As is well-known, Thomas Piketty made some dire predictions for inequality in his book. Roughly speaking, the idea is that low productivity growth will lead to ever-expanding inequality. While Piketty’s empirical contributions are well received (though there are critics), his main conclusions and policy recommendations are based on his theoretical framework, which has been widely criticized. It is thus not an exaggeration to say that the consensus among leading economists is that Piketty’s theory (and consequently his conclusions) are flawed.
So what happens if we study the question of inequality in a standard macroeconomic model? Does inequality increase as growth falls? And if yes, are such increases huge or are they negligible?
One of the biggest economic issues that we’ll have to deal with in the near future is an aging population. This will result in a shrinking working age population that will have to finance the pensions of a growing retired population. Given the magnitude of this issue, it is quite pressing to know how aging will affect the economy.
It is no surprise that – everything else equal – aging will have detrimental effects on the economy. But everything else is usually not equal: people adjust to new situations and this must be accounted for. What adjustment mechanisms will people take? Furthermore, are pension reforms that are in progress in many developed countries (e.g. raising the retirement age) effective? Can these adjustment mechanisms and reforms help us avoid a crisis?
Household borrowing and insolvency has been on the rise lately. This is yet another phenomenon that could not have happened according to baseline neoclassical economic theory, so we need some innovations to explain it.
This post looks at the effect of the “keeping up with the Joneses” phenomenon on excessive borrowing, how borrowing constraints can protect households, and what macroeconomic effects these micro decisions can entail.
In the aftermath of the financial crisis, we have seen two major approaches to bringing the economy back on track. In the U.S. a huge stimulus package was passed, and double-digit deficits were run; in Europe on the other hand government spending was restrained, and austerity ruled, even in countries with no sovereign debt problems. Which one of these approaches is better, and which one is generally recommended by economists?
This post presents the results of an agent-based computational model of the economy, which considers different policies and examines their implications for things like growth, unemployment, or likelihood of crises.
Long-term economic growth has many determinants such as geography, culture, diversity or institutions. The last one, institutions, is probably the most important direct determinant of long-term growth. Institutions influence variables that are highly important for basic stability such as the rule of law, things that are important for sustaining growth levels in the mid-run such as smart investments in infrastructure, and even things that set highly successful countries apart from moderately successful ones such as efficiency of bureaucracies or promoting an entreprenurial/innovative spirit.
The importance of institutions has long been established, but what is still quite an interesting question is how good (inclusive) and bad (extractive) institutions arise. Generally speaking, one must look at the specific history of each country/region to answer this question precisely. For those interested, Acemoglu and Robinson do just that in their book “Why Nations Fail?“. This post, however, is about how a formerly desirable institution can become a rather undesirable institution as the environment changes.
Developed countries have been keeping their inflation low, between 0 and 3%, for some time now. Developing countries have been trying to do the same with varying success. But what is so special about this range? In particular, why is it detrimental if inflation goes above 3%, or below 0%?
This question is indeed a pressing one as cross-country regressions of GDP on inflation fail to find any significant effect of inflation on GDP.
In this final post on dynamic programming I will simulate the model that was developed in the previous post. The simulation is done in Python and then plotted in Excel, but basically any program can be used to do it.
We will see which actual economic patterns the model is able to match and which ones it isn’t.