households. With the same reasoning the GEPU can also be criticized for its relevance to economic uncertainty for Norway. Armelius, Hull, and Köhler (2017) created an index of policy uncertainty for Sweden and found that shocks to foreign economic policy uncertainty indexes impacted Swedish GDP with a one-quarter delay. This could indicate that there might be a delay in the effects of the global uncertainty shocks for Norway. However Norway differs from Sweden with regards to oil price developments and therefore might be affected with a shorter delay. My estimates try to account for this by including the registered unemployment rate. Finding good real time or relatively close to real time proxies for income uncertainty is difficult. Another approach could be to use survey data on households expected income uncertainty. However there are drawbacks to using survey data since it is not observed in real time and might suffer from measurement errors.

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8 Conclusion

The purpose of this thesis has been to investigate the possible effects of an income uncertainty shock on household consumption and saving. The life cycle model used in this paper and the simulations predicts that income uncertainty shocks increases savings and reduces current consumption. How large the effects are depends on how households perceive the shock. The model assumes that households perceive the income shock as permanent. Based on developments in proxies of uncertainty and the observed change in the variance of the income process the model predicts that the increase in income uncertainty after the financial crisis could explain a decrease in consumption of between 1.17 to 2.34 percent in the period from 2008 to 2013. Assuming that the two income shocks are similar in their effects to income uncertainty my analysis predicts two alternative income shocks of the COVID-19 pandemic. The model predicts that the pandemic-induced income uncertainty could yield around a 1.75 to 3.5 percent fall in consumption in the period from 2020 to 2025 in the worst case scenario.

The economic developments of the pandemic will likely be studied for a long time. The effects of both the pandemic itself and the effects of uncertainty in light of the situation are as of this writing uncertain. Many research questions surrounding the concept of uncertainty remains uncertain. Further research including heterogeneity in the effects of the COVID-19 income shock with more advanced proxies of uncertainty are needed.

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References 31

References

Ahn, SeHyoun and Martin B. Holm (2020). “Monetary Policy under Demographic Transitions”. In: Working Paper.

Alexopoulos, Michelle and Jon Cohen (2009). “Uncertain times, uncertain measures”. In:

University of Toronto Department of Economics Working Paper 352.

Alstadsæter, Annette, Bernt Bratsberg, Gaute Eielsen, Wojciech Kopczuk, Simen Markussen, Oddbjorn Raaum, and Knut Røed (2020). “The first weeks of the coronavirus crisis: Who got hit, when and why? Evidence from Norway”. In:

Covid Economics.

Andersen, Asger Lau, Emil Toft Hansen, Niels Johannesen, and Adam Sheridan (2020).

“Pandemic, Shutdown and Consumer Spending: Lessons from Scandinavian Policy Responses to COVID-19”. In:Technical report, University of Copenhagen and CEBI.

Armelius, Hanna, Isaiah Hull, and Hanna Stenbacka Köhler (2017). “The timing of uncertainty shocks in a small open economy”. In:Economics Letters 155, pp. 31–34.

Arulampalam, Wiji, Paul Gregg, and Mary Gregory (2001). “Unemployment scarring”. In:

The Economic Journal 111.475, pp. 577–584.

Baker, Scott R, Nicholas Bloom, Steven J Davis, and Stephen J Terry (2020). “Covid- induced economic uncertainty”. In: Technical report, National Bureau of Economic Research.

Basten, Christoph, Andreas Fagereng, and Kjetil Telle (2016). “Saving and portfolio allocation before and after job loss”. In: Journal of Money, Credit and Banking 48.2-3, pp. 293–324.

Berg, Oddmund (2018). “Empirical essays on the dynamics of consumption and saving”.

PhD thesis. Norwegian School of Economics.

Bertola, Giuseppe, Luigi Guiso, and Luigi Pistaferri (2005). “Uncertainty and consumer durables adjustment”. In:The Review of Economic Studies 72.4, pp. 973–1007.

Bloom, Nicholas (2009). “The impact of uncertainty shocks”. In: econometrica 77.3, pp. 623–685.

– (2014). “Fluctuations in uncertainty”. In: Journal of Economic Perspectives 28.2, pp. 153–76.

Boug, Pål (2010). “Fra kjøpefest til edruelighet: økonomi”. In: Samfunnsspeilet, 2010/5-6.

(39)

32 References

Browning, Martin and Annamaria Lusardi (1996). “Household saving: Micro theories and micro facts”. In:Journal of Economic literature 34.4, pp. 1797–1855.

Carroll, Christopher D and Andrew A Samwick (1997). “The Nature of Precautionary Wealth”. In: Journal of Monetary Economics 40.1, 41–72.

Chamon, Marcos, Kai Liu, and Eswar Prasad (2013). “Income uncertainty and household savings in China”. In:Journal of Development Economics 105, pp. 164–177.

Davis, Steven J (2016). “An index of global economic policy uncertainty”. In: Technical report, National Bureau of Economic Research.

Deaton, Angus (1991). “Saving and Liquidity Constraints”. In: Econometrica 59.5, pp. 1221–1248.

Domeij, David and Paul Klein (2002). “Public pensions: to what extent do they account for Swedish wealth inequality?” In:Review of Economic Dynamics 5.3, pp. 503–534.

Eichenbaum, Martin S, Sergio Rebelo, and Mathias Trabandt (2020). “The macroeconomics of epidemics”. In:Technical report, National Bureau of Economic Research.

Eiendom Norge (2020).url:https://eiendomnorge.no/nyheter/sterk-oppgang-i-boligprisene- i-mai-article1850-919.html (visited on 06/06/2020).

Fagereng, Andreas, Charles Gottlieb, and Luigi Guiso (2017). “Asset market participation and portfolio choice over the life-cycle”. In: The Journal of Finance 72.2, pp. 705–750.

Fagereng, Andreas, Martin Blomhoff Holm, and Gisle James James Natvik (2019). “MPC heterogeneity and household balance sheets”. In: Working Paper.

Fernández-Villaverde, Jesús and Pablo A Guerrón-Quintana (2020). “Uncertainty Shocks and Business Cycle Research”. In: Technical report, National Bureau of Economic Research.

Fernández-Villaverde, Jesús and David Zarruk Valencia (2018). “A practical guide to parallelization in economics”. In: Technical report, National Bureau of Economic Research.

Flavin, Marjorie A (1981). “The adjustment of consumption to changing expectations about future income”. In:Journal of political economy 89.5, pp. 974–1009.

Friedman, Milton (1957). “A Theory of the Consumption Function”. In: Princeton University Press.

Global Economic Policy Uncertainty Index (2020). url: https://www.policyuncertainty.

com/global_monthly.html(visited on 05/30/2020).

(40)

References 33

Gudmundsson, Jørgen and Lisa K Reiakvam (2013). “Husholdningenes sparing etter finanskrisen”. In:Aktuell kommentar Norges Bank, 1-2013.

Guerrieri, Veronica, Guido Lorenzoni, Ludwig Straub, and Iván Werning (2020).

“Macroeconomic Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages?” In:Working Paper.

Guiso, Luigi, Tullio Jappelli, and Daniele Terlizzese (1992). “Earnings uncertainty and precautionary saving”. In: Journal of Monetary Economics 30.2, pp. 307–337.

Guvenen, Fatih, Serdar Ozkan, and Jae Song (2014). “The nature of countercyclical income risk”. In: Journal of Political Economy 122.3, pp. 621–660.

Holden, Steinar, Hilde Christiane Bjørnland, Thomas von Brasch, Katrine Vellesen Løken, Erik Magnus Sæther, Kjersti Næss Torstensen, and Ragnar Torvik (2020). “Covid-19 – Analyse av økonomiske tiltak, insentiver for vekst og omstilling”. In:Technical report, Finansdepartementet.

Holm, Martin Blomhoff (2015). “Effekten av inntektsusikkerhet på husholdningenes sparing i Norge etter finanskrisen”. In:Samfunnsøkonomen 129.6, pp. 22–27.

Juelsrud, Ragnar E and Ella Getz Wold (2019). “The Saving and Employment Effects of Higher Job Loss Risk”. In: Working Paper.

Jurado, Kyle, Sydney C Ludvigson, and Serena Ng (2015). “Measuring uncertainty”. In:

American Economic Review 105.3, pp. 1177–1216.

Keynes, John Maynard (1936). The general theory of employment, interest, and money.

Macmillan and co ltd.

Kimball, Miles S (1990). “Precautionary Saving in the Small and in the Large”. In:

Econometrica 58.1, pp. 53–73.

Knight, Frank H (1921). Risk, uncertainty and profit. Houghton Mifflin Company, New York.

Liu, Kai, Kjell G Salvanes, and Erik Ø Sørensen (2016). “Good skills in bad times: Cyclical skill mismatch and the long-term effects of graduating in a recession”. In: European Economic Review 84, pp. 3–17.

Modigliani, Franco and Richard Brumberg (1954). “Utility analysis and the consumption function: An interpretation of cross-section data”. In:K.K. Kurihara ed., Post Keynesian Economics, Rutgers University Press, New Brunswick, pp. 388–436.