Human Capital and Political Business Cycles

Classical theory considers political business cycle as a result of either opportunistic behavior of government (opportunistic cycle) or aiming policy on certain constituency (partisan cycle). In this paper, we propose an alternative explanation of the phenomenon of political business cycle — experience of government. We propose an illustration that shows that elections infer cycles without any opportunism or ideology of incumbents. We also build a model with endogenous ego-rent. The model explains a channel to increase incentives, when none has commitment — governors need to develop skills to increase their value for public and increase probability to get re-elected. Using fiscal monthly data of Russian regions from 1996 to 2004, we got evidence both of positive effect of experience on performance and opportunistic component of the cycle. We also got evidence of diminishing return on experience.Elections, opportunistic business cycle, experience, sunk cost, Russian regions.

Opportunistic Political Cycles: Test in a Young Democracy Setting

This paper tests the theory of opportunistic cycles in a decade-old democracy, Russia, finds strong evidence of cycles, and provides explanation for why previous literature often found weaker evidence. Using the comprehensive list of Russia's regional elections and regional monthly panel data between 1996 and 2003, we find that: (1) budget cycle is very sizable and short-lived: large expansion and contraction in fiscal spending occur within two months of elections on both sides; (2) the magnitude of the cycle decreases with government transparency, level of regional democracy, and voter awareness; (3) cycle becomes smaller over time; (4) pre-electoral manipulation increases incumbents’ chances for re-election. The results confirm theoretical findings that maturity of democracy, transparency, and voter awareness are important in determining the scope for opportunistic cycles. The short length of the cycle explains underestimation of its size by previous literature because of low frequency data used.Opportunistic political cycles, Maturity of democracy, Russia, Fiscal policy, Government transparency

Opportunistic Political Cycles: Test in a Young Democracy Setting

This paper tests the theory of opportunistic cycles in a decade-old democracy–Russia–finds strong evidence of cycles, and provides an explanation for why previous literature often found weaker evidence. Using regional monthly panel data, we find that: (1) the budget cycle is sizable and short-lived; public spending shifts towards direct monetary transfers to voters; (2) the magnitude of the cycle decreases with democracy, government transparency, media freedom, voter awareness, and over time; and (3) pre-electoral manipulation increases incumbents’ chances for reelection. The short length of the cycle explains underestimation of its size by previous literature because of low frequency data used in previous studies.Opportunistic Political Cycles, Maturity of Democracy, Russia, Fiscal Policy, Government Transparency

Active Labor Market Policies in Russia: Regional Interpretation Determines Effectiveness?

Persistently sizeable unemployment attracts interest to active labor market policy as an instrument to reduce unemployment. Moreover, sustainable economic growth requires an effective re-training system, a part of which is usually associated with state employment offices’ programs. Little is known, however, about the effects of active labor market programs (ALMPs) on the unemployed in Russia. The paper is the first attempt to shed some light on effectiveness of ALMP in Russia from micro perspective. The influence of ALMPs on the probability of re-employment is estimated using administrative individual-level data from employment service register on two Russian regions. Overall and group treatment effects of the programs are estimated using the nonexperimental exact matching approach. Two cases - assuming that the first program has the major effect (single program participation) and examining sequences of programs (multiple program participation) – are considered. A matching design allowing taking advantage of duration nature of administrative data to compensate for informational restrictions associated with the dataset is proposed. We find that the programs under consideration seem to prolong the unemployment spells in one of the regions, and help to leave unemployment quicker in the other, with the size of the effects differing 3-5 times. The sizable difference in treatment effects prompt for substantial institutional differences: there seems to be high discretion in interpretation of employment service role in the local labor market revealed in procedures of program assignment.Active Labor Market Policy, Unemployment, Duration Analysis, Exact Matching, Multiple Programs, Transition

Leveraging aggregate ratings for improving predictive performance of recommender systems

This paper describes an approach for incorporating externally specified aggregate ratings information into certain types of recommender systems, including two types of collaborating filtering and a hierarchical linear regression model. First, we present a framework for incorporating aggregate rating information and apply this framework to the aforementioned individual rating models. Then we formally show that this additional aggregate rating information provides more accurate recommendations of individual items to individual users. Further, we experimentally confirm this theoretical finding by demonstrating on several datasets that the aggregate rating information indeed leads to better predictions of unknown ratings. We also propose scalable methods for incorporating this aggregate information and test our approaches on large datasets. Finally, we demonstrate that the aggregate rating information can also be used as a solution to the cold start problem of recommender systems.NYU, Stern School of Business, Center for Digital Economy Researc

Leveraging Aggregate Ratings for Better Recommendations

The paper presents a method that uses aggregate ratings provided by various segments of users for various categories of items to derive better estimations of unknown individual ratings. This is achieved by converting the aggregate ratings into constraints on the parameters of a rating estimation model presented in the paper. The paper also demonstrates theoretically that these additional constraints reduce rating estimation errors resulting in better rating predictions