FinTech and Consumer Decision-Making in the Information Age
Bruce Carlin∗ Arna Olafsson† Michaela Pagel‡
November 2020
Abstract We exploit the release of a mobile application for a financial aggregation plat- form to analyze how Financial Technology (FinTech) adoption changes consumer financial decision making. Our sample consists of individuals that had been using the platform via a desktop computer long before the mobile app was released. The app reduced the cost of accessing personal financial information, and this was re- sponsible for a significant drop in the use of expensive consumer credit and late payment fees. The leading explanation for our results appears to be mistake avoid- ance, which is supported by a significant reduction in non-sufficient funds (NSF) charges after the app was released. JEL classifications: G5, D14, D83, G02.
∗Department of Finance, Jones School of Business, Rice University, Houston, TX, USA, & NBER. [email protected] †Department of Finance, Copenhagen Business School, 2000 Frederiksberg, Denmark, the Danish Finance Institute, & CEPR. ao.fi@cbs.dk ‡Division of Economics and Finance, Columbia Business School, NY, USA, NBER, & CEPR. [email protected] We thank numerous seminar participants, and our discussants and conference participants at the AF- FECT Conference University of Miami, University of Kentucky Finance Conference, Santiago Finance Conference, Cerge-Ei Prague, 6th ITAM Finance Conference, and the AEA. This project has received funding from Danish Council for Independent Research, under grant agreement no 6165-00020. This project has benefitted from funding from the Carlsberg Foundation. We are indebted to Meniga and their data analysts for providing and helping with the data. We also thank Fedra De Angelis Effrem and Andrea Marogg for outstanding research assistance. 1 Introduction
Does the availability and adoption of new financial technology equip consumers to make better financial decisions? Ostensibly, if people have easier access to information, they should be able to avoid mistakes (Stango and Zinman, 2009; Jørring, 2019). However, while the rate of technology adoption is straightforward to quantify (Carlin et al., 2019; Anderson, 2015), measuring its economic impact is challenging. It is difficult to find settings, especially natural experiments, in which it is possible to calibrate how technology affects people’s behavior and their outcomes. FinTech has advanced at a rapid rate in retail markets and is receiving growing atten- tion (Goldstein et al., 2019). Personal financial management platforms (Gelman et al., 2014; Baker, 2018; Olafsson and Pagel, 2018; Kuchler and Pagel, 2018), mobile payment systems (Agarwal et al., 2019), and robo-advisors (D’Acunto et al., 2019; Loos et al., 2019) are just a few examples of the rapidly changing landscape for consumers. However, while FinTech advances have been shown to be valuable to their innovators (Chen et al., 2019), it remains an open question how consumers are affected. In this paper, we investigate how access to mobile financial apps affect consumer financial decision making. We use individual transaction-level data from a financial ag- gregation platform in Iceland called Meniga. Meniga allows users to link all of their checking, savings, and credit card accounts, and view all spending, income transactions, and account balances in one place. A considerable fraction of adults in Iceland use this service and the user population appears to be representative of the overall population (Olafsson and Pagel, 2018; Carvalho et al., 2019). As we describe in the paper, using data from Iceland has several advantages for our purposes here. We exploit an exogenous shock that made accessing the online platform easier. Before November 2014, access to the personal financial management software was possible only via the Internet on a desktop or laptop computer. However, on November 14, 2014, a mobile application was released, which gave users easier and remote access to bank
1 account information. The effect of the mobile app release on the propensity to log in to the platform is shown in Figure1. Each dot represents the raw average fraction of individuals who logged in using any method each month. There is a clear discontinuous jump in the propensity for users to log in to the platform around the mobile app release.
{Figure1 around here}
Our data set contains time-series information about the frequency and method of access to bank information (computer vs. mobile app), demographics, expenditures by category, income, use of consumer credit (credit cards and checking account overdrafts), and the resultant financial outcomes (consumer debt and bank fees).1 We perform an event study to compare each consumer’s financial behavior before and after they start using the mobile app. In our primary specification, our estimates represent the average monthly effect on bank fees during the first year after the app was released.2 To avoid selection bias, we only analyzed individuals who used the platform for at least a year prior to the app release (37 months on average).3 We include individual fixed effects to control for all time-invariant individual characteristics and calendar fixed effects to take care of seasonal variation. We provide evidence that no other confounding events took place around the same time. After the mobile application was introduced, consumers paid fewer bank fees. Plots of the raw data show that monthly late fees, overdraft interest, and total fees decreased after the app was introduced (Figures2-4). It is likely that the release of an app lowered the cost of logging in to the platform and thereby changed people’s behavior. Indeed,
1Note that, in Iceland, checking account overdrafts are the way most consumers roll over unsecured high-interest consumer debt. Individuals pay interest on overdrafts, but there is no discrete overdraft fee. Credit cards are common but are typically repaid in full each month rather than used to roll over debt. 2We focus on a short period (one year) after the release of the mobile application. We also consider other lengths of time after the app release (3 months, 6 months, 18 months, and 2 years) for the same group and show that the results are qualitatively robust. 3We exclude individuals that were not already using the platform twelve months prior to the mobile app release to address the concern that some users may have started using the platform because they expected an app to be released soon.
2 based on the raw data there appears to be a monotonic, decreasing relationship between the number of logins and the fees that consumers pay (Figure5). 4 Regression estimates suggest that the release of the app reduced average monthly bank fees by $2.22 and average late fees by $1.38. Given that individuals paid $21.95 in average monthly bank fees and monthly late fees of $7.16 before the app was released, the new technology was associated with a 10.1% reduction in monthly bank fees and a 19.3% decrease in late fees. In our sample, the average consumer rolled over $487 in overdraft debt each month. Based on our results, the twelve-month average cumulative decrease in overdraft interest was $50, which represents a 10.27% decrease. As such, our results are statistically significant and economically compelling.
{Figures2,3,4, and5 around here}
Previous work finds support that incurring late fees and paying overdraft interest are often associated with financial mistakes (Stango and Zinman, 2009; Jørring, 2019). To make that conlusion, the authors observe each consumer’s balance at the time the fees are incurred and confirm whether they were avoidable. Unfortunately, in our dataset, infor- mation about fees and interest payments is only available at a monthly frequency, which precludes a similar exercise. So, to investigate whether the app introduction was associ- ated with better financial health and fewer mistakes, we analyze the non-sufficient funds (NSF) charges that consumers paid. As discussed by Carvalho et al.(2019), accruing NSF fees are due to financial mistakes. In our sample, we find that the release of the mobile application was associated with a 14.3% drop in the number of NSF fees, which provides evidence that the new technology helped consumers avoid making financial mistakes. The Meniga platform is exclusively for informational purposes and does not allow for any financial actions (e.g., paying bills). As such, release of the mobile application likely made access to information less costly and promoted more frequent information
4See Olafsson and Pagel(2017) for an in-depth analysis of the within-individual patterns of logins and personal finances.
3 acquisition. It also may have made financial information more salient.5 However, the software did not provide nudge-based interventions (Stango and Zinman, 2014; Karlan et al., 2016; Medina, 2018).6 In the presence of time costs, a reduction in the costs of paying attention to finances may result in a reduction in consumer debt. That being said, the fact that consumers reduced their debt after getting easier access to financial information may contribute to our understanding of why high-interest consumer debt exists in magnitudes that are puzzling from the perspective of standard preferences in life-cycle consumption models (Angeletos et al., 2001; Carroll, 2001; Laibson et al., 2003; Zinman, 2015). Our results may speak to non-standard preferences and overconsumption problems as explanations for the initial use of consumer debt (Laibson et al., 2007), rather than consumption smoothing in response to permanent income shocks when funds are tied up in illiquid savings (Kaplan and Violante, 2014) or consumption smoothing in response to transitory income shocks (Keys, 2010; Sullivan, 2008). The remainder of the paper is organized as follows. In Section 2, we describe the data and provide summary statistics. In Section 3, we explain our identification approach, re- port our main results, and discuss their robustness. Finally, Section 4 provides concluding remarks.
2 Data and summary statistics
We use data from Iceland that are collected by Meniga, a financial aggregation software provider to European banks and financial institutions.7 Meniga’s account aggregation
5Our study is not designed to confirm or rule out this conjecture. Other studies that analyze the de- terminants and effects of attention using logins to retirement, brokerage, and bank accounts are Karlsson et al.(2009); Gherzi et al.(2014); Gargano and Rossi(2017); Olafsson and Pagel(2017). 6Our paper was followed by Levi and Benartzi(2020) who study the effects of a mobile app release of a financial management software on spending habits. 7Meniga was founded in 2009 and is the European market leader of white-label Personal Finance Man- agement (PFM) and next-generation online banking solutions, reaching over 50 million mobile banking users across 20 countries. In the US, comparable software is provided by mint.com, personalcapital.com, and YNAB.com.
4 platform allows bank customers to manage all of their bank accounts and credit cards across multiple banks in one place by consolidating data from various sources. The online platform automatically records all bank and credit card transactions, in- cluding descriptions, as well as balances, overdrafts, and credit limits. Figure6 displays screenshots of the app’s user interface. The left screenshot shows the app’s financial feed including bank account information, the middle one shows the transactions interface, and the right one shows background characteristics that the user provides in the app’s settings. The app’s interface did not change over our sample period and the desktop interface was the same in appearance and functionality. Upon logging in, users first see the financial feed interface. After that, they can tap on "Show Only Transactions" to see the transactions interface. To edit their settings, they can tap on "More" and "Edit Profile."
{Figures6 and7 around here}
Later versions of the interface (offered after our sample period) flagged certain events after individuals logged into the app. These might include unusually high transactions, money deposits, or low balances. Figure7 shows an example screenshot of the feed a consumer might observe with flagged events and a screenshot of the default options for messages and merchant offers. When the app was introduced in Iceland in 2014, there were no merchant offers. It was not until 2017 that Meniga expanded their merchant offer features. Also, the app did not send push notifications, so that individuals would have to log in first to see messages or flagged data.8 Using this data from Iceland has several advantages when studying the effect of new technology on financial decision making. Icelandic consumers use electronic means of payments almost exclusively,9 so this dataset is more representative of the underlying
8It is important to note that the platform looks and functions differently than what is portrayed in advertisements on Meniga’s homepage and in their international demo. Users in Iceland have to log in initially to see all messages and warnings. 9ATM withdrawals make up approximately one percent of spending transactions by amounts or transactions volume.
5 population than data obtained from other financial aggregator apps. This is because: i) all adult individuals in Iceland need to have a bank account;10 ii) bank accounts in Iceland are personal and cannot be shared, even within households; iii) the majority of Icelanders use internet banking and all individuals with an online bank have access to the Meniga software;11 and iv) the software is marketed through all the major banks in Iceland. We start with a sample of 52,545 Meniga users and exclude those with incomplete records, subject to four criteria. First, users must have observable bank account balances and credit limits. Second, they must have observable income streams, which may include both labor income and other sources such as unemployment benefits, pension payments, invalidity benefits, and student loans. Third, key demographic information about the user must be available (age, sex, and postal code). Finally, the consumption stream of each user must be credible, which we validate by requiring at least 5 food transactions per month in at least 23 of the last 24 months. After imposing these requirements, we are left with 13,411 active users with complete records. We confirm that the demographic and economic characteristics of our study population is not different from those who were excluded and is representative of the adult Icelandic population.12 We collect a monthly panel of individual logins, bank fees, credit use, and consumption choices from November 2011 to January 2017. The data include how many times each individual logs in via the mobile app versus through a desktop computer.13 We observe the following categories of fee payments.
1 Late-Payment Interest: Credit card companies charge late-payment interest each day from the date a payment is due until it is paid in full.
10There are many reasons for this. For instance, checks are not used in Iceland and if individuals want to receive salary payments or state benefits, they need a bank account. 11According to Eurostat, 94 percent of Icelanders used internet banking in 2018. Source: http: //appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_bde15cbc&lang=en. We refer the reader to Olafsson and Pagel(2018) and Carvalho et al.(2019), for further discussion of the repre- sentativeness of the sample. 12See Olafsson and Pagel(2018) for an in-depth discussion of the sample representativeness. 13We do not find any evidence of individuals linking more accounts following the app release.
6 2 Non-Sufficient Funds Fees: The bank charges this fee when there are insufficient funds or the overdraft limit is exceeded in the consumer’s current account, when a debit card transaction is attempted. As discussed in Carvalho et al.(2019), incurring such a fee is consistent with a mistake made by a consumer.
3 Late Fees: Fees assessed for paying bills after their due date.
4 Overdraft Interest: This is the primary form of high-interest unsecured consumer debt in Iceland. An overdraft occurs when withdrawals from a current account exceed the available balance. This means that the balance is negative and the bank provides credit to the account holder at an interest rate that is partly based on an individual’s affordability and credit history. At the time of the app release in 2014, the average overdraft interest rate was approximately 13% (see Figure8), which is a mark-up of 7% over the central bank policy rate.14
4 Total Bank Fees: This is the sum of all the fees defined above.
{Figure8 around here}
Table1 reports the distribution of first-time mobile app users over time. As can be seen, 77.6% of people who ever used the mobile app during our sample period started using it during the first 12 months after it was released. This corresponds to 33.7% of the total users of the platform. Table2 displays summary statistics for our sample, where we distinguish between very early adopters of the app (who start using the app within 6 weeks after release), early adopters (who start using the app within 12 months after release), late adopters (who start using the app more than 12 months after release), and non-adopters. As can be seen, adopters within the first year do not appear to
14We obtain a detailed and objective measure of the use of rolled over consumer debt. A big disad- vantage of traditional consumer credit data sources is that it is difficult to obtain such measures. In many datasets, only credit card balances are available and it is not feasible to separate transacting from revolving balances (see, e.g., Zinman, 2009). Survey data are also limited due to low reporting frequency, reporting bias, and inability to separate transacting and revolving balances.
7 be very different from the other groups. Furthermore, adopters and non-adopters have comparable demographic characteristics, income, liquidity, and credit card utilization. Adopters have slightly lower total bank fees, late fees, and overdraft interest.
{Tables1 and2 around here}
As we outline below, however, our empirical set-up compares people to their previous selves before the app release and we include individual fixed effects. As such, these slight differences do not preclude an analysis of the effect of the app release on consumer decision-making. Additionally, we alleviate some concern about selection bias by only considering consumers who used the platform for at least a year before the app was released.15 The average consumer in our sample used the Meniga platform for 37 months before the app was introduced. Especially, given that 33.7% of all Meniga consumers used the app in the first year, it seems unlikely that such a large group experienced a change in their financial situation that spuriously coincided with the app release. Another potential concern is that any effect that we might identify would be due to one specific fraction of the population, such as younger individuals. This does not appear to be the case. For example, Figure9 plots the cumulative adoption of the smartphone technology over time across different generations. Indeed, there is a fairly quick and broad adoption of the app by all generations.
{Figure9 around here}
3 Empirical Analysis
3.1 Empirical Set-Up
Our empirical strategy exploits the discontinuity in individual access to the financial aggregation platform that arose when the mobile application was released on November
15We did not compare adopters to non-adopters to avoid selection bias.
8 14, 2014. As we investigate in Section 3.3, to our knowledge, no other confounding event took place around the same time. The timing of the app release is plausibly exogenous to individual characteristics, but caused some individuals to log in more often and is thus a valuable source of identifying variation. We exploit this to estimate the effect of more convenient, lower cost access to financial information. In what follows, we utilize various regressions based on the specification
Yi,t = α + βP ostF irstUsei + κXi,t + ρi + ξt + it (1)
where i is an individual identifier, t represents time (month-by-year), Yi,t is individual
i’s outcome of interest at time t, and P ostF irstUsei is a dummy variable that takes the value one in periods after individual i used the mobile app for the first time and
zero otherwise. The vector Xi,t includes controls, ρi is an individual fixed effect, and
ξt is a time or month-by-year fixed effect. Our identification thus relies on comparing each individual’s monthly bank fees in the year after the adoption of the mobile app to their fees in previous years. Standard errors are double-clustered at the individual and month-by-year levels. We focus on individuals who used the app in the first year after the mobile app release. Our main specification estimates the average monthly effect on bank fees during the first year after the release. We also consider other lengths of time after the app release (3, 6, 18 and 24 months) for the same group of individuals to determine whether our findings are robust to using different periods after the release. The main coefficient of interest β represents the average monthly effect of the app release on the variable under investigation. Importantly, when we estimate the responses in the months after the app release, we include the observations up to the time the app was introduced so that the interpretation of an individual’s post-app personal finances is estimated relative to their personal finances prior to the app’s existence. We also consider a dynamic model to study how financial decisions evolve over time
9 following a change in technology. To do this, we estimate impulse responses over the first year after the mobile app release and use the specification
Yi,t+k = α + βkF irsti,t + κXi,t+k + ρi + ξt+k + it for k = −3, ..., 12. (2)
As before, i is an individual identifier, t represents time (month-by-year), Yi,t is individual i’s outcome of interest at time t, and F irsti,t is a dummy variable that takes the value one in the month individual i uses the mobile app for the first time and zero otherwise. The vector Xi,t includes controls, ρi is an individual fixed effect, and ξt+k is a time or month- by-year fixed effect. Again, our identification thus relies on comparing each individual’s monthly bank fees in the year after the adoption of the mobile app to their fees in the previous years. Standard errors are double-clustered at the individual and month-by-year levels.
Here, βk is the main coefficient of interest. Each βk represents the effect of the app release on the variable under investigation, k months from the app release. We also compute the cumulative responses by the summing over the βk’s.
3.2 Results
Table3 reports the estimated effect of the mobile app release on bank fees and interest payments. Columns (1) and (2) report the effect on the use of the platform. During the first 12 months after the app was released, individuals that started using the mobile app were 42.0% (with a standard error of 4.4%) more likely to log in to the platform than before. The average number of monthly logins increased by 3.83 (with a standard error of 0.72). Column (3) of Table3 shows the results based on our preferred specification that estimates the average monthly effect among individuals that started using the mobile app in the first 12 months. Release of the app reduced average monthly bank fees by
10 $2.22 (with a standard error of $0.89), while late fees were reduced by about $1.38 (with a standard error of $0.72).16 Columns (4)-(7) show the estimates of the average monthly effect using the same sample, but with a different length of time after the release. The statistical and economic significance of the estimates remain largely unchanged. Figure 10 displays how these estimates vary with the post-app periods from 2 to 24 months. This evidence suggests that the results are not sensitive to the length of period after the app release and do not deviate much from those obtained in the baseline specification.
{Table3 and Figure 10 around here}
According to Table2, individuals in our sample who used the app in the first 12 months after it was released incurred average monthly bank fees of $21.95 prior to the app release and monthly late fees of $7.16. According to our estimates, then, use of the app lowered bank fees by 10.1% and late fees by 19.3%. Figure 11 shows the estimated dynamic responses of late fees, overdraft interest, and total bank fees, as well as their standard errors, among individuals who started using the mobile app in the first 12 months after it was released. The top panel displays the flows, i.e., the estimated within-month responses, and the bottom panel displays the cumulative responses. The twelve-month cumulative response of late fees is a bit more than $100, the estimated cumulative response of overdraft interest is $50, and total bank fees are over $150. All are statistically significant, and given the baseline levels of overdraft usage and bank fees from Table2, this represents a significant economic effect.
{Figure 11 around here}
Based on the results so far, it is natural to consider the underlying mechanism. One potential channel might be the tendency of individuals to use credit cards more instead
16Estimates are reported in ISK. To obtain the USD estimates, the coefficients are simply divided by 100. This adjustment reflects the approximate exchange rate over our sample period.
11 of debit cards when it becomes easier to observe their transactions and balances. For consumers with low savings, use of a credit card may be superior to overdrawing a bank account with a debit card, when managing short-term liquidity needs. Credit cards in Iceland typically provide a 37-day float until interest is charged.17 In contrast, over- draft interest is incurred immediately, as soon as a current account becomes negative. We explore this in Table4, where we measure the impact of logins on the fraction of expenditures paid by credit cards. The release had close to a null effect on the share of expenditures paid with a credit card. This suggests that this substitution effect is unlikely to be responsible for the reduction of bank fees that we observe in the data.
{Table4 around here}
Another possibility is that the app release helped consumers avoid financial mistakes. Indeed, both Stango and Zinman(2009) and Jørring(2019) show that consumers fre- quently make mistakes that inefficiently raise the cost of their credit. In this light, our results would suggest that better access to information reduced these errors because con- sumers were better informed. However, our data is not as granular and cannot be used in the same way as those other studies. Both Stango and Zinman(2009) and Jørring(2019) directly measured the cost associated with each instance a consumer borrowed funds and were successful in detecting whether they made bad choices. In our dataset, bank fees are not available at the transactional level (only monthly), so that a similar exercise cannot be undertaken. To address this, we examine whether the app release changed the tendency for con- sumers to incur non-sufficient funds (NSF) charges. As discussed by Carvalho et al. (2019), incurring an NSF fee is typically a financial mistake. Table5 reports the esti- mated effect of the app release on NSF fees. We find that the app release reduced the
17Credit card periods in Iceland typically end on the 25th each month and bills or due on the 2nd of the month or the first bank day thereafter and thereby provide a grace period of up to about 37 days. Furthermore, in Iceland, almost all credit card customers repay their balance in full each month and if they are short of liquid savings they finance it via checking account overdrafts.
12 number of NSF charges per person by 0.005 (with a standard error of 0.002). Given that the baseline incidence is 0.035 per person, this represents a 14.3% drop. Additionally, as in our baseline regressions, changing the length of the period used to estimate the average monthly effect to 3, 6, 18, or 24 months does not appear to alter the magnitude of the estimates. These findings provide support that the app likely reduced bank fees by helping consumers avoid financial mistakes.
{Table5 around here}
Based on our anaylsis, consumers probably did enjoy an economic benefit as they adopted new technology. Another way to gauge whether the app helped consumers is to compare the benefits and costs of logging in. As a back-of-the-envelope calculation, consider that it takes a consumer one minute to check her balance before paying a bill or making a purchase. On average, consumers increase their number of logins per month by 3.83 and pay $2.22 lower bank fees after starting to use the mobile app. This is equivalent
$2.22×60 to earning $34.78 per hour ( 3.83 ) if logging in takes one minute. As one means of comparison, the average hourly wage for a consumer in our sample is approximately $3,782/(52/12)/40 ≈ $22 per hour, assuming a 40-hour work week. Compared to this, use of the app yields a net benefit. But, since most people do not necessarily use the app at work or need to substitute work time for time using the app, the relative benefit of the app is likely much higher.
3.3 Discussion
A concern that always arises in event studies like this is whether individuals change their behavior for a reason that is outside the model or due to confounding events that took place simultaneously. In our setting, the concern might be that the choice to start using the app is driven by a separate decision to change attention to personal finances (as a means to change financial behavior). If this were the case, consumers would likely increase their frequency of logins before and after November 14, 2014. Surely, there is
13 nothing special about this date in particular. But, if rising attention to personal finances occurred via a separate choice, we would not expect to see such a sharp jump that exists in the data. Additionally, release of the app was not an anticipated event: the date was chosen exogenously by Meniga and was not marketed to consumers until it was first made avail- able. If consumers had prior knowledge that an app was coming and anticipated its release, we might expect them to login more frequently via a desktop in advance of the release to keep track of this. To investigate this, we examine the behavior of the very early adopters of the app (first 6 weeks after release), who would be the most likely to monitor the platform most closely. As can be seen in Figure 12, this type of monitoring did not appear to have taken place.
{Figure 12 around here}
Another concern might be whether our analysis could be confounded by other events around November 2014, such as regulation changes or macroeconomic events. The only regulatory change that we are aware of is a court ruling on December 14, 2014 that addressed deceptive merchant fees.18 However, this did not involve consumer fees directly and the percentage changes in merchant fees were very small. So, we do not think that this is material to our analysis. We also reviewed several macroeconomic indicators and conditions around the event and found only one that might be important. Around the time of the app release, there was a small decrease in interest rates in Iceland (Figure8). Potentially, this could affect overdraft interest rates and therefore the interest expense that consumers in our sample paid. However, most of the reduction in bank fees that we observe in the data are due to decreased late fees, which are not influenced by interest rates. Additionally, in all of our specifications, we include month-by-year fixed effects, which should control for variation in the central bank policy rate. 18Source: Annual Report on Competition Policy Developments in Iceland (2014). Available at https://en.samkeppni.is/resolution/publications/nr/3144.
14 We also ran a Factiva economic news search for Iceland during November 2014 to screen for confounding events. The Appendix displays the results of the search. Many articles did report that the Icelandic central bank took another step in reducing interest rates in November 2014. However, we do not find any other confounding events. The interest rate had been trending downward over 2014 and 2015 (although overdraft interest payments trended upwards). Without doubt, other macroeconomic conditions, such as personal and government consumption, output, and economic sentiment, changed over that period as well, but not discontinuously in November 2014. Notwithstanding, any macroeconomic changes should also be captured by the month-by-year fixed effects in our analysis. Another concern might be that the financial crisis of 2008 affected Iceland differently than other countries where consumer debt is prevalent and this might limit the external validity of our findings. However, the effect of the crisis was not that different in Iceland and the country had more or less recovered a few years after its onset (Olafsson, 2016). The country actually experienced high GDP growth and low unemployment during our entire sample period. Additionally, Iceland is similar to many other economies, including the US, with regard to the use of consumer debt. Individuals who started using the app within 12 months in Iceland held approximately $4,872 in overdrafts conditional on having overdraft debt. As previously mentioned, in Iceland individuals typically pay off their credit card in full and use overdrafts to roll over high-interest unsecured debt. Nev- ertheless, they still enjoy substantial liquidity because they have additional borrowing capacity before hitting their limits: a liquidity of $11,694 on average. In comparison, in the US, the average credit card debt for individuals who roll it over is approximately $4,000 in the Survey of Consumer Finances (SCF) data and individuals also enjoy sub- stantial liquidity. We thus believe that our results can be generalized to the US and other European countries with relatively large consumer debt holdings, such as the UK, Spain, and Turkey. Finally, to increase confidence in our findings, we perform a placebo test imposing a
15 release and adoption of the app at a different time than the actual month it took place. We assume that the app was released in November 2013 and that each user started using the app 12 months before he or she actually did. This choice allows us to consider a placebo that takes place in the same calendar month as the real app release, and gives us the opportunity to collect enough observations prior to the placebo start to include individual fixed-effects. Everything else remains the same as in our main specification. We also provide estimates using the average monthly effect during the first 3 and 6 months after the placebo app release, but cannot use 18 and 24 months after the placebo release because it would overlap with the actual release of the app.
{Table6 around here}
Column (1) in Table6 shows that there was close to a zero effect of the placebo adoption on the number of logins and columns (2)-(4) show that there were no significant effects of this placebo adoption on the average monthly bank fees for individuals in the 12, 3, and 6 months after the placebo release of the mobile app.
4 Concluding Remarks
In this study, we analyze the effect of the release of a mobile app by a financial manage- ment platform. The release ostensibly eases consumers’ plight to gather information and make good choices. We quantify these effects and find a substantial reduction in financial fee payments in response to the reduced cost of accessing information more often. The decrease in financial fee payments is primarily driven by a reduction in late fees. In fact, people reduce their monthly late fees by about 19.3% during the first year after the app release while total bank fees are reduced by about 10.1%. As noted before, since the app did not send reminders or notifications about unpaid billls during our sample period, the decrease in bank fees likely results from easier and more immediate access to information about personal finances.
16 As we discuss, consumers frequently make incorrect financial decisions, which raise the cost of credit. In light of results from Stango and Zinman(2009) and Jørring(2019), and the fact that we see that the release and subsequent use of the mobile app reduced the number of NSF fees by 14.3%, we posit that the release of the new technology helped individuals avoid financial mistakes. Additionally, we may learn about why consumers hold expensive consumer debt in the first place, given that improved access to financial information helps them to reduce it. FinTech advances continue to evolve faster than academics or practitioners can eval- uate them. The results in this paper hopefully encourage future study in this area as financial technology evolves and affects peoples’ lives.
17 References
Agarwal, S., W. Qian, B. Y. Yeung, and X. Zou (2019). Mobile Wallet and Entrepreneurial Growth. AEA Papers and Proceedings 109, 48–53. Anderson, M. (2015). Technology device ownership: 2015. Report, PEW Research Center. Angeletos, G.-M., D. Laibson, A. Repetto, J. Tobacman, and S. Weinberg (2001). The hy- perbolic consumption model: Calibration, simulation, and empirical evaluation. Jour- nal of Economic Perspectives 15 (3), 47–68. Baker, S. (2018). Debt and the response to household income shocks: Validation and application of linked financial account data. Journal of Political Economy 126 (4), 1504–1557. Carlin, B., A. Olafsson, and M. Pagel (2019). Generational differences in managing personal finances. In AEA Papers and Proceedings, Volume 109, pp. 54–59. Carroll, C. D. (2001). A theory of the consumption function, with and without liquidity constraints. Journal of Economic Perspectives 15 (3), 23–45. Carvalho, L., A. Olafsson, and D. Silverman (2019, September). Misfortune and mis- take: The financial conditions and decision-making ability of high-cost loan borrowers. Working Paper 26328, National Bureau of Economic Research. Chen, M., Q. Wu, and B. Yang (2019). How valuable is fintech innovation? Review of Financial Studies 32 (5), 2062–2106. D’Acunto, F., N. Prabhala, and A. G. Rossi (2019). The promises and pitfalls of robo- advising. Review of Financial Studies 32 (5), 1984–2020. Gargano, A. and A. G. Rossi (2017). Does it Pay to Pay Attention? Gelman, M., S. Kariv, M. D. Shapiro, D. Silverman, and S. Tadelis (2014). Harness- ing naturally occurring data to measure the response of spending to income. Sci- ence 345 (6193), 212–215. Gherzi, S., D. Egan, N. Stewart, E. Haisley, and P. Ayton (2014). The meerkat effect: Per- sonality and market returns affect investors’ portfolio monitoring behaviour. Journal of Economic Behavior & Organization 107, 512–526. Goldstein, I., W. Jiang, and G. A. Karolyi (2019). To fintech and beyond. Review of Financial Studies 32 (5), 1648–1661. Jørring, A. (2019). Financial sophistication and consumer spending. Boston College Working Paper. Kaplan, G. and G. L. Violante (2014). A model of the consumption response to fiscal stimulus payments. Econometrica 82 (4), 1199–1239. Karlan, D., M. Morten, and J. Zinman (2016). A personal touch: Text messaging for loan repayment*. Behavioral Science and Policy forthcoming. Karlsson, N., G. Loewenstein, and D. Seppi (2009). The ostrich effect: Selective attention to information. Journal of Risk and uncertainty 38 (2), 95–115. Keys, B. J. (2010). The credit market consequences of job displacement. The Review of Economics and Statistics. Kuchler, T. and M. Pagel (2018). Sticking to your plan: The role of present bias for credit card paydown. Technical report, National Bureau of Economic Research. Laibson, D., A. Repetto, and J. Tobacman (2003). A debt puzzle. In Aghion, Frydman, Stiglitz, and Woodford (Eds.), Knowledge, Information, and Expectations in Modern Economics: In Honor of Edmund S. Phelps., pp. 228–266. Princeton University Press.
18 Laibson, D., A. Repetto, and J. Tobacman (2007). Estimating discount functions with consumption choices over the lifecycle. Technical report, National Bureau of Economic Research. Levi, Y. and S. Benartzi (2020). Mind the app: Mobile access to financial information and consumer behavior. Technical report, Working Paper. Loos, B., A. Previtero, S. Scheurle, and A. Hackethal (2019). Robo-advisers and investor behavior. Working paper, indiana university. Medina, P. (2018). Selective attention in consumer finance: Experimental evidence from the credit card market. Olafsson, A. (2016). Household financial distress and initial endowments: Evidence from the 2008 financial crisis. Health Economics 25, 43–56. hec.3426. Olafsson, A. and M. Pagel (2017, October). The ostrich in us: Selective attention to financial accounts, income, spending, and liquidity. Working Paper 23945. Olafsson, A. and M. Pagel (2018). The liquid hand-to-mouth: Evidence from a personal finance management software. The Review of Financial Studies 31 (11), 4398–4446. Stango, V. and J. Zinman (2009). What do consumers really pay on their checking and credit card accounts? explicit, implicit, and avoidable costs. American Economic Review, Papers and Proceedings 99 (2), 424–429. Stango, V. and J. Zinman (2014). Limited and varying consumer attention: Evidence from shocks to the salience of bank overdraft fees. The Review of Financial Studies 27 (4), 990–1030. Sullivan, J. X. (2008). Borrowing during unemployment unsecured debt as a safety net. Journal of human resources 43 (2), 383–412. Zinman, J. (2009). Where is the missing credit card debt? clues and implications. Review of Income and Wealth 55 (2), 249–265. Zinman, J. (2015). Household debt: Facts, puzzles, theories, and policies. Annual Reviews of Economics 7 (1), 251–276.
19 Figures and Tables
Figure 1: The Mobile App Release and Login Behavior
This figure shows the propensity to use the aggregation platform for early adopters of the app (at least one mobile app login within the 12 months after its release) and inactive users of the platform (individuals that have all their accounts linked to the platform but never log in via the mobile app). Each dot represents the share of individuals who log into the platform at least once each bin (month) within each group and error bars represent the 95% confidence interval around the bin mean. Both adopters and inactive users passed activity and completeness-of-records checks and had linked their accounts to the platform and stayed active for at least one year before the release of the mobile app.
20 Figure 2: The Mobile App Release and Total Bank Fees
This figure shows the evolution of bank fees incurred by users of the financial aggregation software before and after the release of the mobile app. Each dot represents the raw average bank fees (non-sufficient fund fees, late payment interest, late fees, and overdraft interest) paid by individuals within each bin (month) in USD and error bars represent the 95% confidence interval around the bin mean. All individuals included in the sample had linked their accounts to the platform and stayed active for at least one year before before the app was released and passed activity and completeness-of-records checks.
21 22
Figure 3: The Mobile App Release and Overdraft Interest Exenses
This figure shows the evolution of overdraft interest expenses incurred by users of the financial aggregation software before and after the release of the mobile app. Each dot represents the raw average overdraft interest expenses paid by individuals within each bin (month) in USD and error bars represent the 95% confidence interval around the bin mean. All individuals included in the sample had linked their accounts to the platform and stayed active for at least one year before before the app was released and passed activity and completeness-of-records checks.
Figure 4: The Mobile App Release and Late Fees
This figure shows the evolution of late fees incurred by users of the financial aggregation software before and after the release of the mobile app. Each dot represents the raw average late fees paid by individuals within each bin (month) in USD and error bars represent the 95% confidence interval around the bin mean. All individuals included in the sample had linked their accounts to the platform and stayed active for at least one year before before the app was released and passed activity and completeness-of-records checks. 23
Figure 5: Late Fees, Overdraft Interest, and Total Bank Fees vs. Monthly Logins
This figure shows the relationship between averages of (winsorized at the 1% level) late fees (top), overdraft interest (middle), and total bank fees (bottom) and bins of monthly logins. Each dot represents the average amount of fees paid by individuals within the corresponding bin of login frequency and error bars represent the 95% confidence interval around the bin mean. The last bin includes # monthly logins ≥ 10. Figure 6: The Financial Aggregation App: Screenshots of the User Interface
This figure shows screenshots of the mobile app user interface. The left screenshot shows the app’s financial feed including bank account information, the middle one shows the transactions interface, and the right one shows background characteristics that the user provides in the app’s settings. The app’s interface was the same over our sample period. Upon logging in, users first see the financial feed interface. After that, they can tap on "Show Only Transactions" to see the transactions interface. To edit their settings, they can tap on "More" and "Edit Profile."
24 25
Figure 7: The Financial Aggregation App: Screenshots of the New User Interface
This figure shows screenshots of a more recent version of the mobile app transaction feed and its notifi- cation setting. The left screenshot shows the transaction feed. The right screenshot shows the default settings for messages. The transaction feat used during our sample period is displayed in Figure6. Later versions flagged certain events (e.g., unusually high transactions, money deposits and low balance) as displayed in this figure but not in the version used during our sample period. The app did not send push notifications during our sample period.
Figure 8: The Central Bank Policy Rate and Average Overdraft Interest Rates
This figure shows the evolution of the Central Bank policy rate and the average overdraft inter- est rates financial institutions charge individual customers. Data source: Central Bank of Iceland https://www.cb.is/ 26
Table 1: Adoption of the Mobile App over Time
# individuals Cumulative #% individuals Cumulative %
Nov-2014 1,322 1,322 22.4 22.4 Dec-2014 441 1,763 7.47 29.88 Jan-2015 410 2,173 6.95 36.82 Feb-2015 561 2,734 9.51 46.33 Mar-2015 569 3,303 9.64 55.97 Apr-2015 384 3,687 6.51 62.48 May-2015 162 3,849 2.75 65.23 Jun-2015 170 4,019 2.88 68.11 Jul-2015 116 4,135 1.97 70.07 Aug-2015 100 4,235 1.69 71.77 Sep-2015 104 4,339 1.76 73.53 Oct-2015 182 4,521 3.08 76.61 Nov-2015 202 4,723 3.42 80.04 Dec-2015 155 4,878 2.63 82.66 Jan-2016 207 5,085 3.51 86.17 Feb-2016 150 5,235 2.54 88.71 Mar-2016 91 5,326 1.54 90.26 Apr-2016 83 5,409 1.41 91.66 May-2016 62 5,471 1.05 92.71 Jun-2016 45 5,516 0.76 93.48 Jul-2016 56 5,572 0.95 94.42 Aug-2016 79 5,651 1.34 95.76 Sep-2016 45 5,696 0.76 96.53 Oct-2016 50 5,746 0.85 97.37 Nov-2016 46 5,792 0.78 98.15 Dec-2016 44 5,836 0.75 98.9 Jan-2017 65 5,901 1.1 100
Notes: This table reports the distribution of when users of the financial aggregation platform started using the mobile app. There are in total 5,901 mobile app users. All individuals included in the sample passed activity and completeness-of-records checks. 27
Table 2: Comparison of Non-Adopters and Adopters by Time of Adoption
Non- Use in first Use in first Use > first adopters 12 months 6 weeks 12 months
42.7 37.4 38.1 37.4 Age 12.5 11.2 10.9 11.5 Female 0.50 0.46 0.37 0.49 427,810 444,175 572,529 321,563 Total income 583,470 5,231,960 8,255,854 488,107 411,514 379,191 429,629 309,580 Regular income 551,829 379,191 512,809 471,058 164,167 153,840 172,906 124,113 Total spending 173,435 176,720 189,659 175,892 196,639 164,482 165,663 163,721 Current account balance 951,401 582,931 448,506 737,702 354,629 407,748 418,598 422,777 Savings account balance 2,198,573 3,645,245 1,560,023 2,178,933 551,268 572,230 584,261 586,499 Cash 2,546,297 3,713,472 1,661,934 2,339,909 1,137,860 1,169,360 1,320,838 1,112,136 Liquidity 2,727,869 4,060,661 2,921,297 2,581,110 550,268 487,190 536,014 557,877 Overdraft amount (conditional) 971,003 1,143,910 1,677,162 1,043,583 174,208 144,787 158,897 152,035 Overdraft amount (unconditional) 611,235 672,164 965,409 604,090 -177,144 -166,583 -200,842 -148,790 Credit card balance 209,822 208,212 224,062 199,894 294,255 269,578 330,989 235,019 Current account limit 709,103 1,346,520 2,062,938 663,721 469,481 493,958 606,303 439,409 Credit card limit 514,680 635,892 737,250 656,167 0.43 0.42 0.43 0.42 Credit utilization 0.30 0.31 0.30 0.31 2,741 2,195 2,233 1,997 Total bank fees 7,990 6,885 6,333 11,366 832 716 686 677 Late fees 5,540 5,189 4,537 9,533 1,825 1,421 1,491 1,269 Overdraft interest (conditional) 5,147 4,084 4,043 4,658 4,567 4,023 3,918 4,264 Overdraft interest (unconditional) 7,333 6,063 5,784 7,753
# individuals 7,510 4,521 1,763 1,380
Notes: This table reports summary statistics for 13,411 users of the financial aggregation platform, 8,890 of whom never used used the mobile app, 1,763 of whom started using in the mobile app in first six weeks after its release, 4,521 of whom started using in the mobile app in first 12 months after its release, and 1,380 of whom started using the mobile app later during our sample period. All numbers are in USD. Overdraft amounts and interest expenses are reported both with and without conditioning on having overdraft debt. Standard deviations are displayed below mean averages. All individuals included in the sample passed ac- tivity and completeness-of-records checks. Figure 9: Adoption of the Mobile App across different Generations
This figure shows how the use of the mobile app varied across different generations. Each raw data point represents the average number of mobile logins of individuals within each generation. Our sample consists of users who passed activity and completeness-of-records check. The population is split into three generations based on their birth year: Baby Boomers born 1946-1964), Generation X (born 1965-1980), and Millennials (born 1981-2000).
28 29 equals zero for i P ostF irstUse 100 ISK in 2017. *, **, *** denote significance at the 10, 5, and 1 percent level, ≈ 1 Table 3: Bank Fees and the Mobile App Release $ 12 12 12 3 6 18 24 (1) (2) (3) (4) (5) (6) (7) 4,521 4,521 4,521 4,521 4,521 4,521 4,521 (0.044) (0.717)(0.044) (72.825) (0.717) (67.951) (49.954)(0.044) (78.778) (0.717) (61.330) (70.125) (88.734) (52.810) (69.813) (103.697) (45.619) (109.193) (43.454) (110.540) (99.645) 216,825 216,825 216,825 177,000 190,275 243,375 269,925 Logins:0.420*** 3.832***Logins: -137.757*0.420*** Late -249.491*** Fees: 3.832*** -169.558**Logins: -76.303 -124.484*0.420*** Overdraft Interest: -105.673 3.832*** -84.700 -222.400** -76.821 -342.911*** Total Bank Fees: -256.773** -73.999 -206.442* -73.004 -186.254* Months after Observations Individuals Each entry is a separate regression and presents the average effect on monthly bank fees.Estimates Each are column reported shows the in estimated ISK. Notes: This table reports the estimated effect of the adoption of the mobile app of the mobile on bank fees. Our sample respectively. all months prior to the firsteffect use when of using theAll a mobile specifications different app include number andto linear of one controls differ. months of for (12, time the 3, from months the 6, after app 18, its Standard release and first where errors 24 use. we are months) allow clustered the after at slopes the the before app individual and release and after quarter-by-year to the level estimate release and the are effect. within parentheses. consists of individualsto that the used platform thechecks. and mobile stayed app All active in specificationsafter for the include the at first app individual least 12 release oneestimating fixed months to the year effects their after effect before and fees it in that. estimates in was the all therefore They released 12 previous also compare and months months passed had individuals’ after in activity linked the fees our and their mobile in sample completeness-of-records app accounts (the the release). average months number The of indicator months variable included is 49 when # # # App Release: Start R-sqrStart R-sqr 0.440Start 0.380R-sqr 0.440 0.136 0.380 0.440 0.138 0.509 0.380 0.137 0.526 0.320 0.137 0.524 0.322 0.140 0.487 0.322 0.470 0.319 0.318 30
Figure 10: Bank Fees and the Mobile App Release - Different Estimation Windows
This figure shows the average effect on monthly late fees (top), overdraft interest, and total bank fees (bottom), using different length of time after the app release for the estimations (2-24 months). Our estimations sample consists of individuals that used the mobile app in the first 12 months after it was released and had linked their accounts to the platform and stayed active for at least one year before that (N = 4,521 ). Standard errors are clustered at the individual and quarter-by-year level. The specification includes linear controls of time from the mobile app release where we allow the slopes before and after the release to differ. The specification also includes individual fixed effects and estimates therefore compare individuals’ fees in the months after the app release to their fees in all previous months in our sample (the average number of months before the release is 37). Estimates are reported in ISK ($1 ≈ 100 ISK in 2017). All individuals passed activity and completeness-of-records checks and logged in via the mobile app at least once through the mobile app in the first six weeks after its release. 31 Total Bank Fees ). All individuals passed activity and 4,521 = N Overdraft Interest 100 ISK in 2017). Our estimations sample consists of individuals that used the mobile app in the first 12 months Figure 11: Dynamic Responses to the Mobile App Release ≈ 1 $ Late Fees This figure shows the dynamicpanel responses displays of the late cumulative fees,estimates effects. overdraft therefore interest, Standard compare and errors total individuals’ areincluded bank fees clustered is fees 49). at in to the Estimates the theafter individual are mobile months it and app reported after was release. quarter-by-year in released the level. ISK The andcompleteness-of-records app ( top The checks had release panel and specification linked displays to logged their includes the in accounts their individual flows via to fees fixed and the the effects the in mobile platform bottom and all app and at previous stayed least active months for once in at through our the least sample mobile one app year (the before in average the that number first ( of six months weeks after its release. Table 4: The Share of Expenditures paid by Credit Cards and the Mobile App Release
(1) (2) (3) (4) (5) #Months 12 3 6 18 24 after intro:
Credit Ratio: 0.006 0.004 0.006 0.005 0.005 P ostF irstUse i (0.004) (0.006) (0.004) (0.004) (0.004)
#Observations 191,132 151,366 164,613 217,657 244,178 #Individuals 4,521 4,521 4,521 4,521 4,521 R-sqr 0.715 0.734 0.727 0.705 0.696
Notes: This table reports the estimated effect of the release of the mobile on credit card utilization. Our estimations sample consists of individuals that used the mobile app in the first 12 months after it was released and had linked their accounts to the platform and stayed active for at least one year before that (N = 4,521 ). They also passed activity and completeness-of-records checks. All specifications include individ- ual fixed effects and estimates therefore compare individuals’ fees in the months after the app release to their fees in all previous months in our sample (the average number of months included is 49 when estimating the effect in the 12 months after the mobile app release). The indicator variable P ostF irstUsei equals zero for all months prior to the first use of the mobile app and one for the months after its first use. Each entry is a separate regression and presents the average effect on monthly bank fees. Each col- umn shows the estimated effect when using a different number of months (12, 3, 6, 18, and 24 months) after the app release to estimate the effect. All specifications include linear controls of time from the app release where we allow the slopes before and after the release to differ. Standard errors are clustered at the individual and quarter-by- year level and are within parentheses. Estimates are reported in ISK. $1 ≈ 100 ISK in 2017. *, **, *** denote significance at the 10, 5, and 1 percent level, respectively.
32 33
Table 5: Non-Sufficient Funds Charges and the Mobile App Release
(1) (2) (3) (4) (5) #Months after 12 3 6 18 24 first use of APP:
Panel A: Number of Non-Sufficient Funds Charges
-0.005** -0.005* -0.006*** -0.004* -0.004* P ostF irstUse i (0.002) (0.002) (0.002) (0.002) (0.002)
#Observations 216,825 177,000 190,275 243,375 269,925 #Individuals 4,425 4,425 4,425 4,425 4,425
Panel B: Incurrence of Non-Sufficient Funds Charges
-0.003** -0.003 -0.004*** -0.002* -0.002* P ostF irstUse i (0.001) (0.002) (0.001) (0.001) (0.001)
#Observations 216,825 177,000 190,275 243,375 269,925 #Individuals 4,425 4,425 4,425 4,425 4,425
Panel C: Use of Overdrafts
-0.019** -0.026** -0.022** -0.017** -0.015** P ostF irstUse i (0.008) (0.009) (0.008) (0.007) (0.007)
#Observations 216,825 177,000 190,275 243,375 269,925 #Individuals 4,425 4,425 4,425 4,425 4,425
Notes: This table reports the estimated effect of the release of the mobile on the number of non-sufficient funds charges and likelihood of incurring non-sufficient funds charges (using a logit regression), and incurrence of overdraft interest (using a logit regression). Our estima- tions sample consists of individuals that used the mobile app in the first 12 months after it was released and had linked their accounts to the platform and stayed active for at least one year before that. They also passed activity and completeness-of-records checks. All specifi- cations include individual fixed effects and estimates therefore compare individuals’ fees in the months after the app release to their fees in all previous months in our sample (the aver- age number of months included is 49 when estimating the effect in the 12 months after the mobile app release). The indicator variable P ostF irstUsei equals zero for all months prior to the first use of the mobile app and one for the months after its first use. Each entry is a separate regression and presents the average effect on monthly bank fees. Each column shows the estimated effect when using a different number of months (12, 3, 6, 18, and 24 months) after the app release to estimate the effect. All specifications include linear controls of time from the app release where we allow the slopes before and after the release to differ. Standard errors are clustered at the individual and quarter-by-year level and are within parentheses. Estimates are reported in ISK. $1 ≈ 100 ISK in 2017. *, **, *** denote significance at the 10, 5, and 1 percent level, respectively. Figure 12: The Mobile App Release and Total Logins among Very Early Adopters
This figure shows the total number of monthly logins to the financial aggregation platform before and after the release of the mobile app among very early adopters (individuals that started using the mobile app in the first 6 weeks after it was released). Each dot represents the raw average number of monthly logins of individuals, either via their PC of via the mobile app within each bin (month) and error bars represent the 95% confidence interval around the bin mean. All individuals included in the sample had linked their accounts to the platform and stayed active for at least one year before before the app was released and passed activity and completeness-of-records checks..
34 Table 6: Bank Fees and a Placebo Mobile App Release
(1) (2) (3) (4) #Months after 12 12 3 6 placebo release:
#Logins: Total Bank Fees: Total Bank Fees: -0.287 -62.029 -77.623 -64.132 P ostF irstUse i (0.199) (84.367) (86.541) (94.873) R-sqr 0.444 0.352 0.362 0.363
#Logins: Overdraft Interest: -0.287 -34.516 -33.980 -38.092 P ostP F irstUse i (0.199) (64.327) (56.951) (65.325) R-sqr 0.444 0.544 0.588 0.571
#Logins: Late Fees: -0.287 -15.468 -32.463 -13.454 P ostP F irstUsei (0.199) (60.536) (56.565) (63.852) R-sqr 0.444 0.157 0.158 0.161
#Observations 124,776 124,776 106,146 115,071 #Individuals 3,466 3,466 3,466 3,466
Notes: This table reports the estimated effect of the placebo adoption of the mobile app on bank fees. The placebo adoption took place 12 months prior to the actual adoption of the mobile app and assumes the app was released 12 months prior to the actual release. Our esti- mations sample consists of individuals that used the mobile app in the first 12 months after it was released (N = 4,521 ). They also passed activity and completeness-of-records checks. All specifications include individual fixed effects and estimates therefore compare individuals’ fees in the months after the app release to their fees in all previous months in our sample (the av- erage number of months included is 49 when estimating the effect in the 12 months after the mobile app release). The indicator variable P ostP F irstUsei equals zero for all months prior to the first placebo use of the mobile app and one for the months after its first placebo use. Each entry is a separate regression and presents the average effect on monthly bank fees. All specifications include linear controls of time from the app release where we allow the slopes before and after the release to differ. Standard errors are clustered at the individual and quarter-by-year level and are within parentheses. Estimates are reported in ISK. $1 ≈ 100 ISK in 2017. *, **, *** denote significance at the 10, 5, and 1 percent level, respectively.
35 A Appendix
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1. Interview: FTA offers multiple opportunities for Iceland , China Xinhua News Agency, 8:42 AM, 1 November 2014, 1541 words, Svava Jonsdottir, (English) REYKJAVIK, Nov. 1 ( Xinhua) -- The Free Trade Agreement (FTA) between China and Iceland has become one of the topics at the current Arctic Circle Assembly here for bringing Ar (Document XNEWS00020141101eab10063h) + 2 duplicate article(s) identified
2. 14Nov/Már Guðmundsson: Economic outlook, monetary policy, and credit ratings ForeignAffairs.co.nz, 14 November 2014, 2695 words, (English) The Iceland Chamber of Commerce has a long-standing tradition of holding a meeting like this one on economic developments and prospects and monetary policy. The meeting is he and, in latter years... (Document PARALL0020141115eabe000ek) S
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I Icelandic inflation falls to 16-year low in November, increases probability of further interest rate cuts F IHS Global Insight Daily Analysis, 26 November 2014, 487 words, Diego Iscaro, (English) Iceland ’s consumer price index rose by just 1.0% year on year (y/y) in November, according to figures released by Statistics Iceland . This is the lowest increase since October 1998. (Document WDAN000020141126eabq0001g)
4. Central Bank of Iceland cuts interest rates in line with improved inflation outlook IHS Global Insight Daily Analysis, 5 November 2014, 851 words, Diego Iscaro, (English) The Central Bank of Iceland surprised markets by cutting interest rates for the first time since early 2011. Following its November meeting, the Monetary Policy Committee (MPC) of th points. (Document WDAN000020141105eab500020)
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Iceland Cuts Interest Rates WSJ Blogs, 06:50 AM, 5 November 2014, 342 words, Charles Duxbury, Anna L. Molin, By Charles Duxbury and Anna Molin, (English) Iceland became the latest country to join the international struggle against low inflation Wednesday, cutting interest rates for the first time in two years to boost the rate of price increases in the North Atlantic island state. (Document WCWSJB0020141105eab5003e9)
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Central Bank of Iceland cuts interest rates in line with improved inflation outlook IHS Global Insight Daily Analysis, 5 November 2014, 851 words, Diego Iscaro, (English) The Central Bank surprised markets with its first interest rate cut since the start of 2011. IHS perspective Significance The Central Bank of Iceland surprised markets by cutting interest rates for the first time since early 2011. Following ... (Document WDAN000020141105eab500020)
Iceland economy predicted to grow by 2.7% in 2014 PNA (Philippines News Agency), 14 November 2014, 225 words, (English) REYKJAVIK, Nov. 14 -- Iceland's economy was predicted to grow by 2.7 percent in 2014, according to latest economic forecast released Friday by Statistics Iceland. (Document PHILNA0020141115eabe000dk)
Federal Council adopts dispatches on double taxation agreements with Estonia, Ghana, Iceland , Uzbekistan and Cyprus ForeignAffairs.co.nz, 12 November 2014, 184 words, (English) MIL OSI - Source: Switzerland - Department of Finance - Press Release/Statement: Headline: Federal Council adopts dispatches on double taxation agreements with Estonia, Ghana, Iceland, Uzbekistan and Cyprus (Document PARALL0020141113eabc000gz)
Iceland eager to reach free trade agreement with Japan Kyodo News, 05:00 AM, 11 November 2014, 308 words, Mie Sakamoto Mie Sakamoto, (English) TOKYO, Nov. 11 -- Icelandic Foreign Minister Gunnar Sveinsson said Tuesday the Nordic country is eager to reach a free trade agreement with Japan to seek further economic growth, calling on Tokyo to "take some new steps." (Document KYODO00020141111eabb0053d)
Iceland central bank cuts key rate 0.25 pct pt to 5.75% https://global.factiva.com/hp/default.aspx?pp=Print&hc=All 1/12 8/15/2018 Factiva 24 Ore Radiocor-Newswire International Edition, 05:57 AM, 5 November 2014, 174 words, (English) Inflation rate well below target (Il Sole 24 Ore Radiocor) - Reykjavik , 05 Nov - Iceland's central bank today cut its main interest rate 0.25 percentage points, as it seeks to stave off excessively low inflation. (Document SOLRADIN20141105eab5001uq)
Iceland extends deadline for Landsbanki bonds rescheduling Reuters News, 06:19 AM, 18 November 2014, 246 words, (English) REYKJAVIK, Nov 18 (Reuters) - Iceland has agreed with Landsbankinn, the successor of one of three banks that amassed huge debts before collapsing and crashing the economy in 2008, to extend a deadline until the end of the year to ... (Document LBA0000020141118eabi00aar)
Australian FTA just the beginning China Daily-Europe Weekly, 21 November 2014, 727 words, Nie Pingxiang, (English) Landmark deal shows China's willingness to compromise and open the country up to foreign competition On Nov 17, China and Australia announced they had formally completed negotiations on a free trade agreement. They are likely to sign and ... (Document CDEUWE0020141121eabl0000b)
Iceland Inflation Eases In November RTT News, 26 November 2014, 112 words, (English) (RTTNews) - Iceland's consumer price inflation slowed in November after accelerating slightly in the previous month, figures from Statistics Iceland showed Wednesday. (Document RTTNEW0020141126eabq003ux)
Iceland Central Bank Cuts Rates Unexpectedly RTT News, 5 November 2014, 248 words, (English) (RTTNews) - Iceland's central bank lowered its key rate for the first time since February 2011 as growth is set to be weaker than the previous forecast. (Document RTTNEW0020141105eab5003v1)
Iceland 's unemployment rate at 5 percent in October Middle East North Africa Financial Network (MENAFN), 27 November 2014, 96 words, (English) (MENAFN) According to a data by Statistics Iceland, the country's unemployment rate stood at 5 percent in October, showing a slight change over a year earlier, Xinhua reported. (Document MENAFI0020141127eabr0008t)
Iceland 's CPI down 0.52 pct between months of October, November Xinhua News Agency, 09:26 AM, 26 November 2014, 166 words, xiebinbin, (English) REYKJAVIK, Nov. 26 (Xinhua) -- Iceland's Consumer Price Index (CPI) based on prices reported month-on-month experienced a decrease of 0.52 percent as of middle of November, according to statistics issued on Wednesday by Statistics Iceland. ... (Document XNEWS00020141126eabq00899)
UPDATE 1- Iceland 's central bank cuts interest rate as inflation falls Reuters News, 05:13 AM, 5 November 2014, 247 words, (English) (Adds reason for cut) COPENHAGEN, Nov 5 (Reuters) - Iceland's central bank lowered its key interest rate, which has been unchanged for two years, to 5.75 percent from 6.00 percent, saying that real borrowing costs had risen more than ... (Document LBA0000020141105eab500ayd)
Cyprus and Iceland sign Double Taxation Agreement Cyprus News Agency, 14 November 2014, 109 words, (English) The Republic of Cyprus and the government of Iceland signed on Thursday a Double Taxation Avoidance Agreement concerning income. The Finance Ministry issued on Friday an announcement, saying that the agreement contributes in further ... (Document CYPRNA0020141203eabe0002i)
* Iceland Central Bank Cut Main Interest Rate by 25 Bps to 5.75% Dow Jones Institutional News, 03:56 AM, 5 November 2014, 208 words, (English) https://global.factiva.com/hp/default.aspx?pp=Print&hc=All 2/12 8/15/2018 Factiva 5 Nov 2014 04:24 ET Iceland's Central Bank Cuts Main Interest Rate by 25 Bps to 5.75% By Anna Molin Iceland's central bank on Wednesday cut its main interest rate for the first time in two years in an effort to boost inflation, which is ... (Document DJDN000020141105eab5001gv)
Iceland 's unemployment stood at 5 % in October PNA (Philippines News Agency), 27 November 2014, 182 words, (English) REYKJAVIK, Nov. 27 -- Iceland's unemployment rate stood at 5 percent in October 2014, little change compared with the same period last year, according to a labor force survey issued Wednesday by Statistics Iceland. (Document PHILNA0020141127eabr0005e)
Iceland CPI +1.0 pct yr/yr in Nov Reuters News, 04:16 AM, 26 November 2014, 87 words, (English) COPENHAGEN, Nov 26 (Reuters) - Icelandic consumer prices fell 0.5 percent in November from October while annual inflation rose 1.0 percent, the country's statistics office said on Wednesday. (Document LBA0000020141126eabq008vm)
TABLE-Euro zone Q3 GDP growth stronger than expected Reuters News, 05:00 AM, 14 November 2014, 726 words, (English) BRUSSELS, Nov 14 (Reuters) - The European Union's statistics office Eurostat released the following data on gross domestic product growh in the euro zone in the third quarter of 2014. Economst polled by Reuters had expected ... (Document LBA0000020141114eabe008y5)
Iceland central bank cuts key rate 0.25 pct pt to 5.75% -2- 24 Ore Radiocor-Newswire International Edition, 06:04 AM, 5 November 2014, 100 words, (English) Future rate moves could hinge on pay increases (Il Sole 24 Ore Radiocor) - Reykjavik , 05 Nov - The central bank indicated that future rate moves could depend on the impact of pay increases. (Document SOLRADIN20141105eab500209)
Corporate taxes: Double-tax treaties Economist Intelligence Unit - Country Commerce, 1 November 2014, 1381 words, (English) Switzerland has treaties in force with 85 countries (see table below). In addition, it has initiated or signed new treaties with Cyprus, Costa Rica, North Korea, Oman, the United Arab Emirates and Zimbabwe. These treaties provide for ... (Document EIUCC00020141206eab100016)
China's trade with Iceland in September 2014 Xinhua's China Economic Information Service, 02:12 AM, 7 November 2014, 254 words, 瞿宁宁, (English) BEIJING, Nov. 7 (Xinhua) – Following is a table showing the total value of China's trade with Iceland from 2004 to September 2014, released by the General Administration of Customs: (Unit: 1,000 U.S. dollars) (Document XNHA000020141107eab70020p)
ACEA: Romania, 2nd place in EU in terms of increase in number of new car registrations, in October AGERPRES, 04:57 AM, 18 November 2014, 332 words, (English) Brussels, Nov 18 /Agerpres/ - Car sales in the European Union and EFTA countries recorded, in October, the 14th consecutive month of growth as the recovery of the Eurozone's economy encouraged consumers to purchase new models, according to ... (Document ROMPES0020141118eabi0012x)
Central Bank of Iceland -Statement of the Monetary Policy Committee ENP Newswire, 6 November 2014, 379 words, (English) Release date - 05112014 The Monetary Policy Committee (MPC) of the Central Bank of Iceland has decided to lower the Bank’s interest rates by 0.25 percentage points. (Document ENPNEW0020141106eab6000i5)
Iceland central bank cuts rate Global Banking News, 5 November 2014, 100 words, (English)
https://global.factiva.com/hp/default.aspx?pp=Print&hc=All 3/12 8/15/2018 Factiva Iceland's central bank has cut its rate. On Wednesday, the bank cut its main interest rate for the first time in two years to boost inflation. The apex bank said its benchmark seven-day collateralised lending rate would be lowered to 5.75 ... (Document GLOBAN0020141105eab50002w)
Macro Horizons: GOP’s Midterm Win In Focus, But Global Economy Bigger Concern WSJ Blogs, 07:43 AM, 5 November 2014, 1538 words, Michael J. Casey, Alen Mattich, By Michael J. Casey and Alen Mattich, (English) Macro Horizons covers the main macroeconomic and policy news events affecting foreign-exchange, fixed income and equity markets around the world, as selected by editors in New York, London and Hong Kong. (Document WCWSJB0020141105eab5003xp)
Europa-Euro area unemployment rate at 11.5% ENP Newswire, 3 November 2014, 2813 words, (English) Release date - 31102014 The euro area1 (EA18) seasonally-adjusted2 unemployment rate3 was 11.5% in September 2014, stable compared with August 20144, but down from 12.0% in September 2013. (Document ENPNEW0020141103eab3000ad)
Switzerland: Tax regulations Economist Intelligence Unit - ViewsWire, 28 November 2014, 4676 words, (English) Corporate taxes: Overview Switzerland is noted for its moderate taxation of companies. It remains at the lower end of the corporate tax burden among the wealthier countries of OECD. The complexity of the Swiss system is partly due to the ... (Document EIUCP00020141206eabs00006)
Selling Alberta's economy through cultural diplomacy The Globe and Mail, 7 November 2014, 754 words, By TODD HIRSCH, (English) Todd Hirsch is the Calgary-based chief economist of ATB Financial and author of The Boiling Frog Dilemma: Saving Canada from Economic Decline. (Document GLOB000020141107eab70001w)
Landsbankinn hf.: Financial Results January - September 2014 NASDAQ OMX Nordic Exchanges - Company Notices, 11:11 AM, 6 November 2014, 1588 words, (English) Landsbankinn reports an ISK 20 bn profit in the first 9M of 2014 In the first nine months of 2014, Landsbankinn's after-tax profit was ISK 20 bn as compared with ISK 22 bn for the same period in 2013. This lower margin is due mostly to ... (Document NASDQC0020141106eab6003h1)
EU Demands Clear Economic Strategy From Ukraine Dow Jones Institutional News, 02:10 PM, 14 November 2014, 713 words, By Laurence Norman, (English) BRUSSELS--The European Union won't organize a donors' conference until Ukraine's government lays out a clear new economic strategy, the bloc's new enlargement chief said Friday. (Document DJDN000020141114eabe003u8)
DIARY - Major Central Bank Meetings for 2014-15 Reuters News, 04:37 AM, 14 November 2014, 1644 words, (English) For other related diaries, please see; DIARY - U.S. Federal Reserve DIARY - Polling Unit Diary DIARY - Key World Financial Events DIARY - Political and General news DIARY - Index of all ... (Document LBA0000020141114eabe008xc)
Political/commercial background: Foreign investment Economist Intelligence Unit - Country Commerce, 1 November 2014, 1206 words, (English) Switzerland welcomes foreign direct investment (FDI) in manufacturing, services, and research and development, and it treats foreign enterprises in the same way as local companies. Switzerland Global Enterprise (S-GE; previously known as ... (Document EIUCC00020141206eab100008)
DIARY - Major Central Bank Meetings for 2014-15 Reuters News, 08:36 AM, 21 November 2014, 1740 words, (English) For other related diaries, please see; DIARY - U.S. Federal Reserve DIARY - Polling Unit Diary DIARY - Key World Financial Events DIARY - Political and General news DIARY - Index of all ... (Document LBA0000020141121eabl00el3) https://global.factiva.com/hp/default.aspx?pp=Print&hc=All 4/12 8/15/2018 Factiva
USA: 5-year forecast table Economist Intelligence Unit - ViewsWire, 17 November 2014, 4201 words, (English) Data summary: Global outlook Global outlook 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 International assumptions (%) World GDP growth 4.0 2.6 2.0 2.1 2.3 2.9 2.8 2.9 ... (Document EIUCP00020141119eabh0000h)
Good Morning Ms. Yellen, Rand Paul on Line One Dow Jones Top North American Equities Stories, 01:09 PM, 5 November 2014, 2019 words, (English) The Wall Street Journal's Daily Report on Global Central Banks for Wednesday, November 5, 2014: Sign up for the newsletter: http://on.wsj.com/grandcentralsignup. (Document DJTNAE1120141105eab5000ct)
Macro Horizons: GOP's Midterm Win In Focus, But Global Economy Bigger Concern Dow Jones Institutional News, 07:36 AM, 5 November 2014, 1550 words, (English) By Michael J. Casey and Alen Mattich Macro Horizons covers the main macroeconomic and policy news events affecting foreign-exchange, fixed income and equity markets around the world, as selected by editors in New York, London and Hong Kong. (Document DJDN000020141105eab500271)
Political/commercial background: International agreements Economist Intelligence Unit - Country Commerce, 1 November 2014, 1838 words, (English) Switzerland is a member of the Council of Europe, the IMF, the OECD, the Organisation for Security and Co-operation in Europe, the World Bank and the World Trade Organisation (WTO). In May 2009 Switzerland became a member of the ... (Document EIUCC00020141206eab10000a)
Good Morning Ms. Yellen, Rand Paul on Line One Dow Jones Top Global Market Stories, 12:29 PM, 5 November 2014, 2019 words, (English) The Wall Street Journal's Daily Report on Global Central Banks for Wednesday, November 5, 2014: Sign up for the newsletter: http://on.wsj.com/grandcentralsignup. (Document DJTGMS1120141105eab5000bs)
AgriNews Kiev (No. of pages: 23) AgriNews, Kiev, 10 November 2014, 9396 words, (English) CONTENT 1. GOVERNMENT POLICY AND DEVELOPMENT OF LEGISLATION ...... 2 1.1. TOPIC OF THE WEEK ... (Document AGRIKV0020150706eaba000gt)
Custodian REIT PLC Half Yearly Report Regulatory News Service, 02:01 AM, 25 November 2014, 21914 words, (English) TIDMCREI RNS Number : 8695X Custodian REIT PLC 25 November 2014 25 November 2014 Custodian REIT plc ("Custodian REIT" or "the Company") Interim Results (Document RNS0000020141125eabp0001p)
Latest Developments In EU Export Controls And Economic Sanctions Mondaq Business Briefing, 19 November 2014, 1545 words, Laurent Ruessmann, (English) Following is a short update on recent developments in EU export controls and sanctions laws. Our Export Control Team would be glad to assist you further should you have any questions or seek additional information. (Document BBPUB00020141119eabj000mm)
3-page Market special: A departure from Economics 101 The Sunday Business Post, 23 November 2014, 1144 words, (English) Central banks pumping hundreds of billions into retail banks will not get more plumbers or SMEs their overdraft facilities. In the 1950s, the economist Oskar Morgenstern asked a simple question, and got a very strange answer. Morgenstern was ... (Document SBPM000020141123eabn0001j)
https://global.factiva.com/hp/default.aspx?pp=Print&hc=All 5/12 8/15/2018 Factiva Europe Data: New Car Registrations By Country, Y/Y % Chgs Market News International, 02:00 AM, 18 November 2014, 407 words, (English) Release for: October 2014 Source: European Automobile Manufacturers Association Y/Y Jan-Oct Jan-Oct Y/Y Oct14 Oct13 %CHG 2014 2013 ... (Document MARNEW0020141118eabi0004r)
Signs of growth in retail rental re-emerge in regional areas The Sunday Business Post, 2 November 2014, 1062 words, (English) The slow recovery in retail sales has at last begun to ripple out to some of the regional towns and cities. The slow recovery in retail sales has at last begun to ripple out to some of the regional towns and cities, according to Florence ... (Document SBPM000020141102eab20003o)
PH, Switzerland agree to narrow down trade gap Manila Bulletin, 12 November 2014, 621 words, (English) The Philippines and Switzerland governments are working to narrow down the huge trade deficit against the Philippines, whose exporters are still having difficulties meeting the standards and quality requirements of a wealthy trading ... (Document MABULL0020141111eabc0009d)
Landsbankinn hf.: Advisory opinion of the EFTA Court on the inflation indexing provision of a bond NASDAQ OMX Nordic Exchanges - Company Notices, 06:14 AM, 24 November 2014, 929 words, (English) Today the EFTA Court handed down its advisory opinion on the interpretation of provisions in Directive 87/102/EEC on consumer credit, and Directive 93/13/EEC on unfair terms in consumer contracts. The opinion was requested by the Reykjavík ... (Document NASDQC0020141124eabo001md)
Capital Control Plan Irish Independent, 12 November 2014, 82 words, (English) Briefs ICELAND took another step toward easing capital controls in a move that will free creditors in its failed banks as the government moves ahead with a mortgage debt relief programme. (Document IINM000020141112eabc0004p)
Tax-free shopping around the world Manila Bulletin, 3 November 2014, 870 words, (English) People around the world will try to avoid tax as much as they can. In fact, global companies tend to relocate their business operations in countries where taxes are relatively low, or at least business-friendly. (Document MABULL0020141104eab3000ai)
PH, Efta to start free trade talks Philippine Daily Inquirer, 26 November 2014, 189 words, (English) The Philippines and the four member states of the European Free Trade Association (Efta) are set to start negotiations for a free trade agreement, following the successful conclusion of the so-called “scoping exercises” between the two ... (Document AIWPHI0020141126eabq0001h)
Germany to require board quota The Knoxville News Sentinel, 27 November 2014, 338 words, (English) Germany to require board quota BERLIN — Germany’s leading companies will need to have at least 30 percent women on their supervisory boards from 2016, according to a new directive being adopted by the government, Chancellor Angela Merkel ... (Document KXVL000020141127eabr0000m)
Cecilia S. Petersen, Knoxville The Knoxville News Sentinel, 27 November 2014, 1015 words, (English) Cecilia S. Petersen, Knoxville Whatever happened to Thanksgiving? I am dismayed by the increasing commercialism of all holidays. Apparently, if it doesn’t reap retail dollars, it’s just not a holiday. Examine Veterans Day and Thanksgiving. ... (Document KXVL000020141127eabr0001l)
https://global.factiva.com/hp/default.aspx?pp=Print&hc=All 6/12 8/15/2018 Factiva Lithuania's truck sales fall 28 pct in Jan-Oct y/y - ACEA Baltic Business Daily, 04:12 AM, 27 November 2014, 196 words, (English) VILNIUS, Nov 27, BNS - All three Baltic countries recorded a decline in new heavy commercial vehicle sales for the ten months through October, figures from the European Automobile Manufacturers' Association (ACEA) showed on Thursday. (Document BALDAI0020141127eabr0012y)
Equality, Korean style Joins.com, 21 November 2014, 773 words, (English) American-style winner-takes-all business practices have become widespread, deepening inequalities. South Korea achieved two economic miracles from the 1960s through the 1990s. One is the often-cited passage from rags-to-riches. Per capita ... (Document JOONAI0020141120eabl0005p)
Brazil, US on the agenda during EFTA Ministerial ForeignAffairs.co.nz, 24 November 2014, 424 words, (English) MIL OSI - Source: Government of Iceland - Press Release/Statement Headline: Brazil, US on the agenda during EFTA Ministerial 17.11.2014 Gunnar Bragi Sveinsson Minister for Foreign Affairs participated in the biannual Ministerial meeting of the ... (Document PARALL0020141125eabo000ec)
UPDATE 2-EU insurance watchdog warns on low interest rate threat Reuters News, 04:18 PM, 30 November 2014, 515 words, (English) * EIOPA releases results of insurer stress tests * EIOPA does not name individual insurers in results * Sector generally well capitalised, EIOPA says (Adds European industry comment, detail) (Document LBA0000020141130eabu00fz2)
Financial Adviser: The latitude of longevity. Financial Adviser, 20 November 2014, 1433 words, Joseph Lu, (English) The Longevity Science Panel forecasts that life expectancy will continue to rise in the next decade, and so ways have to be found to meet the challenges - and opportunities - of an ageing population (Document FADV000020141127eabk00019)
China and South Korea close to reaching free trade agreement EFE News Service, 10 November 2014, 235 words, (English) Beijing, Nov 10 (EFE).- Chinese President Xi Jinping on Monday met with his South Korean counterpart Park Geun-hye and both announced a tentative free trade agreement, the state Xinhua news agency reported. (Document WEFE000020141110eaba0005m)
Principle of home rule must be backed up by full tax powers The Herald, 5 November 2014, 1132 words, NO BYLINE, (English) THE new cross-party Campaign for Scottish Home Rule (“Home rule supporters vow to win meaningful settlement”, The Herald, November 4) is right to look for a principled approach to further Scottish devolution. These principles could rest on ... (Document GH00000020141105eab50003z)
All roads lead to retail parks The Sunday Business Post, 30 November 2014, 820 words, (English) Increased car sales, better roads and access to parking are driving the increase in rental rates for retail parks, writes Donal Buckley. Ireland’s vastly improved motorway network could well be a key factor driving the latest buying trends ... (Document SBPM000020141130eabu0003p)
New depository scheme from December 15 Business Standard, 15 November 2014, 131 words, BS Reporter New Delhi, (English) Indian corporates, both listed and unlisted, would be able to raise money through depository receipts in 34 foreign jurisdictions from December 15. Notifying the scheme, the finance ministry said any domestic company, both listed and ... (Document BSTN000020141114eabf004fe)
https://global.factiva.com/hp/default.aspx?pp=Print&hc=All 7/12 8/15/2018 Factiva Britain doesn't need to be in Europe to strike important trade deals The Telegraph Online, 10:10 AM, 5 November 2014, 464 words, By Douglas Carswell, (English) What should we make of the proposed Transatlantic Trade and Investment Partnership, asks Douglas Carswell What should one make of the proposed Transatlantic Trade and Investment Partnership or TTIP? (Document TELUK00020141105eab5003k4)
European car sales up again in October. ANSA - English Media Service, 04:24 AM, 18 November 2014, 93 words, (English) (ANSA) - Turin, November 18 - European car sales increased for the 14th consecutive month in October, European Automobile Manufacturers Association (ACEA) said on Tuesday. It said there were 1,112,628 new car registrations in the EU and the ... (Document ANSAEN0020141118eabi000b7)
Norway economy: Coming to terms with falling oil prices Economist Intelligence Unit - ViewsWire, 7 November 2014, 1117 words, (English) Norway is a wealthy economy and has come through previous negative oil shocks in decent shape, partly because the positive spin-offs from a falling krone deliver some necessary rebalancing of the economy. Yet if the sudden drop in global ... (Document EIUCP00020141108eab700033)
Why relying on GDP will destroy the world The Telegraph Online, 11:01 AM, 17 November 2014, 754 words, Lauren Davidson, By Lauren Davidson, (English) GDP is not the only way to measure global growth, and leaning on it too heavily has left the world "teetering on the brink of environmental disaster and filled with anger and conflict." (Document TELUK00020141117eabh003v0)
A 'Tax Day' Americans could love? CNN Wire, 12:11 PM, 26 November 2014, 1119 words, By Edward J. McCaffery, (English) Editor's note: Edward J. McCaffery is Robert C. Packard Trustee Chair in law and a professor of law, economics and political science at the University of Southern California. He is the author of "Fair Not Flat: How to Make the Tax System ... (Document CNNWR00020141126eabq007bx)
Food inflation hits eight-year low as shoppers benefit from price cuts of essentials in supermarket wars Mail Online, 06:48 AM, 5 November 2014, 575 words, CLAIRE HUGHES FOR THISISMONEY.CO.UK, (English) Shoppers are benefiting from the lowest recorded food inflation figures since 2006 with falls in the price of kitchen essentials such as milk, cheese and eggs, new data indicates. (Document DAMONL0020141105eab50053h)
Corporate taxes: Double-tax treaties Economist Intelligence Unit - Country Commerce, 1 November 2014, 948 words, (English) India has comprehensive double-taxation agreements in force with 88 countries, covering all sources of income (however, a few of them specify simply that standard national rates of withholding will be charged). There are 13 agreements ... (Document EIUCC00020141206eab10003f)
India's gender gap is getting worse Sunday Tribune, 2 November 2014, 365 words, (English) NEW DELHI: Indian women still face some of the world's worst inequality in access to health care, education and work, de- spite years of rapid economic growth, a survey of 142 nations revealed. (Document SUNDTB0020141102eab20004c)
All 3 Baltic countries post y/y growth in new car sales in Jan-Oct – ACEA Baltic Business Daily, 04:17 AM, 18 November 2014, 209 words, (English) VILNIUS, Nov 18, BNS - All three Baltic countries posted growth in new car sales in January through October, data from the European Automobile Manufacturers' Association (ACEA) showed on Tuesday. (Document BALDAI0020141118eabi000rt)
DIANNE Sharp, regional director of [...] https://global.factiva.com/hp/default.aspx?pp=Print&hc=All 8/12 8/15/2018 Factiva The Journal, Newcastle, 20 November 2014, 754 words, PETER TROY, (English) DIANNE Sharp, regional director of the CBI makes a good point in her column (The Journal Business, November 11) when she asserts that concern around free movement and migration is at the centre of continued membership of the EU. However, ... (Document THEJOU0020141120eabk0001f)
Government sets its sights on new trade deal, Media release, 14 Nov 2014, Australian Minister for Trade and Investment, The Hon Andrew Robb... ForeignAffairs.co.nz, 17 November 2014, 406 words, (English) MIL OSI - Source: Australian Government - Foreign Affairs and Trade - Press Release/Statement: Headline: Government sets its sights on new trade deal, Media release, 14 Nov 2014, Australian Minister for Trade and Investment, The Hon Andrew ... (Document PARALL0020141117eabh0003o)
World: 5-year forecast table Economist Intelligence Unit - ViewsWire, 21 November 2014, 884 words, (English) Global assumptions Global forecast 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Real GDP growth (%) ... (Document EIUCP00020141123eabl0001l)
Icelandic central bank cuts key interest rate to 5.75 percent Reuters News, 04:07 AM, 5 November 2014, 114 words, (English) COPENHAGEN, Nov 5 (Reuters) - Iceland's central bank lowered its key interest rate to 5.75 percent from 6.00 percent on Wednesday and said inflation was likely to fall further in the next few months and remain at or below target through ... (Document LBA0000020141105eab500akr)
OECD raises growth forecast for Russian GDP in 2014 to 0.7%, lowers to zero for 2015 Interfax: Russia & CIS Business and Financial Newswire, 10:16 AM, 6 November 2014, 254 words, (English) MOSCOW. Nov 6 (Interfax) - The Organization for Economic Cooperation and Development (OECD) is expecting Russia's GDP to grow 0.7% in 2014, followed by zero growth in 2015 and 2% growth in 2016, the organization said in a statement. (Document DAIBN00020141106eab60015r)
Phl, European free trade group set talks The Philippine Star, 24 November 2014, 410 words, By Louella D. Desiderio, (English) MANILA, Philippines – The Philippines and European Free Trade Association (EFTA) are set to hold this month the first round of meetings for a planned free trade agreement (FTA) in line with the aim to start negotiations by next year. (Document PHSTAR0020141123eabo0000l)
World: 5-year forecast table Economist Intelligence Unit - ViewsWire, 1 November 2014, 874 words, (English) Global assumptions Global forecast 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Real GDP growth (%) ... (Document EIUCP00020141102eab100044)
Philippines: High-level Swiss business delegation visit Manila, Cebu Thai News Service, 25 November 2014, 600 words, (English) Section: General News - The visit of Swiss business delegation in Manila and Cebu this month further boosted economic relations between the Philippines and Switzerland. (Document THAINS0020141124eabp0005z)
Investors Chronicle - magazine and web content: RPC packs a punch. Investors Chronicle - Magazine and Web Content, 27 November 2014, 313 words, Daniel Liberto, (English) Success outside Europe and a new acquisition sent shares in the plastic packager up 5 per cent on results day. Rigid plastic package supplier RPC (RPC) overcame flat economic growth in its core European market and high polymer prices to post https://global.factiva.com/hp/default.aspx?pp=Print&hc=All 9/12 8/15/2018 Factiva ... (Document INVR000020141210eabr0003n)
Irish vote against sovereign debt mechanism puzzling The Irish Times, 14 November 2014, 725 words, arthur beesley, Arthur Beesley, (English) A few weeks ago, the United Nations General Assembly adopted a resolution to establish a new international mechanism to restructure the sovereign debt of bankrupt countries, the noble objective being to prevent any repeat of Argentina's ... (Document IRTI000020141114eabe0003l)
EMU Data: Flash GDP By Member State; Q/Q, Y/Y Pct Change Market News International, 05:00 AM, 14 November 2014, 664 words, (English) Release for: Third Quarter 2014(flash) Source: Eurostat Q/Q Pct Change Y/Y Pct Change 4Q13 1Q14 2Q14 3Q14 4Q13 1Q14 2Q14 ... (Document MARNEW0020141114eabe00085)
EU/EMU Data: Harmonised CPI By Nation Market News International, 05:12 AM, 14 November 2014, 470 words, (English) Release for: Otcotber 2014(final) Source: Eurostat Oct14 Sep14 Aug14 Jul14 Oct13 Oct14 y/y y/y y/y y/y y/y ... (Document MARNEW0020141114eabe0008a)
Europe patent for colorectal cancer prognostic technology Scoop.co.nz, 07:35 PM, 25 November 2014, 812 words, Pacific Edge, (English) 26 November 2014 Europe grants Pacific Edge patent for colorectal cancer prognostic technology The European Intellectual Property Office which operates across 38 countries including all European Union members, has awarded Pacific Edge a ... (Document SCCONZ0020141126eabq00065)
What Does IMF's Appointment of Crisis Veteran Say About Europe's Economy? Dow Jones Institutional News, 12:56 PM, 3 November 2014, 263 words, Ian Talley, By Ian Talley, (English) If the International Monetary Fund's latest economic appointment is any indication, Europe's economy must still be in real trouble. IMF chief Christine Lagarde named Poul Thomsen, architect of the controversial Greek bailout and one the ... (Document DJDN000020141103eab3002yx)
FTA’s smoother Chinese capital should give nation a welcome shot in the arm The Australian, 12 November 2014, 1046 words, Glenda Korporaal, Glenda Korporaal, (English) IT is too early to tell just how much of a stimulus the free trade agreement Australia is expected to sign with China will be to our economy. But the deal, which could be signed on Monday at the G20 leaders meeting in Brisbane, has the ... (Document AUSTLN0020141111eabc0004c)
World Furniture Outlook 2014-2015 PR Newswire (U.S.), 04:00 PM, 27 November 2014, 451 words, (English) LONDON, Nov. 27, 2014 /PRNewswire/ -- The World Furniture Outlook 2014-2015 by CSIL provides an overview of the world furniture industry with historical statistical data (production, consumption, imports, exports) and 2014 and 2015 ... (Document PRN0000020141127eabr00048)
TABLE-OECD overview of deficit and inflation forecasts Reuters News, 05:30 AM, 25 November 2014, 331 words, (English) PARIS, Nov 25 (Reuters) - Following are the main public deficit and consumer-price inflation projections from the Organisation for Economic Cooperation and Development's latest Economic Outlook, released on Tuesday. ... (Document LBA0000020141125eabp00blg)
Interfax Russia & CIS Business and Financial Daily Interfax: Russia & CIS Business and Financial Daily, 03:51 PM, 6 November 2014, 13258 words, (English) 07.11.14 06.11.14 Official Exchange Rate of Ruble to Dollar (ruble/$1) 45.1854 44.3993 Official Exchange Rate of Ruble to Euro (ruble/EUR1) 56.5450 55.6234 ... https://global.factiva.com/hp/default.aspx?pp=Print&hc=All 10/12 8/15/2018 Factiva (Document DAFIR00020141107eab600001)
“Who Says We Do Not Work?” (No. of pages: 8) Economic & Political Weekly, 15 November 2014, 9035 words, Sujata Gothoskar, Apoorva Kaiwar, (English) SPECIAL ARTICLE november 15, 2014 vol xlix no 46 EPW Economic & Political Weekly 54 “Who Says We Do Not Work?” Looking at Sex Work Sujata Gothoskar, Apoorva Kaiwar Sex workers’ organisations have argued against trafficking and see it ... (Document ECPOLW0020141202eabf0002t)
Economics Turan Information Agency (Azerbaijan), 08:04 AM, 28 November 2014, 2927 words, (English) Milli Majlis Approves Draft Budget 2015Decline Expected in the Oil and Gas SectorAzerbaijan Exports Iron Ore at a Price 2.5 Times Lower than the World PriceAccord Corporation Launches Kutaisi Bypass RoadPlacing a Large Batch of Bonds of the ... (Document TURNAG0020141128eabs0005q)
Interfax Russia & CIS Business and Investment Weekly Interfax: Russia & CIS Business & Investment Weekly, 04:44 PM, 7 November 2014, 26782 words, (English) 07.11.2014 31.10.2014 Official Exchange Rate of Ruble to Dollar (ruble/$1) 45.1854 43.3943 Official Exchange Rate of Ruble to Euro (ruble/EUR1) 56.5450 54.6378 ... (Document INVST00020141121eab700001)
Investing in real estate abroad and at home Today.az, 28 November 2014, 1832 words, (English) /AzerNews/By Gulgiz Dadashova Buying a house is without a doubt one of the most important decisions that you will make in your life. Besides turning a house into a home, real estate is also an alluring investment. (Document DLSDAZ0020141128eabs0000d)
Why China's Easing Measures Are Failing -- Market Talk Dow Jones Institutional News, 02:27 AM, 6 November 2014, 1532 words, (English) 0727 GMT [Dow Jones] Beijing's targeted easing measures are not bolstering economic growth this year and sentiment remains weak, Standard Chartered economists Li Wei says. "It started as a deleveraging process and a property market ... (Document DJDN000020141106eab600154)
EU passenger car registrations rise 6.5% y/y during October – ACEA IHS Global Insight Daily Analysis, 18 November 2014, 2555 words, Ian Fletcher, (English) Passenger car registrations in the European Union (EU) have grown again in October, but the improvement is very much relative to how poor the performance in this market has been. (Document WDAN000020141118eabi00008)
Analyzing Whether Countries Are Equally Efficient at Improving Longevity for Men and Women American Journal of Public Health, 1 November 2014, 4089 words, Douglas Barthold; Arijit Nandi; José M Mendoza Rodríguez; Jody Heymann, (English) Objectives. We examined the efficiency of country-specific health care spending in improving life expectancies for men and women. Methods. We estimated efficiencies of health care spending for 27 Organisation for Economic Co-operation and ... (Document GAPH000020141106eab10000s)
BBC Monitoring quotes from China, Taiwan press 18 Nov 14 BBC Monitoring Asia Pacific, 01:28 AM, 18 November 2014, 1677 words, (English) The following is a selection of quotes from editorials and commentaries carried in 18 November 2014 website editions of mainland Chinese, Hong Kong and Taiwan newspapers and news portals available to BBC Monitoring. Unless otherwise stated, ... (Document BBCAPP0020141118eabi0012x)
(Insight-EU-Dossier) TiSA: EU pushes to lock in liberalisation of services worldwide By Craig James Willy, dpa https://global.factiva.com/hp/default.aspx?pp=Print&hc=All 11/12 8/15/2018 Factiva dpa Insight EU, 12:57 PM, 20 November 2014, 4321 words, (English) The European Commission is pushing for a deal to free up as many services as possible to international competition, with potential big gains for companies and consumers. But critics fear liberalisation could mean deregulation, privatisation ... (Document DPAINSE020141120eabk00001)
Cyprus’s Shipping Tax Regime Mondaq Business Briefing, 18 November 2014, 1993 words, Philippos Aristotelous, (English) Cyprus is among the world's top shipping centres, with a government policy which actively attracts investment to the island. This article looks at the features and exemptions which make it so attractive to shipping businesses. (Document BBPUB00020141118eabi000ht)
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