National culture and housing credit

Journal of Empirical Finance 56 (2020) 19–41

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National culture and housing credit Chrysovalantis Gaganis a , Iftekhar Hasan b,c,d , Fotios Pasiouras e ,∗ a

Department of Economics, University of Crete, Rethymno, Greece Fordham University, United States c Bank of Finland, Finland d University of Sydney, NY, United States e Department of Financial Management, Law & Accounting, ICORE Building, Montpellier Business School, 2300 Avenue des Moulins, 34185 Montpellier Cedex 4, France b

ARTICLE

INFO

JEL classification: G21 E71 Keywords: Culture Mortgage Housing Hofstede

ABSTRACT Using a sample of around 30 countries over the period 2001–2015, this study provides evidence that deeply rooted cultural differences are significantly associated with the use of mortgage debt. More detailed, we find that power distance and uncertainty avoidance have a negative impact on the value of the total outstanding residential loans to GDP. This finding is robust across various specifications and the use of alternative measures of mortgage debt. In contrast, trust has a positive and robust impact on all the measures of mortgage debt. Other dimensions of national culture like long-term orientation, individualism, and indulgence, also appear to matter; however, their impact depends on the control variables and the employed measure of mortgage debt.

1. Introduction Housing finance varies greatly among countries. For instance, data for 2015 from nearly 90 countries reveal that the ratio of mortgage loans to GDP ranges from 0.08% in Guinea to 121.45% in Switzerland. Even within geographical regions, like Europe, there can be substantial variation, with countries like Romania and Bulgaria having a ratio below 10%. A question that naturally emerges is: what really drives the large variation in housing finance across countries? Answering to this question is important for numerous reasons. For instance, as discussed in Cerutti et al. (2017), many consider access to housing finance as essential to promoting home ownership that can be beneficial to social stability and economic growth. However, at the same time, the global financial crisis started with the subprime mortgage crisis and the housing market collapse in the U.S (Kamin and DeMarco, 2012). Evidence also suggests that national credit and house price cycles feed off each other and translate into more intense episodes (Claessens et al., 2011). Badev et al. (2014) discuss various other reasons for which it is important to explore the determinants of mortgage finance. First, the maturity transformation of short-term liabilities into long-term assets, like long-term housing finance, is at the centre of the financial intermediation theory. Second, mortgage loans constitute a major part of the liability of households in developed countries, and they become central for the transmission of monetary policy. Third, housing finance is an important element of the urbanization process in many developing and emerging economies. As we discuss in more detail in Section 2, earlier research suggests that people often condition their decisions on social factors and the behaviour of others. Therefore, national culture may be an important factor in housing related decisions. However, cross-country ∗ Corresponding author. E-mail addresses: [email protected] (C. Gaganis), [email protected] (I. Hasan), [email protected] (F. Pasiouras). https://doi.org/10.1016/j.jempfin.2019.12.003 Received 12 December 2018; Received in revised form 24 November 2019; Accepted 21 December 2019 Available online 31 December 2019 0927-5398/© 2019 Elsevier B.V. All rights reserved.

C. Gaganis, I. Hasan and F. Pasiouras

Journal of Empirical Finance 56 (2020) 19–41

studies on the determinants of mortgage credit have so far neglected the impact of national culture, and we aim to close this gap in the literature.1 Using a sample of around 30 advanced countries, our results show that most of the cultural dimensions that we consider are associated with the ratio of the value of the total outstanding residential loans to GDP. In general, this finding holds when we control for an array of other country-specific characteristics that have been proposed in past studies as drivers of mortgage debt, like macroprudential regulations, age dependency, interest rates, house prices, and urbanization, among others. The results also hold when we instrument the cultural variables to account for potential endogeneity. We obtain the same results when we consider alternative attributes of the mortgage market like the per capita outstanding residential loans, the percentage of homeowners with a mortgage, the ratio of the outstanding residential loans to the households’ disposable income, with the non-performing loans ratio being the only exception. Therefore, we provide evidence that deeply rooted differences between societies are significantly associated with residential loans. The rest of the paper is as follows. Section 2 provides a review of the relevant literature. Section 3 describes the data and variables used in our study. Section 4 discusses the empirical results. Section 5 concludes. 2. Literature review Our work is motivated by numerous studies from the fields of economics and sociology suggesting that the decisions of people are influenced by social factors and the behaviour of others.2 We hypothesize that national culture, which reflects the unwritten rules of the social game and the ‘‘collective programming of the mind’’ (Hofstede et al., 2010), plays an obvious role in motivation, and subsequently the use of mortgage to acquire a house. In the discussion that follows we refer in turn to various strands of the literature that relate to our work. 2.1. Social environment, culture and the housing market The first strand of the literature consists of studies that relate the social and cultural environment to various housing decisions. For example, Hubka and Kenny (2006) discuss the American dream and the emergence of national housing culture over the period 1900–1930, while Patacchini and Venanzoni (2014) provide empirical evidence that social comparisons are important in shaping the demand for housing quality. The work of Pan and Pirinsky (2015) examines the impact of social influence in the U.S. housing market. However, their work focuses on home ownership rather than the use of mortgage credit. They conclude that: (1) individuals’ homeownership decisions depend on their exposure to homeowners of the same ethnicity in the region, and (2) people whose country of origin is characterized by more unequal, uncertainty-avoiding, and collectivistic cultures are more likely to condition their homeownership decisions on the decisions of their ethnic peers. In a more recent study that uses data from facebook, Bailey et al. (2017) examine the effects of social interactions on individuals’ housing market expectations. They find that decisions like the transition from renting to owing, buying larger houses and paying more for a given house depend on individuals’ social networks. 2.2. Household debt, social interactions and culture The second strand of the literature consists of studies suggesting that the willingness of households to rely on debt may also vary with social interactions and culture. Some of these studies, like the ones by Pan and Pirinsky (2015) and Haliassos et al. (2017), refer to housing decisions. For example, Pan and Pirinsky (2015) highlight that status considerations related to buying a home increase the reward in the upside disproportionally more than the cost in the downside, creating an additional incentive for the use of leverage. Additionally, Haliassos et al. (2017) focus on migrants and refugees to Sweden and reveal differences across cultural groups in how behaviour relates to the following household characteristics: stockholding, debt outstanding, and homeownership. Others take a more general approach to housing debt without focusing on the mortgage market. For instance, Breuer et al. (2015) find evidence that countries with a more long-term oriented culture tend to have shorter household debt maturity. Similarly, using information from a Dutch survey, Georgarakos et al. (2014) reveal a social influence, in terms of perceived income, on household borrowing behaviour. Focusing on debt to a public utility company, Lea et al. (1993) find that serious debtors are less likely to claim Nonconformist, Agnostic or Atheist religious views, they have slightly more permissive attitudes towards debt, they know more other people in debt, and they are less likely to think that their friends or relations would disapprove if they know they were in debt. Finally, Jiang and Lim (2018) use a US sample to show that individuals with higher levels of trust have lower likelihoods of default in household debt and higher net worth. They also conclude that the effect is driven by trust values inherited from cultural and family backgrounds more than by trust beliefs about others. 1 A growing number of cross-country studies examines the association between national culture and financial decisions and outcomes at the country level, like bilateral portfolio investments (Kim et al., 2015), life insurance consumption (Chui and Kwok, 2008), saving rates (Guiso et al., 2006), access to finance (Aggarwal and Goodell, 2014), the development and efficiency of the financial sector and the stock market (Calderon et al., 2002), whether the economy is bank- or market-based (Kwok and Tadesse, 2006), and asset managers’ views and behaviour (Beckmann et al., 2008). In general, these studies conclude that various dimensions of national culture, like uncertainty avoidance, individualism and power distance play an important role in financial decisions. 2 Akerlof (1997) refers to numerous studies linking social interactions with economic theory. Pan and Pirinsky (2015) argue that the idea that people tend to emulate the consumption patterns of their peer groups could be traced back to the works of Smith (1776) and Veblen (1899). Dupor and Liu (2003) argue that social influence, and in particular jealously, can be related to overconsumption. Grinblatt et al. (2008) also find evidence of a social influence in the decision to buy an automobile. However, they conclude that there is little evidence that emotional biases, like envy, account for the observed social influence on consumption. Bucciol et al. (2017) conclude that sociodemographic variables explain observed heterogeneity in household portfolios more than economic ones.

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2.3. Determinants of mortgage credit While our work broadly relates to all the above studies, it is also closely related to studies that examine the determinants of mortgage credit. In general, cross-country evidence in the field is scarce. Two of the studies focus on the European market, the one examining EU-15 (Wolswijk, 2006) and the other examining 19 emerging economies in Central and Eastern Europe (Beck and Brown, 2015). Using larger international samples, Warnock and Warnock (2008) examine around 60 countries and Badev et al. (2014) examine a sample of nearly 100 countries. However, none of these cross-country studies considers national culture. The vast majority of the existing studies focuses on individual countries like U.S. (Ling and McGill, 1998; Bhutta, 2015), Ireland (Fitzpatric and McQuinn, 2007), Netherlands (Cunha et al., 2013), Switzerland (Basten and Koch, 2015), Italy (Jappelli and Pistaferri, 2002), and Canada (Simone and Walks, 2019). In general, these studies neglect the impact of culture; however, a handful number of them considers ethnic characteristics and differences among immigrants. For example, focusing on immigrants in Spain, Rodriguez-Planas (2018) finds that the higher the loan penetration in the country of ancestry, the higher the likelihood of having a mortgage in Spain. Additionally, higher mortgage depth in the country of ancestry translates into higher present value of monthly mortgage payments in Spain. She interprets this as evidence that social norms in the country of ancestry matter in determining immigrants’ mortgage finance in the host country. While not relating it to cultural differences, Simone and Walks (2019) also find evidence that: (i) immigrants bear significantly higher debt burdens that do nativeborn Canadians, and (ii) many neighbours with a high concentration of immigrants, particularly in the metropolitan areas with the tightest housing markets, have significantly higher levels of mortgage debt than other neighbours. Therefore, these two studies illustrate the importance of the social background, but their single country focus does not allow them to consider the role of national culture, and we aim to close this gap within a cross-country setting. 3. Data and variables 3.1. Background discussion and variables selection This section describes the data sources and the variables, it presents descriptive statistics, and discuss our methodology. Our sample consists of EU-28 plus a few other countries that are covered in Hypostat, the EMF-ECBC’s statistical publication of European housing and mortgage markets. The additional countries are: Australia, Iceland, Japan, Norway, Russia, Turkey, and USA. There are two reasons for which we focus on this set of countries. First, their inclusion in Hypostat means that there is a rich amount of standardized information available. Second, the EU countries in our sample have different cultural values; however, at the same time there are ongoing efforts to converge in terms of the macroeconomic and institutional environment. Therefore, it might be easier to isolate the impact of culture on mortgage depth. Following earlier studies, we control for various country-specific attributes (e.g. Wolswijk, 2006; Badev et al., 2014). Data for inflation, GDP growth, interest rates on new residential loans, share of loans with a variable interest rate, and nominal house price indices, are from the Hypostat reports. Data on bank and stock market development are from the Global Financial Development Database of the World Bank. More detailed, we control for bank concentration, foreign banking, stock market capitalization (% GDP), and the stock market turnover ratio. Additionally, we control for real estate related restrictions on bank activities, the existence of a loan to value macroprudential instrument, and the autonomy of the central bank, using information from Barth et al. (2013), Cerutti et al. (2015), and Arnone et al. (2007), respectively. Data relating to population, like urban population (% total), old and young age dependency ratios (% of working-age population), annual population growth, and population density are from the World Development Indicators of the World Bank. Information on tax property is from the Organization for Economic Co-Operation and Development (OECD), whereas information on the percentage of young adults (aged 18–34) living with parents is from Eurostat. Finally, we use information from the Doing Business Report of the World Bank to control for the number of required procedures for construction permits, and the cost for property registration. We rely on the same source for information on the getting credit environment, reflecting the strength of credit reporting systems and the effectiveness of collateral and bankruptcy laws in facilitating lending. The indicator of housing credit, that is our main dependent variable, captures mortgage depth, as measured by the ratio of the value of the total outstanding residential loans to GDP. The data are from various versions of the Hypostat, and they cover the period 2001–2015. We initially considered all 35 countries that are included in the reports. However, in a subsequent step we excluded Cyprus due to lack of data for our key independent variable — i.e. the Hofstede indicators of national culture. Our key independent variables are interpersonal trust and the following six dimensions of national culture: Power Distance Index, Individualism versus collectivism, Masculinity versus femininity, Uncertainty Avoidance Index, Long-Term versus Short-Term Orientation, and Indulgence versus restrained. These dimensions are the outcome of the work in Hofstede (1980, 1991, 2001), Hofstede and Bond (1988), and Hofstede et al. (2010), and we obtain the data from Hofstede Insights. In the discussion that follows, we outline these dimensions and their association with financial decisions. The power distance dimension can be defined as ‘‘the extent to which the less powerful members of institutions and organizations within a country expect and that power is distributed unequally’’ (Hofstede et al., 2010, p.61).3 A general norm in societies with low power distance is that inequalities among people should be minimized. Therefore, such societies are characterized by an effort to equalize 3 According to Hofstede et al. (2010), institutions are the basic elements of society, such as family, the school, and the community; organizations are the places where people work.

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Journal of Empirical Finance 56 (2020) 19–41

the distribution of power, which is associated with a demand for justification of inequalities of power. Among the cross-country studies from the field of financial economics, Chui and Kwok (2008) reveal that power distance has a negative effect on insurance consumption, whereas firm-level studies like the one by Arosa et al. (2014) show that countries with higher levels of power distance have higher levels of long-term debt to book value of total assets. Some other studies, focusing on income inequality, find support for the ‘‘keeping up with the Joneses’’ hypothesis. Using US data, Christen and Morgan (2005), find a strong positive effect of income inequality on household debt relative to disposable income as well as on the components of household debt, like mortgage debt, credit card debt, car loans, etc. They attribute this to the effort of lower income households to keep their consumption relative to higher income households. Fligstein et al. (2017) provide similar evidence while focusing on housing costs and debt. Through the analysis of 4000 residential moves in the US housing market from 1997 to 2007 they find that in areas where income inequality was higher, all movers went deeper into debt and increased their monthly housing costs to live in more desirable neighbours. Due to their wealth, the higher income people were able to take less debt to keep their position in the status quo; however, everyone below them who made a move to buy a house took on more debt in an attempt to keep up and maintain their social status and lifestyle. In the context of the present study, we expect that social comparisons and the desire for equality through the ownership of a house in low power distance societies will result in higher mortgage debt. The individualism versus collectivism dimension is another dimension of national culture identified by Hofstede. At the country level, individualism is defined as a preference for a loosely-knit social framework in which individuals are expected to take care of only themselves and their immediate families. In contrast, collectivism represents a preference for a tightly-knit social framework in which individuals can expect their relatives or members of a particular group to look after them in exchange for unquestioned loyalty. A society’s position on this dimension is reflected in whether people’s self-image is defined in terms of ‘I’ or ‘we.’ Individualism has been positively associated to long-run economic growth (Gorodnichenko and Roland, 2017), life insurance consumption (Chui and Kwok, 2008), higher mortgage default rate (Tajaddini and Gholipour, 2017), and overconfidence of individuals (Ferris et al., 2013). Hofstede et al. (2010) also discuss that in individualist cultures, children expect and are expected to move out of their parents’ home and live on their own when they start higher education. Additionally, they mention that in individualist societies home is considered to be one’s castle. Finally, de Mooij (2011) reports that personal loans are more frequent in individualistic cultures. This discussion leads us to the hypothesis that people in individualist societies will be more willing to take a mortgage to buy a house. The dimension of masculinity versus femininity refers to ‘‘the distribution of emotional roles between the genders (Hofstede, 2011). At the country level, the masculine side of this dimension represents a preference in society where men are assertive, tough, focused on material success, as well as a society that is more competitive. Hofstede et al. (2010) mention that status purchases are in general more frequent in masculine cultures, whereas feminine cultures spend more on products for home. Therefore, the impact of this dimension on housing decisions might depend on whether the house is considered an indicator of status or a necessity for quality of life that serves a functional role, a home that is also a social need. Yet, in both cases, we would expect that people would be willing to take more debt to buy a house either as a symbol of status or a social need. Yet, it is possible that in the first case, the amount of mortgage would be higher to finance the purchase of a more expensive house. The uncertainty avoidance index is defined as ‘‘the extent to which a culture programmes its members to feel either uncomfortable or comfortable in unstructured situations’’ (Hofstede, 2001, p.19). Hofstede et al. (2010) mention that this is expressed through nervous stress, and in a need for predictability: a need for written and unwritten rules. Consequently, societies with a high uncertainty avoidance index are characterized by rigid codes of belief and behaviour, and are intolerant of unorthodox behaviour and ideas. Aggarwal and Goodell (2014) conclude that uncertainty avoidance has a negative impact on access to finance, while Hofstede et al. (2010) point out that when it comes to financial matters, people from high uncertainty avoidance countries take fewer risks, and they have more worries about money. Therefore, we expect that people from high uncertainty avoidance countries will not be willing to take debt in the form of mortgage financing given the uncertainty that is associated with its repayment. The fifth dimension refers to long-term versus short-term orientation. It was incorporated into Hofstede’s framework in 1991, and it is related to the choice of focus for people’s efforts: the future or the present and past (Hofstede, 1991, 2011).4 On the one hand, societies with a score low on this dimension show a preference to maintain time-honoured traditions and norms, and they view societal change with suspicion. On the other hand, societies with a high score take a more pragmatic approach; they encourage thrift and efforts in modern education to prepare for the future. The impact of this dimension on mortgage credit is ambiguous. On the one hand, Hofstede et al. (2010) point out that people in high long-term oriented countries invest more in real estate, which is long term commitment, while people in low long-term oriented show a preference for mutual funds. This could possibly imply a positive relationship of long-term orientation with mortgage debt to finance the purchase of real estate, like a house. On the other hand, de Mooij and Hofstede (2002) mention that long-term orientated cultures are cash or debit card cultures, not credit card cultures. This possibly implies a dislike towards debt. At the same time, Hofstede et al. (2010) argue that in long-term orientated societies children should learn to save money and other assets, and these societies have large savings quotes and funds available for investments. Taken together, these characteristics could imply a negative association between long-term orientation and the willingness or the need to take a mortgage to finance the purchase of a house. The sixth dimension of national culture is known as indulgence versus restrained, and it was included in Hofstede’s model in 2010. Societies with a high degree of indulgence are characterized by relatively free gratification of basic and natural human drives related to enjoying life and having fun. In contrast, restrained societies suppress gratification of needs and regulate them by means of strict social norms. In their analysis of credit/debt discourses and practices of white middle-class consumers in the U.S., Penaloza and Barnhart (2011), mention that at one end is self-discipline, in limiting the use of market-supplied resources in relation to their 4

A new version of this fifth dimension, based on World Value Surveys became available in Hofstede et al. (2010) and it is the one used in the present study. 22

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own, with a moral quality deserving respect in controlling themselves and delaying gratification. At the other end is indulgence in expending relatively high levels of credit relative to their resources in pursuing their desires and pleasures. Using 279 questionnaires with answers from residents in Oxford, Lunt and Livingstone (1991) find that the more importance people assign to enjoyment as a value in their lives, the less they save. Additionally, Livingstone and Lunt (1992) discuss that self-indulgence is a common representation of debtors as well as that those in debt not only experience pleasure in consumption but also express their social worth and social relations through consumption. Therefore, we expect that the ratio of mortgage debt to GDP will be higher in societies characterized by higher indulgence. Finally, we examine the impact of interpersonal trust on housing credit. Knack (2001) argues that social ties and interpersonal trust can reduce transactions costs, enforce contracts, and facilitate credit at the level of individual investors. Indeed, earlier empirical studies show that trust matter for economic and financial investment decisions. For example, Guiso et al. (2004) conclude that in areas of Italy with high levels of social trust, households are more likely to use checks, invest less in cash and more in stock, have higher access to institutional credit, and make less use of informal credit. Delis and Mylonidis (2015) also conclude that trust matters for investments and insurance policy decisions in the case of Dutch households. While earlier studies have documented the role of trust in stock market and other financial investments, the link between trust and housing credit has been largely overlooked.5 We expect that higher interpersonal trust will be positively associated with residential loans. To measure interpersonal trust we construct an index based on information from the European Social Survey (ESS). The ESS asks participants in each country to answer the following question: ‘‘Generally speaking, would you say that most people can be trusted, or that you can’t be too careful in dealing with people?’’ The answers are on a predetermined eleven points scale which ranges, from ‘‘you can’t be too careful’’ to ‘‘most people can be trusted’’. To obtain an overall index of trust, we assign the values of 1–11 to the eleven potential answers and we then weight them by the proportion of respondents from each country who provided each answer. For ease of interpretation, we then scale the index in such a way that it takes values between 0 and 100, with higher values denoting higher trust.6 3.2. Descriptive statistics Table 1 presents descriptive statistics of the variables.7 Appendices B and C provide further information for the dependent and key independent variables on a per country basis. The average ratio of total outstanding residential loans to GDP is 36.25%, associated with a standard deviation of 24.04%. The value of the ratio ranges from 2.84% to 81.96%. Thus, there is substantial variation across countries in terms of mortgage depth. We observe the same in the case of the cultural indicators. For example, the mean value of power distance is 50.23 (standard deviation of 20.11), ranging from 11 in Austria to 100 in Slovakia. Similarly, the mean value of individualism is 60.03, associated with a standard deviation of 18.02. The minimum value of 27 is observed in the cases of Portugal and Slovenia, whereas the maximum value of 91 is observed in the case of the United States. We observe similar variations in the rest of the cultural indicators which we do not discuss further to conserve space. The figures in Table 2 reveal that some cultural indicators have moderate to high correlation coefficients. For instance, PDI is negatively correlated with INDIV (−0.574), and positively correlated with UAI (0.638), whereas the correlation between INDIV and UAI is −0.620. 4. Empirical results 4.1. Main results In the regressions that follow, as in Badev et al. (2014), we use the log of mortgage depth as our dependent variable. In all the cases, we use random effects estimations, and standard errors clustered at the country level.8 To avoid multicollinearity issues, we introduce the cultural dimensions in the regressions one by one. Table 3 presents the results of the baseline regression, where we control for inflation and the share of urban population. In Tables 4–8, we present additional estimations while controlling for various country-specific attributes. The results in Table 3 show that power distance, uncertainty avoidance, and long-term orientation have a negative and statistically significant association with mortgage depth. In contrast, individualism, indulgence, and trust appear to be positively associated with mortgage depth. These findings are consistent with the discussion in Section 3. In a sense, our findings provide 5 In general, interpersonal trust has received a lot of attention in the literature with past studies linking it not only to finance phenomena but also to economic ones, like economic growth (Dearmon and Grier, 2009), the shadow economy (D’Hernoncourt and Méon, 2012), and technology transfers (García-Vega and Huergo, 2017). 6 This approach is similar to the one used in Fungáčová et al. (2019). To give an example, in the case of Austria in 2002, the responses are as follows: 1 (6.3%), 2 (3.7%), 3 (5.8%), 4 (9.4%), 5 (11.2%), 6 (21.7%), 7 (10.4%), 8 (14.8%), 9 (10%), 10 (3.6%), 11 (3.3%). Thus, the trust index for Austria in 2002 is calculated as: (1 × 6.3) + (2 × 3.7) + (3 × 5.8) + (4 × 9.4) + (5 × 11.2) + (6 × 21.7) + (7 × 10.4) + (8 × 14.8) + (9 × 10) + (10 × 3.6) + (11 × 3.3) = 608.4. Taking into account the maximum (801.3) and minimum (329.6) values of this index in the sample, we convert it into the 0–100 scale as follows: {[(100 − 0)∕(801.3 − 329.6)] × (608.4 − 801.3)} + 100 = 59.11. 7 All the variables in the form of ratios, including the dependent variables, where capped at the 5th and 95th percentile to lessen the potential influence of outliers. 8 We do not consider the use of fixed effects, because national culture is time invariant. This is a common assumption in the studies that use the Hofstede and other cultural indicators. Beugelsdijk et al. (2015) point out that the argument that culture is stable over time (e.g. Hofstede, 2001) is related to the claim that value differences between societies are deeply rooted in history and drive socioeconomic developments rather than the other way around. After replicating Hofstede’s dimensions for two birth cohorts with data from the World Values Survey, Beugelsdijk et al. (2015), conclude that: (i) countries’ scores on the Hofstede dimensions relative to the scores of other countries have not changed very much, and (ii) using Hofstede’s data in international research is as relevant now as it was when his work was first published.

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Journal of Empirical Finance 56 (2020) 19–41

Table 1 Descriptive statistics. Dependent variables

Obs.

Countries

Mean

Std. Dev.

Min

Max

Mortgage Mortgage Mortgage Mortgage Mortgage

489 350 261 312 192

34 29 26 25 19

1.41 4.04 30.00 4.06 2.70

0.41 0.49 18.23 0.69 2.96

0.45 2.50 2.3 2.01 0.10

1.91 4.69 63.1 5.16 13.5

489 489 489 489 489 489 430

34 34 34 34 34 34 30

50.23 60.03 46.10 69.35 54.83 46.38 54.38

20.11 18.02 25.37 21.60 19.13 18.88 21.42

11.00 27.00 5.00 23.00 21.00 13.00 0.00

100.00 91.00 100.00 100.00 88.00 78.00 100.00

489 489 489 489 489 489 354 354 354 209 437 437 437 436 434 434 434 434 434 474 474 474 489 489

34 34 34 34 34 34 29 29 29 23 33 33 33 33 30 30 30 30 30 34 34 34 34 34

2.69 74.57 2.43 0.26 6.25 6.68 4.37 99.63 1.73 59.40 69.42 35.32 56.89 64.10 24.15 24.68 0.33 135.11 47.22 25.41 4.23 6.49 0.33 2.01

2.22 11.82 1.12 0.44 2.05 1.15 2.00 17.88 1.03 31.35 19.85 33.99 37.21 49.17 3.98 3.39 0.67 122.23 14.79 23.24 3.01 1.08 0.47 3.46

−0.28 54.01 1.00 0.00 1.00 3.00 2.14 64.83 0.35 6.58 34.24 0.00 10.88 1.35 16.38 20.51 −0.89 3.19 19.20 7.80 0.30 4.00 0.00 −14.81

8.50 94.07 4.00 1.00 8.00 8.00 12.04 135.17 3.64 100 99.53 95.00 133.48 164.21 31.26 31.75 1.71 483.87 68.70 76.90 11.25 8.00 1.00 11.90

Depth (in logs) Density (in logs) Penetration Affordability (in logs) NPLs

Main independent variables Power distance Individualism Masculinity Uncertainty avoidance Long term orientation Indulgence Trust index Control variables Inflation Urbanization Restrictions on real estate Dummy for LTV ratio CB political autonomy CB economic autonomy Interest rates House prices Property taxation Share of variable interest rate Bank concentration Foreign banks Stock market development Stock market turnover Age dependency-old Age dependency-young Population growth Population density Young adults with parents Construction permits Registering property Getting credit Banking crises GDP growth

Note: Descriptive statistics are based on the actual number of observations used in the corresponding regression. In the case of variables used in several regressions, the statistics are calculated on the basis of the regression with the highest number of observations. The full sample includes the EU-27 countries (i.e. EU-28 excluding Cyprus) plus Australia, Iceland, Japan, Norway, Russia, Turkey, and USA. The sample period is 2001–2015 in the case of mortgage depth, mortgage density, and mortgage affordability; 2003–2015 in the case of mortgage penetration; 2005–2015 in the case of NPLs (%). Variables are defined in Appendix A.

Table 2 Correlation coefficients of cultural indicators.

1. 2. 3. 4. 5. 6. 7.

Power distance Individualism Masculinity Uncertainty avoidance Long term orientation Indulgence Trust index

1

2

3

4

5

6

7

1.000 −0.574*** 0.205*** 0.638*** 0.286*** −0.557*** −0.711***

1.000 0.090** −0.620*** −0.122*** 0.451*** 0.573***

1.000 0.223 0.200 −0.100** −0.422

1.000 0.272*** −0.486*** −0.726***

1.000 −0.526*** −0.244***

1.000 0.601***

1.000

Note: ***Statistically significant at the 1% level, **Statistically significant at the 5% level, *Statistically significant at the 10% level. Variables are defined in Appendix A.

cross-country evidence that national culture, which reflects a nation’s deep-rooted social and psychological characteristics that were shaped many years ago, explains differences in mortgage loan decisions across countries. The only cultural dimension that does not have a statistically significant relationship with the ratio of residential mortgage to GDP is masculinity. Fig. 1 presents a visual 24

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Journal of Empirical Finance 56 (2020) 19–41

Table 3 National culture and mortgage depth: Baseline regression.

Inflation Urbanization Power distance

(1)

(2)

(3)

(4)

(5)

(6)

(7)

−0.0228*** (0.0086) 0.0146*** (0.0040) −0.0087*** (0.0026)

−0.0227*** (0.0086) 0.0159*** (0.0044)

−0.0226*** (0.0086) 0.0194*** (0.0042)

−0.0232*** (0.0085) 0.0172*** (0.0032)

−0.0234*** (0.0086) 0.0190*** (0.0035)

−0.0234*** (0.0085) 0.0135*** (0.0047)

−0.0207** (0.0088) 0.0111** (0.0055)

Individualism

0.0069** (0.0035)

Masculinity

−0.0009 (0.0014)

Uncertainty avoidance

−0.0075*** (0.0022)

Long-Term orientation

−0.0063** (0.0026)

Indulgence

0.0083*** (0.0031)

Trust index Constant

0.8127** (0.3803)

−0.1365 (0.3290)

0.0539 (0.3605)

0.6986** (0.2823)

0.3975 (0.3351)

0.0776 (0.2863)

0.0123*** (0.0033) −0.0440 (0.3314)

Observations Number of countries R-sq overall R-sq within R-sq between

489 34 0.528 0.082 0.626

489 34 0.444 0.083 0.528

489 34 0.382 0.085 0.437

489 34 0.512 0.084 0.599

489 34 0.458 0.085 0.532

489 34 0.489 0.081 0.574

430 30 0.537 0.178 0.617

Notes: ***Statistically significant at the 1% level, **Statistically significant at the 5% level, *Statistically significant at the 10% level, Robust standard errors in parentheses; Random effects estimations with standard errors clustered at the country level. The dependent variable is the log of mortgage depth, measured by the ratio of total outstanding residential loans to GDP.

representation of the baseline results, showing the correlation between the cultural variable and mortgage depth conditional on the control variables (i.e. partial correlation).9 In Table 4, we control for prudential and macroprudential policies. Allen et al., 2020 show that macroprudential policies matter for housing finance demand in Canada. Others find that policies such as maximum LTV can be effective in restraining a real estate boom (Cerutti et al., 2017). Additionally, Wolswijk (2006) argues that deregulation removes obstacles to a competitive mortgage supply. In the present study, we include an index of real estate restrictions on bank activities from Barth et al. (2013). This index takes values between 1 and 4, and reveals the extent to which banks may engage in real estate investment, development and management. Higher values indicate higher restrictions.10 Furthermore, we use a dummy that takes the value of one when a loan-to-value ratio is in place, and the value of zero otherwise. Information is from Cerutti et al. (2015). The rationale for its inclusion in the analysis is that such a ratio constrains highly levered mortgage downpayments by enforcing or encouraging a limit or by determining regulatory risk weights. The results show that the existence of an LTV ratio is positively associated with the ratio of residential mortgage to GDP. However, the restrictions on the real estate activities of banks have no influence on mortgage depth. The inclusion of these two variables in the regression does not influence our main results.11 In Table 5 we control for various housing market conditions, like the annual average interest rates on new residential loans, the nominal house price indices, and tax on property (% GDP).12 , 13 As discussed in Wolswijk (2006) low mortgage interest rates enhance the capacity of households to borrow while keeping debt servicing costs, as percentage of their income, stable. Turning to house prices, Wolswijk (2006) argues that there are various channels through which they can influence mortgage debt. For instance, high house prices may decrease mortgage demand of starters on the housing ladder but may have the opposite effect on current owners. Additionally, higher house prices increase the amount of mortgage to be financed by new buyers. At the same time, buyers may have incentives to buy now if they anticipate a future increase in prices. Finally, higher taxation on property may affect both the 9

The Figure was prepared with the use of the Stata user-written command xtavplot (Gallup, 2020). The engagement of banks in real estate activities can be: (i) Unrestricted, which takes the value of 1, and means that the full range of activities can be conducted directly in the bank; (ii) Permitted, which takes the value of 2, and means that the full range of activities can be conducted, but some or all must be conducted in subsidiaries, (iii) Restricted, which takes the value of 3, and means that less than full range of activities can be conducted in the bank or subsidiaries; and (iv) Prohibited, which takes the value of 4, and means that the activity cannot be conducted in either the bank or subsidiaries. 11 In unreported regressions we also control for the political and economic autonomy of the central bank. The underlying idea is that is the independence of the Central Bank can be a critical factor in monetary policy and bank regulations, which in turn might have a significant impact on housing market activity (Ume, 2018). These variables enter the regressions with insignificant coefficients, and their inclusion in the regressions of Table 4 does not influence our main findings. 12 Tax on property is defined as recurrent and non-recurrent taxes on the use, ownership or transfer of property. These include taxes on immovable property or net wealth, taxes on the change of ownership of property through inheritance or gift and taxes on financial and capital transactions. 13 In unreported regressions we also control for the share of loans (in terms of amount) with a variable interest rate (or fixation period of up to 1 year). We do not include this simultaneously with tax on property (% GDP) because it reduces the sample size by more than 40%. Our results indicate that the share of loans with a variable interest rate is insignificant in all the cases, and the main findings hold. 10

25

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Journal of Empirical Finance 56 (2020) 19–41

Table 4 National culture and mortgage depth: Controlling for regulations.

Inflation Urbanization Restrictions on Real Estate Dummy for LTV ratio Power distance

(1)

(2)

(3)

(4)

(5)

(6)

(7)

−0.0192** (0.0091) 0.0129*** (0.0040) 0.0192 (0.0217) 0.1702*** (0.0370) −0.0104*** (0.0024)

−0.0195** (0.0091) 0.0143*** (0.0045) 0.0174 (0.0218) 0.1607*** (0.0376)

−0.0195** (0.0091) 0.0179*** (0.0041) 0.0183 (0.0219) 0.1571*** (0.0400)

−0.0201** (0.0091) 0.0164*** (0.0033) 0.0176 (0.0216) 0.1531*** (0.0396)

−0.0205** (0.0091) 0.0181*** (0.0033) 0.0163 (0.0216) 0.1512*** (0.0402)

−0.0199** (0.0091) 0.0101** (0.0044) 0.0174 (0.0216) 0.1722*** (0.0377)

−0.0184** (0.0092) 0.0092* (0.0049) 0.0243 (0.0255) 0.1479*** (0.0349)

Individualism

0.0077** (0.0039)

Masculinity

−0.0011 (0.0015)

Uncertainty avoidance

−0.0076*** (0.0024)

Long-Term orientation

−0.0063** (0.0028)

Indulgence

0.0106*** (0.0031)

Trust index Constant

0.9302** (0.4017)

−0.1586 (0.3327)

0.0781 (0.3743)

0.6761** (0.3249)

0.3660 (0.3543)

0.0676 (0.2799)

0.0125*** (0.0029) −0.0205 (0.3179)

Observations Number of countries R-sq overall R-sq within R-sq between

489 34 0.528 0.170 0.611

489 34 0.404 0.169 0.466

489 34 0.348 0.168 0.376

489 34 0.474 0.167 0.536

489 34 0.421 0.166 0.470

489 34 0.486 0.169 0.553

430 30 0.507 0.253 0.570

Notes: ***Statistically significant at the 1% level, **Statistically significant at the 5% level, *Statistically significant at the 10% level, Robust standard errors in parentheses; Random effects estimations with standard errors clustered at the country level. The dependent variable is the log of mortgage depth, measured by the ratio of total outstanding residential loans to GDP. The variables are defined in Appendix A.

decision to purchase a house but also its financing, as the buyers will have to consider both current and future payable taxes. All three variables enter with a statistically significant coefficient, being negative in the case of interest rates, and positive in the case of the price index and property taxation. While most of the variables retain their sign and significance, individualism and indulgence become insignificant in Table 5 In Table 6, we control for bank and stock market characteristics. The results show that only concentration in the banking sector has a negative and statistically significant association with mortgage depth. In contrast, foreign banks and stock market development do not appear to influence our dependent variable.14 The main results are comparable to the ones of the baseline regression in Table 3. Then we control for socio-economic characteristics by including in the regression, the young and old age dependency ratios, annual population growth, population density, and the percentage of young adults (aged 18–34) living with parents. We present the results in Table 7. The age dependency-old (-young) ratio is positive (negative) and statistically significant in all cases, a finding that is consistent with Badev et al. (2014). The other three socio-economic variables are insignificant. As before, the inclusion of these control variables does not influence the association between national culture and mortgage depth. Table 8 presents regressions while controlling for the contractual and informational environment. Consistent with the results of Badev et al. (2014) we find a negative association between mortgage depth and the number of procedures to obtain a construction permit and an insignificant relationship with the other two variables.15 The rest of the findings remain the same. In further regressions, we control for (i) banking crises,16 and (ii) GDP growth. The dummy variable for the banking crisis enters with a positive and statistically significant coefficient. Possibly, this is associated with the significant decrease in GDP that most countries experience during a banking crisis, resulting in a higher mortgage to GDP ratio.17 GDP growth enters the regression with a negative and statistically significant coefficient.18 In both cases, our main results hold. 14 In unreported regressions we re-estimate the specifications of Table 6 with the inclusion of one more proxy for stock market conditions, this being the stock market turnover. This variable enters the regression with an insignificant coefficient, and it does not influence the main results. 15 The index of the number of procedures refers to procedures required for a business in the construction industry to build a warehouse. Thus, the index does not capture the conditions in the housing market; however, it can serve as a proxy for similar requirements in the case of housing. 16 Information for the banking crises are from the IMF database by Valencia and Laeven (2012). Thus, it allows the years of the crisis to vary by country. 17 Using data from 35 countries, Demirguc-Kunt et al. (2006) examine what happens to the banking systems following a banking crisis. They conclude that credit as a share of GDP remains significantly above pre-crisis levels for the entire aftermath period, which they attribute to the less rapid decrease of credit relatively to output. 18 To conserve space, we do not tabulate these results; however, they are available upon request.

26

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Journal of Empirical Finance 56 (2020) 19–41

Fig. 1. Baseline regressions (partial correlations). Note: The figure presents the correlation between the cultural variable and mortgage depth conditional on the control variables (i.e. partial correlation). Plots were prepared with the use of the xtavplot Stata user-written command by Gallup, 2020.

27

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Journal of Empirical Finance 56 (2020) 19–41

Table 5 National culture and mortgage depth: Controlling for housing market conditions.

Inflation Urbanization Interest rate House prices Property taxation Power distance

(1)

(2)

(3)

(4)

(5)

(6)

(7)

−0.0017 (0.0047) 0.0073** (0.0033) −0.0206*** (0.0078) 0.0030*** (0.0007) 0.0776*** (0.0275) −0.0066** (0.0027)

−0.0013 (0.0047) 0.0084** (0.0038) −0.0213** (0.0084) 0.0029*** (0.0007) 0.0709** (0.0290)

−0.0016 (0.0047) 0.0093*** (0.0032) −0.0198** (0.0081) 0.0029*** (0.0007) 0.0781*** (0.0277)

−0.0014 (0.0045) 0.0083*** (0.0026) −0.0208*** (0.0080) 0.0029*** (0.0007) 0.0774*** (0.0262)

−0.0011 (0.0047) 0.0099*** (0.0027) −0.0218*** (0.0081) 0.0029*** (0.007) 0.0711*** (0.0270)

−0.0014 (0.0047) 0.0074* (0.0041) −0.0215*** (0.0081) 0.0029*** (0.0007) 0.0695** (0.0292)

−0.0021 (0.0053) 0.0064** (0.0031) −0.0189** (0.0087) 0.0027*** (0.0007) 0.0695** (0.0268)

Individualism

0.0043 (0.0041)

Masculinity

−0.0023** (0.0011)

Uncertainty avoidance

−0.0058*** (0.0020)

Long-Term orientation

−0.0038* (0.0021)

Indulgence

0.0046 (0.0032)

Trust index Constant

0.9445*** (0.3196)

0.2929 (0.2544)

0.5935** (0.2598)

0.9532*** (0.2566)

0.6701** (0.2620)

0.4228** (0.2129)

0.0059*** (0.0022) 0.4014** (0.2008)

Observations Number of countries R-sq overall R-sq within R-sq between

354 29 0.3129 0.5138 0.4599

354 29 0.3133 0.4184 0.3720

354 29 0.3137 0.4047 0.3883

354 29 0.3131 0.5333 0.4875

354 29 0.3128 0.4308 0.4169

354 29 0.3128 0.4313 0.4225

318 26 0.3288 0.5883 0.5006

Notes: ***Statistically significant at the 1% level, **Statistically significant at the 5% level, *Statistically significant at the 10% level, Robust standard errors in parentheses; Random effects estimations with standard errors clustered at the country level. The dependent variable is the log of mortgage depth, measured by the ratio of total outstanding residential loans to GDP. The variables are defined in Appendix A.

To lessen concerns about omitted variable bias, Table 9 presents estimations that include simultaneously all the control variables that were statistically significant in earlier regressions. The results show that even when we account simultaneously for an array of country-specific attributes, there appears to be an association between deeply rooted cultural differences and mortgage depth, with individualism being the only variable of interest that is not statistically significant. 4.2. Endogeneity In this section we re-estimate the specifications of Table 9 using a G2SLS random-effects IV estimator with standard errors clustered at the country level. The issue of reverse causality, i.e. an effect running from mortgage lending to national culture, should not be a major concern in our context. This is because the cultural dimensions are rather stable over time, formed many years ago, and they reflect attributes that are deeply rooted in societies. Nonetheless, one may still argue that major prior events related to the country’s mortgage lending and housing could affect social norms leading to reverse causality. A more important concern in our analysis relates to the impact of omitted variables. While in Table 9 we control simultaneously for various country-level attributes, our analysis lack controls for micro-level characteristics of borrowers. Therefore, to address concerns about confounding effects we re-estimate our specifications with instrumented national culture variables. Based on theory, past studies on culture and finance, and data availability, we carefully select instruments that are: (i) unlikely to have a direct influence on mortgage lending, therefore satisfying the exogeneity requirement of an instrument; (ii) correlated with the cultural dimensions, therefore satisfying the relevance requirement of an instrument. We instrument power distance with religion, history of communist rule, and pronunciation. Carl et al. (2004) provide a detailed discussion on the association between various religions and the acceptance of a higher or lower degree of power distance. For example, they mention that the Protestant religions support, in general, the concept of equality of status before God, egalitarianism of access to God, individualist assertion, and hence lower power distance before other human beings. In contrast, Catholicism supports the status quo in many societies, and it continues to recognize women as unsuitable to hold the highest positions within the Church establishment. Therefore, societies that have been primarily Roman Catholic have a tendency towards high power distance. Religion has been used, in general, as an instrument of cultural dimensions in various studies (Kwok and Tadesse, 2006; Li et al., 2013; Mourouzidou-Damtsa et al., 2019), and as an instrument of power distance in Boubakri et al. (2017). We use the percentages of a country’s populations being Catholics, Muslims, Protestants. Nash and Patel (2019) suggest the history of communist rule as another 28

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Journal of Empirical Finance 56 (2020) 19–41

Table 6 National culture and mortgage depth: Controlling for bank and stock market characteristics.

Inflation Urbanization Bank concentration Foreign banks Stock market development Power distance

(1)

(2)

(3)

(4)

(5)

(6)

(7)

−0.0189** (0.0091) 0.0159*** (0.0048) −0.0045** (0.0018) −0.0002 (0.0014) −0.0006 (0.0006) −0.0103*** (0.0033)

−0.0181** (0.0092) 0.0184*** (0.0050) −0.0044** (0.0019) −0.0001 (0.0015) −0.0008 (0.0006)

−0.0190** (0.0092) 0.0221*** (0.0049) −0.0045** (0.0020) −0.0001 (0.0015) −0.0008 (0.0007)

−0.0185** (0.0091) 0.0191*** (0.0044) −0.0047** (0.0020) −0.0004 (0.0015) −0.0090 (0.0007)

−0.0183** (0.0091) 0.0234*** (0.0041) −0.0046** (0.0019) 0.0007 (0.0015) −0.0007 (0.0006)

−0.0170* (0.0090) 0.0167*** (0.0049) −0.0047** (0.0019) 0.0016 (0.0016) −0.0010 (0.0006)

−0.0145* (0.0086) 0.0130*** (0.0046) −0.0066*** (0.0018) −0.0004 (0.0013) −0.0012* (0.0006)

Individualism

0.0075* (0.0042)

Masculinity

−0.0021 (0.0019)

Uncertainty avoidance

−0.0088*** (0.0029)

Long-Term orientation

−0.0082** (0.0033)

Indulgence

0.0119*** (0.0036)

Trust index Constant

1.1384** (0.5072)

−0.0243 (0.4600)

0.2500 (0.4937)

1.0357** (0.4340)

0.5018 (0.4204)

−0.0148 (0.3932)

0.0151*** (0.0030) 0.1853 (0.3948)

Observations Number of countries R-sq overall R-sq within R-sq between

437 33 0.1739 0.5226 0.4404

437 33 0.1767 0.3790 0.3152

437 33 0.1742 0.2929 0.2658

437 33 0.1752 0.4763 0.4067

437 33 0.1816 0.3897 0.3431

437 33 0.1888 0.4393 0.3711

393 30 0.3085 0.4967 0.4407

Notes: ***Statistically significant at the 1% level, **Statistically significant at the 5% level, *Statistically significant at the 10% level, Robust standard errors in parentheses; Random effects estimations with standard errors clustered at the country level. The dependent variable is the log of mortgage depth, measured by the ratio of total outstanding residential loans to GDP. The variables are defined in Appendix A.

instrument for power distance. The underlying rationale is that communism is a form of government that strongly emphasizes and perpetuates inequalities in society (Carl et al., 2004). Therefore, we use a dummy variable indicating whether a country has a history of communism rule. Kashima and Kashima (1998) relate the use of pronouns to social distance and various cultural dimensions. The underlying idea is that rules associated with the use of first-person and second-person singular pronouns maps onto the self-other relationship. In other words, languages requiring person-indexing pronouns (such as ‘‘I’’ in English) place greater emphasis on the subject by explicitly distinguishing the speaker from the general context. They also highlight that ‘‘Earlier in history, a higher status person called a lower status person by T, whereas the lower status person addressed the higher status person by V ’’ (p. 467).19 Therefore, as in Klasing (2013) we use the pronoun drop rule as an instrument for power distance. The first two instruments that we use in the case of individualism, are pronunciation and the presence of pathogens (Nash and Patel, 2019). As discussed earlier, pronunciation has been associated with culture, and Kashima and Kashima (1998) document that cultures with pronoun drop languages tend to be less individualistic than nonpronoun drop languages. Davis and Abdurazokzoda (2016) confirm this finding. Therefore, we follow past studies and we use ‘‘pronoun drop’’ as an instrument for Individualism (e.g. El Ghoul and Zheng, 2016; Gorodnichenko and Roland, 2017). Turning to the presence of pathogens, Fincher et al. (2008) find that the regional prevalence of pathogens has a strong positive association with collectivism and a strong negative association with individualism. They suggest that this is due to specific behavioural manifestations of collectivism (e.g. tradition, conformity). Accordingly, as in Boubakri and Saffar (2016), Boubakri et al. (2017), Zheng et al. (2013) and Gorodnichenko and Roland (2017), we use pathogen prevalence as an instrument for individualism. The third instrument that we use for individualism versus collectivism is agricultural potential. Meggers (1954) mentions that in places that are unfit for agriculture, subsistence derived from hunting, fishing and gathering will normally support only small groups that must be constantly on the move. Therefore, social organization is formed around kinship lines, with the social unit being a single family, or at best an extended family or lineage. Hofstede et al. (2010) also point out that farmers had to collaborate in monotonous, season-bound work, and they lived in much greater numbers than hunter-gatherers or herders. As they mention, this situation requires a certain meekness, possibly related to larger collectivism. Along the same lines, Ang (2019) empirically documents that agricultural legacies have structured the individualist traits among individuals, pre-industrial ethnic groups, and countries. Within this context, the instrument that we use in the present study is the maximum potential caloric yield attainable given the set of crops that were suitable for cultivation in the pre-1500 period (Galor and Ozak, 2016). 19

These two types of second-person pronouns originated from 𝑡𝑢 and 𝑣𝑜𝑠 in Latin (Kashima and Kashima, 1998). 29

C. Gaganis, I. Hasan and F. Pasiouras

Journal of Empirical Finance 56 (2020) 19–41

Table 7 National culture and mortgage depth: Controlling for socio-economic characteristics.

Inflation Urbanization Age dependency - old Age dependency – young Population growth Population density Young adults with parents Power distance

(1)

(2)

(3)

(4)

(5)

(6)

(7)

−0.0093 (0.0076) 0.0082 (0.0098) 0.0295** (0.0140) −0.0563*** (0.0201) 0.0459 (0.0352) 0.0006 (0.0007) 0.0011 (0.0061) −0.0118** (0.0052)

−0.0090 (0.0075) 0.0102 (0.0102) 0.0299** (0.0141) −0.0575*** (0.0200) 0.0481 (0.0361) −0.0007 (0.0007) 0.0005 (0.0062)

−0.0094 (0.0075) 0.0117 (0.0095) 0.0292** (0.0141) −0.0588*** (0.0203) 0.0499 (0.0364) 0.0005 (0.0007) −0.0015 (0.0062)

−0.0085 (0.0073) 0.0100 (0.0089) 0.0305** (0.0140) −0.0573*** (0.0199) 0.0465 (0.0354) 0.0007 (0.0006) 0.0015 (0.0061)

−0.0087 (0.0076) 0.0121 (0.0085) 0.0298** (0.0140) −0.0626*** (0.0196) 0.0377 (0.0363) 0.0008 (0.0008) −0.0025 (0.0057)

−0.0086 (0.0073) 0.0034 (0.0094) 0.0311** (0.0135) −0.0641*** (0.0191) 0.0193 (0.0367) −0.0002 (0.0006) 0.0020 (0.0067)

−0.0083 (0.0076) 0.0075 (0.0098) 0.0283** (0.0130) −0.0615*** (0.0179) 0.0303 (0.0363) 0.007 (0.006) 9.78e−06 (0.0058)

Individualism

0.0112** (0.0057)

Masculinity

−0.0041 (0.0039)

Uncertainty avoidance

−0.0133*** (0.0033)

Long-Term orientation

−0.0130*** (0.0042)

Indulgence

0.0202*** (0.0068)

Trust index Constant

1.9440* (1.0695)

0.6774 (1.0504)

1.4677 (1.0250)

2.0960** (0.9807)

2.0831** (1.0223)

1.0186 (0.9377)

0.0106*** (0.0037) 1.0293 (0.9602)

Observations Number of countries R-sq overall R-sq within R-sq between

434 30 0.4617 0.2899 0.2842

434 30 0.4549 0.2107 0.2138

434 30 0.4500 0.1460 0.1762

434 30 0.4604 0.3583 0.3553

434 30 0.4511 0.2787 0.2927

434 30 0.4724 0.3606 0.3473

419 29 0.4975 0.3378 0.3353

Notes: ***Statistically significant at the 1% level, **Statistically significant at the 5% level, *Statistically significant at the 10% level, Robust standard errors in parentheses; Random effects estimations with standard errors clustered at the country level. The dependent variable is the log of mortgage depth, measured by the ratio of total outstanding residential loans to GDP. The variables are defined in Appendix A.

Following the empirical study of El Ghoul and Zheng (2016) and the suggestions of Nash and Patel (2019) the first instrument for masculinity is genetic diversity. The underlying idea is that parents transmit culture together with genes to their offspring, making genetic distance a suitable instrument for cultural differences. As suggested in Nash and Patel (2019), we use Japan as the frontier, since it is the country with the highest masculinity score. The second instrument for masculinity is agricultural potential, as defined above. Hansen et al. (2015) propose and empirically document that societies with long histories of agriculture are characterized by higher gender inequality because of more patriarchal values and beliefs regarding the proper role of women in society. They also refer to other studies which support that agriculture enhanced gender inequality (Diamond, 1987) or suggest that hunter-gatherer societies were characterized by more independent women as compared to agricultural societies (Iversen and Rosenbluth, 2010). In general, as Hansen et al. (2015) point out, it seems that the move to agriculture led to different roles within the family, where the man used his physical strength in food production and the woman took care of other family-related duties. The authors also refer to various studies that examine the degree to which a society relies on hunting and gathering for the accumulation of the daily calorie intake, and the associated value attributed to women. In a similar vein, one may argue that as the societies moved to agriculture, the maximum potential caloric yield attainable became an important factor, in shaping beliefs about the two genders, and hence the masculinity versus femininity cultural dimension of a society. The third instrument is religion. As before, we use the percentages of the three most widely spread religions in the world in 1980. Hofstede et al. (2010) highlight that issues related to the masculinity-femininity dimension are central to any religion. Paxton (1997) also points out that religions are differentially conservative or patriarchal in their views about the place of women, both in the church hierarchy and in society. For example, her results indicate that Roman Catholicism, which is considered patriarchal in nature, has a negative impact on the number of women in politics. Following the recommendation of Nash and Patel (2019) we instrument uncertainty avoidance with religion. Using religion is consistent with past empirical studies (Li et al., 2013; El Ghoul and Zheng, 2016), and the underlying idea is that religious beliefs help people to avoid anxiety and accept uncertainties against which they cannot defend themselves (Hofstede et al., 2010). Therefore, we use the percentages of the three most widely spread religions in the world in 1980. In the case of long-term orientation, we use a total of four instruments. First, we use agricultural potential, motivated by the findings of Galor and Ozak (2016) who document that higher potential crop yield experienced by ancestral populations during 30

C. Gaganis, I. Hasan and F. Pasiouras

Journal of Empirical Finance 56 (2020) 19–41

Table 8 National culture and mortgage depth: Controlling for contractual and informational environment.

Inflation Urbanization Construction permits Registering property Getting credit Power distance

(1)

(2)

(3)

(4)

(5)

(6)

(7)

−0.0148** (0.0065) 0.0039 (0.0050) −0.0041*** (0.0007) −0.0057 (0.0102) −0.0135 (0.0364) −0.0121*** (0.0029)

−0.0149** (0.0065) 0.0031 (0.0056) −0.0041*** (0.0007) −0.0065 (0.0107) −0.0211 (0.0467)

−0.0154** (0.0067) 0.0095* (0.0050) −0.0039*** (0.0007) −0.0046 (0.0108) 0.0301 (0.0431)

−0.0163** (0.0067) 0.0092** (0.0041) −0.0040*** (0.0007) −0.0000 (0.0097) 0.0032 (0.0340)

−0.0156** (0.0067) 0.0102** (0.0043) −0.0039*** (0.0007) −0.0048 (0.0102) 0.0152 (0.0420)

−0.0154** (0.0065) 0.0014 (0.0053) −0.0041*** (0.001) −0.0069 (0.0101) 0.0336 (0.0375)

−0.0143** (0.0068) 0.0036 (0.0057) −0.0039*** (0.0008) 0.0004 (0.0081) 0.0153 (0.0371)

Individualism

0.0118*** (0.0044)

Masculinity

−0.0017 (0.0016)

Uncertainty avoidance

−0.0085*** (0.0025)

Long-Term orientation

−0.0068** (0.0026)

Indulgence

0.0140*** (0.0032)

Trust index Constant

1.9814*** (0.5911)

0.7712* (0.4493)

0.7376 (0.5052)

1.4354*** (0.4634)

1.0761** (0.5017)

0.6172 (0.4252)

0.0112*** (0.0027) 0.5420 (0.4190)

Observations Number of countries R-sq overall R-sq within R-sq between

474 34 0.507 0.401 0.556

474 34 0.390 0.403 0.426

474 34 0.395 0.388 0.439

474 34 0.515 0.385 0.552

474 34 0.451 0.387 0.481

474 34 0.507 0.405 0.538

425 30 0.575 0.444 0.613

Notes: ***Statistically significant at the 1% level, **Statistically significant at the 5% level, *Statistically significant at the 10% level, Robust standard errors in parentheses; Random effects estimations with standard errors clustered at the country level. The dependent variable is the log of mortgage depth, measured by the ratio of total outstanding residential loans to GDP. The variables are defined in Appendix A.

the pre-industrial era, enhanced the long-term orientation of their descendants in the contemporary era. Then, following Shi and Veenstra (2015) we use religion and ethnic fractionalization, as well as a dummy variable indicating whether a country has been a British colony.20 Being a relatively new dimension, indulgence has received considerably less attention. The first instrument that we use is again agricultural potential. Hofstede et al. (2010) mention that the stronger sense of freedom and happiness of societies of huntergatherers and horticulturalists, may be partly explained by the fact that they were not burdened by the evils of intensive agriculture. Within the same context, they highlight that intensive agriculture is associated with attributes like restrained discipline, planning and saving for the future, indifference to leisure, and tight social management. The second instrument is religion, which may impose various restrains. For example, as discussed in Hofstede et al. (2010), different religions have taken different positions towards the pleasure side of sex, they may penalize marriage with nonadherents or more generally condemn the pursuit of happiness and consider it a waste of time. Furthermore, Djankov and Nikolova (2018) show that Eastern Orthodox believers are less happy compared to those of Catholic and Protestant faith. The third instrument is a dummy variable that takes the value of one in the case of countries with a history of communist rule and zero otherwise. There various reasons for which communism might be related to indulgence versus restraints. For example, Luthar (2006) highlights that ‘‘Among the strongest individual memories of life under state socialism is the lack of desired goods, the ‘culture’ of shortages, and the ‘dictatorship’over needs’’ (p. 229), whereas Paldam and Svendsen (2001) argue that totalitarian regimes have created a strong atmosphere of fear. Trust is also instrumented with two country-specific attributes. First, we use religion, and in particular, the percentage of Protestants in the country. This is consistent with: (i) the findings of Uslaner (2002) and Bjørnskov (2006) who show that Protestantism increases trust, and (ii) the suggestions of Nash and Patel (2019) and previous studies that use measures of religious 20 Ashkanasy et al. (2004) highlight that the dominant religion in a society determines the future orientation of its members, and they discuss differences between Confucianism, Islam, Protestants and Catholics. Therefore, we use the religion fractionalization index by Alesina et al. (2003), to capture such differences. Ethnic fractionalization has also been used in numerous studies as an instrument for cultural dimensions (e.g. Kwok and Tadesse, 2006; Li et al., 2013; Shi and Veenstra, 2015). Finally, Nash and Patel (2019) refer to several studies that suggest that colonization by the British had profound social implications including a predisposition for pragmatic change (Licht et al., 2005, 2007; Acemoglu et al., 2001).

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Table 9 National culture and mortgage depth: Simultaneous inclusion of control variables. (1) Power distance

(2)

(3)

(4)

(5)

(6)

Individualism

0.0064 (0.0039)

Masculinity

−0.0032** (0.0015)

Uncertainty avoidance

−0.0082*** (0.0021)

Long-Term orientation

−0.0052*** (0.0022)

Indulgence

0.0101*** (0.0035)

Trust index Inflation Urbanization Dummy for LTV ratio Interest rate House prices Property taxation Bank concentration Age dependency – old Age dependency - young Construction permits Banking crises GDP growth Constant Observations Number of countries R-sq overall R-sq within R-sq between

(7)

−0.0083*** (0.0029)

0.0061 (0.0043) 0.0011 (0.0040) 0.0370 (0.0271) −0.0135** (0.0062) 0.0015** (0.0007) 0.0876*** (0.0153) −0.0010 (0.0009) 0.0110 (0.0104) −0.0005 (0.0108) −0.0012*** (0.0004) −0.0110 (0.0241) −0.0113*** (0.0020) 1.4321*** (0.4493)

0.0067 (0.0042) 0.0014 (0.0046) 0.0300 (0.0281) −0.0142** (0.0068) 0.0015** (0.0007) 0.0780*** (0.0155) −0.0009 (0.0009) 0.0124 (0.0108) 0.0001 (0.0107) −0.0012*** (0.0003) −0.0132 (0.0244) −0.0114*** (0.0020) 0.5820 (0.3961)

0.0059 (0.0043) 0.0030 (0.0041) 0.0327 (0.0281) −0.0130* (0.0071) 0.0015** (0.0007) 0.0876*** (0.0163) −0.0012 (0.0009) 0.0114 (0.0107) 0.0001 (0.0116) −0.0012*** (0.0004) −0.0126 (0.0247) −0.0113*** (0.0020) 1.0316** (0.4443)

0.0061 (0.0040) 0.0020 (0.0037) 0.0254 (0.0291) −0.0113* (0.0064) 0.0015** (0.0007) 0.0887*** (0.0148) −0.0012 (0.0009) 0.0142 (0.0107) −0.0049 (0.0106) −0.0011*** (0.0004) −0.0146 (0.0246) −0.0112*** (0.0019) 1.5567*** (0.3807)

0.0065 (0.0043) 0.0044 (0.0035) 0.0261 (0.0283) −0.0140** (0.0067) 0.0015** (0.0007) 0.0798*** (0.0150) −0.0010 (0.0009) 0.0129 (0.0107) −0.0021 (0.0114) −0.0011*** (0.0004) −0.0185 (0.0253) −0.0114*** (0.0020) 1.0894*** (0.3844)

0.0063 (0.0042) −0.0021 (0.0051) 0.0316 (0.0278) −0.0118* (0.0067) 0.0014** (0.0007) 0.0761*** (0.0154) −0.0009 (0.0009) 0.0147 (0.0108) −0.0060 (0.0111) −0.0012*** (0.0004) −0.0132 (0.0248) −0.0111*** (0.0019) 0.8615** (0.3978)

0.0056*** (0.0017) 0.0052 (0.0043) 0.0008 (0.0043) 0.0163 (0.0294) −0.0102 (0.0072) 0.0013* (0.0007) 0.0793*** (0.0159) −0.0014 (0.0010) 0.0134 (0.0107) −0.0045 (0.0102) −0.0011*** (0.0004) −0.0085 (0.0256) −0.0108*** (0.0020) 0.8367*** (0.3607)

351 29 0.5412 0.4568 0.4259

351 29 0.5406 0.3354 0.3155

351 29 0.5389 0.3261 0.3504

351 29 0.5426 0.5147 0.4817

351 29 0.5367 0.3728 0.3806

351 29 0.5481 0.3998 0.4276

315 26 0.5563 0.5571 0.4860

Notes: ***Statistically significant at the 1% level, **Statistically significant at the 5% level, *Statistically significant at the 10% level, Robust standard errors in parentheses; Random effects estimations with standard errors clustered at the country level. The dependent variable is the log of mortgage depth, measured by the ratio of total outstanding residential loans to GDP. The variables are defined in Appendix A.

affiliation as instruments for the cultural attribute of trust (Guiso et al., 2006, 2009). Second, we use the agricultural potential. Our selection is driven by the findings of Litina (2016) who concludes that lower level of land productivity in the past is associated with higher levels of current social capital, measured by generalized trust.21 Table 10 presents the results of the G2SLS random-effects IV estimator, along with tests for under-identification, weak identification, and over-identification. Both the first stage (Panel A) and second stage (Panel B) estimations include the same control variables as the specifications of Table 9, which are not shown to conserve space. Except for the long-term orientation, all the remaining instrumented cultural indicators and the trust index appear to have a statistically significant impact on mortgage depth.

21 Litina (2016) finds that a 10% point increase in land suitability, is associated with a 2% points decrease in probability that an individual is trustful. She suggests that the results of her study show that the origins of social capital (i.e. trust) can be traced to large-scale cooperation, which emerged thousands of years ago, for the development of agricultural infrastructure that could mitigate the adverse effect of the natural environment in places with lower levels of land productivity.

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Table 10 Accounting for endogeneity: G2SLS random-effects IV estimator. (1)

(2)

(3)

Panel: 1st stage

(4)

Power distance Individualism Masculinity Communist history Catholic (%) Muslim (%) Protestants (%) Pronunciation

1.6602 (5.0462) 0.0345 (0.0711) 0.0919* (0.0534) −0.1815*** (0.0612) 15.8332** (6.2314)

Pathogens Agricultural potential

0.0680 (0.1589) −0.2812** (0.1282) −0.4274*** (0.1261) −10.1895** (4.9660) −2.1330*** (0.5259) 0.0327*** (0.0045)

Genetic distance

(6)

Uncertainty Avoidance Long-Term Orientation Indulgence

(7)

0.0337*** (0.059) −283.9161** (123.258)

Trust

−25.2858*** (5.7863) 0.0321 (0.0739) −0.0199 (0.0677) 0.1376* 0.3932*** (0.0765) (0.0543)

0.0295 (0.1371) 0.1863 (0.1135) −0.3475** (0.1455)

−0.0297** (0.0142)

Ethnic fractionalization

0.0094** (0.0035)

−0.2941** (0.1293)

YES YES

YES YES

44.1288*** (16.5692) 39.4788*** (10.0304) −33.5275*** (10.7673)

Religion fractionalization British colony

Control variables Constant

(5)

Instrumented cultural dimension

YES YES

YES YES

YES YES

YES YES

YES YES

Panel B: 2nd stage Power distance_instrumented

−0.0173*** (0.0045)

Individualism_instrumented

0.0170*** (0.0056)

Masculinity_instrumented

−0.0079*** (0.0024)

Uncertainty avoidance_instrumented

−0.0139*** (0.0037)

Long-Term orientation_instrumented

-(0.0052) (0.0034)

Indulgence_instrumented

0.0125** (0.0051)

Trust index_instrumented Control variables Constant Observations Number of countries R-sq overall R-sq within R-sq between Underidentification test Kleibergen-Paap rk LM statistic p-value Weak identification test Kleibergen-Paap rk Wald F statistic Stock–Yogo weak ID test critical values 5% maximal IV relative bias 10% maximal IV relative bias 10% maximal IV size 15% maximal IV size Test of overidentifying restrictions Sargan–Hansen statistic p-value

YES YES 320 26 0.5731 0.4272 0.3619

YES YES 316 26 0.5699 0.2029 0.1777

YES YES 340 28 0.5467 0.2111 0.2578

YES YES 340 28 0.5464 0.4312 0.3995

YES YES 351 29 0.5383 0.3582 0.3674

YES YES 340 28 0.5516 0.3597 0.3912

0.0103*** (0.0019) YES YES 304 25 0.5116 0.6707 0.5884

18.155 0.0028

7.931 0.0475

11.900 0.0362

9.736 0.0210

14.055 0.0071

11.464 0.0429

9.788 0.0075

39.593

38.373

34.891

23.016

23.332

38.005

27.822

18.37 10.83 26.87 15.09

13.91 9.08 22.30 12.83

18.37 10.83 26.87 15.09

13.91 9.08 22.30 12.83

16.85 10.27 24.58 13.96

18.37 10.83 26.87 15.09

n.a. n.a. 19.93 11.59

2.060 0.7246

1.819 0.4028

2.783 0.5948

2.094 0.3510

2.157 0.5404

7.188 0.1263

0.727 0.3938

Notes: ***Statistically significant at the 1% level, **Statistically significant at the 5% level, *Statistically significant at the 10% level, Robust standard errors in parentheses; G2SLS random-effects IV estimations with standard errors clustered at the country level. The dependent variable is the log of mortgage depth, measured by the ratio of total outstanding residential loans to GDP. Both the 1st stage and 2nd stage regressions include a constant, and the following control variables, which are not shown to conserve space: Inflation, Urbanization, Dummy for LTV ratio, Interest rate, House prices, Property Taxation, Bank concentration, Age dependency-old, Age dependency-young, Construction permits, Banking crises, GDP growth. The variables are defined in Appendix A.

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Table 11 National culture and mortgage density, penetration and affordability. (1)

(2)

(3)

(4)

(5)

(6)

(7)

Panel A- Dependent variable: Mortgage density Power distance

−0.0135*** (0.0037)

Individualism

0.0093* (0.0049)

Masculinity

−0.0038 (0.0026)

Uncertainty avoidance

−0.0131*** (0.0028)

Long-Term orientation

−0.0099*** (0.0037)

Indulgence

0.026*** (0.0046)

Trust index Observations Number of countries R-sq overall R-sq within R-sq between

0.0087*** (0.0021) 350 29 0.6033 0.5801 0.5614

350 29 0.6078 0.4068 0.3965

350 29 0.6018 0.4178 0.4264

350 29 0.6123 0.5778 0.5536

350 29 0.6054 0.4907 0.4907

350 29 0.6295 0.6360 0.6525

314 26 0.6176 0.6118 0.6118

Panel B - Dependent variable: Mortgage penetration Power distance

−0.3909*** (0.1188)

Individualism

0.2562 (0.2176)

Masculinity

−0.2111** (0.1041)

Uncertainty avoidance

−0.3832*** (0.0949)

Long-Term orientation

−0.2957*** (0.1100)

Indulgence

0.6441*** (0.1241)

Trust index Observations Number of countries R-sq overall R-sq within R-sq between

0.1756*** (0.0660) 261 26 0.2434 0.6437 0.6602

261 26 0.2470 0.5276 0.5371

261 26 0.2518 0.5537 0.5981

261 26 0.2562 0.6365 0.6449

261 26 0.2154 0.6428 0.6976

261 26 0.2792 0.6538 0.7175

261 26 0.2138 0.7390 0.7367

Panel C - Dependent variable: Mortgage affordability Power distance

−0.0173*** (0.0061)

Individualism

0.0090 (0.0102)

Masculinity

−0.0090* (0.0047)

Uncertainty avoidance

−0.0189*** (0.0047)

Long-Term orientation

−0.0108* (0.0065)

Indulgence

0.0227** (0.0089)

Trust index Observations Number of countries R-sq overall R-sq within R-sq between

0.0128*** (0.0039) 312 25 0.5413 0.4335 0.4169

312 25 0.5414 0.2490 0.2671

312 25 0.5413 0.3259 0.3479

312 25 0.5472 0.4746 0.4700

312 25 0.5359 0.3513 0.3402

312 25 0.5515 0.3868 0.4066

297 24 0.5606 0.4242 0.4114

Notes: ***Statistically significant at the 1% level, **Statistically significant at the 5% level, *Statistically significant at the 10% level, Robust standard errors in parentheses; Random effects estimations with standard errors clustered at the country level. All the estimations include a constant, and the following control variables, which are not shown to conserve space: Inflation, Urbanization, Dummy for LTV ratio, Interest rate, House prices, Property Taxation, Bank concentration, Age dependency-old, Age dependency-young, Construction permits, Banking crises, GDP growth. The variables are defined in Appendix A.

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Thus, while we cannot completely rule out endogeneity, we believe that these results mitigate concerns about omitted variable bias driving our findings. 4.3. Alternative dependent variables In the results presented so far, we focused on the depth of the mortgage market, using an indicator that reveals the importance of the mortgage finance market relative to the total economic activity. In this section we consider alternative dependent variables to capture slightly different aspects of the market. First, we use an indicator of mortgage density, that is the logarithm of ratio of the value of the total outstanding residential loans divided by the population (over 18 years) of a country. Thus, this indicator captures the value of the residential loans on a per capita basis. Second, we use a measure of housing loan penetration, using the percentage of homeowners with an outstanding mortgage.22 In a sense, this indicator captures the access dimension, revealing how widely used housing finance services are. Third, we use the logarithm of the country-level ratio of the value of outstanding residential loans to the households’ disposable income. This serves as an indicator of debt affordability, but also the attitude of households to accumulate mortgage debt relative to their income.23 Finally, we use the percentage of non-performing mortgage loans.24 Once again, we observe some differences in the rate of non-performing mortgage loans across countries. For example, over the 2005–2015 period, the average mortgage non-performing rates was as low as 0.4% in Netherlands and as high as 14.3% in Greece. While several economic factors and the crisis could drive such differences, culture may also have a role. For example, Italy, Portugal and Spain, also experienced a crisis over this period; however, the average mortgage NPL ratio remained considerably lower than the one of Greece, being 3.6%, 2.0% and 2.7%, respectively. We re-estimate the full model of Table 9 with these alternative dependent variables, and we present the results in Table 11. All the regressions include the same control variables as the ones in Table 9, which are not shown to conserve space. The results in Table 11 are comparable to the ones presented in Table 9. Therefore, culture and trust have a statistically significant impact on mortgage per capita, mortgage value to households’ disposable income, and the percentage of homeowners with an outstanding mortgage. Given that in all the cases, higher values of the dependent variable indicate higher use of mortgage lending, the interpretation is similar to the one of mortgage depth. Therefore, to avoid repetition and conserve space, we do not discuss these results further. In the case of NPLs, the impact of both trust and the cultural dimensions appears to be insignificant.25 One potential explanation is that in the case of NPLs, owing to data availability, we use a smaller number of countries and shorter timer period. This results in small variation, not only among countries, but also within a country during the period of the analysis.26 Having said that, it should be noted that our results are in general consistent with the results of Tajaddini and Gholipour (2017), who use a sample from more countries and a different time period.27 5. Conclusion Countries differ in terms of the use of the mortgage loans market. This paper uses cross-country data from around 30 countries to assess whether and how national culture influences the residential loans to GDP ratio. In further analysis, we examine additional aspects of the mortgage market like mortgage density, mortgage penetration, mortgage affordability, and mortgage non-performing loans. The results show that with the exception of mortgage NPLs, the national culture dimensions and interpersonal trust are important drivers of the use of housing finance lending. The results hold when we control for an array of country-specific attributes capturing among other things socio-economic, regulatory, housing, and banking sector conditions. The results are also robust to the use of G2SLS random-effects IV estimator. Therefore, our findings provide support to studies that highlight the importance of social factors in economic decisions, as well as to the literature that associates national culture with the development and other attributes of the financial system. The study could be extended towards various directions. First, it would be interesting to conduct a large scale, standardized, cross-country survey that would allow comparisons while considering not only country-attributes but household characteristics as well. So far, such studies are usually limited to individual countries. Second, it would be interesting to examine characteristics of the mortgage loans like their maturity or the contractual arrangements. Unfortunately, such data are not available in our case, but we hope that future research will improve upon this. 22 As discussed in the 2016 Hypostat report there is a mixed pattern across European countries in terms of the use of mortgage financing to purchase a property. In the case of mortgage penetration, the sample period is 2003–2015 due to data availability. In the case of several countries, data are available as of 2005. 23 The December 2005 monthly bulletin of the European Central bank highlights that Euro area households’ mortgage debt has increased rapidly as a percentage of their income in the past, raising concerns about the sustainability of debt positions. 24 In this case, the estimations are limited to the period 2005–2015 due to data availability in Euromonitor International. 25 These results are not reported to conserve space. They are available from the authors upon request. 26 For example, the NPL ratio ranges from 2.4% to 2.6% in the case of France, from 2.7% to 3% in the case of Germany, from 2.2% to 3.2% in Austria, etc. While there are some notable exceptions like Greece and Romania that experienced a large increase in NPLs during the financial crisis, it appears that such countries do not drive the results into finding a statistically significant association between NPLs and culture. 27 Tajaddini and Gholipour (2017) show that individualism has a statistically significant impact on mortgage NPLs. While they also find that long-term orientation and indulgence have an impact on NPLs, these are statistically significant at the 10% level only. The other three cultural dimensions that they consider in their work (i.e. power distance, masculinity, uncertainty avoidance) are insignificant. Still, there are two notable differences between the sample used in the present paper and the one of Tajaddini and Gholipour (2017). First, their dataset consists of 42 countries, including many developing economies. Second, their analysis is conducted using the average NPL ratio over the period 2010–2013.

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CRediT authorship contribution statement Chrysovalantis Gaganis: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review & editing. Iftekhar Hasan: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Writing - original draft, Writing - review & editing. Fotios Pasiouras: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review & editing. Acknowledgements We would like to thank Rossen I. Valkanov (Editor), an anonymous referee, and participants at the 2019 meeting of the Society of the Economics of the Household for valuable comments and suggestions on earlier versions of the manuscript. Hasan acknowledge the financial support from the Australian Research Council via Discovery Grant DP170101413 for this research. Any remaining errors are our own. Montpellier Business School (MBS) is a founding member of the public research center Montpellier Research in Management, MRM (EA 4557, Univ. Montpellier). Appendix A. Definitions of variables

Dependent variables

Definition

Mortgage depth Mortgage density

Log of ratio of Total Outstanding Residential Loans’ Value to GDP (%). (Source: Hypostat) Log of ratio of Total Outstanding Residential Loans’ Value divided by population (over 18 years). (Source: Hypostat) Mortgage penetration Percentage of homeowners with an outstanding mortgage. (Source: Eurostat) Mortgage affordability Log of ratio of Total Outstanding Residential Loans’ Value to Disposable Income of Households. (Source: Hypostat) Mortgage NPLs Non-performing mortgages/housing loans (%). (Source: Euromonitor International). Main Independent Variables Power distance National Culture Indicator of the extent to which the less powerful members of institutions and organizations within a country expect and that power is distributed unequally. (Source: Hofstede Insights) Individualism National Culture Indicator of the degree to which individuals are supposed to look after themselves or remain integrated into groups, usually around the family. (Source: Hofstede Insights) Masculinity National culture indicator that refers to the distribution of emotional roles between the genders. Masculinity represents a preference in society for achievement, heroism, assertiveness, and material rewards for success. Society at large is more competitive. (Source: Hofstede Insights) Uncertainty avoidance National culture indicator that expresses the degree to which the members of a society feel uncomfortable with uncertainty and ambiguity. (Source: Hofstede Insights) Long-term orientation National culture indicator that is related to the choice of focus for people’s efforts: the future or the present and past. Societies with high score in this dimension take a pragmatic approach: they encourage thrift and efforts in modern education as a way to prepare for the future. (Source: Hofstede Insights) Indulgence Indulgence stands for a society that allows relatively free gratification of basic and natural human drives related to enjoying life and having fun. (Source: Hofstede Insights) Trust Index Indicator of interpersonal trust calculated on the basis of information from the European Social Survey that asks participants in each country to answer the following question: ‘‘Generally speaking, would you say that most people can be trusted, or that you can’t be too careful in dealing with people? The answers are on a predetermined eleven points scale which ranges, from ‘‘you can’t be too careful’’ to ‘‘most people can be trusted’’. To obtain an overall index of trust, we assign the values of 1–11 to the eleven potential answers and we weight them by the proportion of respondents from each country who provided each answer. For ease of interpretation, we then scale the index in such a way that it takes values between 0 and 100, with higher values denoting higher trust(Source: ESS) Control Variables Inflation Inflation, consumer prices (annual %). (Source: Hypostat) Urbanization Urban population (% of total). (Source: WDI) Restrictions on Real Estate Index of real estate restrictions on bank activities. It takes values between 1 and 4, and reveals the extent to which banks may engage in real estate investment, development and management. Higher values indicate higher restrictions. (Source: Barth et al., 2013) Dummy for LTV ratio Dummy variable that takes the value of 1 if there is a Loan-to-Value Ratio macroprudential instrument and the value of 0 otherwise. (Source: Cerutti et al., 2015)

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Dependent variables

Journal of Empirical Finance 56 (2020) 19–41

Definition

CB Political Autonomy

Index of political independence of central bank, with higher values (indicating higher autonomy. The index is estimated by assigning one point for each of the following criteria if satisfied: (1) Governor appointed without government involvement, (2) governor appointed for more than five years, (3) central bank board (CBB) appointed without government intervention, (4) CBB appointed for more than five years, (5) no mandatory participation of government representatives in CBB, (6) no government approval is required for formulation of monetary policy, (7) central bank legally obliged to pursue monetary stability as one of its primary objectives, (8) legal protections that strengthen the CB’s position in the event of a conflict with government. (Source: Arnone et al., 2007) CB Economic Autonomy Index of economic independence of central bank, with higher values indicating higher autonomy. The index is estimated by assigning one point for each of the following criteria if satisfied: (1) No automatic procedure for government to obtain direct credit from CB, (2) when available credit extended to government at market interest rates, (3) credit is temporary, (4) and for limited amount, (5) CB does not participate in primary market for public debt, (6) CB responsible for setting policy rate, (7) central bank has no responsibility to oversee banking sector (two points) or shares responsibility with other institutions (one point). (Source: Arnone et al., 2007) Interest Rates Representative Interest Rates on New Residential Loans, annual average based on monthly figures, percent. (Source: Hypostat) House Prices Nominal House Price Indices, 2006 = 100. (Source: Hypostat) Property taxation Tax on property as a percentage of GDP. Tax on property is defined as recurrent and non-recurrent taxes on the use, ownership or transfer of property. These include taxes on immovable property or net wealth, taxes on the change of ownership of property through inheritance or gift and taxes on financial and capital transactions. (Source: OECD) Share of variable interest rateAmount of gross lending with a variable interest rate (fixation period of up to 1 year), percent. (Source: Hypostat) Bank concentration Assets of three largest commercial banks as a share of total commercial banking assets (%). (Source: GFD-WB) Foreign banks Foreign bank assets among total bank assets (%). (Source: GFD-WB) Stock market development Total value of all listed shares in a stock market as a percentage of GDP. (Source: GFD-WB) Stock market turnover Total value of shares traded during the period divided by the average market capitalization for the period. (Source: GFD-WB) Age dependency, old Ratio of older dependants (people older than 64) to the working-age population (those ages 15–64). (Source: WDI-WB) Age dependency, young Ratio of younger dependants (people younger than 15) to the working-age population (those ages 15–64). (Source: WDI-WB) Population growth Population growth (annual %). (Source: WDI-WB) Population density Population density (people per sq. km of land area). (Source: WDI-WB) Young adults with parents Percentage of young adults aged 18–34 living with their parents. (Source: Eurostat) Construction permits Number of procedures required for a business in the construction industry to build a warehouse. Examples of procedures are: (i) Obtaining all plans and surveys required by the architect and the engineer to start the design of the building plans (for example, topographical surveys, location maps or soil tests), (ii) Obtaining and submitting all relevant project-specific documents (for example, building plans, site maps and certificates of urbanism) to the authorities, (ii) Obtaining all necessary clearances, licenses, permits and certificates, (iv) Submitting all required notifications for the start and end of construction and for inspections. . (Source: DB–WB) Registering property This indicator is related to the cost of the procedures necessary for a business (the buyer) to purchase a property from another business (the seller) and to transfer the property title to the buyer’s name so that the buyer can use the property for expanding its business, use the property as collateral in taking new loans or, if necessary, sell the property to another business. Cost is recorded as a percentage of the property value, assumed to be equivalent to 50 times income per capita. Only official costs required by law are recorded, including fees, transfer taxes, stamp duties and any other payment to the property registry, notaries, public agencies or lawyers. Other taxes, such as capital gains tax or value added tax, are excluded from the cost measure. Both costs borne by the buyer and the seller are included. If cost estimates differ among sources, the median reported value is used. (Source: DB–WB) Getting credit Indicator of the legal rights of borrowers and lenders with respect to secured transactions through one set of indicators and the reporting of credit information through another. The first set of indicators measures whether certain features that facilitate lending exist within the applicable collateral and bankruptcy laws. The second set measures the coverage, scope and accessibility of credit information available through credit reporting service providers (such as credit bureaus or credit registries. (Source: DB–WB) 37

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Dependent variables

Definition

Banking Crises

Dummy variable that takes the value of 1 if there is a banking crisis in a specific country, and the value of 0 otherwise. (Source: Valencia and Laeven, 2012) GDP growth (annual %). (Source: Hypostat)

GDP growth Variables used as instruments History of Communist rule Dummy variable that takes the value of 1 in the case of countries with a history of communist rule and 0 otherwise. (Source: Authors from various sources) Catholic religion Percentage of country’s population following Catholic religion. (Source: La Porta et al., 1999) Muslim religion Percentage of country’s population following Muslim religion. (Source: La Porta et al., 1999) Protestant religion Percentage of country’s population following Protestant religion. (Source: La Porta et al., 1999) Pronunciation Indicator of pronoun drop. Dummy variable that takes the value of 1 when pronoun drop is permitted and the value of 0 otherwise. This indicator combines information from Kashima and Kashima (1998) and the World Atlas of Language Structures. (Source: Davis and Abdurazokzoda, 2016) Pathogens Index of the prevalence of pathogens. It focuses on seven classes of pathogens (leishmanias, trypanosomes, malaria, schistosomes, filariae, spirochetes and leprosy) and codes the relative prevalence of each specific pathogenic disease within each class. A total of 22 specific pathogenic diseases are coded, each on the same three point prevalence scale. These values were summed within each region to create a composite index estimating the contemporary prevalence of pathogens. (Source: Fincher et al., 2008) Agricultural potential Maximum potential caloric yield attainable given the set of crops that were suitable for cultivation in the pre-1500 period. The raw figures were divided by 1.000.000 to be expressed in millions (Source: Galor and Ozak, 2016) Genetic distance FST genetic distance between countries. We use the distance between the plurality ethnic groups of each country in a pair, i.e. the groups with the largest shares of each country’s population. We use the pairs relatively to either the US (FST_USA) or to Japan (FST_Japan). (Source: Spolaore and Wacziarg, 2018) Ethnic fractionalization Index of Ethnic heterogeneity that takes values between 0 and 1. Higher values correspond to higher heterogeneity. (Source: Alesina et al., 2003) Religious fractionalization Index of Religious heterogeneity that takes values between 0 and 1. Higher values correspond to higher heterogeneity. (Source: Alesina et al., 2003) British colony Dummy variable that takes the value of 1 if a country has been under British colonial power and 0 otherwise. (Source: Hensel, 2018) Notes: GFD: Global Financial Development database, WDI: World Development Indicators database, DB: Doing Business database, WB: World Bank, ESS: European Social Survey. Appendix B. Dependent variables, average by country

Country

Mortgage depth

Mortgage density

Mortgage penetration

Mortgage affordability

Mortgage NPLs

Australia Austria Belgium Bulgaria Croatia Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Italy Japan Latvia Lithuania

47.59 23.34 38.89 8.96 13.84 10.68 75.62 27.37 34.69 33.86 46.78 28.67 15.23 68.51 55.74 17.12 38.40 23.87 14.31

23,860.16 9,898.50 15,941.29 n.a. n.a. 2,099.50 46,293.01 4,798.36 15,094.13 12,897.35 17,413.53 6,742.88 2,047.05 37,676.23 33,462.36 6,345.366 14,338.91 3,320.55 2,106.41

n.a. 26.18 40.55 n.a. n.a. 15.04 52.42 16.49 41.78 28.65 27.67 14.61 17.77 67.53 34.23 15.33 n.a. 8.03 6.62

n.a. 37.41 65.22 n.a. n.a. 21.93 176.41 58.26 61.22 50.82 70.05 43.14 29.79 n.a. 126.87 28.43 n.a. 44.24 26.28

0.58 2.54 n.a. n.a. n.a. 2.30 1.39 n.a. n.a. 2.53 2.83 14.26 7.15 n.a. n.a. 3.58 0.81 n.a. n.a.

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Journal of Empirical Finance 56 (2020) 19–41

Country

Mortgage depth

Mortgage density

Mortgage penetration

Mortgage affordability

Mortgage NPLs

Luxembourg Malta Netherlands Norway Poland Portugal Romania Russia Slovakia Slovenia Spain Sweden Turkey UK USA

41.14 34.21 80.45 57.96 12.36 55.68 5.06 3.06 13.97 9.01 53.05 65.11 3.93 68.08 69.90

47,922.21 n.a. 43,884.34 61,022.70 2,024.41 11,437.38 n.a. n.a. 2,731.69 2,564.793 12,853.25 32,327.04 1,563.398 2,8268.80 33,604.82

42.04 n.a. 58.75 64.07 7.74 31.39 n.a. n.a. 7.93 7.27 32.37 56.70 5.68 41.98 n.a.

115.61 n.a. 191.72 142.99 27.20 81.53 n.a. n.a. 28.80 19.01 74.54 131.71 n.a. 99.26 89.57

n.a. n.a. 0.36 1.20 1.71 2.01 n.a. n.a. n.a. n.a. 2.69 0.12 0.47 1.69 5.32

Note: Descriptive statistics are based on the actual number of observations used in the corresponding regression. In the case of variables used in several regressions, the statistics are calculated on the basis of the regression with the highest number of observations. All the figures are the raw ones, and not the ones with logarithm, to be more informative. The sample period is 2001– 2015 in the case of mortgage depth, mortgage density, and mortgage affordability; 2003–2015 in the case of mortgage penetration; 2005–2015 in the case of NPLs (%); n.a. denotes that the variable is not available for this particular country; Variables are defined in Appendix A. Appendix C. Key independent variables, average by country

Country

PDI

INDIV

MAS

UAI

LTO

INDUL

TRUST

Australia Austria Belgium Bulgaria Croatia Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Italy Japan Latvia Lithuania Luxembourg Malta Netherlands Norway Poland Portugal Romania Russia Slovakia Slovenia Spain Sweden

38 11 65 70 73 57 18 40 33 68 35 60 46 30 28 50 54 44 42 40 56 38 31 68 63 90 93 100 71 57 31

90 55 75 30 33 58 74 60 63 71 67 35 80 60 70 76 46 70 60 60 59 80 69 60 27 30 39 52 27 51 71

61 79 54 40 40 57 16 30 26 43 66 57 88 10 68 70 95 9 19 50 47 14 8 64 31 42 36 100 19 42 5

51 70 94 85 80 74 23 60 59 86 65 100 82 50 35 75 92 63 65 70 96 53 50 93 99 90 95 51 88 86 29

21 60 82 69 59 70 35 82 38 63 83 45 58 28 24 61 88 69 82 64 47 67 35 38 28 52 81 77 49 48 53

71 63 57 16 33 29 70 16 57 48 40 50 31 67 65 30 42 13 16 56 66 68 55 29 33 20 20 28 48 44 78

n.a. 59 57 23 43 45 98 66 90 46 53 34 42 84 66 50 n.a. 39 54 58 n.a. 76 93 36 33 32 39 37 40 57 83

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Journal of Empirical Finance 56 (2020) 19–41

Country

PDI

INDIV

MAS

UAI

LTO

INDUL

TRUST

Turkey UK USA

66 35 40

37 89 91

45 66 62

85 35 46

46 51 26

49 69 68

5 63 n.a.

Notes: PDI =power distance, INDIV = individualism, MAS =masculinity, UAI =uncertainty avoidance, LTO =long-term orientation, INDUL =indulgence, TRUST =Trust index; n.a. denotes that the variable is not available for this particular country; Further information about the variables is available in Appendix A.

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