Book review: Econometrics for Dummies

I just finished reading Roberto Pedace's 2013 book Econometrics for Dummies. As you might expect from a book written as part of Wiley's 'Dummies' series, the book is written as a basic introduction to its topic, starting from the basics of probability distributions, and ending with a brief primer on how to conduct an econometrics research project, as well as common mistakes to avoid in applied econometrics.

The book mostly hits the mark as a good introduction. However, Pedace clearly has a high opinion of dummies, because he assumes a great deal of statistical understanding. The book also has a lot of mathematical formulae to negotiate. To be fair to Pedace, it would be difficult to teach econometrics without the formulae without turning it into a 'recipe book' of steps to follow that would not help readers to understand. Nevertheless, I feel like the book could have been pitched perhaps a little lower, as more of a stepping stone between basic statistics and a full introduction to econometrics.

Nevertheless, the book is well written and easy to follow. Pedace does use some unusual terminology though. I struggled with his reference to categorical variables as "qualitative variables". In my mind, qualitative is something quite different. The book is also a little repetitive at times, in part because Pedace has written it in a way where the reader need not necessarily linearly follow through each chapter, but instead can jump directly to the bits of most relevance without missing out on important details that are hidden earlier in the text.

The book may be an introduction for dummies, but Pedace certainly stretches the dummies. I really appreciated that it included a discussion of difference-in-differences (and regular readers of this blog will recognise that this is a research approach that is applied quite commonly in research papers). I also thought that Pedace gave the clearest description of the difference between fixed effects and random effects models for panel data, as well as the Hausman test. Although applications of econometrics to panel data are a feature of every econometrics textbook, in my mind most do not clearly explain these models. At least, not as well as Pedace does.

Finally, the book offers an online 'cheat sheet', although I was disappointed that this seemed to simply be a static webpage, and not really a sheet that can be downloaded or printed.

Overall, this book is a good accompaniment to a full econometrics textbook, or as a memory aid for those who did econometrics some years ago and want the simple details quickly. Or, for those who want an intermediate step between introductory statistics and a full econometrics book such as Mastering 'Metrics (which I reviewed here) or Mostly Harmless Econometrics (which I reviewed here).

Broadband coverage and rural fertility

I recently expressed some scepticism at a paper showing that the release of the iPhone explained a third or more of the decline in US fertility. So, I was interested to read this new working paper by Gokhan Kumpas (California State University, Los Angeles), which looks at the effect of broadband coverage on fertility. 

Specifically, Kumpas studies broadband expansion to rural areas through the USDA's Broadband Initiatives Program (BIP) and the National Telecommunications and Information Administration’s Broadband Technology Opportunities Program (BTOP), which accepted applications in 2009-10 for broadband expansion. Kumpas identifies counties where applications for the programmes were rejected, using a subset of those counties as a control group to compare with counties where applications were successful. Moreover, Kumpas limits the control group counties to those that most closely matched the pre-treatment trajectory of the treated counties in terms of population growth, in order to deal with any problems of mean reversion.

Kumpas compares fertility among teenage women (aged 15 to 29 years) between 2010-13 and 2018-19, leaving out the intervening years where broadband was being expanded. In his main specification, he finds that broadband rollout:

...reduced the rural teen birth rate by approximately 1.6 per 1,000 in the pre-COVID post period (CHR release years 2018–2019), or about 3 percent of the pre-period baseline of 48.6 per 1,000.

The teenage birth rate for the treated counties fell from 48.6 to 34.0 per 1,000, so based on these results the effect is equivalent to about 11 percent of the 14.6-point decline in the teenage birth rate. Notice that this is a much more modest estimated contribution to fertility decline than that in the iPhone and fertility paper. Moreover, Kumpas has a good theoretical basis for believing that broadband would affect fertility, based on the opportunity cost of fertility work of Kearney and Levine (see here, for example), which:

...predicts that any local shock that meaningfully expands the perceived economic or informational opportunity set young women face should depress teen fertility. Broadband Internet expands the perceived opportunity set through several channels.

First, broadband expands informational access to contraceptive methods, to family-planning service locations and scheduling, and to the comparative costs and consequences of different reproductive choices...

Second, broadband expands access to schooling and credentialing options beyond what is locally available, including online community-college coursework, remote tutoring and test preparation, financial-aid information, and credential-program advertising. The downstream consequence is to raise expected returns to schooling and to deferring family formation.

Third, broadband expands the labor-market opportunity set by making non-local jobs visible and (with telework) accessible.

Kumpas finds results consistent with the first two of these channels, with the effects concentrated in counties that had at least one Title X family planning clinic (with no statistically significant effect in counties without a clinic), and individual-level evidence that high school completion and college attendance increased in treated counties. However, there was no evidence for changes in the adult labour market. These results are suggestive that the contraception-access and education channels explain the results, although they are not definitive.

Now, the analysis relies critically on comparing counties covered by successful applications with those covered by unsuccessful applications. This rejected-applicant comparison is appealing, but it relies on the assumption that the treated and control counties would have followed similar trends in the absence of funding. Kumpas provides considerable evidence in support of that assumption, including by selecting control counties with similar pre-treatment population trends, but it cannot be tested conclusively. Funding decisions were based partly on project benefits, viability, and sustainability, and the applications would have included information about subscriber projections and local demographics. It is therefore possible that factors related to anticipated demographic change influenced both the likelihood of receiving funding and subsequent fertility trends. This is similar to the concern I raised about the iPhone and fertility paper.

Nevertheless, if we take the results at face value it appears that broadband access contributed to reduced teenage births in rural US counties. Let's not get carried away though - broadband explains only around 11 percent of the decline in the teenage birth rate. That is a meaningful proportion, but most of the decline in teenage births happened for other reasons. In other words, fertility would have declined substantially even without the contribution of broadband access.

Read more:

• The pandemic 'baby bust' in other countries
• Cohort effects and the downturn in US fertility since the Great Recession
• The new economics of fertility
• The economics of the falling total fertility rate in New Zealand
• The economics of fertility in high-income countries
• Can fertility return to replacement levels?
• You can make future population decline disappear just by changing the way you categorise people and fertility
• The modest impact of Australia's baby bonus on fertility timing
• My take on that iPhone-fertility paper

This week in research #134

Here's what caught my eye in research over the past week:

• Wu and Lee use a theoretical model to show that higher military spending stimulates aggregate demand, thereby promoting employment and income growth, particularly among low-income groups, and consequently reducing income inequality
• Wang (open access) finds using data from British Columbia that having larger proportions of female peers has a large positive effect on students' choice of a STEM major
• Clark and Nielsen (open access) conduct a meta-analysis on the returns to education, including 79 studies that use changes in the minimum school leaving age to identify effects, finding that the average return to an additional year of education is 8.2 percent
• Giuranno and Manni (open access) review the literature on the impact of TikTok on elections, concluding that TikTok functions as a fast-moving marketplace for political ideas in which algorithmic incentives may shape conditions relevant to electoral integrity
• Collischon and Zimmermann (open access) unsurprisingly find zero effects of western Zodiac signs on wages, education, and managerial status among workers in Germany
• Garcia et al. find that both social trust and civic pride contribute to the willingness to pay to prevent the relegation of a professional football (soccer) team, with social trust more relevant for those who attend games

Also, I am delighted to report that my Master's student, Josh McNamara, won the Jan Whitwell Prize for the best student research paper at the New Zealand Association of Economists conference last week. Josh has blossomed into a standout research student over the last couple of years, and will no doubt do great things in a PhD programme in the near future. Congratulations Josh!

Finally, I can also report a marker of my own mild fame. While in Scotland last week, I visited my ancestral family lands at Achnacarry in the western highlands, and the Clan Cameron Museum. My wife asked if they would add me to their list of famous Camerons, and after looking me up online (including my blog!), they agreed! So, I join the likes of former UK prime minister David Cameron, Pearl Jam drummer Matt Cameron, and others on the list.

Spark's new overseas roaming charges and price discrimination

I've just gotten back home from three weeks in Europe. One irksome but necessary aspect of travelling is mobile phone roaming. While I was away, Spark introduced new roaming charges, and their new options both increase the price per day of roaming for most overseas trips, and price discriminate so that those staying overseas for longer pay a higher price for roaming. As the New Zealand Herald reported:

Spark customers travelling overseas for the school holidays face new charges to stay connected, with the telco scrapping its cheapest $25 fortnightly roaming pack.

The company is overhauling its roaming plans, with the new charges taking effect this Friday, including an automatic $10-a-day fee if customers don’t turn roaming off...

Previously, pay monthly customers could use 2GB of data on one of the provider’s 14-day roaming packs, priced at $25 and $30...

Three new packs will replace the old plans, alongside a new daily roaming option.

A $30 14-day pack will still be available to prepaid customers.

The other options will provide travellers with 20GB to use over 30 days, a change the company believes will make roaming simpler and more predictable.

“This helps our customers to stay connected for longer, with fewer top-ups, less uncertainty, and greater confidence about what they’ll pay.”

While the $50 and $65 packs have a higher upfront cost, customers would receive five times more data to use than under the previous plans, the spokesperson said.

If you look at the price per gigabyte of data, the new roaming packs are clearly much better value. Customers are paying twice the price, but getting ten times the data. So, high data users are likely to be better off under these plans. I want to focus instead on travellers who are not using large amounts of data (and for simplicity, I'm going to focus on the data-only packs, not the more expensive packs that include roaming calls and texts). For those travellers, when you look at the cost per day of roaming, the new packs are far more expensive. This is illustrated in the diagram below, which shows the costs for up to 30 days of roaming. The bold green line shows the existing pricing for a 14-day data-only roaming pack ($25 for each 14-day period). The light blue dashed line shows the cost using the new $10 daily roaming rate. The orange dashed line shows the cost for the new 30-day data-only roaming pack ($50 for each 30-day period).

For a Spark customer roaming for one or two days only, the new daily roaming pack is the cheapest option. So, if you're travelling to Australia for a day or two of shopping or to attend a concert or a sporting event, the new option is a better deal than what was previously on offer. With the new options, daily roaming is lower cost than buying a 30-day pack for up to four days of roaming, and the same cost as the 30-day pack for five days of roaming. Beyond that, you would be better off buying the 30-day roaming pack, even if you are only roaming for seven days.

The comparison between the old 14-day roaming pack and the 30-day roaming pack makes it clear that anyone roaming between five days and 14 days will now be paying twice as much as before. From 15 to 28 days, the cost of roaming with the new packs is the same as for the old packs. For someone like me, who typically goes overseas for a conference and might be away for 10-14 days at a time, this is clearly going to increase the cost of roaming.

It may be that Spark has determined that the new pricing options better reflect actual customer usage. That is what a Spark spokesperson argues in the New Zealand Herald article. However, it is also clearly an example of price discrimination in action. Travellers going overseas for a few days likely have more elastic demand for roaming than travellers going overseas for a longer time. That's because of the availability of close substitutes. If you go overseas for a few days, you could make use of free hotel and airport WiFi, or be prepared to just switch off mobile data for the time you are away, rather than paying for roaming. So, travellers who go overseas for a few days are likely to be relatively price sensitive. Travellers going overseas for a longer time are less likely to be able to switch off mobile data for that length of time, making them less price sensitive. The optimal pricing therefore is to set a higher price for travellers going overseas for a longer time than for those going overseas for a few days.

Price discrimination is very common in practice. In this case, Spark is using price discrimination and that will likely increase their profits. And that means that many travellers who are not high data users, myself included, will be paying more for roaming in the future.