metadata: authors: 0: author: Beckmann,Elisabeth
count: Europe
docna: 0: docna: Household Savings in Central Eastern and
            Southeastern Europe : How Do Poorer Households Save?
docty: Policy Research Working Paper
subtopic: Educational Sciences,Capital Markets and Capital Flows,Capital Flows,Non Bank Financial Institutions,Mutual Funds,Financial Literacy,Insurance & Risk Mitigation,Social Funds and Pensions
teratopic: Education,International Economics and Trade,Finance and Financial Sector Development,Social Protections and Labor
abstracts: cdata!: Based on a survey of households in 10
            Central Eastern European and Western Balkan countries, this
            paper presents new and unique evidence on which households
            have savings and how they save. The paper shows that the
            percentage of savers is low, and savings are frequently
            informal. Formal savings are dominated by bank savings, and
            participation in contractual and capital market savings is
            very low in comparison to high-income countries. Poor
            households are significantly less likely to have any
            savings; income also has an effect, albeit smaller, on the
            choice of formal versus informal savings. With a high
            density of bank branches in Central Eastern European and
            Western Balkan countries lack of physical access to banks
            does not explain the lack of formal savings. Lack of trust
            in banks reduces the probability of formal savings,
            especially bank savings.
repnme: repnme: Household Savings in Central Eastern and
            Southeastern Europe : How Do Poorer Households Save?
display_title: Household Savings in Central Eastern
            and Southeastern Europe : How Do Poorer Households Save
listing_relative_url: /research/2019/02/30844198/household-savings-central-eastern-southeastern-europe-poorer-households-save
topicv3: Pension Funds m1326066 611,Mutual Funds m1326970 2180,Education m1327048 470,Financial Literacy m1504979 3391,Life Insurance m1505058 3837
publishtoextweb_dt: 2019-02-19T00:00:00Z
historic_topic: Education,International Economics and Trade,Finance and Financial Sector Development,Social Protections and Labor
no_of_pages: 36
author_keys: 0: author_key: 1413818
model_extractions: Policy Research Working Paper 8751 Household Savings in Central Eastern and Southeastern Europe How Do Poorer Households Save?

_Elisabeth Beckmann_ Europe and Central Asia Region Office of the Chief Economist February 2019
Policy Research Working Paper 8751

# **Abstract**

Based on a survey of households in 10 Central Eastern European and Western Balkan countries, this paper presents new and unique evidence on which households have savings and how they save. The paper shows that the percentage of savers is low, and savings are frequently informal. Formal savings are dominated by bank savings, and participation in contractual and capital market savings is very low in comparison to high-income countries. Poor households are significantly less likely to have any savings; income also has an effect, albeit smaller, on the choice of formal versus informal savings. With a high density of bank branches in Central Eastern European and Western Balkan countries lack of physical access to banks does not explain the lack of formal savings. Lack of trust in banks reduces the probability of formal savings, especially bank savings.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The author may be contacted at elisabeth.beckmann@oenb.at.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Household Savings in Central Eastern and Southeastern Europe: How Do Poorer Households Save?**<sup>**1**</sup>

Elisabeth Beckmann<sup>2</sup>

**Keywords** : Household savings, portfolio choice, shared prosperity, survey data, Central Eastern Europe and Western Balkans

**JEL** : O16, D12, D14, G11, P34

> 1 This paper was written while the author was on secondment at the Chief Economist Office, Europe and Central Asia Region in 2015. It is a background paper for the flagship report “Gould, D. and M. Melecky (2017) Risks and returns: Managing financial trade-offs for inclusive growth in Europe and Central Asia. Washington, DC, World Bank.”

> 2 Foreign Research Division, Oesterreichische Nationalbank, Vienna, Austria. Tel.: +43 1 40420 5252, <u>elisabeth.beckmann@oenb.at</u> The views expressed in this article are those of the author and do not necessarily reflect those of the Oesterreichische Nationalbank or the Eurosystem of Central Banks. I am very grateful to David Gould for his support and guidance in writing this paper. Thorsten Beck, Ross Levine, Davide Mare, Martin Melecky, Ugo Panizza, Hans Timmer and Hernan Winkler provided helpful comments and suggestions.
# **1. Introduction**

Globally in 2014, the majority of adults reported having saved in the past 12 months, however, only half of these savers held their savings at a formal financial institution (Demirguc-Kunt et al., 2015). In Europe and Central Asia (ECA), only 38% of adults save and only 8% save formally. ECA’s comparatively low level of formal savings raises concerns both in the context of its dependence on foreign funding and in the context of its aging population.

In a recent literature review on the determinants of savings by poor households, Karlan et al. (2014) highlight five factors which may hinder the adoption of formal saving products: transaction costs, lack of trust and regulatory barriers, information and knowledge gaps as well as social constraints and behavioral biases. The literature has shown that the experience of banking and currency crises during transition from planned to market economies has led to a low level of trust in banks and lack of trust in the stability of the local currencies, which is one important reason for the high percentage of cash savings and savings in foreign currencies in transition economies (Stix, 2013; Coupe, 2011; Brown and Stix, 2015).

In this paper, we will focus on how poorer households in ECA save. Our sample covers 10 Central Eastern European (CEEU) and Western Balkan (WB) countries. Referring to the World Bank’s goal of “shared prosperity” that seeks to foster income growth among the bottom 40 percent (B40), we analyze how saving behavior among B40 differs from saving behavior among the top 60 percent (T60). Specifically, we address the following questions: Which households save and how do they save? What are the main differences in the saving behavior of B40 and T60 households? Is the limited use of formal bank and capital market saving products among B40 due to transaction costs or lack of trust in the financial system?

We show that in CEEU and WB countries the percentage of savers is low, and savings are frequently informal. Formal savings are dominated by bank savings; participation in non-bank formal savings is very low. Compared to empirical evidence for high income countries, our data reveal even lower participation rates in capital market savings. Diversification in terms of the number of saving instruments held by households is very low. Foreign currency savings are widespread. B40 households are significantly less likely to have any savings. Compared to this effect, income exerts a much smaller impact on access to formal savings. With a high density of bank branches in CEEU and WB, transaction costs in terms of physical distance do not explain the lack of formal savings. Internet access is correlated with formal savings. However, internet access mainly has an informational role which can only be utilized by financially literate individuals. Lack of trust in banks reduces the probability of formal savings and in particular bank savings. However, trust in deposit safety is higher than trust in banks and there is some indication that households who mistrust banks not only save informally but also resort to non-bank formal savings – if they trust the stability of the financial system.

Given the importance of saving as a potential source of investment and thus economic growth, a large body of research has emerged looking at the determinants of savings both at the macro- and microeconomic levels. To the best of our knowledge, ours is the first study to bring together three strands of this literature: The literature which focusses on the determinants of saving for poorer households, the literature which studies access and information costs in the choice between formal versus informal savings, and finally the literature which emphasizes the importance of trust for 2
financial decisions. Regarding trust, research looks at advanced economies and the role of trust for participation in the stock market and risky financial assets more broadly, on the other hand it shows that the experience of previous economic crises can lead to higher levels of mistrust and therefore reluctance to participate in financial markets – a phenomenon which is particularly relevant for transition economies. We contribute to the literature by presenting new and unique evidence on savings in 10 Central Eastern and Southeastern European countries, distinguishing not only between formal and informal savings but also further distinguishing formal savings by bank savings and contractual savings (life insurance and pension funds) and capital market investments (stocks, bonds and mutual funds). Based on this evidence we can bridge the literature which analyzes the role of mistrust for informality and the literature which analyzes the role of mistrust for lack of capital market participation. Finally, we contribute to the research which investigates transaction costs by simultaneously analyzing the role of physical access and internet access. Our findings are descriptive and have constraints in providing causal explanations but will be of interest to policy makers in their endeavors to promote formal savings in ECA.

While we only focus on a subsample of countries in ECA, the results we obtain are at least to some extent applicable to the region more generally due to the common experience of transition from planned to market economies. Furthermore, our measure of savings is based on survey data and looks at whether households hold certain saving instruments, but we do not know the amount invested in the respective saving instrument. Therefore, our analysis only partially relates to the large body of research which studies savings in terms of flows, i.e. as the residual between income and current consumption, and analyzes saving motives (see Browning and Lusardi (1996) for an overview and Le Blanc et al. (2015) for a recent analysis of saving motives in the euro area, which highlights the importance of precautionary savings, savings for old age and credit constraint). With these limitations in mind, the remainder of the paper is structured as follows: The next section presents a brief literature review of the research on household saving which is relevant to our research questions. Section 3 provides details on the data source, presents descriptive evidence on the distribution of household savings in CEEU and WB and documents participation rates across a range of saving instruments including informal savings, bank savings, contractual savings and capital market investments. The econometric specification is outlined in section 4. Section 5 discusses why the percentage of savers is so low and analyzes the determinants of participation in specific saving instruments before we conclude.

# **2. Literature Review**

Poor households can and do save (Banerjee and Duflo, 2007), however, they may face higher constraints in using formal saving instruments including transaction costs which may include pecuniary costs, e.g., account opening fees, or non-pecuniary costs, e.g. travel time to the nearest bank. Bank branch density in CEEU and WB is high on average, but distribution of bank branches within countries is very heterogeneous (Beckmann et al., 2018). Three recent papers have highlighted the role of transaction costs in terms of geographical distance and the type of banks surrounding households: Allen et al. (2013) show that local presence of a bank has a positive and significant impact on households’ use of bank accounts and bank credit. Brown et al. (2015) study the expansion of a microfinance bank branch network in Southeastern Europe and show a positive 3
effect on the use of bank accounts by low income households. Alimukhamedova et al.(2015) look at Uzbekistan analyzing the effect of proximity to a microfinance bank on households’ businesses.

Even if distance does not constitute a barrier to formal savings, poorer households in particular may still face higher costs in terms of information barriers. Guiso and Jappelli (2005) develop a model of financial market participation emphasizing information barriers: Investors can learn about assets from distributors or through social interaction. Distributors inform investors depending on the probability that investors buy the asset and on the cost of information production. Regarding cost of information, Guiso and Jappelli (2005) add that the incentive to advertise decreases if gains from spreading information can be appropriated by competitors, i.e., if product substitutability is high and market power is low.

Bogan (2008) argues that improvements in internet access lowered transaction and information costs for stock market participation – estimating that the increased probability of participation due to overall improvements in internet access was equivalent to $27,000 additional household income. Looking at a longer time horizon, Glaser and Klos (2013) back up this finding by causal evidence employing instrumental variable techniques to address the endogeneity of internet access. In addition, analyzing the channels via which internet access facilitates stock market participation they argue that internet diffusion lowers information costs and helps households to make better financial decisions. They corroborate this finding by showing that internet access interacts with financial literacy. Assuming that early internet adopters tend to have higher financial literacy, providing universal internet access would therefore not be a simple policy solution to promote stock market participation (Glaser and Klos, 2013). Liang and Guo (2015) link the effect of internet usage to the research on social interaction and its informational benefit for households’ financial decisions (Hong et al., 2004) and further back up the finding that internet access mainly has an informational role by showing that it acts as a substitute for social interaction. Deuflhard et al. (2015) show for the Netherlands that financially literate individuals, who use the internet and online accounts achieve higher returns.

In transition economies in particular, non-participation in formal savings may also be driven by lack of trust. Malmendier and Nagel (2011) highlight the importance of past experience of economic crises for households’ financial decisions. Stix (2013) and Brown and Stix (2015) show that memories of previous economic crises during transition have an impact on trust in the financial system and affect the demand for informal versus formal savings as well as the currency of savings. For advanced economies, a growing body of research shows that more trusting individuals and those who trust in the stock market are more likely to participate in the risky assets (Guiso et al., 2008; Georgarakos and Pasini, 2011).

# **3. Data and descriptive evidence**

# **3.1. OeNB Euro Survey**

The main data source for the analysis is the “OeNB Euro Survey” conducted by the Austrian central bank since 2007 on a regular basis as a repeated cross-sectional survey in 10 Central, Eastern and Southeastern European countries: 6 EU Member States which are not part of the euro area (Bulgaria, Croatia, Czech Republic, Hungary, Poland, Romania) and four (potential) candidate 4
countries (Albania, Bosnia and Herzegovina, the Former Yugoslav Republic Macedonia, Serbia). 3 In each country and wave, a nationally representative sample of 1,000 individuals aged 15 years or older is polled based on multistage random sampling procedures. For the purpose of this analysis, we exclude respondents who are younger than 18, as these will probably lack experience in significant saving decisions. Data weighting is used to ensure a nationally representative sample for each country. Sampling weights use population statistics on gender, age and region and where available education and socioeconomic status as well as ethnicity.

We employ data from 2 surveys conducted in fall 2012 and 2013, as these two waves include our main variable of interest as well as a comprehensive set of necessary control variables. Thus, our analysis focuses on 10 countries and around 20,000 individuals.

# **3.2. Measuring household saving behavior**

We measure household savings behavior looking at the percentage of households that hold a diverse set of saving instruments conditional on having any savings. The central variables of our analysis are based on two questions reported in Table 1.

> 3 Further details on the survey can be found at: https://www.oenb.at/en/Monetary-Policy/Surveys/OeNB-Euro-Survey.html

5
**Table 1:** Measures of household saving behavior

|1) [<br>can<br>etc.<br>toge<br>Yes|ASK ALL] There are several ways in which one can hold savings. For example, one<br>hold cash, use bank accounts, have life insurances, hold mutual funds, pension funds,<br>Do you currently have any savings? Please refer to savings you hold personally or<br>ther with your partner.<br>/ No / Don’t Know / No Answer|
|---|---|
|2) [I<br>you<br>you<br>–<br>–<br>–<br>–<br>–<br>–<br>–<br>–<br>–<br>–<br>–|f 1=Yes] Please take a look at this card that lists various savings instruments – could<br>please select the ones you are currently using and rank them according to the amounts<br>have saved on the respective instrument.<br>Cash<br>Current Account / transaction account / wage card<br>Savings deposits / savings accounts (in foreign or in [LOCAL CURRENCY])<br>Life insurance<br>Mutual funds<br>Stocks<br>Pension funds (voluntary contributions)<br>Bonds<br>Other (e.g. gold)<br>Do not know<br>No answer|

Using the responses to the above questions, we employ seven binary variables of households’ saving choice: _any savings_ takes the value one if the respondent has savings, _cash_ takes the value one if the respondent has savings but only saves in cash, _formal savings_ measures whether the respondent has any savings excluding cash; _bank savings_ captures whether the respondent has savings in a savings deposit or current account; _contractual savings_ shows whether the respondent saves using a life insurance or pension fund; _capital market savings_ measures whether the respondent has stocks, mutual funds or bonds; finally, _>1 formal savings_ indicates whether the respondent holds more than one of the saving instruments excluding cash. We consider cash as an indicator of informal savings as the amounts are saved outside the formal financial system.

As the question in Table 1 shows, in contrast to household wealth surveys, the surveys contain information on the existence of savings and assets but not on amounts. Thus, percentages reflect participation rates only and not amounts invested in the respective assets. A further difference in comparison to household wealth surveys is that the questionnaire focuses on individuals rather than households. However, the questionnaire partly accounts for this issue by asking whether individuals hold financial assets alone or together with their partner. Also, in contrast to wealth surveys, we do not impute missing values but assume that non-response is random. For household income, we take this into account by including a dummy variable for those respondents who refuse to answer the question on income.

# **3.3. Household savings are low in Central, Eastern and Southeastern Europe**

There are substantial heterogeneities between countries in the percentage of individuals with savings – with the highest percentage of savers in a European Union member state (Czech Republic) followed by a candidate for accession (FYR Macedonia) (Figure 1). In the European 6
<!-- Start of picture text --> oo<br>2<br>st<br>TM<br>e<br>BG HR Ce HU PL RO AL BA MK RS<br>BE any savings EE formal savings<br>EEE informal savings<br><!-- End of picture text -->
correlation which could be interpreted as evidence that better social safety nets reduce (precautionary) savings. Thus, we conclude that our indicator of savings, while partially based on respondents’ perceptions, is a meaningful measure.

# **3.4. Informal and bank savings are the predominant saving instruments**

Theoretical models of household investment behavior predict that all households should participate in all financial markets and thus hold a diversified portfolio which includes risky financial assets (Panizza, 2015). Empirical evidence on actual investment behavior including investment in risky assets is scarce and mainly limited to high income countries. Guiso and Sodini (2013) gather evidence on direct and indirect stock holding for 12 countries; more recently the Household Finance and Consumption Survey in the euro area collected new data on the saving behavior of households in 15 euro area countries. On average 33% of households in the euro area have any contractual savings and 20.2 % of households across the euro area hold capital market savings (Arrondel et al, 2014).

In CEEU and WB, among those individuals who save (40% on average, see Figure 1), an equal percentage of households have informal savings (74.6%) and bank savings (74.1%). The very high percentage of informal savings is dominated by savings in cash, a feature of households’ saving behavior in transition economies which has been documented and analyzed in depth by previous research (Stix, 2013). With three out of four savers holding bank savings, current accounts and/or saving deposits constitute the predominant form of formal savings. Contractual savings are held by 23% of savers or 9% of households, thus well below the euro area average. In line with the wellknown “stock-market participation puzzle”, participation in capital market savings is very low at less than 3 % of households or 7% of savers.

Heterogeneities across countries in the use of different saving instruments are substantial. Bank savings are held by only 50% of savers in Serbia and up to 90% of savers in Bulgaria, Czech Republic and FYR Macedonia. The differences between countries are even larger for participation in pension funds or life insurance, ranging from below 1% of savers in Albania to above 50% of savers in the Czech Republic. Finally, less than 1% of savers in Albania invest in stocks, bonds or mutual funds compared to 18% in Croatia.

While we do not know the amount invested in the respective saving instrument, we can still shed some light on whether households “diversify” their investments from the number of saving instruments they invest in. Figure 2 (left panel) shows that on average savers hold 1.36 formal saving instruments. Czech Republic stands out with the highest diversification. On the other hand, Romania, Albania, Bosnia and Herzegovina as well as Serbia are significantly below the average. Finally, the right panel of Figure 2 highlights a well-known feature of household savings in Central, Eastern and Southeastern Europe and more generally transition economies. On average 44% of saving deposits are denominated in foreign currency and 60% of those who save in cash have foreign currency cash.

8
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# **4. Econometric specification**

Which factors drive households’ saving behavior and what determines the choice of saving instruments? We relate the indicators of saving behavior S�,� of individual i in country c to household characteristics X� controlling for country level determinants by including interacted country and survey wave fixed effects: First, we estimate probit models and calculate average marginal effects for the determinants of participation in savings based on the full sample of individuals. Then we analyze the choice of saving instruments. Given that a large fraction of households does not save, our analysis on the choice of saving instruments might suffer from selection bias (Palia, 2014). Following Shum and Faig (2006), we exclude households that do not have sufficient funds to save. We estimate probit models and calculate average marginal effects for participation in saving instruments looking at the subsample of savers for each of the following dependent variables: _formal savings_ , savings in _cash only_ , _banks savings_ , _contractual savings_ , _capital market savings_ , more than _1 formal saving instrument_ . All reported estimation results are based on standard errors which account for clustering at the primary sampling unit and time level.

We check for the robustness of our results by estimating a Heckman selection model where we jointly estimate the probability of having savings and the probability of holding specific asset categories following Allen et al. (2012). We further conduct robustness checks by including control variables step-by-step instead of jointly. To ensure our results are not driven by a particular country, we repeat estimations dropping one country at a time. Finally, we exclude that our dependent variables capture insignificant savings by utilizing the information from the survey question on the ranking of saving instruments in terms of amounts and repeat estimations only including up to three saving instruments.

# **4.1. Control variables**

In our analysis of the determinants of household savings behavior, we control for a rich set of behavioral and demographic characteristics as well as indicators of transaction costs. All our estimations include information on socio-demographics which have been shown to influence the choice of saving instruments: age, gender, the size of household, whether there are any children in the household, marital status and whether the respondent is in charge of managing household finances (Halko et al., 2011; Love, 2010; Sundén and Surette, 1998). Brown and Taylor (2016) suggest that there is an intertemporal relation between saving behavior during childhood and the probability of saving and the amount saved as an adult. Beckmann et al. (2013) investigate households’ saving decisions and find that age is a significant determinant of saving and that the relation is hump-shaped, in line with the life-cycle hypothesis.

Following Palia et al. (2014), we further control for factors affecting background risk – the labor market status, ownership of housing and private business. We further control for education and in robustness analyses control for financial literacy (van Rooij et al., 2011). Taking into account the findings by Guiso et al. (1996), our estimations include information on whether the respondent has a loan or plans to take out a loan.

Karlan et al. (2014) highlight that inter alia transaction costs may hinder the adoption of formal saving products. Following Brown et al. (2015), we proxy for transaction costs by including 10
geographic proximity to the nearest banks (see Beckmann et al., (2018) for a detailed account how these data are compiled). We further include information on light intensity at night which Henderson et al. (2012) show is a useful proxy for local economic activity.

Previous research has shown that the experience of financial turmoil during transition led to lack of trust in financial institutions and this influences the financial behavior of households (Stix, 2013; Coupe, 2011; Brown and Stix, 2015). We employ four indicators to capture the different levels of trust in financial institutions: trust in the safety of deposits, trust in domestically owned banks, trust in foreign owned banks and trust in the stability of the financial system in general.

Finally, following the recent paper by Balloch et al. (2015), we control for a wide range of behavioral characteristics including risk aversion, expectations and trust in other non-financial institutions which allows us to isolate the effect of trust.

All variables are defined in Table A3 in the Annex.

# **5. Results**

We first discuss what the main factors are that determine whether a respondent holds savings. We focus, in particular, on how income affects savings. We then move on to the choice of saving instruments and discuss what factors drive the choice of saving instruments. What are the main differences in the saving decisions of bottom 40 and top 60 households? Is the limited use of formal bank and capital market saving products due to lack of access or lack of trust in the financial system?

# **5.1. Who saves? Why is the percentage of savers so low?**

Figure 1 shows that on average across countries the percentage of savers is relatively low but that there are large differences between countries. What could drive these surprisingly low savings? The EBRD’s Transition Report of 2011 found that households in Central, Eastern and Southeastern Europe were hit much harder by the crisis than those in Western Europe (EBRD, 2011). Between 2009-2013 private consumption declined in seven of the 10 countries; the exceptions were Poland, Albania and FYR Macedonia. Households’ ability to save declined between 2008 and 2013 (Figure 4, left panel). Furthermore, 43% of households had to reduce the amount set aside for savings and a quarter of households utilized savings or sold possessions due to the crisis (Figure 4, right panel). Although we cannot compare results with other countries, these findings confirm the EBRD’s conclusion that the macroeconomic environment indeed exerts a particularly strong impact on households in CEEU and WB.

11
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**Table 2:** Determinants <u>of savings</u>

|**Dependent variable**|**any savings**<br><br>|
|---|---|
|age|**(1)**<br>**(2)**<br>-0.004**<br>-0.004**<br> <br>|
|age squared|(0.002)<br>(0.002)<br>0.005**<br>0.007***<br>0003<br>0002|
||(.)<br>(.)<br> <br>|
|female|-0.012<br>-0.014<br>(0.008)<br>(0.009)|
|1 person HH|<br> <br>-0.033*<br>-0.012<br> <br>|
||(0.018)<br>(0.02)|
|2 person HH|-0021*<br>-0017|
||.<br>.<br>(0.012)<br>(0.012)|
|children in HH|0.008<br>0.008|
||(0.009)<br>(0.012)<br>**<br>**|
|married|0.031<br>0.032|
||(0.013)<br>(0.013)|
|head of household|<br> <br>-0.001<br>0.002<br> <br>|
|B40|(0.008)<br>(0.011)<br>-0.118***<br>-0.098***|
||(0.018)<br>(0.014)<br> <br>|
|income answer refused|-0.080***<br>-0.067***<br>(0012)<br>(0015)|
|own house|.<br>.<br>0.049***<br>0.037**<br> <br>|
||(0.017)<br>(0.017)|
|own other real estate|<br> <br>0.091***<br>0.065***<br> <br>|
||(0.013)<br>(0.012)|
|loan|0.039**<br>0.031**|
||(0.017)<br>(0.014)<br>|
|unemployed|-0.104***<br>(002)|
|self-employed|.<br>0.068***<br>0.056***<br> <br>|
||(0.018)<br>(0.017)|
|retired|<br> <br>-0.032<br>0.044**<br> <br>|
|employed|(0.02)<br>(0.02)<br>0.072***|
||(0015)|
|secondary|.<br>0.087***<br>0.051***|
|education|(0.016)<br>(0.012)|
|tertiary<br>|<br> <br>0.185***<br>0.141***<br> <br>|
|education|(0.019)<br>(0.017)|
|regular income in euro|0.108***<br>0034|
|receives remittances|(.)<br>0.133***<br>(002)|
|plan a loan|.<br>0.071***<br>|
|Muslim|(0.018)<br>0006|
||.<br>(0.028)|
|risk averse|0.035**|
||(0017)|
|exp econ growth|.<br>0.049***<br>(0011)|
|trust in government|.<br>0.028**<br>(0.014)|
|trust in EU|<br>0.032***<br>|
|deposits safe|(0.012)<br>0.047***|
||(0011)|
|financial system stable|.<br>0.050***<br>(0.011)|
|financial loss during transition|<br>0.084***<br>|
||(0.015)|
|internet access|0.096***<br>|
|distance to nearest bank (log)|(0.012)<br>-0.008***|
||(0.003)<br>|
|nightlight|0.001<br>(0.007)|
|Pseudo-R2<br>|0.19<br>0.22<br> <br>|
|N<br>P(DepVar=1)|17646<br>9893<br>041<br>043|
|<br>Note: Cluster standard errors in pare|.<br>.<br>ntheses.. *** p<0.01, ** p<0.05, * p<0.01.|

13
The labor market status exerts a strong impact on the probability of having savings. Employed individuals are more likely than unemployed individuals to save, also self-employed individuals are more likely to save. Retired respondents are more likely to hold savings. Compared to respondents with primary education, individuals with secondary or tertiary education are more likely to save.

Income strongly affects the overall stock of savings. Households in the bottom 40 are 10 percentage points less likely to have any savings – a sizeable effect equal to 23% of the sample mean. Looking more closely at the types of income shows a role for foreign income. Some of the WB countries have a significant inflow of remittances. Recipients of remittances are 13 percentage points more likely to have any savings, and this effect remains significant and of similar magnitude even when eliminating individual countries from the sample which have high / low frequency of remittance receivers. Around 5% of households receive some income in euros, 3% receive a regular income in euros. These households are 11 percentage points more likely to have any savings. Finally, looking at non-financial wealth we find the ownership of real estate increases the probability of having savings. We investigate the role of income in more depth in Table 3 which shows how income interacts with other socio-demographic characteristics in determining savings. Education does not have a differential impact on B40 savers (Models 1 and 2). Retired B40 individuals are less likely to have savings compared to non-retired B40 individuals (Model 5). There appears to be a substitution effect between investment in real and financial assets for B40 savers. B40 individuals who own other real estate are 8 percentage points less likely to save compared to B40 who do not own other real estate which is equal to 17% of the sample mean.

In column 2 of Table 2 we study what other, non-socioeconomic factors, influence whether individuals have savings. We focus on trust and transaction costs.

Trust is an important factor guiding the financial decisions of households, as a growing body of household finance research shows (e.g. Guiso et al., 2008; Balloch et al., 2015; Delis and Mylonidis, 2015; Stix, 2013). To exclude that trust is not a proxy for other determinants of saving instrument participation, we control for risk aversion and individual expectations following Guiso et al. (2008). In addition, Stix (2013) and Brown and Stix (2015) stress that experience of previous economic crises during transition influences saving decisions.

We find that those who think that the economic situation of their country will improve are 5 percentage points more likely to save. Risk averse individuals are 4 percentage points more likely to save. Those who experienced a financial loss during transition are more likely to have savings. Trust has a strong and significant impact on participation in saving instruments even after controlling for risk aversion, expectations and experience of economic crises. We distinguish between trust in non-financial and financial institutions. Respondents who trust their government or the EU are 3 percentage points more likely to save (Table 2, column 2). Looking at whether trust has a differential impact for B40 or T60 households (results not shown), we find that B40 households who trust in the government have a lower propensity to have savings. Unfortunately, we do not have information on which households receive government benefits which would allow a better understanding of the effect.

14
Turning to the role of trust in financial institutions, we find that individuals who trust deposit safety and those who trust the stability of the financial system are 5 percentage points more likely to have savings compared to a sample mean of 43%.<sup>4</sup> One dimension of transaction costs is physical access to financial institutions (Karlan et al., 2014). Controlling for local economic activity, following Henderson et al. (2012), by using average stable night lights as a proxy, Table 2, column 2 shows that households who live further away from the nearest bank are less likely to have savings.

> 4 For descriptive evidence on the different measures of trust, see Figure 5.

15
# **Table 3:** Are some B40 individuals more likely to have savings?

|**Mo**|**del 1**|**Mod**|**el 2**|**M**|**odel 3**|**M**|**odel 4**|**M**|**odel 5**|**M**|**odel 6**|**M**|**odel 7**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|B40|-0.097***|B40|-0.102***|B40|-0.096***|B40|-0.116***|B40|-0.086***|B40|-0.066**|B40|-0.083***|
||(0.013)||(0.017)||(0.013)||(0.017)||(0.014)||(0.031)||(0.013)|
|tertiary|0.143***|secondary|0.048***|self-employ|ed 0.063***|employed|0.060***|retired|0.060***|house|0.047**|other real|0.083***|
|education<br>B40*tertiary|(0.016)<br>-0.011|education<br>B40*secondary|(0.013)<br>0.008|B40*self-|(0.019)<br>-0.035|B40*employe|(0.017)<br>d 0.034*|B40*retired|(0.021)<br>-0.052**|B40*house|(0.02)<br>-0.037|estate<br>B40*other|(0.013)<br>-0.075***|
|education|(0.033)|education|(0.021)|employed|(0.038)||(0.021)||(0.026)||(0.031)|real estate|(0.027)|
|Log-L|-5295|Log‐L|-5295|Log-L|-5294.7|Log-L|-5293.7|Log-L|-5292.8|Log-L|-5294.4|Log-L|-5290.2|
|N|9893|N|9893|N|9893|N|9893|N|9893|N|9893|N|9893|
|Further<br>controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|P(DepVar=1)|0.43|P(DepVar=1)|0.43|P(DepVar=1|) 0.43|P(DepVar=1|) 0.43|P(DepVar=1|) 0.43|P(DepVar=1|) 0.43|P(DepVar=1|) 0.43|

Note: Cluster standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.01. The dependent variable for all models is any savings. All models include country-time fixed effects and further individual level controls.

16
# **5.2. How do savers save? Determinants of saving instrument choice**

Table 4 investigates what determines the choice of saving instruments for those respondents who have savings. We first study socio-economic determinants of the choice of saving instruments.

The life–cycle hypothesis implies a hump-shaped wealth-age profile. In a frictionless setting, demographic characteristics do not play a role for portfolio choice; however, the models of portfolio choice which include “real life” assumptions would suggest that portfolio allocations do change over the life cycle (see e.g. Cocco et al., 2005). With the exception of _cash,_ age has a significant, hump-shaped effect on savings; the effect on _cash_ is significant and U-shaped.

_Married_ couples are less likely to save formally, however, the effect is 4 percentage points compared to a sample mean of 80 percent, i.e. relatively small. Single households are significantly less likely to have contractual savings or diversify savings. In contrast to evidence on the US (Love, 2009), _children in the household_ do not influence the choice of saving instruments. In fact, in contrast to the euro area, households with children are less likely to own their primary residence which may be related to the specific characteristics of real estate markets in CEEU and WB.<sup>5</sup> Gender does not have a significant impact on the choice of saving instruments except for capital market investments, which, again, is in line with results for advanced, non-transition economies (Halko et al., 2011).

The labor market status not only exerts an impact on the probability of having savings but also on the choice of saving instruments. _Employed_ individuals are more likely than unemployed individuals to save formally. The effect is sizeable at 5.3 percentage points or 24% of the sample mean. No significant effect of employment on capital market investments is detected with some heterogeneity between countries: the positive marginal effect becomes significant at the 10 percent level when Bulgaria, Romania or Albania are dropped from the sample.

_Self-employed_ individuals, if saving, are more likely to save using life insurance or pension funds, or invest in stocks, bonds or mutual funds. Albania is an exception: Excluding Albania we find a positive and significant effect of self-employment on formal savings and bank savings and a negative and significant effect on saving in cash only. This is likely related to the nature of selfemployment in Albania: Excluding Albania, 14% of self-employed individuals are working as farmers, gardeners or fishermen and 15% work as lawyers, doctors or accountants. By contrast, in Albania 28% of self-employed individuals work as farmers, gardeners or fishermen and 5% work as doctors, lawyers or accountants.

_Retired_ respondents are more likely to save formally and less likely to save in cash. This may _ex ante_ be surprising as these are the individuals most likely to remember banking crises during transition. However, we control for experience of economic turbulence during transition ( _financial loss during transition_ ). The positive and significant effect of _retired_ on investments in capital

> 5 Estimation results for real estate ownership are not reported but available on request from the author. The negative effect of children on ownership of the primary residence is economically small at 2 percentage points with a sample average of 88% homeowners.

17
markets may be related to privatization of companies through internal share-holding schemes during transition.

Turning to the role of income for the choice of saving instrument we find that, conditional on having any savings, B40 households are 5 percentage points less likely to have any formal savings (6% of the sample mean), 4 percentage points more likely to save in cash (25% of sample mean) and 7 percentage points less likely to save at banks (9% of the sample mean). For contractual savings and capital market savings, income does not have a significant effect, as very few B40 households have any of these saving instruments. Considering the large variation across countries in the probability of the dependent variable, we repeat estimations dropping one country at a time from the sample to ensure results are not driven by a particular country. The Czech Republic is an outlier in terms of contractual savings - income is a significant determinant of contractual savings in all countries except the Czech Republic. The significant impact of income on cash savings is mainly driven by Albania. Excluding Albania, income is insignificant. This may indicate that in contrast to the results for other countries where lack of trust drives cash savings (Stix, 2013), the lack of formal savings in Albania is also significantly driven by transaction costs. Those with regular income in euros are more likely to save in cash, which is probably related to the high percentage of foreign currency cash savings (see Figure 2). In Croatia, FYR Macedonia and Bosnia and Herzegovina the high percentage of cash savings does not appear to be related to “commuters” as the effect of income in euros on cash savings become insignificant if either of these countries is excluded.

Ownership of the primary residence does not influence the choice of saving instruments. However, ownership of other real estate increases the probability of investments in capital markets and diversification of formal saving instruments, which supports interpreting this variable as an indicator of wealth.

In order to shed more light the role of income for the choice of saving instrument, Table 5 presents results repeating the regressions in Table 4 seven times but with different interactions of B40 and socio-economic characteristics. It shows that among B40 savers those with tertiary education are more likely to have bank savings. Otherwise education does not have a differential impact on B40 savers. The labor market status has a significant and strong impact on the choice of saving instrument of B40 savers. Self-employed and retired B40 savers are more likely to save informally; the former likely relates to the nature of self-employment. By contrast, employed B40 savers are more likely to save formally, but less likely to hold contractual savings. Ownership of the primary residence does not affect the choice of saving instruments of B40 savers. However, the substitution effect between real and financial assets of Table 3 is corroborated. B40 individuals who own other real estate are less likely to have formal savings and more likely to save informally.

18
**<u>Table 4:</u>** <u>The choice of saving instruments</u>

|<br>**Dependent variables**|<br>**formal savings**|**cash only**|**bank savings**|**contractual**<br>|**capital market**<br>|**>1 formal saving**|
|---|---|---|---|---|---|---|
|B40|-0.049***|0.044***|-0.068***|**savings**<br>0.008|**savings**<br>0.008|-0.013|
|income no answer|(0.014)<br>-0.013|(0.013)<br>-0.012|(0.016)<br>-0.026|(0.015)<br>0.020|(0.011)<br>0.010|(0.016)<br>-0.004|
||(0.017)<br>|(0.015)<br>|(0.018)<br>|(0.019)<br>|(0.011)<br>|(0.016)<br>|
|house|0.014|0.003|0.023|0.015|0.021|0.021|
||(0017)|(0016)|(0020)|(0019)|(0015)|(0018)|
|other real estate|.<br>0.011|.<br>-0.006|.<br>0.004|.<br>-0.003|.<br>0.039***|.<br>0.042***|
|l i i|(0.014)<br>0048|(0.013)<br>001*|(0.015)<br>0011|(0.015)<br>0001|(0.008)<br>002|(0.013)<br>0041|
|reguar ncome n euro|-.<br>(0.032)|.5<br>(0.029)|-.<br>(0.035)|-.<br>(0.037)|.5<br>(0.020)|.<br>(0.032)|
|receives remittances|<br>0002|<br>0003|<br>-0006|<br>0015|<br>-0003|<br>0009|
||.<br>(0.022)|.<br>(0.019)|.<br>(0.025)|.<br>(0.023)|.<br>(0.016)|.<br>(0.022)|
|have a loan|0.045***|-0.028**|0.008|0.081***|0.013|0.038***|
||(0.013)|(0.012)|(0.014)|(0.013)|(0.009)|(0.013)|
|plan a loan|0.038|-0.021|0.008|0.026|0.009|0.032*|
|<br>|(0.027)<br>|(0.025)<br>|(0.028)<br>|(0.019)<br>|(0.011)<br>|(0.018)<br>|
|medium education|0.044***|-0.042***|0.043***|0.020|0.028***|0.038**|
||(0.014)|(0.013)|(0.015)|(0.016)|(0.010)|(0.015)|
|high education|<br>0.078***|<br>-0.083***|<br>0.071***|<br>0.071***|<br>0.053***|<br>0.107***|
||(0.017)|(0.017)|(0.019)|(0.018)|(0.012)|(0.017)|
|employed|<br>0.053***<br>|<br>-0.051***<br>|<br>0.063***<br>|<br>0.061***<br>|<br>0.023<br>|<br>0.062***<br>|
||(0.018)|(0.016)|(0.021)|(0.021)|(0.015)|(0.020)|
|self-employed|0004|0007|0005|0056***|0030**|0050***|
||.<br>(0.018)|.<br>(0.016)|.<br>(0.021)|.<br>(0.019)|.<br>(0.013)|.<br>(0.016)|
|retired|0.063**|-0.067***|0.095***|-0.040|0.046**|0.020|
||(0025)|(0024)|(0028)|(0029)|(0022)|(0029)|
|age|.<br>0.009***|.<br>-0.010***|.<br>0.007**|.<br>0.012***|.<br>0.004**|.<br>0.008***|
||(0003)|(0002)|(0003)|(0003)|(0002)|(0003)|
|age squared|.<br>-0.009***|.<br>0.011***|.<br>-0.008***|.<br>-0.011***|.<br>-0.004*|.<br>-0.007**|
||(0.003)|(0.003)|(0.003)|(0.003)|(0.002)|(0.003)|
|fl|<br>0012|<br>0006|<br>0010|<br>0014|<br>0023***|<br>0006|
|emae|-.<br>(0.012)|.<br>(0.013)|-.<br>(0.014)|.<br>(0.013)|-.<br>(0.008)|-.<br>(0.011)|
|married|<br>-0.038**<br>|<br>0.035**<br>|<br>-0.031*<br>|<br>-0.030*<br>|<br>-0.012<br>|<br>-0.024*<br>|
||(0.016)|(0.016)|(0.017)|(0.018)|(0.011)|(0.014)|
|1 person HH|-0.057**|0.061**|-0.046|-0.074***|-0.007|-0.059**|
|<br>|(0.025)<br>|(0.026)<br>|(0.028)<br>|(0.027)<br>|(0.020)<br>|(0.028)<br>|
|2 person HH|-0.007|0.018|-0.017|-0.012|-0.001|-0.013|
||(0015)|(0016)|(0018)|(0016)|(0012)|(0017)|
|children in HH|.<br>0.009|.<br>-0.009|.<br>-0.001|.<br>0.013|.<br>0.001|.<br>0.008|
|head of HH|(0.014)<br>0.013|(0.014)<br>-0.011|(0.015)<br>0.004|(0.015)<br>0.009|(0.010)<br>0.001|(0.014)<br>0.010|
||(0.014)|(0.015)|(0.015)|(0.014)|(0.009)|(0.013)|
|Muslim|<br>-0.053**<br>|<br>0.051***<br>|<br>-0.073***<br>|<br>0.071*<br>|<br>0.042*<br>|<br>0.051<br>|
||(0.022)|(0.020)|(0.026)|(0.040)|(0.024)|(0.038)|
|risk averse|0.019|-0.016|0.019|-0.001|-0.017|0.001|
||<br>(0.018)|<br>(0.018)|<br>(0.020)|<br>(0.020)|<br>(0.012)|<br>(0.020)|
|exp econ growth|0.025**|-0.017|0.027**|0.027**|0.013|0.023*|
||(0012)|(0012)|(0014)|(0013)|(0009)|(0012)|
|trust in government|.<br>-0.003|.<br>0.000|.<br>0.008|.<br>-0.001|.<br>-0.007|.<br>-0.007|
||(0014)|(0013)|(0016)|(0016)|(0012)|(0015)|
|trust in EU|.<br>-0.003|.<br>0.004|.<br>0.008|.<br>0.000|.<br>0.018**|.<br>0.018|
||(0.015)|(0.014)|(0.016)|(0.014)|(0.009)|(0.013)|
|deposits safe|<br>0.041***<br>|<br>-0.033***<br>|<br>0.053***<br>|<br>-0.004<br>|<br>0.008<br>|<br>0.012<br>|
||(0.014)|(0.013)|(0.015)|(0.015)|(0.009)|(0.013)|
|financial system stable|<br>0016|<br>-0018|<br>0025|<br>0039**|<br>0018*|<br>0062***|
|<br>financial loss during transition|.<br>(0.016)<br>0.037**|.<br>(0.015)<br>-0.037**|.<br>(0.018)<br>0.037*|.<br>(0.016)<br>0.031**|.<br>(0.011)<br>0.041***|.<br>(0.015)<br>0.059***|
|<br>|(0.018)<br>|(0.018)<br>|(0.019)<br>|(0.015)<br>|(0.010)<br>|(0.014)<br>|
|internet access|0.024<br>(0016)|-0.009<br>(0016)|0.033*<br>(0018)|0.043**<br>(0020)|0.032**<br>(0014)|0.069***<br>(0020)|
|distance to nearest bank (log)<br>ihlih|.<br>0.003<br>(0.003)<br>1**|.<br>0.000<br>(0.003)<br>*|.<br>0.001<br>(0.004)<br>1**|.<br>0.011***<br>(0.004)<br>21**|.<br>0.006**<br>(0.003)<br>|.<br>0.007**<br>(0.003)<br>14*|
|ngtgt|0.05<br>|-0.009<br>|0.09<br>|0.0<br>|0.003<br>|0.0<br>|
||(0.006)|(0.006)|(0.008)|(0.010)|(0.006)|(0.008)|
|Log-L<br>|<br>-1629.7<br>|<br>-1529.8<br>|<br>-1944.8<br>|<br>-1698.2<br>|<br>-929.2<br>|<br>-1541.7<br>|
|N|4241|4241|4241|4241|4178|4241|
|P(DepVar=1)|081|016|075|024|008|019|
|Note: Cluster standard errors in pa|. <br>rentheses.*** p<0.01|.<br>, ** p<0.05|.<br>, * p<0.01.|.|.|.|

19
**Table 5: Heterogeneities among B40 savers**

|**Dependent**|**formal**|**cash only**|**bank**|**contractual**|**capital**|**>1 formal**|
|---|---|---|---|---|---|---|
|<br>**variables**|**savings**||**savings**|**savings**|<br>**market**<br>|**saving**|
|**Sample**|**Respondents**|**with savings**|||**savings**||
|<br>B40|<br>-0.055***|<br>0.047***|-0.080***|0.014|0.014|-0.005|
||(0.015)|(0.014)|(0.017)|(0.017)|(0.011)|(0.017)|
|tertiary|0.072***|-0.080***|0.059***|0.077***|0.057***|0.113***|
|<br>education|<br>(0.018)|<br>(0.017)|<br>(0.020)|<br>(0.018)|<br>(0.013)|<br>(0.017)|
|B40*tertiary|<br>0.045|<br>-0.024|<br>0.094**|<br>-0.046|<br>-0.037|<br>-0.053|
|education|(0.038)|(0.036)|(0.043)|(0.046)|(0.030)|(0.041)|
|Log-L|-1629.0|-1529.5|-1942.5|-1697.6|-928.4|-1540.9|
|B40|-0.044**|0.042**|-0.065***|0.015|0.011|-0.019|
||(0.019)|(0.018)|(0.022)|(0.022)|(0.014)|(0.021)|
|secondary|<br>0.047***|<br>-0.042***|<br>0.044***|<br>0.024|<br>0.029***|<br>0.035**|
|<br>education|(0.015)|(0.015)|(0.017)|(0.017)|(0.011)|(0.016)|
|B40*secondary|<br>-0.009|<br>0.003|<br>-0.005|<br>-0.015|<br>-0.004|<br>0.012|
|<br>education|(0.025)|(0.024)|(0.028)|(0.029)|(0.020)|(0.028)|
|Log-L|<br> -1629.7|<br> -1529.7|<br> -1944.7|<br> -1698.0|<br> -929.1|<br> -1541.7|
|B40|-0.034**|0.028**|-0.051***|0.014|0.010|-0.008|
||(0.015)|(0.014)|(0.018)|(0.016)|(0.010)|(0.016)|
|self-employed|<br>0.030|<br>-0.021|<br>0.034|<br>0.068***|<br>0.033**|<br>0.058***|
||(0.020)|(0.019)|(0.022)|(0.020)|(0.013)|(0.018)|
|B40*self-|-0.118***|0.124***|-0.148***|-0.094|-0.023|-0.067|
|employed|(0.037)|(0.036)|(0.044)|(0.058)|(0.033)|(0.051)|
|Log-L|-1625.4|-1524.4|-1939.7|-1696.8|-928.9|-1540.9|
|B40|-0.072***|0.057***|-0.112***|0.058**|0.005|0.012|
||(0.020)|(0.019)|(0.024)|(0.024)|(0.016)|(0.026)|
|employed|0.039**|-0.043**|0.036|0.089***|0.021|0.075***|
||(0.020)|(0.018)|(0.023)|(0.023)|(0.016)|(0.023)|
|B40*employed|<br>0.043*|<br>-0.025|<br>0.080***|<br>-0.078**|<br>0.005|<br>-0.038|
||(0.026)|(0.024)|(0.030)|(0.032)|(0.020)|(0.031)|
|Log-L|-1628.4|-1529.3|-1941.1|-1694.8|-929.1|-1540.9|
|B40|-0.041**|0.036**|-0.047***|-0.017|0.011|-0.026|
||(0.016)|(0.015)|(0.018)|(0.018)|(0.011)|(0.017)|
|retired|<br>0.072***|<br>-0.075***|<br>0.118***|<br>-0.071**|<br>0.048**|<br>0.005|
||(0.027)|(0.026)|(0.031)|(0.031)|(0.021)|(0.029)|
|B40*retired|<br>-0.038|<br>0.036|<br>-0.097***|<br>0.134***|<br>-0.013|<br>0.072**|
||(0.033)|(0.031)|(0.036)|(0.036)|(0.023)|(0.035)|
|Log-L|<br> -1629.1|<br> -1529.1|<br> -1941.3|<br> -1691.6|<br> -929.0|<br> -1539.7|
|B40|-0.081**|0.074*|-0.122***|-0.021|0.002|-0.067|
||<br>(0.040)|<br>(0.039)|<br>(0.043)|<br>(0.041)|<br>(0.035)|<br>(0.046)|
|house|<br>0.004|<br>0.012|<br>0.007|<br>0.008|<br>0.019|<br>0.010|
||(0.022)|(0.022)|(0.025)|(0.021)|(0.016)|(0.021)|
|B40*house|<br>0.036|<br>-0.034|<br>0.060|<br>0.033|<br>0.007|<br>0.060|
||(0.041)|(0.040)|(0.045)|(0.042)|(0.035)|(0.048)|
|Log-L|<br> -1629.4|<br> -1529.4|<br> -1943.9|<br> -1697.9|<br> -929.1|<br> -1540.8|
|B40|-0.030*|0.027*|-0.043**|0.010|0.024**|0.006|
||(0016)|(0015)|(0018)|(0018)|(0011)|(0018)|
|other real estate|.<br>0.026*|.<br>-0.020|.<br>0.023|.<br>-0.001|.<br>0.048***|.<br>0.053***|
||(0.015)|(0.014)|(0.017)|(0.015)|(0.009)|(0.013)|
|B40*other|<br>-0.069**|<br>0.061**|<br>-0.091***|<br>-0.011|<br>-0.054***|<br>-0.073**|
|real estate|(0.029)|(0.027)|(0.033)|(0.035)|(0.020)|(0.034)|
|Log-L|-1626.9|-1527.2|-1940.8|-1698.1|-926.2|-1539.1|
|N|4241|4241|4241|4241|4178|4241|
|P(DepVar=1)|0.81|0.16|0.75|0.24|0.08|0.19|

Note: Cluster standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.01.

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Turning to the role of trust for the choice of saving instruments, we again control for risk aversion, expectations and experience of economic crises to lessen the probability that trust is a proxy for other determinants of saving instrument choice (Guiso et al., 2008). We find that those who experienced a financial loss during transition are more likely to have formal savings, in particular contractual savings (3 percentage points compared to a sample mean of 24%) and capital market savings (4 percentage points compared to a sample mean of 8%). The effect on bank savings is much smaller at 4 percentage points or 5% of the sample mean. This “preference” for non-bank formal savings is likely related to the experience of banking crises during transition. Experience of economic crises exerts the strongest effect on the propensity to hold a diversified saving portfolio at 6 percentage points compared. Those who think that the economic situation of their country will improve are 3 percentage points more likely to hold formal savings – at banks or in pension funds / life insurance.

In analyzing the role of trust for the choice of saving instrument, we distinguish between trust in non-financial and financial institutions. Trust in government does not affect the choice of saving instruments; however, those who trust the EU are 2 percentage points more likely to invest in capital markets, a sizeable effect compared to the sample average of 8%. Stix (2013) shows that mistrust in the banking system due to the experience of economic crises during transition is one of the main factors driving informal savings in cash. Figure 5 presents descriptive results on the four indicators of trust in financial institutions included in the Euro Survey. Trust in both foreign and domestic banks is low compared to a non-transition economy.<sup>6</sup> In seven of 10 countries, trust in domestic banks is higher than trust in foreign banks. However, trust in the safety of deposits is higher than trust in banks and trust in the overall financial system is higher than trust in banks.<sup>7</sup> Regression results in Table 4 show that trust in financial institutions has a strong effect on savings and the choice of saving instruments. Conditional on having savings, trust in deposit safety increases the probability of holding formal savings by 4 percentage points and the probability of saving at banks by 5 percentage points – an effect similar in magnitude to having secondary education. In line with Stix (2013), we find that trust in the safety of deposits decreases the probability of saving in cash by 3 percentage points. Interestingly, those who trust financial stability are more likely to invest in life insurance or pension funds (4 percentage points) and stocks, bonds or mutual funds (2 percentage points). Compared to the sample mean of 24% and 8% the effect is sizeable, although not as large as that reported for the USA by Guiso et al. (2008). Trust in the

> 6 See Knell and Stix (2015) for comparable indicators of trust in domestic banks, foreign banks, deposit safety and financial stability.

> 7 Unfortunately, it is beyond the scope of this paper to examine the determinants of trust in financial institutions and in particular the puzzling difference between trust in the safety of deposits and trust in the stability of the financial system. Preliminary regression analyses show that memories of economic crises during transition indeed are highly correlated with the low level of trust in banks. Further regression analyses show that trust in government is highly correlated with trust in the safety of deposits. Trust in the stability of the financial system is also correlated with trust in government. However, the correlation is weaker and by contrast the correlation with trust in the EU is stronger. This may indicate that respondents are aware on the one hand of possible spillover effects from instability in the euro area and on the other hand believe that the EU institutions have a stabilizing cross-border effect.

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stability of the financial system has the largest effect on diversification of saving instruments at 6 percentage points or a third of the sample mean.

<!-- Start of picture text --> Trust<br>BG HR CZ HU PL RO AL BA MK RS<br>stability of financial system deposit safety<br>domestically owned banks foreign owned banks<br>.8<br>.6<br>.4<br>of respondents<br>.2<br>0<br><!-- End of picture text -->

**Figure 5: Trust in financial institutions**

This figure presents the percentage of respondents who say they trust in (i) the stability of the financial system, (ii) deposit safety, (iii) domestically owned banks, and (iv) foreign owned banksSource: OeNB Euro Survey, 2012-2013. Note: All percentages are weighted by sampling weights.

We analyze these findings further by including the indicators of trust separately. Figure 6 shows the average marginal effect for each trust indicator on the respective saving instrument. The left panel shows that trust in banks (whether foreign or domestic) has the strongest impact on whether respondents hold any savings. The decision to save formally is influenced by trust in domestic banks and trust in deposit safety. By contrast, lack of trust in deposit safety increases the probability of saving in cash only. Turning to the choice of formal saving instrument, the right panel illustrates that trust in the stability of the financial system has the strongest impact on the decision to hold non-bank formal savings and to diversify savings. We also investigate whether trust has a differential impact for B40 versus T60 households (results not shown). We find that this is not the case.

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<!-- Start of picture text --> formal<br>savings<br>any savings<br>>1 formal bank<br>savings savings<br>cash only<br>capital<br>formal savings market contractual<br>savings<br>savings<br>‐0.05 0 0.05 0.1<br>deposit safety financial stability deposit safety financial stability<br>domestic banks foreign banks domestic banks foreign banks<br><!-- End of picture text -->

**Figure 6:** Average marginal effect of trust in financial institution on respective saving instruments

While lack of trust in financial institutions could lead to “voluntary exclusion” from formal savings, high transaction costs might play a role for “involuntary exclusion”, especially for low-income households. In CEEU and WB, between 49% (Bosnia and Herzegovina) and 74% (Hungary) of households live within 1km to the nearest bank branch. These percentages increase to 56% and 86% for a radius of 2km (Beckmann et al., 2018). These figures would suggest that physical access to banks is not a significant constraint to formal savings, however, up to 27% of households (Bosnia and Herzegovina) do not live within 5km to the next bank branch.

After controlling for local economic activity (night lights), Table 4 confirms that distance to the next bank does not significantly affect participation in bank savings or, looking at the reverse effect, informal savings. This result holds for all countries. There is some indication that households with poorer physical bank access are more likely to use alternative formal savings, however, the size of the effect is small. We investigate this further by considering whether internet access perhaps substitutes physical access. While we control for a long list of characteristics including trust, risk aversion and education, internet access may still be endogenous and proxy other unobserved characteristics. Therefore, results should be interpreted with caution. We find that internet access is correlated with formal savings and in particular contractual and capital market savings as well as diversification of formal savings. Excluding internet access from the model does not change the observed insignificance of physical access. In robustness analyses, we follow Glaser and Klos (2013) and Liang and Guo (2015) in investigating the effect of financial literacy and social interaction on the observed internet-saving correlation (Annex, Table A5). These robustness analyses indicate that the effect of internet access on savings instruments is robust to including indicators of financial literacy and indicators of network effects: Households may acquire information on saving products from neighbors. Including the percentage of respondents in the primary sampling unit who hold formal savings also does not affect the significance or magnitude 23
of the “internet effect”. Results in Table A5 further suggest that internet access only increases the probability of holding contractual savings, capital market savings and a diversified saving portfolio for financially literate individuals. It does not influence participation in bank savings and it does not have an effect on the saving behavior of those who are financially illiterate.

In further robustness analyses, we repeat estimations of Table 2 country-by-country (Table A4).<sup>8</sup> We further use a Heckman selection model where we jointly estimate the probability of having savings and the probability of holding specific asset categories following Allen et al. (2012). Finally, we repeat the estimations including control variables step-by-step instead of jointly. None of these modifications qualitatively changes our main results.

# **6. Conclusions**

The evidence we present shows that there is significant scope for promoting household savings in CEEU and WB. The percentage of savers is low, and this is particularly true for bottom 40 households. Informal savings are widespread also among top 60 households. Improving physical access to banks is unlikely to increase bank savings. However, there is some indication that lack of information regarding formal savings could be counteracted by improving internet access. However, this will likely only have an effect on financially literate individuals.

Mistrust in banks is widespread and has a significant negative impact on formal savings. However, trust in deposit safety is higher than trust in banks, suggesting policies regarding deposit insurance have been successful. Furthermore, overall trust in the stability of the financial system is higher than trust in banks, and there is some indication of a substitution effect between bank savings and non-bank formal savings for those who mistrust the banking system.

> 8 In these country-specific regressions, we only include socio-economic control variables, as the number of observations is too low to estimate the full model presented in Table 2.

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