Research writing is seldom perfect. But some of it is better at getting the job done. Let us look at what makes this true.
Skim each version below and think about how the writing makes it easier (or harder) to learn what the paper contributes to the literature.
Intro Version A
The EITC is a refundable tax credit for low to moderate income working individuals and families administered through the income tax system. In 2010, 27.4 million tax filers received a total of $59.6 billion in EITC payments (Internal Revenue Service 2012, Table 2.5). In fact, the EITC is the largest poverty reduction program in the United States: almost 21 million American families received more than $36 billion in payments through the EITC in 2004. The effects of responses to the EITC have been debated in the literature. Evidence on intensive-margin responses is mixed (Meyer and Rosenbaum 1999, Bollinger, Gonzalez, and Ziliak 2009, Rothstein 2010). At the same time, several studies have shown that the EITC clearly increases labor force participation – the extensive-margin response (Eissa and Liebman 1996, Meyer and Rosenbaum 2001, Grogger 2003, Hotz and Scholz 2006, GelberandMitchell2012). Surveys show that the knowledge that working can yield a large tax refund is much more widespread than knowledge about the non-linear marginal incentives created by the EITC (Liebman 1998, Ross Phillips 2001, Romich and Weisner 2002, Smeeding, Ross Phillips, and O’Connor 2002, Maag 2005). Thus, knowledge about the increased return to working diffused more quickly than knowledge about how to optimize on the intensive margin.
The pattern of knowledge diffusion is consistent with a model of rational information acquisition, as re-optimizing in response to a tax reform on the extensive margin has first-order (large) benefits, whereas reoptimizing on the intensive margin has second-order (small) benefits (Chetty 2012). Intensive-margin responses may therefore take more time to emerge. Our analysis contributes to the literature on estimating behavioral responses from non-linearities in the budget set and bunching at kink points (e.g., Hausman 1981, Saez 2010, Chetty et al. 2011, Kleven and Waseem 2012). As wage-earners cannot control earnings perfectly, the impact of taxes on the wage earnings distribution is diffuse and does not produce visible bunching at kinks. As a result, traditional non-linear budget set methods would again lead to the conclusion that taxation does not generate intensive- margin responses.
Research on the impacts of tax policy must confront two important empirical challenges. First, it is difficult to find counterfactuals to estimate causal effects because federal tax policies often do not vary cross-sectionally, Second, many individuals respond slowly to tax changes because of inattention and other frictions. This makes it difficult to identify steady-state impacts from short-run changes in behavior around tax reforms.
In this paper, we overcome these challenges to show that prior studies of short-run responses to tax reforms may have underestimated the importance of intensive-margin responses. Recent work suggests that a lack of knowledge about changes in the tax code and other adjustment frictions can lead to sluggish adjustment of labor supply, especially on the intensive margin (Chetty 2012). We develop a new research design that overcomes these frictions, and find intensive- margin responses to taxation that are similar in magnitude to previously documented...
Intro Version B
A widely accepted view in the literature on labor supply is that income taxation leads to much larger responses on the extensive margin (participation) than on the intensive margin (hours of work or earnings conditional on working). This finding has important implications for understanding the macroeconomic impacts of taxation and for the optimal design of tax and transfer policies (e.g., Piketty and Saez 2012). In this paper, we show that prior studies of short-run responses to tax reforms may have underestimated the importance of intensive-margin responses. Recent work suggests that a lack of knowledge about changes in the tax code and other adjustment frictions can lead to sluggish adjustment of labor supply, especially on the intensive margin (Chetty 2012). We develop a new research design that overcomes these frictions, and find intensive- margin responses to taxation that are similar in magnitude to previously documented extensive-margin responses.
Our research design is based on a simple idea: individuals with no knowledge of a tax policy’s marginal incentives behave as they would in the absence of the policy. Hence, we can identify the causal effect of a policy by comparing behavior across cities that differ in knowledge about the policy but are otherwise comparable. We apply this method to analyze the impacts of the Earned Income Tax Credit, the largest means- tested cash transfer program in the United States, on earnings behavior and inequality. We exploit fine geographical heterogeneity across ZIP codes by using data from U.S. population tax records spanning 1996-2009, which include over 75 million unique EITC eligible individuals with children and 1 billion observations on their annual earnings.
Our empirical analysis proceeds in two steps. First, we develop a proxy for local knowledge about the marginal rate structure of the EITC. Ideally, one would measure knowledge directly using data on individuals’ perceptions of the EITC schedule. Lacking such data, we proxy for knowledge using the extent to which individuals manipulate their reported income to maximize their EITC refunds by reporting self-employment income. Self-employed tax filers have a propensity to report income exactly at the first kink of the EITC schedule, the point that maximizes net tax refunds (Saez 2010). We show that the degree of “sharp bunching” by self-employed individuals at the first kink varies substantially across ZIP codes. For example, 6.5 percent of EITC claimants in Chicago, IL in 2008 are self-employed and report earnings exactly at the refund-maximizing level, com- pared with 0.6 percent in Rapid City, SD. Consistent with knowledge diffusion, bunching spreads across the country over time: the degree of bunching is almost three times larger in 2009 than in 1996.
The key assumption needed to use sharp bunching as a proxy for knowledge about the EITC schedule is that individuals in low-bunching neighborhoods believe that the EITC has no impact on their marginal tax rates. We present two pieces of evidence supporting this assumption. First, we show that the spatial heterogeneity in bunching appears to be driven by differences in knowledge about the first kink of the EITC schedule. We find that those who move from low-bunching to high-bunching neighborhoods report incomes that yield larger EITC refunds on average after they move. In contrast, those who move from high-bunching to low-bunching neighborhoods experience no change in average EITC refunds after they move, consistent with learning and memory. More- over, we find that bunching is highly correlated with predictors of information diffusion, such as the density of EITC recipients and availability of professional tax preparers. Second, we show that individuals in low-bunching areas are unaware not just about the refund-maximizing kink but about the EITC schedule more broadly. In particular, when individuals become eligible for a much larger EITC refund after having their first child, the distribution of their reported self-employment income remains virtually unchanged in low-bunching areas.
So which version of the Intro did a better job and why? You may or may not like the style of either, but the question here is about structure: which version made it easier to quickly reap the major insights of the paper and why? List examples of things that worked. (FYI, one version is made up and the other one is here)
Now, let us zoom in to the level of the paragraph. Skim both versions of this paragraph and think about what makes one easier to learn from:
In US data, a 1-percent decrease in X increases Y by 4.2 percent, causes a fall in Z by 4.3 percent. There is a reduction in M by 8.6 percent. This is measured by the Z-Y ratio. The model generates T as observed in the data. Second, a 1-percent decrease in X only decreases G by 0.7 percent, implying that G are as responsive in the model as in the data. Simulated and empirical Bs are identical at -0.89.
The fit of the model is good along three critical dimensions. First, the model amplifies T as observed in the data. For example, in US data, a 1-percent decrease in X increases Y by 4.2 percent, decreases Z by 4.3 percent, and decreases M by 8.6 percent. Second, G are as responsive in the model as in the data: a 1-percent decrease in X only decreases G by 0.7 percent. Third, simulated and empirical Bs are identical at -0.89.
Please select a paragraph from an Introduction you have writtten — any paragraph apart from the first 2 — that you would like to improve.
Please include some ideas for how you would like to improve this para (you can rewrite it or write up a plan for what you want to improve and how).