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on Informal and Underground Economics |
| By: | Daniel Straulino; Juan C. Saldarriaga; Jairo A. G\'omez; Juan C. Duque; Neave O'Clery |
| Abstract: | Knowledge of the spatial organisation of economic activity within a city is key to policy concerns. However, in developing cities with high levels of informality, this information is often unavailable. Recent progress in machine learning together with the availability of street imagery offers an affordable and easily automated solution. Here we propose an algorithm that can detect what we call 'visible firms' using street view imagery. Using Medell\'in, Colombia as a case study, we illustrate how this approach can be used to uncover previously unseen economic activity. Applying spatial analysis to our dataset we detect a polycentric structure with five distinct clusters located in both the established centre and peripheral areas. Comparing the density of visible and registered firms, we find that informal activity concentrates in poor but densely populated areas. Our findings highlight the large gap between what is captured in official data and the reality on the ground. |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2104.04545&r=all |
| By: | Tiziana Marie Gauci; Noel Rapa (Central Bank of Malta) |
| Abstract: | The paper applies two commonly used methods in the literature to estimate the shadow economy in Malta, the Currency Demand Approach and the Multiple Indicator Multiple Causes (MIMIC) model. Given the unobservable nature of the shadow economy, estimates are surrounded by a considerable degree of uncertainty. While these two methods differ somewhat on the historical evolution of the size of the Maltese shadow economy, which in turn can be traced back to their different underlying assumptions, both suggest that it has remained relatively stable over the last decade, standing at just below 21% of official GDP in 2019. Where possible, these estimates are compared to other studies on the same subject where we find that the dynamic properties of our variable follow those found in the literature. |
| JEL: | C32 E26 H26 O17 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:mlt:wpaper:0220&r=all |
| By: | David Szakonyi (George Washington University) |
| Abstract: | Cracking down on corruption has become a key tool for politicians to build popular support. But little is known about whether anti-corruption measures actually change political behavior. This paper evaluates the effects of a common reform -- financial disclosures -- using data on 25,724 elections in Putin-era Russia. I argue that financial disclosures function like a personal audit, generating information for journalists and prosecutors to investigate illicit gains earned inside and outside of government. Exploiting staggered elections, I find that the passage of a disclosures requirement led to roughly 25% fewer incumbents seeking re-election and 10% fewer candidates with suspicious financial histories. Greater media freedom and law enforcement capacity further increase the risk of corruption and tax evasion being exposed, resulting in even fewer candidacies from those criminally exposed. Increasing transparency changes the incentives for serving in elected office, even in settings where other political motives may be at play. |
| Keywords: | corruption; anti-corruption; Russia; reforms; elections |
| JEL: | D7 H40 D73 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:gwi:wpaper:2020-21&r=all |