Women have the purchasing power now

Pallavi Shastry

July 07, 2021

Women have the purchasing power now

The number of single women who’re buying homes in America compared to their male counterparts is growing at a remarkable rate. A new report by LendingTree has found that in 50 of the largest metropolitan areas of the United States, single women own more homes than single men do. And the average age of women buying homes for the first time is 33. Young, independent, and financially educated women are taking the real estate world by storm!

![Women have the purchasing power now.png](/uploads/Women_have_the_purchasing_power_now_a4908ecf98.png) Given how women’s space in the professional world, as well as home, has grown multifold (there’s still a long way to go though), a growing, and in some cases, a major part of big decisions like home buying, investments, and insurance are being made by women today. When couples browse for their new home, it’s often the woman who eventually decides whether or not they’re buying that house.

Regardless of where they are personally, women have become important decision-makers with trillions in spending power today. Single women, especially those with children, are the second-largest set of homebuyers after married couples. Today’s women have more independence – financially and socially. And that has made them realize the importance of making the right choices that will never render them dependent on anyone else.

According to LendingTree's study , in America's top metropolitan areas, single women own 22% of homes while single men own 13%. It’s a new kind of gender gap we’re seeing here. Breaking the norm of married couples buying homes to build their family, single women are slowly changing the rules for themselves today.

As women take the lead in reshaping and restructuring our society, they are still the ones to think about their dependents. Whether it’s buying a home for themselves or their family, or buying insurance, women have carefully considered what’s best for the future of their families. Despite the persisting wage gap, women have still found ways to empower themselves financially and with them buying homes now more than ever, we’re going to see a big shift in financial power.

Reasons for this gap between men and women with homeownership aren't completely clear yet. But it’s safe to say that women prioritize buying a home from a young age. If you’re a woman reading this, you should know that buying a home is always going to be a big objective and we have a few tips for you to keep in mind while you achieve this goal.


The first step to making a big purchase or an investment is to find out as much information as you possibly can to know what you’re about to get into. From mortgage to insurance, make it your priority to know everything. Information is the best tool you can have.


Sit back and think about your ideal day in your home. You may be living in the busiest part of town right now but is that where you want to buy a house? Or do you want to move to the suburbs? What are the benefits of buying a house in either of these locations? Do you want pets? There’s a lot to factor in.


Your budget can help you zero in on the location and size of your home. Look at your finances and get help understanding them to set the right budget. Learn about what you can and can’t fit in your budget to lessen the pressure.

We hope this helps you move in the right direction towards buying a home for yourself. It’s a big move but it will be the best move for you.

Why Life Insurance should be a priority for single parents

Pallavi Shastry

July 07, 2021

Why Life Insurance should be a priority for single parents

As if life isn’t hard enough already, it’s even harder for single parents. The seemingly simple idea of managing home and work is a whole different story when it comes to single parents. It can be a stressful, lonely effort – home expenses, bills, kids’ tuition, clothing, mortgage, the list goes on. It’s tough, but you make it work. You make it work for yourself and for your children. Now imagine this well-oiled parental love machine coming to an abrupt halt if you’re not in the picture. You wouldn’t want your kids to be left to fend for themselves – or, entirely at the mercy and kindness of others. Getting a life insurance policy is an ideal safeguard and backstop in a scenario where, if the unthinkable happens, your kids will be okay. You would have taken care of their financial future with the right coverage at the right time. **Can you afford Life Insurance?** It’s a misconception that life insurance is expensive. This couldn’t be further from the truth. Depending on your financial needs, age and overall health condition life insurance may cost you as little as a week’s worth of coffee. For instance, if you want a $100,000 coverage, your premium could be starting off at $29. Isn’t that something you could put on your grocery list? ![6b54a6_00089797dd8444c191aa82c6764df620_mv2 (1).jpeg](
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**How much coverage do you need?** Now that we’ve established how life insurance can be affordable, let’s look at your finances a little closer. - Mortgage - Medical bills - Kids’ future college tuition - Other debt Consider all the above factors before picking a policy. Ensure your coverage amount covers all these future expenses and more. Don’t buy life insurance because you hear people say how important it is. Buy it because you understand why it’s important. When you know how much coverage you need and why you need it, life insurance becomes a lifesaver for your loved ones. Bubble’s automated guidance platform can help sort out all the intricacies for you. You might say you have “enough” saved up for your children’s future needs. But do you? - Would it be enough to cover your child’s college education? - Would it be enough to pay off your mortgage and any other debt you leave behind? If you can’t answer any of these questions, you might want to consider buying life insurance with coverage that can help keep your children financially looked after when you're no more. Yes, nobody and nothing can replace a parent’s love, care, and protection. You are your kids’ safety net now; but if you’re no more, life insurance can at least partially serve as one...

You don't* need a medical test for Life Insurance

Pallavi Shastry

July 07, 2021

You don't* need a medical test for Life Insurance

The most important factors that determine the cost and kind of life insurance policy you can get are age and health. You are most likely to be in good health in your younger days – 35 and under, let’s say. That’s why the younger you are, the more affordable your life insurance can be. With a good health record and age on your side, you can very well lock in a low premium for the rest of your life. At the same time, if you are young and healthy, life insurance companies do not need to carry out intrusive, messy tests to determine if you are healthy enough to qualify for a policy. Just a few questions usually suffice. Not many people, especially young people, are aware of this fact. Buying insurance has always had the bad rep of being a long, worn out, and expensive process with too many people to talk to and too many hoops to jump through - and yes, a medical test at the end of it all.

Being in the digital age has proven to be a blessing. Insurance buying has become a less complicated and more affordable process. At Bubble, that’s exactly what we’re doing to help every single one of you get the right Life Insurance policy - at the right age, at the right time.

![Probability to no health test.png](/uploads/Probability_to_no_health_test_b37a6a01e8.png)

The above graph shoots it straight – life insurance gets more expensive and difficult to buy with age. The younger you are, the cheaper it is. And the probability of needing a medical test done is negligible. With an enjoyable user experience, ease of purchase, and no medical test there isn’t really a reason or excuse why you shouldn’t buy life insurance before you’re 30. It’s one of the best things you can do for yourself and your loved ones when you’re young and healthy.

By [eliminating medical tests](, life insurance becomes more affordable, convenient, and quickly issued to you.

When you’re 30 or under, you are more likely to be in good health than say when you’re 33 even. And your premium might easily jump from $10 to $30. Of course, there are other factors like whether you’re a smoker or not and other such. This is why getting something like life insurance out of the way as early as you possibly can is the best advice you can get.

If you want a reminder of why you need life insurance, here it is. Your family’s financial future.

Life is short. Don’t let important life decisions fall short too.

How technology has made it easy to buy insurance

Pallavi Shastry

June 28, 2021

How technology has made it easy to buy insurance

Remember the old days? One had to call restaurants and shops to order food, groceries, and other staples. More serious matters like insurance required you to call various insurance providers and get quotes or receive cold calls out of the blue to purchase insurance from them. Sure, many of us would still take the older route to traditional purchases, but with almost everything fathomable going digital – insurance is right up there with the rest.

Businesses have become more efficient and with small and large insurance providers embracing technology, we have far less complicated, quicker, and more affordable insurance policies tailor-made for everyone. Research by McKinsey says that automation can reduce the cost of claims' journey by as much as 30%. What it means is – The ease of filing claims, getting approvals, and money in the claimant’s account is becoming as easy as ordering pizza – well, almost as easy. With more millennials turning homeowners, insurance companies are adapting to methods more convenient and accessible to their generation.

With technology come lower costs, greater convenience, happy families, and of course, satisfied customers. A new customer – a young one at that – expects far more than the traditional customer from a few decades ago. If you are one, you’ve done your research, you have incredible options, and you have every insurance provider’s attention. And to make your life (insurance) easier, Bubble’s AI and matching-pairing guidance algorithms help select the best policies for you.

Just like technology has gotten us to order food online, eliminating the need to dial a number and talk to someone at the restaurant, it has now gotten us to understand what policies work for us, quote and apply online, even get approved online in minutes, get us faster claims, and help us process a traditional financial product in a much simpler and effective way. Not just claims, but with the digitization of the entire insurance buying cycle, post-purchase service is also great today. For instance, with Bubble, you can be assured of continuous support, starting with picking the right policy. Even after the policy is purchased and goes into effect, we are there for you - through nudges, useful information, and advisories

Besides requiring less facetime, delivering more value in policies, and offering faster purchases and claims, digitization has made insurers more creative with their offerings. For instance, you can easily add more coverage in the future without underwriting, so you only pay for the coverage you need and when you need it. With the data they have, they’re able to know their customers better, which in turn helps them create better financial products. And this means smoother processes, easier purchases, and tailor-made policies – I can smell a win-win situation.

There’s a policy for everyone today - Customized policies for people with loved ones with different needs. That's why technology has helped speed the process and makes it less complicated than it used to be. Everyone needs insurance. And it needs to be more accessible to everyone – the ones who understand it, the ones who don’t, and everyone in between.

Dipping your toes in Term Life Insurance

Pallavi Shastry

March 02, 2021

Dipping your toes in Term Life Insurance

As the name suggests, it’s a life insurance policy for a duration of a term and not your entire lifetime. Term insurance lets you buy the policy for a few years and pay a certain sum of money every year till the term ends. If anything unfortunate happens to you during the term, your family will receive the death payout.

Buying a new house is a stressful, overwhelming experience. Particularly, as a first-time home buyer, making a significant investment with a mortgage can be a daunting experience. For most, it’s the largest debt to take on. A scary thought to leave your family vulnerable when something unexpected happens – I mean death. Around a third of Americans would end up in such a situation if they lost the primary earner of the household.

A life insurance policy can cushion the blow related to some of the worry associated with buying a house. It grants your family a sense of security and peace of mind by paying off debts, providing for your kids' future – like college tuition, and supplementing your retirement, among other things. Home expenses typically run in the thousands per month – mortgage, insurance, tax, utilities, etc. Term life policies can cost as little as 1-2% of home expenses for Millennial's- a small additional cost for peace of mind – that’s priceless!

The average length of a mortgage is 30 years, indicating the need for long-term protection – a life insurance policy. Most term life policies are also for 30 years. In the process of getting home insurance, first-time homebuyers should also get life insurance because life insurance premiums increase exponentially with age. Buyers in their late 20s and 30s can lock in low rates for the next 30+ years term policy, which gives protection until their kids have graduated college. You’ve got to think ahead. Bubble makes it easy to buy both life and home policies at the same time, saving you time and money, ensuring your home and family are both protected.

Using various online life insurance tools, homeowners can quickly determine the amount of coverage they may need, customize their policy, and estimate the cost of a mortgage life insurance quote. Factor in the mortgage amount, location, age, kids and their ages (or how many you plan to have and when), other debt (like a student loan), lifestyle expenses, etc.

One cost-effective option is to consider incremental policies – buy the base amount policy now, then add on additional coverage of varying amounts and terms when you have kids, buy a second home, parents become dependents, etc. This allows you to only pay for what you need when you need it. Whatever path you choose to take, using the cost estimator on the Bubble website, you can get a quote for both life and home insurance.

Investing in term life insurance can mitigate some of the anxiety involved in purchasing a home. Bubble makes it easy to not only get a quick quote but also purchase life insurance when first-time homebuyers are getting home insurance. It’s a lot more convenient now than ever to buy life and home insurance. Technology has made it easier for insurance providers and buyers so everyone can benefit from it.

LifeScore - How location matters for Life and Health

Pallavi Shastry and Sahana Subramanian

February 17, 2021

LifeScore - How location matters for Life and Health

You must have heard the cliché “Location location location!” with regard to real estate. But that cliché can extend to health too. Where you live matters a great deal if you care about your health. It should not come as a surprise that proximity and accessibility to parks, gyms, cycling tracks, trekking routes, among others can determine your fitness and health goals. But there is also an increasing recognition that, there are some other intangible attributes of your location that also matter for your quality of life, and mental and physical health. They are the level of community engagement in your locality, the connectedness that you have with your neighbors, the density of social cultural and other interactions and the willingness of neighbors to help each other etc. etc. All of these play a significant role in your life journey.

What is a LifeScore?

Bubble LifeScores are county-level indicators, based on all the factors mentioned above. They reflect the local population’s life expectancy, quality of life and overall health prospects. The scores are gender and age specific with higher scores indicating better life and health outlook for that gender and age group, relative to other counties. Now that we have the heavy-duty technical stuff out of the way, let’s understand how the LifeScore can help you assess your locality’s life and health profile.

Let’s look at it county-wise, shall we?

The LifeScore of a woman aged between 35-45 in Santa Clara, CA is 942 (The cores range from 600 to 950). There are some underlying location-specific factors that contribute to this score, the most important of which happens to be a high median income. The county also enjoys good community engagement including volunteering and recreational group activities which are known to boost overall physical and mental well-being. A high LifeScore like this also reflects the overall community’s health - their access to exercising opportunities, lower mental distress and excellent primary care, which lead to better patient outcomes and a lower mortality rate. In general, the people in this county lead a healthy lifestyle overall.

But there’s only so much one can do about the county they live in (you could pick up your life and move, but we’ll save that for another day). What if there aren’t any trekking routes for the outdoorsy people? What if there aren’t any outlets around for the gym rats?

To drive home the point of location influencing the health and lifestyle of people, let’s look at the LifeScore of a woman in the same age group (35-45) in Lake County, CA which is 767.. A lower LifeScore means higher health risks – directly or indirectly. Our data show that it has much lower median income, and there is a lower rate of physical activity in Lake County as opposed to Santa Clara. It has long been known that preventive healthcare and exercising is a more practical and feasible approach leading to significant health benefits. The county also ranks low on social factors, such as community connectedness, family support and safety. The two examples indicate how the location can impact a person’s physical and mental well-being which ultimately influences one’s overall health and lifestyle.

Men are at similar or more risk than women. From the time they’re out of their diapers it begins. Men die at an average of 5 years earlier than women (sorry, guys). They take the lead in most leading causes of death. Not to scare you or anything – but decades' worth of studies on this subject have been consistently telling us this. Again, all the more reason to buckle down and get healthy. Be it cycling for 30 minutes, a trek on the weekend, or simply a walk in the park – if where you live has any of these opportunities, it’s time to get cracking.

Although everyone, including the youth are prone to lifestyle-based diseases, they also have the opportunity to build good health and take preventive measures. Have you tried yoga? As we observed previously, living in the vicinity of places with high LifeScores confers benefits with lower health and life risks, provided you take advantage of them. This does not imply that those who live in places with lower scores are condemned to an unhealthy life. We are on the verge of several lifestyle-related disease epidemics including diabetes, heart disease, certain cancers. Regardless of gender, age or location, the only way to battle them is to get on the road - literally, if you’d like to run - to better health. Be it actively or preventatively, all of us can forge our way to a long and healthy life!

Dear Homeowner, here’s why you need life insurance

Pallavi Shastry

February 01, 2021

Dear Homeowner, here’s why you need life insurance

Do you own a home? Congratulations! It couldn’t have been easy, coming up with the financing, finding the perfect house in the right neighborhood, moving and settling in, and so much more. I completely understand how many hoops of fire you must’ve gone through to be able to buy a home – a safe haven – for you and your family. But you’ll agree - and would-be home buyers take note - homeownership is THE most rewarding thing we do in our lifetime. Of course, you must have home insurance too. It’s a given, isn’t it? You did all of this to keep your family safe, to protect them on a rainy day (quite literally!), and to secure your future.

Speaking of securing your future, what about life insurance? A recent survey says 89% believe that the primary wage earner needs life insurance but most people don’t get it because of the complexities of applying and getting approved for life insurance. According to Deloitte’s survey in 2015, you’re more likely to buy life insurance with a reliable income and when certain important life events have taken shape like marriage, children, buying a home. That’s what we’re focusing on – homeowners. Now, if you’re a homeowner already, then it’s safe to say you have a fairly stable and reliable income source. There’s almost no reason why you shouldn’t be on your way to get life insurance!

Before you look away with an eye roll and be a part of a disappointing statistic, let’s talk about why you should be getting life insurance when you already have home insurance.

1. It’s just easy!

When you have home insurance, getting life insurance is easy. The process isn’t tedious or complicated at all. In fact, you’ll be surprised how easy and quick it is. It’s just a matter of getting online and on to the right provider. Like I said earlier about food being delivered to your doorstep with a few clicks, it’s not that far-fetched to get life insurance the same way. No one to sell you anything, no going back and forth, no high premium, and no nonsense.

2. Two lesser things to worry about

When you’ve been through getting a house and home insurance, why should life insurance be left behind? Just give me a second here – think of the peace you’d have, knowing that your home, as well as your family, is taken care of. We’ve got a lot going on, considering the circumstances the last year has thrown us in. And we need security now more than ever. Why stay behind?

3. Benefit of being young

I’m not talking about high metabolism here. When you have youth on your side – 20s & 30s to be specific – you can get life insurance at a relatively lower premium and it gets better. You can lock it at that price till you’re well into your 60s in some cases. This is definitely one of those things you might look back at and regret not having done it. As I said, it’s easy to get it done without the hassle of a long process.

4. Advantages of having a family

Your hopes and dreams are on a high when you enter marriage. Your first home takes you on a new high. A kid comes along and completes the picture you’ve been drawing for years. How could you not want to protect this picture with everything you’ve got? Well, it won’t take everything you’ve got. Just a smidgen…of your grocery bill, maybe. Being married with kids and a house makes it easier to get life insurance. In fact, you’re the ideal candidate.

5. Pandemic and all the likes

The word pandemic has seeped into our lives like our everyday coffee. 2020 was a year no one could’ve predicted. Not even in their nightmares. Like me, aren’t you nervous about tomorrow? If something like Covid-19 happened to us when we’re on an all-time high with technological advancement as a nation, who knows what another year in the near future might bring? The only saving grace is life insurance. It helped anyone who was unfortunate and it can help you and your family too.

We think we’re invincible – especially when we’re young. Only when something serious affects our lives do we think about what we should’ve done to secure our family. Don’t wait. It’s never worth it. Life is precious. Life insurance, priceless.

Shelter, Risk and Uncertainty: Housing and Insurance in the Covid and Post-Covid Era

Ashok Bardhan and Robert Edelstein

January 21, 2021

Shelter, Risk and Uncertainty: Housing and Insurance in the Covid and Post-Covid Era

The COVID-19 epidemic has affected every aspect of daily activity. It has adversely impacted a wide range of economic sectors and occupations, especially those which involve face to face, proximate interaction, collective gatherings, services which need physical proximity and contact, and economic activities that require collective operations in close spaces.

The overall economy has taken a significant hit, with the 2020 forecasted GDP slated to decrease by 3.5%; as of November, there are 8 million fewer people employed than in the month of February prior to the onslaught of the pandemic; and in the month of November, there were 14.8 million people who were unable to work because the employer closed or lost business due to the pandemic, of which 12.7 million did not receive pay*.

Many sectors and occupations have suffered significant decreases in output and employment, with transportation, personal services, restaurants, hospitality, and tourism especially adversely affected. On the other hand, a number of economic activities have seen a major bump in demand and output. The most well-known, of course, is the information technology sector, which, by definition involves distanced, remote activity, both individual and collective; companies whose business models rely on online marketing and provision, e.g., E-Commerce firms; companies active in the remote supply of entertainment, media and gaming; and of course those that facilitate the entire infrastructure of online activity, from delivery to distanced learning, to Internet-based collaborative tools, such as Cisco Webex or Zoom.


Somewhat less known are sectors that have benefited because of specific attributes related to the spread and impact of coronavirus. The shelter in place policies have generated a dramatic increase in the “value” and importance of your ultimate refuge - your home. If most of the activities are now home-based, with a substantial part of the work that was formerly conducted at offices and workplaces, and shopping done in retail outlets now launched from home, then the “time value” of housing has surged significantly, everything else being equal**.

The following charts demonstrate how the housing market has been changing in the past few quarters during the Covid era. Many of the housing variables that reflect the

Figure 1
Figure 2

The Case Shiller Index underscores the fact that it is not just the usual urban coastal housing markets, such as the Bay Area and New York, but other urban areas as well that have surged in 2020.

Google mobility data, gleaned from cell phone locations, provides movement trends and time spent by geography across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. The data shows how the number of visitors to (or time spent in) categorised places changed compared to the baseline days (before COVID restrictions), i.e., the median value from the 5-week period Jan 3 – Feb 6, 2020.

The daily data shows that the urban counties of San Francisco, Arlington county outside Washington, DC, and Queens county, New York City were some of those which had the highest increase in time spent in residential spaces. The two spikes are in April and then again in December. There’s been an overall increase ranging between 30 and 40% over baseline in terms of time and duration spent at home. Unsurprisingly, these are also the places which have experienced the largest drop in workplace visitationsranging between 70 and 90%. Indeed, the following chart shows the change in online work trends from home due to COVID-19 in the US.

Figure 3

The Covid 19 pandemic has impacted housing and mobility in another way as well. There has been an outflux of people from dense central-city environments, which were once attractive because of the many entertainment, cultural, and recreational activities available. Former urban dwellers have headed to the peri-urban, suburban and outlying suburban areas, thereby increasing both rents and housing prices in these markets. A number of former renters in cities have bought housing in these new places. As an example of the impact of urban/suburban mobility and migration, our models demonstrate that while most Bay Area and outer Bay Area housing markets, such as Sacramento moved in tandem in earlier years, in 2020 the downturn in rentals in San Francisco and the upswing in housing market metrics in places like Sacramento has been remarkably different, displaying a strong migratory impact. The following chart shows the results of the survey taken on recent homebuyers during the pandemic.

Figure 4

At the same time, the heightened uncertainty and risk awareness regarding health has given a fillip to the demand for insurance products. Some surveys show the increase in anxiety and consumer apprehensions, as can be seen from the following chart****:

Figure 5

While the chart above shows the state of mind from survey results and Internet search queries provide information about the intentions of consumers, the table below shows people voting with their wallet. What are the products and services that people have been spending more money on, particularly relative to previous years?

Figure 6

Data show that there is a correlation between searches for both housing and insurance products, sanitation products, cleansing and medical preventative products, on the one hand, and the regions where the coronavirus spread has been particularly devastating. It’s too early to determine definitively whether risk assessment revisions have led to any change in insurance premium pricing (except in the case of automobile insurance premiums, which have been dramatically impacted negatively since driving overall has been curtailed resulting in lower claims.)

Looking Ahead

The pandemic has had many direct and indirect impacts on household behavior, occupations and economic sectors. In this blog we reflect on how the combination of shelter in place behavior in response to health risks and directives of local authorities, as well as perception of increased risk and uncertainty have caused the housing and insurance sectors to surge.

We believe that “the time value” of housing services has increased because of Covid and the subsequent shelter in place policies; on the other hand, there is increased perception of risk and perils; your Home is the last refuge in an uncertain world, and there will continue to be spillover increased demand for products that can be employed to manage, control, and mitigate risk.

While the Covid era is extending into 2021, we believe that both housing and insurance markets will display hysteresis, even as the post Covid period emerges; that is, even after the underlying cause - the Covid epidemic - subsides, many of its impacts will remain. These impacts might be manifested in pent-up demand for housing, the willingness of people to spend more on housing, everything else being equal, as well as heightened desire to purchase insurance and hedge against risk. We believe there will be a significant burgeoning demand for bundled products that straddle both Housing and Insurance. At the same time, increased data availability, ubiquitous digitization, AI augmented risk and pricing models combined with sound financial and economic theory, and a heightened focus on customer interactions will help develop new kinds of financial products –new kinds of mortgages bundled with other products, targeted insurance products and cross-risk applications. These next-generation products will plug gaps in underserved markets, provide comprehensive security and “peace of mind,” and help customer deal with risks to her home, property, family and life.

2 The American Time Use Survey Data show that an employed person in normal times spent between 13-15 hours in “home related” activity in a 24 hour period. In the Covid era, it should be well over 20, if not close to 24! ** level of activity of the housing market have surged in 2020 relative to 2019. These include sales, prices, days on market, offers per listing, etc***. ***The lower mortgage rates, designed to keep the economy afloat have also played a part in the increase in homeownership and housing market outcomes. ****Google Trends data show spikes in March to June 2020 for searches relating to anxiety, uncertainty and risk. Also, see, for accounts of double-digit increases in the number of life insurance policies sold during the Covid-19 pandemic relative to last year.

Does Location Matter?

Ashok Bardhan and Avi Gupta

January 11, 2021

Does Location Matter?


While the very first case of infection by coronavirus will probably never really be accurately known, by the 31st of December 2019 the city Municipal Health Commission of Wuhan, a city in central China which lay at the junction of dense industrial and trade networks, reported an outbreak of cases of pneumonia. A new kind of coronavirus was subsequently identified. By mid-January, the first recorded cases were confirmed outside of China, starting with Thailand. On January 22, the WHO mission to China issued a statement stating that there was some evidence of human to human transmission, but more investigations were needed.

In the United States, the first confirmed case occurred on January 22, in King county, Washington, followed by Cook county in Illinois. The first confirmed death, as we know it today, occurred on 6 February in Santa Clara, California. As of June 1, there had been 103,717 deaths and 1,800,497 confirmed cases all over the US. The regional variation, the extent and intensity of both the spread of COVID cases and COVID related mortality, and the differences by state and county have been apparent right from the beginning. Since the origins of the virus lay outside the borders of the United States, the initial outbreaks were dependent on the “initial conditions”, and were locationally somewhat idiosyncratic. But over time, certain patterns have emerged, which we can analyze to detect location-specific attributes that have either aided or hindered the spread and impact of the virus.

As of June 1, only 197 of the 3195 US counties have been spared the scourge of the virus, and more than half - 1770 counties - have experienced at least one death from COVID. Figure 1 shows the top 5 counties with the highest prevalence rate among all counties in the nation in terms of confirmed cases per million, while Figure 2 shows the same for the top 10 countries with sizeable populations (over 100,000 people).

Motivation: The Social Factors Impacting Spread and Mortality

Figure 3 shows the top ten counties with the highest COVID mortality rates among counties with sizeable populations. Both flu/influenza and COVID have similar transmission channels when it comes to transference from person to person, with coughing, sneezing, and airborne infected droplets being the primary vehicles. Because the transmission for both is interpersonal, it is therefore socially “determined” and has significant externalities. For the sake of comparison, we show in Figure 3 the flu mortality rate superimposed on the top ten counties with highest COVID mortality rate. The transmission channels may be similar, but the geographic distribution of flu mortality is quite different. The highest flu mortality rates are in counties in Louisiana, South Carolina and Tennessee and the lowest in Collier County and Martin counties in Florida.

All the transmission factors mentioned above – coughing, sneezing, exposure to infected persons, etc. – suggest that there are network externalities that matter. Social interaction and networks are a mixed blessing: on one hand, they contribute to the spread; on the other, social relationships and community connections help provide mutual aid and support, which is vital at any time of crisis. There are attributes of the daily structures of social life, and the nature of built-up space, that predispose one to exposure and facilitate the spread of the virus. At the same time, there is increasing recognition that social connections among individuals – “social networks and the norms of reciprocity and trustworthiness that arise from them" have a salutary effect on many kinds of outcomes, including health related. Some specific features of localities and neighborhoods around the home – the socio-cultural ethos, economic and communal health and civic engagement – have a positive impact on health and in battling disease spread. In short, we attempt to tackle the question: what are the social determinants of the spread of COVID and what role do external, social factors play in determining both prevalence and mortality rate, over and above individual-specific physical, genetic, behavioral and other vulnerabilities?

Most of the debate hitherto, and understandably so, has revolved around the speed of the spread, the required preventive measures, the mode of transmission, the search for a vaccine, and projections of prevalence and mortality over time. Most of these and other epidemiological models are primarily time series models that forecast and predict spread based on parameters such as symptomatic or asymptomatic transfers, and reproduction numbers. However, the “cross-sectional” aspect, i.e. variation not over time, but over space, or the regional and local aspects of the spread of the virus has attracted attention only in so far as it focuses attention on the tragic situation in specific urban areas, such as New York.

Bubble develops health, life and homeowner risk analysis and scores for the insurance industry, taking into account both individual as well as external locality-based social and economic drivers affecting risks associated with home, and the life and health of individuals. We are not medical professionals, but the medical community has come to accept that where we live, and our social and environmental surroundings, impact our lifestyle, health-related behavior and hence, health outcomes. Bubble has therefore collected an extensive amount of local data impinging on these vital issues, for more than 3,000 counties and 30,000 zip codes across the US.

In this blog post, we take a cross-sectional view – snapshots at various points in time across the entire gamut of over 3,000 counties in the US, to analyze the local and county-level social determinants of the coronavirus spread. Does location matter for spread and for mortality, and if so, which social, environmental factors facilitate spread and which hinder it, and which community or socio-economic variables impact mortality positively or adversely? Since this is not an academic paper but rather a blog post, our objective here simply is to begin to demonstrate the salience of external factors and social interaction in the spread and impact of the virus.


What are some of the influential local features and attributes that could impact the spread of the infection, given what we know about the transmission channels? A few obvious features have been mentioned in the media – e.g. the density of population and the frequency with which people collect in crowded spaces, whether for dining, sports or socio-cultural engagements. We proxy the duration and intensity of population interaction, through the reliance on public transportation and other metrics. On the other hand, we look at social cohesiveness, and proxy measures of mutual aid, support and assistance, such as inequality, segregation, and number of social associations. To provide a sense of the rich yield possible from the data, we highlight some sample statistics and charts that showcase key contextual features that are most relevant in the statistical models to follow:

The most densely populated county in the US is New York county followed by Kings County and Bronx county with 48,600 people per square mile, 26,800 people per square mile and 25,000 people per square mile, respectively, while a number of counties in Alaska have the lowest densities (see Figure 4).

New York county also has the highest share (%) of people traveling by public transportation – which frequently throws people together in close proximity – with about 32% doing it regularly, followed by Kings County, NY with 28% and Queens county, NY with 25% (see Figure 5).

Figure 6 highlights the physically active nature of populations residing in the mountain states of Colorado, Utah and New Mexico. Physical activity is, of course, broadly recognized as a singularly effective lifestyle behavioral choice in promoting good health and fighting against any illness 1.

Income equality ratios and racial segregation indices are a measure of fractiousness of a community and hence its collective capacity to counter a local emergency. The former shows a wide geographic range with Eureka County in Nevada being the most unequal, followed by New York county and Clinch county, Georgia, while Roberts county, Texas and Wheeler county, Nebraska are those with very low-income inequality ratios. Figure 7 highlights the parts of the country with the highest measures of black-white residential segregation, which shows a very “diverse” range of counties in many different regions and states of the country.

Figure 8 shows the counties with the highest number of social associations, societies, clubs and other such organizations, per 1,000 persons. Social associations are a measure of civic and civil society participation, important both as a metric of interaction (and hence, spread of infections), and for the probability of mutual aid and support which is relevant in lowering the mortality rate.

Many experts believe that places with higher concentrations of single-person households or smaller households stand a greater chance than places with larger households in stemming the spread of this infectious disease, because there are fewer persons to infect in closed indoor spaces. Figure 9 shows the top counties with the highest share of these smaller households.

Another example of a data vignette of relevance, and potentially associated with both the spread of the virus and the mortality rate, is the general level of affluence. While places with higher incomes have denser international linkages and more global travel, leading to higher probability of exposure, they also have resources to deal with the aftermath and during the treatment stage; a similar story can actually hold for dense urban networks. The Virginia counties on the outskirts of Washington DC, together with Santa Clara and San Mateo counties in Silicon Valley in California are among the most affluent in the country. As an apex measure of health, lifestyle and healthcare, life expectancy is the highest in San Miguel county, Colorado at 98 years and South Dakota has a cluster of three counties with the lowest life expectancy ranging between 62 and 65 years.

A quick look at the correlation of two of the causal factors listed above with the spread of the virus as of Monday, May 25 (we checked for every week starting in mid-April, and results are by and large the same) across the 3,000 plus counties, shows a positive relationship between population density and confirmed cases, as well as positive correlation between the share of the population taking public transportation and the number of confirmed cases (see Figures 10 and 11). Put simply, this suggests that higher confirmed cases are associated with counties that are densely populated and counties with higher usage of public transport, albeit without taking into account, as yet, any conflating factors. As the previous figures showed, urban counties in the New York metro area score high on both these counts, as well as on the prevalence of confirmed cases.

Discussion of COVID Prevalence and Mortality by County

We next run multivariate regressions on two different sets of dependent variables – a) on confirmed cases of Covid-19, and b) confirmed deaths from Covid-19. We recognize that confirmed cases is a crude variable and potentially biased by testing, for which we do not have reliable data for same granularity and frequency. Hence, the need to run mortality models that also include confirmed cases in recent past plus other variables. We ran the former for each Monday starting April 20 through May 11, and the latter starting three weeks later, from May 11 through June 1. An early sign that the results were robust and not a result of a momentary artifact of cross-sectional data was that we get similar results for each week for the dependent variable, with neither the signs of coefficients or the significance changing much. This seems to suggest a stable, time-invariant location specific social dynamic in the spread of the virus 2. The tables present the average coefficient magnitudes for the four weekly models. All the coefficients shown are significant at 95% confidence level.

For the first set of models on the determinants of Covid prevalence figures by county, some of our key independent variables of interest are those area-specific external social attributes that could have a bearing on the infection spread, such as population density, share of people taking public transportation, share of single-person households, and share of people with severe housing problems, while controlling for population, median household income, age distribution, share of people involved regularly in physical activity etc. Table 1 shows the averaged-out coefficients for the variables of interest. As expected, the independent variables that are positively associated with Covid cases are population density, the population share that takes public transportation and share of people in county with severe housing burden (a proxy for overcrowding, bad sanitation, poor kitchen facilities), while share of single person households is negatively associated with infection spread. The overall fit is quite good for a cross-sectional study and the results are robust 3. The coefficient magnitudes are not sizeable, except for the public transportation variable, which is particularly striking, with NY counties driving most of the result; excluding them still keeps the overall result but reduces the coefficient magnitude to the hundreds.

Table 2 lays out the results of Covid confirmed death models averaged out over 4 weeks, as mentioned earlier. The first version of the model uses similar set of variables as for the models involving spread. Since mortality is a subset and nested within the larger infected population, we also use nested models, with the addition in the second specification, of confirmed cases three weeks before, as a predictor variable. Not surprisingly, the first model gives very similar results to the model of spread determinants in Table 1, with density, share of people regularly taking public transportation, percent of population as single person households and those with severe housing burden all behaving in a similar fashion.

Table 3 includes two additional features. The role that social capital plays in creating and nurturing social support systems, mutual aid and collective response networks is captured in a tentative manner in our specification by the Social Cohesion Index, which we create from the urbanization rate, the number of social, non-profit and non-religious organizations, residential segregation, and income inequality ratio, and which has a salutary impact on mortality 4. The inclusion of confirmed cases by county from three weeks earlier, and whose determinants we are already familiar with, explains in large part the mortality rate three weeks later, and helps resolve some of the testing bias concerns. Median household income, a proxy for local area affluence and resources that can be brought into the fight with COVID is significant; more affluent areas have lower mortality, everything else being equal. Different measures of air pollution were associated with Population density, and single person households still seem to matter for mortality once the spread has taken place, while the public transportation variable is no longer relevant. Could this be because by this time most public transportation was severely curtailed? Our next post will investigate these factors further, including more interactions, outlier/residual analysis, more recent real time social and network data etc.


We show that an approach involving analysis of social determinants of health outcomes is promising even in the case of the recent onslaught of the COVID pandemic. Location matters. Where you live has a signal impact on the probability of contracting the infection and the likelihood of successful recovery in case one is infected. The fact that the US is a highly mobile society and US localities are not the traditional settled localities you see in many parts of the world would, at first glance, tend to undermine the locality-specific “social culture” that we rely on. However, even in the US, there is a continuity in locality features, not just the “hard” ones, such as density and environment, but the “soft” ones, such as non-profit memberships. While individual behavior, state of health, resources and general lifestyle are critical, they do not operate in a vacuum and are constrained and shaped by external social factors.

About the Authors

Avi Gupta, co-founder of Bubble Insurance, is a serial entrepreneur who has founded and grown businesses across diverse industries including real estate data/analytics and software, SaaS, e-commerce and automated CAD platforms. He holds a Ph.D. from Univ. of Michigan, Masters from UC Berkeley, and bachelor’s from I.I.T. Kharagpur, India, all in computer science & engineering.

Ashok Bardhan, co-founder of Bubble Insurance, is a seasoned economist and data scientist. He has consulted for several financial, data/analytics and technology companies, prior to which he was a senior economist at UC Berkeley’s Haas School of Business. Ashok holds a Ph.D. in economics from UC Berkeley, an M.Phil. in international relations from JNU-India and an MS in physics and mathematics from PFU-Russia.

1 Our data does not differentiate between outdoor activity and gym-based physical activity; the latter, in closed spaces, can actually facilitate spread.
2 The median household income control (not shown) is the only one that is unstable and changes sign; early on it has a positive association with spread (albeit statistically insignificant) but starting early April and for the mortality models it is negative. Without making too much of this it does fit in with the initial spread in affluent places due to global travel links, but then the resource story, the ability to fight both spread and mortality, comes into play. The reason for naming some of the variables as controls is two-fold. First, there is no specific externality involved; e.g. the share of population above 65 does not directly impinge on an individual’s vulnerability. Second, these are variables available at the level of the individual (age, income, exercise etc.), not social features, therefore more amenable to individual level models. A number of interactions were tried but did not provide additional insights. We plan more complex features in the next iteration. We do not show the statistical models we run for flu, but the fact that there is a robust locality-persistent effect is borne out in the results of our flu models. Run only on available mortality data (confirmed cases data was not available) for 3 separate years 2005, 2010 and 2014, we get stable, consistent results. All coefficients retain their signs and significance. There are some similarities and some differences. Density and public transportation have similar positive signs but are not significant statistically. Single person households is strongly negatively associated.
3 Statistical significance implies the results are not due to chance occurrence. Adjusted R2 of 0.61 implies that our model captures/explains 61% of the variation in data.
4 The first two enter positively and the latter two negatively; all are individually ranked, and the SCI is equal weighted; at the present stage it’s the direction of impact that is important, not the magnitude.

Our Story

Avi Gupta and Ashok Bardhan

January 11, 2021

Our Story

Nobel economist Richard Thaler observed that people are predictably irrational, and do not always make rational decisions, even if they may be good for them. They need a nudge, at the right time, in the right way.

Guilty as charged. I know that I put off making some of the most important financial decisions of my life - forming a trust & will, getting a financial advisor, purchasing life insurance, to name a few - for longer than I should have. The only reassurance was that I was not alone - when I realized that nearly 43% of American adults do not have any life insurance coverage for their families. And another 18% take the shortest path by signing up with whatever their employers offer - often Group Life - which is rarely enough, and typically goes away when you change jobs.

The reasons surprised me even more. Many Americans believe life insurance is 3 to 5 times more expensive than it actually is. Most are never informed about life insurance. And for those that are, it is often through their financial advisors who typically enter their lives when they are older, by when, life policies have already got exponentially more expensive. Not to mention, getting a life insurance policy has never been easy - from answering pages upon pages of questions, to going through a blood test or health check, waiting days for an accurate quote, and going back and forth with your agent to finalize.

We figured it's time to change all that. Make it simple - even fun - to get life insurance, when it makes most sense to us. Such as when we buy a home. Or think about getting married. Or start planning a family.

Bubble's mission is exactly that - to be the simplest, easiest & most convenient way for people to protect themselves, their families & properties, for total peace of mind.We are launching Bubble with one of the most natural ways to secure your family's future with life insurance - by seamlessly bundling it with home owner insurance when you buy a house or refinance your mortgage - often the largest financial transaction, and liability, for most of us.

Bubble will make it easy and quick to get an accurate quote for a bundled home and life insurance policy - tailored to your exact needs. And those that qualify can purchase it instantaneously without any hassle. Change your coverage as your life evolves. And best of all, set it and leave it - have your life insurance premium paid automatically with your home insurance premium, through your mortgage escrow account, so your policy never lapses accidentally.

Now you can sleep at ease in that beautiful home - knowing that both your family and your home are well protected.