Tax policies seen in developing countries are puzzling on many dimensions. To begin with, revenue/GDP is surprisingly small compared with that in developed economies. Taxes on labor income play a minor role. Taxes on consumption are important, but effective tax rates vary dramatically by firm, with many firms avoiding taxes entirely by operating through cash in the informal economy and others facing very high liabilities. Taxes on capital are an important source of revenue, as are tariffs and seignorage, all contrary to the theoretical literature.In this paper, we argue that all of these aspects of policy may be sensible responses if a government is able in practice to collect taxes only from those firms that make use of the financial sector.Through use of the financial sector, firms generate a paper trail, facilitating tax enforcement. The threat of disintermediation then limits how much can be collected in taxes. Taxes can most easily be collected from the firms most dependent on the financial sector, presumably capital-intensive firms. Given the resulting differential tax rates by sector, other policies would sensibly be used to offset these tax distortions. Tariff protection for capital-intensive firms is one. Inflation, imposing a tax on the cash economy is another. Roger Gordon

Background Anorexia nervosa (AN) and body dysmorphic disorder (BDD) are characterized by distorted body image and are frequently comorbid with each other, although their relationship remains little studied. While there is evidence of abnormalities in visual and visuospatial processing in both disorders, no study has directly compared the two. We used two complementary modalities – event-related potentials (ERP) and fMRI – to test for abnormal activity associated with early visual signaling. Methods We acquired fMRI and ERP data in separate sessions from 15 unmedicated individuals in each of three groups (weight-restored AN, BDD, and healthy controls) while they viewed images of faces and houses of different spatial frequencies. We used joint independent component analyses to compare activity in visual systems. Results AN and BDD groups demonstrated similar hypoactivity in early secondary visual processing regions and the dorsal visual stream when viewing low spatial frequency faces, linked to the N170 component, as well as in early secondary visual processing regions when viewing low spatial frequency houses, linked to the P100 component. Additionally, the BDD group exhibited hyperactivity in fusiform cortex when viewing high spatial frequency houses, linked to the N170 component. Greater activity in this component was associated with lower attractiveness ratings of faces. Conclusions Results provide preliminary evidence of similar abnormal spatio-temporal activation in AN and BDD for configural/holistic information for appearance- and nonappearance-related stimuli. This suggests a common phenotype of abnormal early visual system functioning, which may contribute to perceptual distortions.

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In 2001, the Securities and Exchange Commission (SEC) required market centers to publish monthly execution-quality reports in an effort to spur competition for order flow between markets. Using samples of stocks trading on several markets, we investigate whether past execution quality affects order-routing decisions and whether the new disclosure requirements influence this relationship. We find that routing decisions are associated with execution quality; markets reporting low execution costs and fast fills subsequently receive more orders. Moreover, the reports themselves appear to provide information that was unavailable previously. Our results are consistent with active competition for order flow that can be influenced by public disclosure. (JEL G24, G28, K22)The U.S. Securities and Exchange Commission (SEC) frequently relies on public disclosure to achieve policy objectives. In defining themselves, the Commission states that, ''. . . the SEC is concerned primarily with promoting disclosure of important information, enforcing the security laws, and protecting investors . . .'' 1 A theme appearing repeatedly in SEC activities is that well-informed individuals make decisions enhancing security-market efficiency. Recently enacted SEC Rule 11Ac1-5 illustrates this approach. Equity-market trades frequently occur at prices other than those quoted, but brokers/traders find it difficult to anticipate We thank an anonymous referee,

We use the move of Israeli stocks from call auction trading to continuous trading to show that investors have a preference for stocks that trade continuously. When large stocks move from call auction to continuous trading, the small stocks that still trade by call auction experience a significant loss in volume relative to the overall market volume. As small stocks move to continuous trading, they experience an increase in volume and positive abnormal returns because of the associated increase in liquidity. Overall, though, a move to continuous trading increases the volume of large stocks relative to small stocks. CHOOSING AMONG ALTERNATIVE TRADING mechanisms is an issue of growing concern to financial economists. 1 Continuous trading increases the frequency of trading, thereby enabling immediate execution during the entire business day. Call auctions, on the other hand, lead to temporal aggregation of trades at predetermined points in time. 2 Brennan and Cao~1996! show that a move

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Stock movement prediction is a hot topic in the Fintech area. Previous works usually predict the price movement in a daily basis, although the market impact of news can be absorbed much shorter, and the exact time is hard to estimate. In this work, we propose a more practical objective to predict the overnight stock movement between the previous close price and the open price. As no trading operation occurs after market close, the market impact of overnight news will be reflected by the overnight movement. One big obstacle for such task is the lacking of data, in this work we collect and publish the overnight stock price movement dataset of Reuters Financial News. Another challenge is that the stocks in the market are not independent, which is omitted by previous works. To make use of the connection among stocks, we propose a LSTM Relational Graph Convolutional Network (LSTM-RGCN) model, which models the connection among stocks with their correlation matrix. Extensive experiment results show that our model outperforms the baseline models. Further analysis shows that the introduction of the graph enables our model to predict the movement of stocks that are not directly associated with news as well as the whole market, which is not available in most previous methods.