Earth systems models (ESMs), which can simulate the complex feedbacks between climate and fires, struggle to predict fires well for tropical rainforests. This study provides equations that predict historic carbon monoxide emissions from Amazon rainforest fires for 2003–2018, which could be implemented within ESMs’ current structures. We also include equations to convert the predicted emissions to burned area. Regressions of varying mathematical forms are fitted to one or both of two fire CO emission inventories. Equation accuracy is scored on r2, bias of the mean prediction, and ratio of explained variances. We find that one equation is best for studying smoke consequences that scale approximately linearly with emissions, or for a fully coupled ESM with online meteorology. Compared to the deforestation fire equation in the Community Land Model ver. 4.5, this equation’s linear-scale accuracies are higher for both emissions and burned area. A second equation, more accurate when evaluated on a log scale, may better support studies of certain health or cloud process consequences of fires. The most accurate recommended equation requires that meteorology be known before emissions are calculated. For all three equations, both deforestation rates and meteorological variables are key groups of predictors. Predictions nevertheless fail to reproduce most of the variation in emissions. The highest linear r2s for monthly and annual predictions are 0.30 and 0.41, respectively. The impossibility of simultaneously matching both emission inventories limits achievable fit. One key cause of the remaining unexplained variability appears to be noise inherent to pan-tropical data, especially meteorology.

Abstract Early childhood health conditions have large effects on the cognitive development, education and earnings of individuals. This paper assesses the impact of cognitive development on national economic development. Each risk factor for cognitive development also causes child mortality, making child survival a viable proxy for good cognitive development conditions. The cognitive development of current workers happened decades earlier when they were children. Child survival from a generation ago is one of the strongest correlates of economic growth in both low and high income countries. This evidence suggests that early cognitive development plays a major role in economic development.

It’s complicated. Tropical diseases have unusually intricate life cycles because most of them involve not only a human host and a pathogen, but also a vector host. The diseases are predominantly tropical due to their sensitivity to local ecology, usually due to the vector organism. The differences between the tropical diseases mean that they respond to environmental degradation in various ways that depend on local conditions. Urbanization and water pollution tend to limit malaria, but deforestation and dams can exacerbate malaria and schistosomiasis. Global climate change, the largest environmental change, will likely extend the range of tropical climate conditions to higher elevations and near the limits of the tropics, spreading some diseases, but will make other areas too dry or hot for the vectors. Nonetheless, the geographical range of tropical diseases will be primarily determined by public health efforts more than climate. Early predictions that malaria will spread widely because of climate change were flawed, and control efforts will probably cause it to diminish further. The impact of human disease on economic development is hard to pin down with confidence. It may be substantial, or it may be misattributed to other influences. A mechanism by which tropical disease may have large development consequences is its deleterious effects on the cognitive development of infants, which makes them less productive throughout their lives.

Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade-off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade-off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers.

In this article, I extend the theory of added-variable plots to three panel-data estimation methods: fixed effects, between effects, and random effects. An added-variable plot is an effective way to show the correlation between an independent variable and a dependent variable conditional on other independent variables. In a multivariate context, a simple scatterplot showing x versus y is not adequate to show the relationship of x with y, because it ignores the impact of the other covariates. Added-variable plots are also useful for spotting influential outliers in the data that affect the estimated regression parameters. Stata can display added-variable plots with the command avplot, but it can be used only after regress. My new command, xtavplot, is a postestimation command that creates added-variable plots after xtreg estimates. Unlike avplot, xtavplot can display a confidence interval around the fitted regression line.

An added-variable plot is an effective way to show the correlation between an independent variable and a dependent variable conditional on other independent variables. For multivariate estimation, a simple scatterplot showing x versus y is not adequate to show the partial correlation of x with y, because it ignores the impact of the other covariates. Added-variable plots are especially effective for showing the correlation of a dummy x variable with y because the dummy variable conditional on other covariates becomes a continuous variable, making the relationship easier to visualize. Added-variable plots are also useful for spotting influential outliers in the data that affect the estimated regression parameters. Stata provides added-variable plots after ordinary least-squares regressions with the avplot command. I present a new command, avciplot, that adds a confidence interval and other options to the avplot command.

avciplot creates an added-variable plot (a.k.a. partial-regression leverage plot, partial regression plot, or adjusted partial residual plot) after regress. It differs from avplot by adding confidence intervals around the regression line and various options. indepvar is an independent (x) variable (a.k.a. predictor, carrier, or covariate) that may or may not be included in the preceding regression. The user would choose an indepvar not already in the regression to evaluate whether it is worthwhile to include it. avciplot shows the partial correlation between one indepvar and the depvar controlling for all the other regressors in an multiple linear regression. Besides showing the relationship between the indepvar and the depvar controlling for the other regressors, avciplot is useful for visually identifying which outlier observations have a big effect on the estimated coefficient.

xtavplot creates an added-variable plot (a.k.a. partial-regression leverage plot, partial regression plot, or adjusted partial residual plot) after xtreg, fe (fixed-effects estimation), xtreg, re (random-effects estimation) or xtreg, be (between-effects estimation). xtavplot cannot be used after xtreg, mle or xtreg, pa. indepvar is an independent (x) variable (a.k.a. predictor, carrier, or covariate) that may or may not be included in the preceding estimation. xtavplot shows the partial correlation between one indepvar and the depvar from a multivariate panel regression. Besides showing the relationship between the indepvar and the depvar controlling for the other regressors, xtavplot is useful for visually identifying which outlier observations have a big effect on the estimated coefficient. After fixed-effects estimation, the plotted e(x|X) values are the residuals from the regression of x on the other X variables in the original regression, and the plotted e(y|X) values are the residuals from the regression of y on the other X variables.

Purpose The purpose of this paper is to investigate the tourism-led growth hypothesis in Laos. Design/methodology/approach The authors test the tourism-led growth hypothesis using autoregressive distributed lag (ARDL) cointegration estimation (Pesaran et al., 2001) and Granger causality tests. Findings The results of this paper show that when tourism is forcing variable, there is no long-run relationship between tourism development and economic growth. The Granger causality test demonstrates that there is a uni-directional causality running from economic growth in tourism. Social implications The empirical results and policy recommendation may be useful for other small developing countries. Originality/value This study is the first study to investigate the relationship between tourism development and growth in Laos, using a relatively new econometric approach – ARDL bound testing.

Complex statistical tables often must be built up by parts from the results of multiple Stata commands. I show the capabilities of frmttable and outreg for creating complex tables, and even fully formatted statistical appendices, for Word and TeX documents. Precise formatting of these tables from within Stata has the same benefits as writing do-files for statistics commands. They are reproducible and reusable when the data change, saving the user time.

bidensity produces bivariate kernel density estimates and graphs the result using a twoway contourline plot, optionally overlaying a scatterplot. The default kernel is Epanechnikov; all of the kernels provided by -kdensity- are also available. Compared to Baum's -kdens2- (SSC), which was recently enhanced to produce contourline plots, -bidensity- computes the bivariate kernel densities much more efficiently through use of Mata, and provides a choice of kernel estimators. The estimated densities can be saved in a Stata dataset or accessed as Mata matrices.

What happens to income distribution during the course of economic development? New higher quality international data show a marked pattern of inequality convergence, where inequality becomes more similar across countries as income levels rise. Inequality has tended to fall in high inequality countries as their economies grow, and inequality has tended to rise in low inequality countries. This is clear in linear regression trends, piecewise trends, and stochastic kernel estimation. The stochastic kernel estimation models the evolution of inequality in the income domain, rather than the more typical time domain, and allows for complex dynamics. The evidence of inequality convergence is confirmed in numerous robustness checks. The pattern of convergence is consistent with both rising inequality in many high-income countries and falling inequality in high inequality developing countries, such as many Latin American countries. JEL Codes: D31, O15, O47

The frmttable command is a tool for experienced users and programmers to create formatted tables from statistics and write them to Word or LAT(E)X files. My objective is to provide as much control over the layout and formatting of the statistical tables as possible in both file formats while keeping the syntax simple. Users can create rectangular tables with any configuration of data and text; specify numeric formats, font sizes, and font types at the table cell level; specify row spacing; and place lines in or around the table. A complex table can be built by merging or appending new statistics to an existing table, and multiple tables can be included in the same document, making it possible to create a fully formatted statistical appendix from a single do-file. In this article, I provide examples of the ways in which programmers call frmttable to create formatted tables of statistics.

I present an entirely rewritten version of the outreg command, which creates tables from the results of Stata estimation commands and generates formatted Microsoft Word or LATEX files. My objective is to provide as complete control as is practical over the layout and formatting of the estimation tables in both file formats. outreg provides a wide range of estimation statistics (including confidence intervals and marginal effects), can control the number and arrangement of the statistics displayed, and can merge subsequent estimation results into the same table. Users can specify numeric formats, font sizes, and font types at the table cell level, as well as lines in the table and row spacing. Multiple tables can be written to the same document, making it possible to create a fully formatted statistical appendix from a do-file. I demonstrate in examples the numerous formatting options for the outreg command.

This addition to Stata enables extensive formatting of statistical tables created within Stata, creating native Word or TeX tables. Users can specify font sizes, font types, text justification, table cell height and width, cell boundary lines (of different styles), titles, labels, and footnotes, among other attributes. New data can be merged or appended to existing tables to create more complex tables. This system can provide full formatting for statistical tables similar to the way that Stata provided granular formatting for graphics starting in Stata 8. Users could use this to create a complete statistical appendix in print-ready form with a single command within Stata. This system is for use by programmers. The table formatting system is implemented in Mata for speed and compact memory use. Mata string matrices made for efficient coding. I have reimplemented the -outreg- ado program using this system, as well as written a program to create formatted cross-tabulation tables like those created by -tabulate-. I also plan to write a program to create formatted summary statistics tables.

Regional disparities in earnings persist over time. This paper assesses the sources of persistence that impede people from migrating despite the income gains they would realize. A decision-making model is developed that is consistent with the underlying microeconomic theory and incorporates dynamic aspects of the migration decision. Unobserved heterogeneity is estimated by a new method, suitable for longitudinal data on individuals, which avoids the problems of parametric two-step techniques. The estimator is a simple, general method for calculating individual effects in nonlinear models. Malaysian male migrants exhibited risk averse behavior, avoiding regions with high earnings variance, despite any search-theoretic motive to seek them out. The unobserved characteristics which raise people's earnings (unobserved heterogeneity) made these people more likely to migrate, in a way quite similar to education. Who is left behind by the regional disparities in growth? All but the young and unmarried, those who must travel far or have never moved before, and to a lesser degree, the less educated.