Behavioral responses to public pension cuts : evidence from the Greek financial crisis

Behavioral Responses to Public Pension Cuts:

Evidence from the Greek Financial Crisis

by

Iason Zaverdinos

Submitted to the Department of Economics

in partial fulfillment of the requirements for the degree of

Master of Science in Economics

at the

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

May 2020

c

Iason Zaverdinos, MMXX. All rights reserved.

The author hereby grants to MIT permission to reproduce and to distribute

publicly paper and electronic copies of this thesis document in whole or in

part in any medium now known or hereafter created.

Author . . . .

Department of Economics

May 7, 2020

Certified by . . . .

James M. Poterba

Mitsui Professor of Economics

Thesis Supervisor

Accepted by . . . .

Amy Finkelstein

John & Jennie S. MacDonald Professor of Economics

Chairman, Departmental Committee on Graduate Studies

Behavioral Responses to Public Pension Cuts: Evidence from the

Greek Financial Crisis

by

Iason Zaverdinos

Submitted to the Department of Economics on May 7, 2020, in partial fulfillment of the

requirements for the degree of Master of Science in Economics

Abstract

The austerity measures that were adopted in the 2010-2015 period in response to the Greek financial crisis included cutbacks in the public sector on a scale that has rarely been seen in other nations. Pensions for those receiving benefits were cut by up to 50.1% and monthly salaries for public sector employees were reduced substantially. Most of the analysis of these reforms has focused on the macroeconomic effects on the government budget. This project uses data from household budget surveys to assess the effects of these cutbacks on individuals approaching retirement as well as those who had retired under the prior system. I find that both income and consumption of households with higher initial earnings decreased disproportionately more than those with lower earnings, in line with the austerity measures implemented. I also find that the income changes were negatively correlated with mortality rates.

Thesis Supervisor: James M. Poterba Title: Mitsui Professor of Economics

Acknowledgments

I am very grateful to my advisers Jim Poterba and Marios Angeletos for their guidance and support. I thank Nikos Vettas, Platon Tinios, Panos Tsakloglou, Costas Meghir, and Yannis Ioannides for their guidance. I would like to thank Efi Chantsouli, Georgios Ntouros and Athanasios Thanopoulos at the Hellenic Statistical Authority for their help with the data. I thank Eva Vourvachaki and Dimitris Papageorgiou at the Bank of Greece for their help with the data and the reforms. I thank David Rajnes from the United States Social Security Administration and Colin Gray for their help with the reforms. I thank participants at the MIT Public Finance Lunch for their useful comments. I am grateful to the Onassis Foundation for the financial support it has provided me during my studies.

Contents

1 Introduction . . . 12

2 National accounts trajectories . . . 15

3 Pension benefit cuts . . . 18

3.1 Description of the pension system . . . 18

3.2 Benefit cuts . . . 19

3.3 Cumulative changes calculations . . . 21

4 Data . . . 23

4.1 Datasets description . . . 23

5 Results . . . 26

5.1 Household Income & Consumption . . . 27

5.2 Average Individual Income by Age . . . 31

5.3 Mortality Rates by Age . . . 33

List of Figures

1 Greek Government Budget Balance as a Percentage of GDP, 2002-2016 . . . 13 2 Real GDP of Greece, 1995-2018 . . . 16 3 Real Private Final Consumption in Greece, 1995-2018 . . . 17 4 Relative GDP and private final consumption with 2009 basis . . . 17 5 Cumulative reductions of pensions by type, May 2010 - September 2015 . . . 21 6 Average yearly household income & consumption in 2009 prices . . . 27 7 Relative changes in real GDP and average yearly household income . . . 28 8 Relative changes in real PFC and average yearly household consumption . . 29 9 Average of yearly household income by 5-year age group in 2009 prices . . . 30 10 Average of yearly household consumption by 5-year age group in 2009 prices 30 11 Average of monthly income of individuals in 2009 prices by 5-year age group 33 12 Mortality rates per 1000 people by 5-year age group: 40-64 & total . . . 34 13 Mortality rates per 1000 people by 5-year age group: 65+ . . . 35 14 Mortality rates & average individual income by 5-year age group . . . 36

List of Tables

1 Distribution of pensioners by total pension size, May 2015 . . . 23 2 Effect of income reduction on mortality rates: OLS results . . . 38 3 Difference in mortality rates and average income by 5-year age group from

2008-2017 . . . 39 4 Mortality change and income change: OLS results . . . 40

1

Introduction

The Greek financial crisis began in late 2009. It was set off by the turmoil of the Great Recession along with revelations that previous data on government debt levels and deficits had been under-reported by the Greek government.

In particular, the official forecast for the 2009 budget deficit was less than half the final value as calculated in 2010, while after revisions according to Eurostat methodology, the statistics showed that Greece from 2000 to 2010 had exceeded the Eurozone stability criteria, with yearly deficits which went above the recommended maximum limit at 3.0% of GDP, and with the debt level significantly above the limit of 60% of GDP. This led to a crisis of confidence, indicated by a widening of bond yield spreads and rising cost of risk insurance on credit default swaps compared to the other Eurozone countries, particularly Germany.

Unable to borrow in private capital markets and under the pressure of high levels of pub-lic debt to GDP ratio and the high current budget deficit the government resorted to the “Troika” i.e. the European Commission, the European Central Bank (ECB) and the In-ternational Monetary Fund (IMF) for funds. The Troika agreed to lend Greece and save it from sovereign default subject to the condition that it would implement a certain number of austerity measures in order to ensure the correction of the structural weaknesses of the Greek economy and payback of the loans. The government proceeded to enact 12 rounds of tax increases, spending cuts and reforms from 2010 to 2016. The cutbacks were severe and at times triggered local riots and nationwide protests. In all, the Greek economy suffered the longest recession of any advanced capitalist economy to date, overtaking the US Great Depression1.

The economic and social effects of these austerity measures on the Greek population are the main topic of this paper. Understanding whether there is a causal link between income reduc-tion and increased mortality rates among the elderly is especially relevant for public policy. In addition, the behavioral responses to these cutbacks, as measured by household income and consumption are equally important. What was the response of household consumption

Figure 1: Greek Government Budget Balance as a Percentage of GDP, 2002-2016

Source: European Commission, Eurostat. Notes: The primary budget deficit excludes interest payments on debt.

to these measures and how did it differ across pensioners who faced different cutbacks? What was the effect of these reductions on health status?

During the crisis, media attention was focused on the pension reform which included big changes for future pensioners and large cutbacks to current retirees. However, the austerity measures did not affect all pensioners equally and most of them ended up actually improving their relative economic position in society. In particular, the reforms were highly progressive and pensioners receiving low benefits initially, 65.7% of the pensioner population, saw up to 22% cumulative reductions in their income. Pensioners receiving very high benefits, only 1.9% of the pensioner population, suffered reductions larger than 30% and up to 50.1%. Over the same time period, wages decreased by 30% on average, the unemployment rate rose to 27.7%, GDP fell by 26.4% and private final consumption by 29.3%. Hence, the majority of pensioners was affected by the crisis less than most other members of society. On the other hand, a minority of pensioners receiving large benefits was affected more than the rest of society.

Hellenic Statistical Authority (ELSTAT). The Household Budget Sets survey, which ELSTAT administers for Greece, is a survey carried out by every member-State of the European Union. It provides data on income and consumption at both the household and the individual level. ELSTAT also has data on mortality counts for the entire country as well as CPI data. I use the data to shine light on changes in income and consumption throughout the criti-cal time period during which the reforms were enacted. I compute average income at the household and individual level and consumption at the household level. I also compute these averages by 5-year age group. I find that both income and consumption dropped more than GDP and private final consumption did, respectively. Moreover, the reductions are larger for groups who were, on average, earning more initially; consistent with the reforms. In addition, consumption dropped by approximately the same amount as income suggesting that the whole income drop was absorbed by consumption.

In order to study the effects of the cutbacks on health status, I study the change in mortality rates over the sample period. Mortality is a standardized and well-documented measure of health status in the literature. I compute the rates by 5-year age groups, just like with income and consumption. Graphically, the linear trends show very small changes in the mortality rates over time. The regression results however, controlling for trends and age-groups, pro-vide some epro-vidence that income fluctuations are negatively correlated with mortality rates. I also perform a robustness check using differences of income and mortality and I obtain the same result.

There is an extensive literature in economics, both academic publications as well as non-academic work about the Greek financial crisis. Costas Meghir et al. (2017) in their book ”Beyond austerity: Reforming the Greek Economy (2017) provide a thorough description of the structural weaknesses of the Greek economy before the crisis. They analyze the previous and current drawbacks of each sector of the economy and propose solutions for their recovery. Several papers (Chodorow-Reich et al. (2019), Gibson et al. (2012) & (2014)) study the Greek Depression from a macroeconomic perspective.

Platon Tinios, has done extensive work covering the details of the pension system both before and during the crisis. Tinios (2015) documents the pension reforms and cuts that

were implemented and computes the cumulative reductions by original benefit level and by sector. Some parts of section 3 of this paper are heavily based on the work of Tinios. The income analysis of section 4 is perhaps most similar to Matsaganis et al. (2018). In that publication, the authors analyze data from the Survey of Income and Living Conditions (SILC) which is also administered by the Hellenic Statistical Authority for the European Union. They study the income changes across different age groups, professions, regions etc. in the 2003-2014 period. They also study employment changes. Unlike this paper though, they do not study the effects of the cutbacks on consumption and health status.

As far as the mortality analysis is concerned, there is an extensive literature both in eco-nomics and other sciences on this topic. It has established that those with lower incomes have poorer health outcomes and higher mortality rates (Kitagawa & Hauser (1973), Duleep (1986), Wolfson et al. (1993), McDonough et al. (1997), Deaton & Paxson (1998), Chetty et al. (2016)). There also exists a large literature about the correlation between income and health in an elderly population, the particular interest of this paper (Mare (1990), Menchik (1993), Smith & Kington (1997)). This paper’s findings trend closely with the literature. I find a negative association between income and mortality rates after controlling for age groups and trends.

The rest of the paper is organized as follows: In section 2, I show the national account trajectories for the past 25 years, section 3 contains the reforms to the pension system and a cumulative changes calculation, section 4 presents the datasets I use for the analysis and section 5 contains the results I obtain from analyzing the household and individual data on income, consumption and mortality. Section 6 concludes.

2

National accounts trajectories

In this section, I present the trajectories of national account variables for Greece. I obtained the (publicly available) data from the Federal Reserve Bank of St. Louis2_.

Figure 2: Real GDP of Greece, 1995-2018

Source: European Commission, Eurostat

Figure 2 shows the real GDP of Greece in annual, seasonally adjusted values, in billions of chained 2010 euros. In the 1995-2007 period, real GDP was growing steadily with an average growth rate of 3.9%. The financial crisis however, which started in 2008, caused Greece’s GDP to decline sharply. The economy experienced large negative growth rates until 2013, with the largest decrease being a 9.2% decline in 2011. The total real GDP reduction was of the order of 26.4% in the 2007-2013 period. Over the next three years, the growth rate was close to zero, until it returned to positive values in 2017.

Figure 3 depicts Real private final consumption in Greece, in annual, not seasonally adjusted values, in billions of 2009 Euros in the 1995-2018 period. The private final consumption data was obtained from the OECD. I then deflated the values using the CPI from ELSTAT, which uses 2009 as a basis3. Real consumption grew steadily until 2008, just like real GDP. The total increase over this period was 55.7%. When the crisis started however, consumption began to decline and continued to do so until 2015. The total decline was 29.3% over the 2008-2015 period and the largest decrease was a 10.7% reduction in 2011. Since 2015 real private final consumption has basically stayed the same.

Figure 3: Real Private Final Consumption in Greece, 1995-2018

Source: OECD

Figure 4: Relative GDP and private final consumption with 2009 basis

Source: Author’s calculations. Notes: The figure depicts real GDP and real private final consumption for Greece in the 1995-2017 period in constant 2009 values. For each of the two variables, the value of each year has been divided by the 2009 value of the variable generating a ratio. I standardize the variables in this way to allow for an easier comparison of the trends of real GDP and real private final consumption.

Figure 4 depicts real GDP and real private final consumption from 1995-2018 in constant values. To construct this graph, for each variable and for every year I take the ratio of the value of that year to its 2009 value. Doing this for both variables, allows me to compare the changes in them across time, irrespective of quantitative differences or units of measurement.

So for example, the 2009 ratio is 1 for both variables but the 1995 to 2009 ratio is 0.65 for consumption and 0.66 for real GDP. Hence, real private final consumption was farther from its 2009 value compared to GDP. The trajectories are very similar for the two variables suggesting that income changes caused a 1-1 response by consumption. For the 1995-2007 period, the total increase in consumption was slightly smaller than the increase in GDP. The subsequent drop is a little bit larger for consumption suggesting that savings increased during the crisis.

3

Pension benefit cuts

In this section, I present the details of the pension benefit reductions, including a cumulative changes calculation.

In 2010, under the pressure of an increasing public debt, Greece was forced to resort to the Troika, which is the designation of the triumvirate comprising the European Commission (EC), the European Central Bank (ECB), and the International Monetary Fund (IMF). The Troika agreed to provide Greece with financial help, on special terms recorded in a Memorandum of Understanding (MoU) between the Greek Government and the three-party entity. These special terms included severe cutbacks in wages and pension benefits.

3.1

Description of the pension system

In Greece, there are three pillars to the pension system. The second pillar accounts for Occupational Schemes (IORPS) and the third for Private Insurance. Neither of the two is very popular though, thus the first pillar on Social Security accounts for more than 99% of the whole system. The latter operated as a Defined Benefit Pay-as-You-Go System until recently (DB PAYG) and provided three types of benefits:

(i) Main pension provision - which includes 10 social insurance schemes, which cover, on a mandatory basis, salaried employees and self-employed persons grouped in certain pro-fessions/occupations; (ii) Auxiliary pension provision, which includes a number of social

insurance schemes, each of which corresponds to a main social security scheme and runs in parallel with it; and (iii) Social solidarity grant provision (EKAS), a means-tested scheme, which covers residents of Greece who get no or low income. In Greece, almost 99% of the total pension expenditure concerns the above three public provision arrangements4_.

The system used to work on 14-time-a-year deposits. Employees would be paid 14 times a year, contributions would be made accordingly, and pensions were also paid 14 times a year. They became 12 in 2010. The sources of funds are the following: (a) Employees contribute 6.67% of covered monthly earnings (8.87% if in arduous or unhygienic occupations), (b) employers contribute 13.33% of covered monthly payroll (14.73% of covered monthly payroll if the employee is in arduous or unhealthy work) and (c) the government contributes 10% of annual payroll as an employer and a guaranteed annual state subsidy.

The normal retirement age was 65 for men and 60 for women with at least 4,500 days of contributions; age 62 (men) or age 57 (women) with at least 10,000 days of contributions; or age 58 (men and women) with at least 10,500 days of contributions. The full pension was also paid at any age to insured persons with at least 11,100 days of contributions and at the pensionable age to insured persons with specified disabilities and at least 4,050 days of contributions. The age limit and required number of days of contribution differ for people working in arduous or unhealthy occupations and for the disabled5_.

3.2

Benefit cuts

Starting in 2010 and until 2015, several different austerity measures were implemented which reduced the benefits of current retirees. The cuts were imposed on top of the previous ones meaning that if a person had their pension benefit cut then the next law would impose further cuts based on her new benefit level. The measures were the following:

- Abolition of the 13th and 14th pension payments for all pensioners, which were initially

4_{More details could be found at the Greek Pension System Fiche by the EU, compiled by the National}

Actuarial Authority of Greece (NAA) here, the SSA Social Security Programs Throughout the World Reports here and a World Bank document reviewing the Greek pension reform strategy here

replaced by an annual allowance of 800 euros, for an annual pension lower than 30,000 euros. Later, the annual allowance was fully abolished for all pensioners.

- Law 3869/2010 reduced auxiliary pensions immediately in a progressive manner ranging from 3% cuts for auxiliary benefits exceeding 300 euros per month to 10% for auxiliary benefits exceeding 650 euros per month. The law also introduced a special solidarity levy to the pensioners. This levy was again progressive and ranged from 3% for pension amounts exceeding 1400 euros per month to 13% for monthly pensions exceeding 3500 euros.

- In 2011 and 2012 laws L3986/2011, L4002/2011, L4024/2011, L4051/2012 and L4093/2012 introduced pension cuts. These pension cuts affected monthly amounts over 1.000 euros. The changes were the following:

July 2011: Progressive reductions for pensioners who were younger than 60. The reductions ranged from 6% for monthly pension amounts exceeding 1700 euros to 10% for monthly pension amounts exceeding 3000 euros. November 2011: 40% reduction in the monthly main pension exceeding 1,000 euros, for pensioners who had not attained the age of 55 and 20% reduction in the monthly main pension exceeding 1,200 euros for pensioners aged 55 and over. Law 4024/2011 also imposed reductions on auxiliary benefits. 20% reduction on monthly benefits exceeding 150 euros for public sector pensioners, 30% reduction for ETEAM (an auxiliary pension fund) pensioners and 15% reductions for the pensioners who were part of the rest of the private sector auxiliary funds.

Further reduction by 12% to the amount of main pension exceeding 1,300 euros in January 2012. Additional cuts to the entire (monthly) pension amount i.e. main + auxiliary came in November 2012, namely 5% in the [1000, 1500] bracket, 10% in the [1500, 2000] bracket and 15% for the income exceeding 2000 euros. The same law imposed further reductions to auxiliary benefits. In particular, for monthly benefits below 250 euros per month there was a 10% reduction, for benefits in the 251-300 range the reduction was 15% and for amounts greater than 300 euros the reduction was 20%.

- In July 2014, law 4254/2012 was implemented. It imposed a zero deficit clause in supple-mentary pensions. Hence pensioners who were part of any auxiliary fund suffered a horizontal

5.2% reduction in their auxiliary pensions.

- Finally, law L4336/2015 increased contributions to EOPYY (health care contributions). Specifically, contributions increased by 2 percentage points (6% from 4%) and they applied to the amount of main pension they were receiving before 2010. For the auxiliary pensions, a 6% contribution was imposed on the amount they were receiving after the cuts6_.

I have omitted from this list special clauses of the measures for people with disabilities and survivor benefits.

3.3

Cumulative changes calculations

Figure 5: Cumulative reductions of pensions by type, May 2010 - September 2015

Source: Tinios, 2015. Notes: The figure reports reductions of the total annual pension benefits. The total reduction may differ according to the type of auxiliary pension.

The measures described in the previous section resulted in large cumulative reductions for pensioners. Each cut was imposed upon the already reduced benefits that the pensioners were receiving so the same person’s pension was cut multiple times. Figure 5 (taken from Tinios 2015) shows the cumulative reductions of pensions by type and by initial monthly benefit level. It does not show the differential effect the cuts had based on age. It is also not a comprehensive list of all reductions but rather a depiction of the differences in the cuts across different income levels using examples for every range.

The measures were highly progressive, so pensioners who had large initial benefits saw much larger reductions than pensioners with lower initial benefits. The very low pension of farmers was reduced by 8.4%. Low pensions i.e. pensions in the 700-1000 euros per month range decreased by 19.9%-23.8% in the 5-year period of the reforms. Intermediate benefit level pensions were reduced by more, namely pensions in the 1500-2000 euros per month range saw cumulative reductions of up to 35%. The largest reduction were for the highest benefit levels. The total reductions were larger than 35% for all people in this category and reached 47% for private sector pensioners and 50.1% for public sector pensioners.

During roughly the same period, as mentioned in section 2, real GDP declined by 26.4%. Coupled with the fact that wages in both the private and the public sector were reduced by approximately 30% and the unemployment rate rose to over 27%, we can draw the conclusion that low benefit level pensioners were the winners of these reforms while high benefit level pensioners were the losers. During a time when the economy was crumbling, low benefit level pensioners saw smaller reductions in their income relative to other members of society so their relative economic position improved. On the other hand, large benefits were reduced substantially, even more than 50% in some cases, worsening the position of the pensioners receiving high benefits compared to the rest of society. The fact that most of the reforms discriminated based only on the benefit levels, led to a large decrease in inequality across pensioners. The burden of the financial crisis was not allocated uniformly across groups but rather closed the gap between the poor and the rich, mostly by worsening the position of the rich.

Table 1 shows the distribution of pensioners by their total pension size in May of 20157_.

There were 2,654,500 pensioners in 2015, 65.7% of whom were earning less than 1000 euros per month in benefits. 44.6% of pensioners, those who were earning less than 700 euros, only lost the holiday bonuses (14% annual cut, or less). 65.7% of pensioners (pensions < 1000 euros) lost less than the fall in GDP. 86.5% of pensioners (pensions < 1500 euros) lost less than the fall in average earnings (30%). The very large reductions i.e. cumulative cuts larger than 40%, affected only 1.9% of the pensioner population.

Table 1: Distribution of pensioners by total pension size, May 2015 Monthly pension benefit in euros Number of pensioners in each bracket (in thousands) Percentage of pensioners in each bracket Cumulative distribution (number) Cumulative distribution (percentage) < 400 356.3 13.4% 356.3 13.4% 400-700 828.3 31.2% 1184.6 44.6% 700-1000 558.4 21.1% 1742.9 65.7% 1000-1500 552.5 20.8% 2295.5 86.5% 1500-2000 308.6 11.6% 2604.1 98.1% 2000-3000 47.9 1.8% 2652.0 99.9% > 3000 2.5 0.1% 2654.5 100% Total 2654.5 100%

Source: Helios database, May 2015

4

Data

In this section, I introduce the data sets I use in order to study the changes in income and consumption before, during and after the time the aforementioned austerity measures were implemented. I use two datasets, the household budget survey and the mortality counts data obtained from the Hellenic Statistical Authority.

4.1

Datasets description

This project uses two main data sources. For the household and individual income & con-sumption analysis, I use the households budget sets survey data (HBS) for the years 2004 and 2008-2018. To evaluate the impact of income reduction on mortality, I use the Hel-lenic Statistical Authority (ELSTAT) mortality data from 2000-2017. For both parts of the analysis, I focus on adults aged 40 and older.

collects information from a representative sample of households on their composition, the employment of their members, their housing conditions and, most importantly, their living expenses and their income, giving a picture of living conditions in the European Union (EU). It is carried out by each Member State and is used to compile weightings for important macroeconomic indicators, such as consumer price indices (used as measures of inflation) and national accounts8_.

The HBSs in the European Union are sample surveys where the statistical units of interest are private households and which are carried out regularly under the responsibility of the National Statistical Offices (NSIs) in each of the twenty eight EU Member States plus some from the European Statistical System. Since the survey is conducted based on a gentle-men’s agreement, each Member State decides the objectives, methodology and frequency of conduction of the survey. Although there have been continuous efforts towards harmoniza-tion, differences remain. The surveys vary between countries in terms of frequency, timing, content or structure etc.9_.

For Greece, the survey is administered by the Hellenic Statistical Authority (ELSTAT)10_{. It}

is the oldest statistical survey in Greece, having been first conducted in 1957-1958. It was conducted every 5-7 years until 2008, but has been conducted every year since 2008. For this project, I only use the surveys from 2004 and 2008-2017.

The survey is based on the rotational integrated design, which was selected as the most suitable for a single cross-sectional and longitudinal survey. The sample for any year consists of 4 replications (panels), each one representative of the population, which has been in the survey for 1-4 years. In every two consecutive years there is a 75% overlap of the panels. Each year, one of the 4 panels from the previous year is dropped and a new one is added. With the exception of the first three years of the survey, any particular panel remains in the survey for 4 years. In order to have a complete sample the first year of survey, the four panels began simultaneously.

8_{The general description of the survey can be found at the official website of EU statistics (EUROSTAT):}

here and here

9_{The user manual for the scientific-use files can be found at:User manual. It contains all information}

about the stricture of the dataset, the questionnaire, the variables etc.

For the HBS longitudinal component, the people who were selected initially are interviewed for a period of four years, equal to the duration of each panel. The HBS survey is based on a two-stage stratified sampling of households from a frame of sampling which has been created on the basis of the results of the 2011 population census. The first level of stratifi-cation is the geographical stratifistratifi-cation based on the division of the total country area into thirteen (13) standard administrative regions corresponding to the European NUTS 2 level. The two major city agglomerations of Greater Athens Area and Greater Thessaloniki Area constitute two separate major geographical strata. The second level of stratification entails grouping municipalities and communes within each NUTS 2 Region by degree of urbaniza-tion i.e. according to their populaurbaniza-tion size. The survey is conducted on a representative random sample of all private households of the country, irrespective of their size or socio-economic characteristics. The survey does not cover the institutional households of all types (hotels, hospitals, boarding houses, elderly homes, prisons, rehabilitation centers, camps, etc.), the households with more than five lodgers and the households with foreigners serving in diplomatic missions11.

The sample size has varied over the years, depending on availability ranging from 3513 house-holds and 8400 members of househouse-holds to 6556 househouse-holds and 17387 household members. In 2016, there were 6073 households which corresponds to a sampling fraction of 1.5h of the estimated total number of households in the country. In 2004 this figure was 1.89h. The non-response rate before substitutions was 33.7% (2,049 households refused to co-operate, were absent or unable to communicate due to illness etc.) in 2016 and 39.7% in 2004. The interviews are conducted over a period of two weeks. For each household, a reference person (the head of the household, unless he/she is not economically active) fills out the household questionnaire under the guidance of a researcher.12_{. All expenses are recorded}

and all information entered is then checked by both the researcher and the reference per-son.As mentioned in the survey manual, income is one of the variables that is most likely to be misreported. The researchers in charge of the household however communicate to the

11_{More information can be found here}

12_{The household questionnaire for 2018 can be found here. The 2004 questionnaire is available here. The}

household member that the data is confidential, is going to be anonymized and will only be used for research purposes. The basic structure of the questionnaire has been the same, though it has been gradually enriched over the years with more detailed questions about specific variables. The data is then anonymized and codified. The household spending infor-mation follows the EUROSTAT recommended classification system (COICOP-HBS)13_{and is}

very detailed. That is, no information is collected on categories of expenditure as a whole such as “nutrition”, “clothing - footwear”, “health expenditure” etc., but for each expen-diture separately, e.g. white bread, fresh whole milk, men’s shoes, microbiological tests, medicines etc..

The HBS variables are grouped into three categories: (a) Basic variables at the household level, which provide information about identification (i.e. survey year, region, identification number of the household etc.), weighting, demographic characteristics of the households, income, household consumption expenditure, household consumption in quantities , (b) basic variables at the member level, which provide information about demographic characteristics (gender, age, marital status, country of birth, citizenship and of residence), education (level of studies completed and currently followed), activity (current activity status, hours worked, type of work contract, economic sector, occupation, status in employment) and income, (c) derived variables at household level i.e. household size and equivalent size type of household, activity and economic situation.

5

Results

In this section, I present the main results of the paper. I begin by computing average income and consumption at the household level and compare the trajectories with the national accounts ones presented in section 2. Moreover, I compute the income and consumption changes by 5-year age group and do the same for income at the individual level. Finally, I compute the mortality rates by 5-year age group over the sample period and run a regression of mortality on income.

5.1

Household Income & Consumption

Figure 6: Average yearly household income & consumption in 2009 prices

Source: Author’s calculations.

In this section, I present the results I obtained from using the household data from ELSTAT. ELSTAT collects information about the household income components through the question-naire described above and then sums these variables to generate the total household yearly income. I divided these values by the CPI, which uses 2009 as the base year to generate the real income changes across time. Consumption is broken down in 12 subcategories in the survey. The respondents report the household consumption for each one and then ELSTAT sums up the numbers to generate total yearly household consumption. I divided the values by the 2009 CPI to generate the real consumption changes across time.

Figure 6 presents the average household income and the average household consumption across the entire sample size for each of the following years: 2004, 2008-201714_{. Both income}

and consumption declined by approximately the same amount though the income decline was smoother i.e. more gradual and less volatile. This might suggest that households were decreasing their consumption in anticipation of further income reductions. The largest reduction in average income was in 2009 when it dropped from 29,830.56 euros (2009 prices) to 24,610.89 euros, a 17.5% drop. The subsequent reductions were smaller, leading to a

39.4% total decrease in the 2009-2017 period. The consumption reduction was largest in the 2009-2012 period, a 34.2% decline. Consumption decreased at a smaller rate over the next 5 years. The total reduction in the 2008-2017 period was 39.5%, the same as the income reduction.

Figure 7, shows the relative changes of the real GDP and the real average household income obtained from the survey. I standardize the variables by diving by the 2009 value of each as in section 2. I do this in order to compare the household survey variable changes to the national accounts changes more easily. The average income in the survey drops by a larger amount than the GDP and the decrease continued until the end of the sample period. Namely, the GDP reduction was of the order of 26.4% in the 2008-2014 period but the average household income continued to drop until 2017 and the total reduction was 39.4%. The inconsistency might be the result of misreporting at the household level.

Figure 7: Relative changes in real GDP and average yearly household income

Source: Author’s calculations

Figure 8 shows the results we obtain from performing the same exercise for consumption. Again, the drop in real household consumption is larger than the one in the national accounts. Namely, real private final consumption dropped by 29.3% in the 2008-2015 period but started increasing slightly afterwards. Real household consumption on the other hand, continued to drop until 2017 with a total reduction of 39.5% in the 2009-2017 period.

Figure 8: Relative changes in real PFC and average yearly household consumption

Source: Author’s calculations

by 5-year age group, based on the age of the head of the household in the 2004-2017 period, with the exception of 2005-2007 (the survey was not conducted during those years). To construct these graphs, I had to match up the age of the reference person of the household to the household (the reference person of the household is the head of the household unless he/she is not economically active i.e. not part of the labor force). ELSTAT provides a household file and a members file for each year. The household file contains the income and consumption of the household and the members file includes the age of every person in the household. I sorted out the reference person of each household and then matched up the age of the person to the household income and consumption by using the corresponding household identifier which is provided in both files15. After that, I sorted the data by the age of the reference person of the household and took the average of the income and the consumption of all households in each 5-year age group starting from 40-year-olds. I also grouped all 85+ year-olds together, since the survey does not provide the exact age of those who belong in this age group.

The largest household income reductions were for the households with a younger reference

15_{For some data points more than one person were listed as the reference person, so I corrected for those}

Figure 9: Average of yearly household income by 5-year age group in 2009 prices

Source: Author’s calculations

Figure 10: Average of yearly household consumption by 5-year age group in 2009 prices

Source: Author’s calculations

person which on average had higher incomes pre-crisis. This result is consistent with the reforms which followed a progressive schedule i.e. the reforms reduced higher salaries and pensions by more than the lower ones. In addition, the pension cuts were a lot more severe for people who were younger than 55 and had already retired. This led to the shrinking of the differences in income across age groups between 2008 and 2017. The largest decrease

was for the 50-54 year-olds who were initially the highest earners, a 48.2% reduction from 43,302.51 euros per year (in 2009 prices) to 22,431.55 euros per year in the 2009-2017 period. The income reductions of the 40-44, 45-49, 55-59 & 60-64 year-old groups were also large. As with individual income, the largest reductions were in the 2009-2013 period which coincides with the period that most austerity measures were implemented. In the 2013-2017 period when there weren’t many reforms, income remained roughly constant for most age groups. For the 65+ year-olds, income levels actually increased until 2010. Starting in 2010 however, the incomes of the 65+ year olds decreased with the biggest reductions being suffered by the younger groups who were initially earning more. These trends are consistent with the reforms since the major pension cutbacks occurred in the 2010-2013 period and most people in these age groups rely on their pension benefits. The smallest decrease was for the 80-84 year-olds who saw their income drop from 21,008.65 euros per year in 2010 to 14,991.85 euros, a 28.6% decrease.

The household consumption changes are similar to the household income changes but are generally smoother. The reductions follow the same progressive pattern i.e. those of the 40-64 year old groups were quite large while the reductions of the 65+ year-olds who were initially consuming less, were a lot smaller. The largest reduction was again for the 50-54 year-olds who were consuming the highest amounts initially. They experienced a drop from 43,463.89 euros yearly in 2009 to 23,974 euros in 2017, a 44.8% decline. The consumption of the 85+ year-olds decreased the least, from 14,547.36 euros in 2010 to 12,062.75 euros in 2017, a 17% reduction.

5.2

Average Individual Income by Age

The HBS dataset also includes a members file for each survey year, in addition to the household file. The file includes, among others, the age, the occupation and the monthly income of each member of the household16_{. The main variable of interest is the individual}

income. The mortality data presented in the next section, includes the number of deaths at

16_{The individual questionnaire for people older than 14 years old can be found here. There is a separate}

an individual level and not at a household level. Hence, the individual income is the more appropriate variable to study the effect of the benefit cuts on mortality.

The monthly income for each person is reported in that year’s prices and includes income from all sources17_{. The user manual does not address issues of joint income and how it is}

split among the members of the household. It is possible to infer this information since for every household both the total household income is reported (from all sources) and the income of every individual member of the household. In my calculations, I do not account for such issues however and I use the values directly from the dataset. It is also puzzling that the individual income is reported in monthly terms but the household income is reported in yearly terms. No explicit reason has been given for this distinction, though it is most likely for convenience (labor income is paid on a monthly basis but taxes on household income are filled for the yearly income).

I proceeded to sort the data for each year by 5-year age groups and then I computed the average income for each of these age groups. Next, I deflated these averages by the CPI which uses 2009 as the reference year. The CPI data is again obtained from ELSTAT. Figure 11, shows the total monthly individual income per year by 5-year age group.

The individual income graph is similar to the household income graph. The patterns are similar and the income reductions are consistent with the implemented reforms as part of the austerity measures during the 2010-2016 period. The pension reform, as seen above, was basically uniform and discriminated based on age of the pensioner and his/her pension benefits in a progressive manner. That is why there are larger reduction for the younger age groups, who had higher income levels before the reforms. Average monthly individual income was in the [630, 945] euros per month interval for all age groups in 2017. The largest reduction was for the 40-44 year-olds who experienced a 53.8% reduction in their monthly income from 1824 euros per month in 2008 to 842.9 euros per month in 2017. All age groups seem to be following the same pattern in their reduction. The largest decrease was in the 2010-2013 period which is the same time period during which most reforms were enacted. Since 2015, the incomes remained at the same levels. There weren’t any other reforms which

Figure 11: Average of monthly income of individuals in 2009 prices by 5-year age group

Source: Author’s calculations. Notes: Each dot represents the average monthly income of individuals in the 5-year age group, in 2009 prices. The source for the data is the EU-HBS for Greece, administered by ELSTAT.

affected income during this period, so the data is consistent with that.

5.3

Mortality Rates by Age

The mortality data provided by ELSTAT contains the total number of deaths for the entire country per year in the 2000-2017 period. The source of the data is the Civil Registry Offices of all Greek Municipal Authorities18_{. ELSTAT collects the data and then stratifies it by (1)}

region, (2) nationality and (3) age of death. In this project I focus on the 3rd stratification i.e. when it is sorted by the age of death. This data file contains the total number of deaths per 5-year age group for each year from 2000 until 2017. The data is freely available.19_.

In order to convert the mortality counts into rates, I use the population estimates from ELSTAT20_{. ELSTAT estimates the population for January 1st of each year on the basis of:}

(a) the revised estimates of the population of Greece for the period 1991-2014, in line with

18_{Data collection information can be found at: Vital Statistics 2018} 19_{The data can be found here}

the results of the 2011 Population Census21_{, (b) the annual data on the registration of vital}

events (number of deaths and births) for 2017 and (c) estimations of annual migration flows of 2017 (immigration and emigration)22_{. The data is stratified based on age, gender, country}

of birth, nationality and region. I focus only on the stratification by age. Again, the data is reported in 5-year age groups for each year from 2001 to 201823_.

I then use those estimates to compute the mortality rates. In particular, for every year in the sample period and for each 5-year age group, I divide the number of deaths by the population of the age group, in that year. I then multiply by 1000 to standardize the numbers (mortality rates are usually reported per 1000 people). The available data for population and number of deaths does not completely overlap so I drop years 2000 and 2018. Figures 12 and 13 show the results.

Figure 12: Mortality rates per 1000 people by 5-year age group: 40-64 & total

Source: Author’s calculations. Notes: Each dot represents the mortality rate for the age group in that particular year. I split the age sample into two for better exposition, because the mortality rates for older people are much higher. The mortality counts were obtained from ELSTAT.

During the sample period, the income reductions do not appear to have increased the mortal-ity rates. There is, however, a small break in the declining trend for most age groups in the

21_{The population census can be found here}

22_{A description of the derivation can be found here}

Figure 13: Mortality rates per 1000 people by 5-year age group: 65+

Source: Author’s calculations. The mortality counts were obtained from ELSTAT.

2010-2012 period. The mortality rates for people in the 40-44, 45-49 and 50-54 age groups were on decline since 2001. The income reductions did not break this trend. For these age groups, the mortality rates are slightly lower than they were in 2001. For the 55-59 year-olds and 60-64 year olds there was a reversal. Mortality rates for these groups were on a general decline since 2001 but the trend is reversed in 2010. For the 55-59 year olds, the mortality rate in 2017 is slightly lower than in 2010 but for the 60-64 year-olds it was higher. For the 65-69 years old group, the mortality rate has been fairly constant during the entire sample period though there was a slight increase in the 2010-2012 period. The 70-74 year-olds, 75-79 year-olds and 80-84 year-olds have had a declining mortality rate in the sample period, with a small reversal in the 2010-2012 period which coincides with the income reduction period. The 85+ group had a constant mortality rate during the 2010-2012 period but the rate was more volatile during other periods. Over the entire sample period there was a slight decline. In order to summarize the relationship between income changes and mortality changes, I run an OLS regression. I use the mortality rate per 1000 people for a particular age-group in a particular year, coupled with the average individual monthly income of the age group in that year and consider the pair as one observation. There are 10 age groups and 11 years

Figure 14: Mortality rates & average individual income by 5-year age group

Source: Author’s calculations

in the sample (I have income data for years 2004 & 2008-2017 so I restrict the sample to those years) totaling 110 observation in the mortality-income space. Figure 14 shows the scatter plot of mortality and monthly individual income. For the low levels of mortality there appears to be very little variation in the rates when income varies. In general, there is a declining trend but it is confounded by the large variation in mortality rates across different age groups.

The following specification is used to obtain the OLS results from regressing mortality on income: mortalityij = α + β · incomeij + 9 X i=1 γidgi+ 10 X j=1 δjdyj + ij

where mortalityij is the mortality rate per 1000 people for age group i in year j, incomeij is

the average individual (monthly) income in euros of age-group i in year j, dgi is the dummy

variable for age-group i and dyj is the dummy variable for year j. The mortality rates

increase by age so I include the age-group dummy variables to prevent the large variation in the rates across age from being attributed to the income differences. The specification also

includes year dummies to capture the fact that other variables which I do not observe in my data, e.g. improvements in the health care system or changes in diet over time, may have contributed to a change in mortality rates.

Table 2 shows the results from the OLS regression of mortality on income. The regression generates a negative coefficient of -0.0118 for income which is statistically significant at all levels. The interpretation is that a 1000 euro increase in average individual monthly income will decrease the mortality rate of the age-group by 11.8 per thousand people or 1.18% which is quite large. The difference of 11.8 deaths per a thousand people is like the difference in mortality between the 50-54 year-old age group and the 70-74 year-old age group. A causal interpretation is difficult however, because the variation in income is associated with the pension reforms as well as other economic shocks which took place during the financial crisis. The result may also be subject to reverse causality from mortality to income. Higher mortality might be correlated with poorer health for those in an age cohort because poor health drove income down.

The age-group dummy variables coefficients are large, positive and statistically significant for all the age groups and capture most of the variation in mortality. The coefficients are gradually larger for older age-groups which is intuitive. The largest coefficient, which is for the 85+ year-olds, is equal to 150.3744. The year dummy coefficients are negative and statistically significant for all years except 2008. They are also increasing in magnitude over the years. The year coefficients capture underlying effects which I do not observe, most likely improvements in the health care system and/or changes in diet over time since healthier foods are cheaper in Greece. The constant which is equal to the base group coefficient i.e. the mortality rate of 40-44 year-olds in 2004 is also statistically significant.

As a robustness check, I also compute the changes in income and mortality from 2008 to 2017 for all age-groups and I run a regression on these differences.

To construct the changes, I take the difference of the mortality rate per thousand people of each age-group in 2017 from the mortality rate of the age group in 2008. I do the same for the average monthly individual income of each age group. Table 3 shows the differences. Mortality rates fell substantially for the oldest three groups but did not really drop as much

Table 2: Effect of income reduction on mortality rates: OLS results Variable Coefficient Average of monthly individual income -0.0118 (0.0019) Constant 22.28 (3.45) 45-49 age group dummy 1.45

(0.49) 50-54 age group dummy 3.28

(0.47) 55-59 age group dummy 4.73

(0.49) 60-64 age group dummy 6.11

(0.59) 65-69 age group dummy 8.59

(0.77) 70-74 age group dummy 14.61

(0.78) 75-79 age group dummy 29.15

(0.85) 80-84 age group dummy 61.83

(1.26) 85+ age group dummy 150.37

(1.61) 2008 year dummy -0.28 (1.01) 2009 year dummy -3.64 (1.14) 2010 year dummy -1.90 (0.89) 2011 year dummy -3.98 (1.06) 2012 year dummy -5.31 (1.45) 2013 year dummy -9.04 (1.60) 2014 year dummy -9.38 (1.61) 2015 year dummy -10.20 (1.76) 2016 year dummy -11.63 (1.96) 2017 year dummy -10.88 (1.89) R2 _{= 0.9986}

Source: Author’s calculations. Notes: The dependent variable is mortality rate per 1000 people. Standard errors are in parentheses.

for the younger groups. Regarding income, the younger groups had much larger income losses than the older groups. Hence, the large income cuts for the younger groups did not drive up mortality for them but only raised it assuming that the decline in mortality for the oldest groups would have taken place for the younger groups as well.

Table 3: Difference in mortality rates and average income by 5-year age group from 2008-2017 Age-group Difference in mortality rates Difference in income

40-44 -0,3301 -981,12 45-49 -0,5034 -816,41 50-54 -0,175 -780,83 55-59 -0,1114 -843,5 60-64 -0,2794 -915,61 65-69 0,004 -563,9 70-74 -1,8222 -476,06 75-79 -7,6000 -466,88 80-84 -12,8477 -458,32 85+ -3,8152 -363,95

Source: Author’s calculations. Notes: The table contains the differences in the mortality rates by 1000 people and the average monthly individual income per 5-year age groups in euros.

For the OLS regression, I use the following specification:

∆mortalityi = α + β · ∆incomei+ i

where ∆mortalityi is the difference in the mortality rate per 1000 people between 2008 and

2017 for age group i, ∆incomei is the difference in average individual monthly income in

euros between 2008 and 2017 for age-group i.

almost identical to the coefficient I obtained in the previous specification. It is statistically significant at the 5% level. The interpretation is that a 1000 euro increase in average indi-vidual monthly income will decrease the mortality rate by 12 per thousand people or 1.2%. The constant is large and statistically significant at the 5% level. It captures the underlying time trend.

Table 4: Mortality change and income change: OLS results Variable Coefficient

∆income -0,012_(0,005) Constant -10,77

(3,70) R2 = 0, 3929

Source: Author’s calculations. Notes: The dependent variable is change in mortality rate per 1000 people between 2008 & 2017. Standard errors are in parentheses.

The result is consistent with the literature, which documents a negative correlation be-tween income and mortality rates (Kitagawa & Hauser (1973), Duleep (1986), Wolfson et al. (1993), McDonough et al. (1997), Deaton & Paxson (1998)). Isolating income as the causal element in this relationship has been difficult (Smith (1999)). Some research suggests that the causality relationship might be inverse: health shocks reduce earnings and increase health care spending and therefore low income and low wealth may be caused by poor health (Haveman et al. (1995), Bound (1989), Smith (1999)). In addition, low income and high mortality may reflect outcomes of the same process, thereby subjecting the income mortal-ity relationship to an omitted variables bias. This paper’s findings trend closely with the literature but also suffer from the same problems of clean identification.

6

Conclusion

In this paper, I have analyzed the consequences of large income reductions on retirement-age households and individuals.

I presented a detailed list of the pension benefit cuts which were implemented during the period of the financial crisis in Greece and then computed the changes in basic economic variables of interest. To do this I used micro data, at the household and individual level, from the Hellenic Statistical Authority. I found that income reductions were consistent with the reported benefit reductions and followed a progressive schedule. Inequality decreased over the time period the reforms were implemented, mostly by worsening the position of the rich. Compared to the rest of society, most pensioners ended up better off, with reductions of 22% or lower. However the previously rich saw very large reductions, larger than 35% and up to 50.1% in their benefits and I observe a similar pattern in the data.

These reductions are staggering compared to what has previously been documented in devel-oped countries. Consumption dropped in a 1-1 fashion with respect to income for most age groups, implying that consumption absorbed the entire effect of the cuts. In terms of health status, I find that these start cutbacks were negatively correlated with mortality, which is suggestive of a large negative effect.

The results may have important implications for policy. Across different income levels and ages, benefit cuts have resulted in direct consumption reductions, equal in magnitude to the income reductions. Furthermore, health status was negatively affected during this pe-riod in the sense that there was a reversal on the negative time trend which was occurring. Future plans on this research topic include calculation of reductions by initial income lev-els, difference-in-differences analysis by different income levels/cuts and calculation of labor supply responses.

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