Paying over the odds at the end of the fiscal year: Evidence from Ukraine (with Margaryta Klymak)
Governments are the largest buyers in most countries and they tend to operate budgets that expire at the end of the fiscal year. They also tend to spend disproportionately large amounts right at year-end. This use-it-or-lose-it spending pattern has been observed in a number of countries and is considered a problem due to possible waste. This could be the case if firms increase their prices to profit from a government’s greater demand at the end of the fiscal year. We investigate this previously unexplored possibility using a novel granular dataset of Ukrainian government procurement auctions over the period between 2017 and 2021. First, we document that the prices bid by firms are significantly higher in the last month of a fiscal year. Second, we employ a neural network technique to infer supplier costs from bidding behaviour. We estimate that suppliers charge around a 7.5% higher margin on less competitive tenders at the end of a fiscal year. Third, we demonstrate how results change depending on the type of the procured good, the length of the buyer-supplier relationship, and whether the procurement was expedited as a result of the Covid-19 pandemic. Our findings imply that substantial government funds could be saved if the extent of the year end spending could be moderated.
Keywords: procurement , fiscal year distortions , Ukraine , government spending
This has been circulated in the Department of Economics Discussion Paper Series, University of Oxford.
High frequency data typically exhibit asynchronous trading and microstructure noise, which can bias the covariances estimated by standard estimators. While a number of specialised estimators have been developed, they have had limited availability in open source software. HighFrequencyCovariance is the first Julia package which implements specialised estimators for volatility, correlation and covariance using high frequency financial data. It also implements complementary algorithms for matrix regularisation as well as functions to estimate a covariance matrix blockwise and combine the results. This paper first presents the issues associated with exploiting high frequency financial data. We then describe the volatility, covariance and regularisation algorithms and demonstrate their implementation in the HighFrequencyCovariance package. We perform a Monte Carlo experiment, which shows the accuracy gains that are possible in different settings. Finally, we show that different estimators can be combined to form an ensemble estimator which can produce more accurate estimates with lower variance than any of the individual estimators.
JEL Codes: C22, C58, G11, G12
Keywords: covariance estimation, correlation, volatility, high-frequency financial data, Julia
This was presented at JuliaCon 2021. The video is here.
In markets where firms sell similar goods to their competitors, firms may be able to free-ride off the costly price signalling of competitor firms by engaging in price comparative advertising. As the goods are similar consumers can reason that if one good is high quality (revealed for instance through price signalling) then so is the other. This paper models this phenomenon and finds that in equilibrium there will be firms price signalling as well as freeriding firms that signal through advertising. Surplus is strictly higher in markets where advertising firms are active relative to pure price signalling markets. In some cases advertising markets can be even more efficient than full information markets as advertisers surrender market power to avoid costly price signalling.
JEL Codes: D82, D83, M37
Keywords: Comparative advertising, Price Signalling
For a nontechnical explanation of this paper please press here