Good Days, Bad Days: Stock Market Fluctuation and Taxi Tipping Decisions

Using taxicab tipping records in New York City (NYC), we develop a novel measure of real-time utility and quantitatively assess the impact of wealth change on the well-being of individuals based on the core tenet of prospect theory. The baseline estimate suggests that a one-standard-deviation increase in the stock market index is associated with a 0.3% increase in the daily average tipping ratio, which translates to an elasticity estimate of 0.3. The impact is short-lived and in line with the wealth effect interpretation. Consistent with loss aversion, we find that the impact is primarily driven by wealth loss rather than gain. We exploit Global Positioning System and timestamp information and design two difference-in-differences tests to establish causal inference. Exploitation of the characteristics of individual stocks suggests that the effect of wealth change on real-time utility is more pronounced in the stocks of firms with large market capitalization. Finally, our aggregate estimate suggests that annual tip revenue in the NYC taxi industry is associated with stock market fluctuations, ranging from −$17.5 million to $12.9 million. This paper was accepted by Tyler Shumway, finance.

Effectiveness of Fiscal Incentives for R&D: Quasi-experimental Evidence

We exploit a 2008 UK policy reform that increased the tax incentives for R&D in medium-sized enterprises relative to large ones, to overcome the endogeneity of exposure to such tax credits. We estimate a difference-in-difference design on the universe of corporation tax filings in the United Kingdom, combined with other datasets. We find a positive and significant impact of tax credits for R&D, implying a user-cost elasticity estimate of around −1.6. This magnitude implies around $1 in additional private R&D spending per dollar foregone in tax revenue. (JEL H25, H32, K34, L25, O32)

Tax Incentives and Charitable Giving: Evidence from New Survey Data

A new survey data set is used to obtain estimates of the tax price elasticity of personal giving to tax-deductible charitable causes. Like other surveys, the data here yield a large elasticity estimate, roughly two and a half for a representative household when Tobit estimation is used. One hypothesis for the discrepancy between such large estimates and values close to unity found in tax data is that there is an “itemization effect” reflecting nonrandom selection in tax data; the Florida data do not support this hypothesis. Another explanation for the discrepancy between tax-file-based and survey-based estimates is that the standard use of OLS rather than Tobit biases the elasticity more in survey data, where many people report zero gifts. For the Florida data, using OLS increases the estimated elasticity by about 30%; while this effect cannot explain why the Florida data produce such large elasticities, it suggests that OLS estimates in earlier studies should be used with caution.

Price and Income Elasticities of Consumer Goods Imports of Pakistan

Estimation of disaggregated import elasticities for developing countries presents a formidable data-handling problem. The available studies on the subject are concerned mostly with the estimation of income and price elasticities of imports at a disaggregated level corresponding to the one-digit level of the Standard International Trade Classification (SITC), see, e.g., Khan [I], Melo and Vogt [4], Nguyen and Bhuyan [5). Consequently, they apply a common elasticity estimate to all commodity sub-groups..

Application of Price Elasticities To Farm Policy Analysis: Reply

Huang's comment on our article in a recent issue of this journal [2] points out two ideas that could have been more clearly stated. We will reply to his comments in the order in which they appear.First, our illustration of the two approaches to examining the market was to demonstrate how economic theory could be used to show the importance of the world market to producers of cotton and soybeans. One approach separates the total market into domestic and export components. We pointed out that the elasticity estimates used for the domestic (—.35) and export (—1.76) markets and the production data for 1976 give a total elasticity estimate for the U.S. of -.98 if the world elasticity is -.5. The other approach (the primary subject of Huang's comment) uses a single world market and illustrates how the U.S. affects or is affected by it. Use of the second approach gives a U.S. elasticity of —1.0 when the world elasticity is —.5. It was not our intention to compare the two approaches as to consistency of estimate, although the example may have implied such an argument. Given the same data base and consistent methods in estimating the separate demand functions, the results should be similar.