The magic of Nizhny Novogorod

Don’t call it just Novgorod, our guide advised me. You can say Nizhny, but just Novgorod is wrong. Nizhny has always been ‘the city’. The meaning is that, founded in 1211, it has always meant to be a city; it was not created just as a settlement. In 1932 Stalin decided to rename the City into Gorky, i.e. it was given the name of the famous Russian writer. Gorky himself, still alive at that time strictly opposed the idea. And believe it or not, soon after the city turned from Nizhny Novgorod into Gorky, Gorky started to fell ill. Bad Karma. Eventually the city was renamed again in 1991. (So Nizhny Novgorod = Gorky = Nizhny Novgorod; I will use both terms interchangeably).















In the Great Patriotic War 1941-1945 Gorky made highly significant contributions to the production of military equipment. One out of 2 submarines, every third tank, including famous T-34, and one out of four airplanes were produced in the cities’ numerous factories. German Wehrmacht never reached the City (Plan Barbarossa to capture Moscow in a Blitzkrieg failed already in 1941 as a result of the battle of Moscow), but military planes did; the city was bombarded 43 times.

Eternal Flame

A large chunk of the magic of Nizhny derives from the fact that the city is located on the mighty Volga River, which is the largest river in Europe, some 3,500 km long. And Volga River meets with the river Oka, some 1,500 km long. The location where they meet is a must see. You can easily do so by visiting the Kremlin.



Due to its military importance the Gorky was a closed city until 1991, i.e. it was invisible to foreign tourist. It was not displayed on foreign maps. And cruisers on the Volga River passed the City at night, when most tourists were sleeping.


Nizhny consists of two parts. The Kremlin, the old city, and the Higher School of Economics are located in the first one on the one side of the River Oka. The second part, industrial part of the City on the other side of the River Oka, was only merged with Nizhny in 1920. Nizhny is the 5th largest city of Russia, with some 1.5 registered inhabitants (Gastarbeiter and sans-papier come on top of that). Though Nizhny has a Metro, the public transportation system strongly relies on public buses, and to some extent tramways. In fact the first Tramway in Russia was established in Nizhny in 1896. They proudly display this fact on the railcars of a particular line. Nizhny will host some of the matches of the Soccer World Championships in 2018. This is expected to lead to a large increase in infrastructure investment in the years to come.

Until 1917 Nizhny was famous for its international fair. Back then Moscow was usually said to be the heart of Russia, St. Petersburg was the Brain (until today, a lot of senior federal decision makers graduated from St. Petersburg, just take the President, and the Prime minister as examples) and Nizhny was the pocket or the valet (because Nizhny provide so much goods, and merchants made a lot of money there). The fair was closed in 1917, reopened again 1922-1930 during a period of economic policy experiments, and then again closed.

So Nizhny is worth a trip. You can reach the City by bullet speed train within 3 h 48 minutes from Moscow Kurskaya. Fast, clean, safe, still reasonable price. And no boarding time.

Nizhny_Frau_entspannt (c) Tim Jaekel, 2015


What’s new in econometrics? (Part 2)

Day two of the 2nd International conference on Modern Econometric Tools and Application at the Higher School of Economics in Nizhny Novgorod.

Mikhail Zitlukhin from Steklov Mathematical Institute in Moscow addresses a question which is relevant for any actor in the financial market: How to invest money optimally? The common approach is to compare scores assigned to different investment strategies. From that you choose an optimal portfolio. Now, investment decisions face a tradeoff between profit and uncertainty. The objective of financial market research has been to provide a good answer to the question how to measure the trade-off. The Sharpe Ratio is a well-known measure. The expected return, EX, is divided by standard deviation of returns (square-root of Var(X)): S(X) = EX / Var(X)^0.5. A basic assumption is that future returns are known. The Sharpe Ratio is famous because it is easy to understand, and good for practice. But it has disadvantages: not monotone, symmetric, and not flexible. And it is just one measure. Mikhail pointed out that we want to have a family of performance measures, not just one. Mikhail contributes to the literature by defining a simple monotonic profit-to-uncertainty ratio. This approach builds on Theory of coherent risk Measures, the concept of a worst scenario expectation (what is the profit in the worst case), and the logic of Average Value at Risk, among others.

Svetlana Makarova from University College London and co-authors proposed a new test for evaluating economic policy effects. The traditional approach to evaluation is to include some variables representing policy action into the equations. Svetlana and co-authors start from two assumptions which contradict the traditional logic: The time lag between policy action and impact on the economic process is not known well enough. And past decisions might not affect the mean value of the process but its stochastic, unpredictable part. An example is targeting inflation. Svetlana use ARMA-GARCH models to inform an alternative testing strategy.

Kirill Furmanov from Higher School of Economics and his co-author presented results from research on a model for mortgage survival. The response variables are timing of default (3 months without payment), pre-termination (both are modeled separately). They exploit a data set with some 280,000 observations, so large sample trap is an issue. Covariates include debtors and mortgage characteristics. The focus of the paper is on the methodology of comparing survival or life time models. However, they find no satisfactory measure of predictive accuracy of survival model.

And a last snapshot, because this is the kind of topic everybody is interested in: Evgenii Gilenko from St. Petersburg State University and Elena Mironova from University of Amsterdam investigated whether items like gender, driving experience, and even car color should be reflected in future tariff formulation of Russian Motor Own Damage insurances (pictured). So far, mostly driver’s age, car brand and car age and prior claim are reflected in tariff calculation. The Russian car insurance market is a very competitive one, due to recent Rouble deprecation prices for foreign car parts have skyrocketed, while profitability of insurance products has sharply decreased accordingly. Exploiting a dataset of some 3,000 contracts they found no significant effects of driver’s gender on damage claim frequency. Nonetheless the insurance company where the data come from introduced a discount for female clients. The reasoning was that women tend to cheat less, and the claim severity is lower compared to male peers. (Footnote: In the European Union unisex nowadays triumphs over such reasonable nuances. It is not allowed anymore to (positively) discriminate insurance customers based on gender characteristics).


What’s new in econometrics?

The 2nd International Conference on Modern Econometric Tools and Applications (EC2015) is currently taking place at the Higher School of Economic Campus in Nizhny Novgorod (pictured), some 500km east of Moscow.

Svetlana Bryzgalova from Stanford University presented a quite impressive and sophisticated approach to estimate the consumption risks both of bonds and stocks. Dean Fantazzini from Moscow State University had a closer look on the reasons for the recent sharp decline in oil prices. 3 Factors are usually held to account: First of all, there was too much oil in the market. Shale oil production in US is at a peak level, although the oil companies are making losses and accumulating a lot of debt (indicating that shale oil production is just not efficient, but anyways). A short break in the civil war in Libya resulted in an unexpected additional inflow of oil on the market. And Saudi Arabia decided not to cut supply. Secondly, demand for oil turned out to be lower than expected. This was mainly due to economic slow-down in Europe and Asia. On top of that there was a strong dollar, which further put pressure on the oil price. But beyond these commonly stated factors, Dean showed that there is some variation left. He proposed a bubble detection strategy; including a decomposition of a variable into its components. He finds evidence on bubble behavior; apart from basic economics this bubble behavior puts additional pressure on the oil market.

In the spatial econometrics sessions Olga Demidova, from Higher School of Economics, and her co-authors investigated unemployment clubs in Russian Regions. This is relevant because the Federal concept of Regional Development until 2020 asks for a balanced socio-economic regional development and a reduction of interregional disparities. In the same session I present a paper on Performance Gaps, Peer Effects and Innovative Behavior in Public Sector Organizations. I develop a model in which managerial actions result from spillover due to pure learning or strategic mimicry and a cost-benefit calculus of instrumental benefits vs. reputational costs of innovation adoption. Propositions are tested against participation data in a Swedish benchmarking exercise.

Also interesting from a policy-perspective was that – using time series analysis – Rajarshi Mitra and co-authors find that foreign aid has a significantly negative effect on personal income in Bangladesh in the 1971-2001 period. Seemingly overseas development assistants (ODA) was not very effective has rather harmed than benefited Bangladesh. A “bad policy-environment” is considered to be an explanation for this finding.