Shuaizhang Feng

Linkages among climate change, crop yields and Mexico–US cross-border migration

Climate change is expected to cause mass human migration, including immigration across international borders. This study quantitatively examines the linkages among variations in climate, agricultural yields, and peoples migration responses by using an instrumental variables approach. Our method allows us to identify the relationship between crop yields and migration without explicitly controlling for all other confounding factors. Using state-level data from Mexico, we find a significant effect of climate-driven changes in crop yields on the rate of emigration to the United States. The estimated semielasticity of emigration with respect to crop yields is approximately −0.2, i.e., a 10% reduction in crop yields would lead an additional 2% of the population to emigrate. We then use the estimated semielasticity to explore the potential magnitude of future emigration. Depending on the warming scenarios used and adaptation levels assumed, with other factors held constant, by approximately the year 2080, climate change is estimated to induce 1.4 to 6.7 million adult Mexicans (or 2% to 10% of the current population aged 15–65 y) to emigrate as a result of declines in agricultural productivity alone. Although the results cannot be mechanically extrapolated to other areas and time periods, our findings are significant from a global perspective given that many regions, especially developing countries, are expected to experience significant declines in agricultural yields as a result of projected warming.

Recent Trends in Top Income Shares in the USA: Reconciling Estimates from March CPS and IRS Tax Return Data

Although the majority of research on US income inequality trends is based on public-use March CPS data, a new wave of research using IRS tax return data reports substantially higher levels of inequality and faster growing trends. We show that these apparently inconsistent estimates are largely reconciled if the inequality measure and the income distribution are defined in the same way. Using internal CPS data for 1967-2006, we closely match IRS data-based estimates of top income shares reported by Piketty and Saez (2003). Our results imply that any inequality increases since 1993 are concentrated among the top 1 percent of the distribution.

Using the P90/P10 Index to Measure US Inequality Trends with Current Population Survey Data: A View from Inside the Census Bureau Vaults

The March Current Population Survey (CPS) is the primary data source for estimation of levels and trends in labor earnings and income inequality in the USA. Time-inconsistency problems related to top coding in theses data have led many researchers to use the ratio of the 90th and 10th percentiles of these distributions (P90/P10) rather than a more traditional summary measure of inequality. With access to public use and restricted-access internal CPS data, and bounding methods, we show that using P90/P10 does not completely obviate time-inconsistency problems, especially for household income inequality trends. Using internal data, we create consistent cell mean values for all top-coded public use values that, when used with public use data, closely track inequality trends in labor earnings and household income using internal data. But estimates of longer-term inequality trends with these corrected data based on P90/P10 differ from those based on the Gini coefficient. The choice of inequality measure matters.

Levels and Long-Term Trends in Earnings Inequality: Overcoming Current Population Survey Censoring Problems Using the GB2 Distribution

Over its history, the March Current Population Survey (CPS) has increasingly captured the upper tail of the distribution of all sources of income. This, together with time-consistency problems in top coding, means that users of both the public-use and restricted-access CPS will understate the level of wage earnings and income inequality in earlier years and overstate their growth over time. We address this problem by modeling the personal earnings of full-time, full-year workers using the generalized beta distribution of the second kind, calculating Gini coefficients from the estimated parameters, and comparing them with past findings.

Applying statistical models to the climate–migration relationship

We are grateful to Auffhammer and Vincent (1) for having pointed out that Eqs. 1 and 2 in Feng et al. (2) do not represent the model actually used to produce the estimates presented in Tables 1 and 2 of the latter paper (2). What we actually estimated was a model without time dummies. Thus, gt and ht should have been omitted from Eqs. 1 and 2. We regret this mistake.

Measuring Inequality Using Censored Data: A Multiple Imputation Approach

To measure income inequality with right-censored (top-coded) data, we propose multiple-imputation methods for estimation and inference. Censored observations are multiply imputed using draws from a flexible parametric model fitted to the censored distribution, yielding a partially synthetic data set from which point and variance estimates can be derived using complete-data methods and appropriate combination formulae. The methods are illustrated using US Current Population Survey data and the generalized beta of the second kind distribution as the imputation model. With Current Population Survey internal data, we find few statistically significant differences in income inequality for pairs of years between 1995 and 2004. We also show that using Current Population Survey public use data with cell mean imputations may lead to incorrect inferences. Multiply-imputed public use data provide an intermediate solution.

Measuring Labor Earnings Inequality Using Public-Use March Current Population Survey Data: The Value of Including Variances and Cell Means When Imputing Topcoded Values

Using the Census Bureaus internal March Current Population Surveys (CPS) file, we construct and make available variances and cell means for all topcoded income values in the public-use version of these data. We then provide a procedure that allows researchers with access only to the public-use March CPS data to take advantage of this added information when imputing its topcoded income values. As an example of its value we show how our new procedure improves on existing imputation methods in the labor earnings inequality literature.

Generalised percentile ratios as robust measures of labour earnings inequality

We propose a class of generalised percentile ratios as an alternative to the P90 / P10 ratio for measuring labour earnings inequality. We show that they are more robust to sampling the variation and rounding error prevalent in interview-based surveys, as demonstrated through a Monte Carlo simulation and with Current Population Survey labour earnings data from 1987 to 2005. We find a smoother upward trend in the P90 / P10 ratio over this period than what is shown with conventionally measured P90 / P10 ratios.