The Drivers of Current Account Surplus in Germany and the Politics of Rebalancing in the Eurozone. EU Centre, Singapore Working Paper No. 24, April 2015

Current account deficits have caught the public’s attention as they have contributed to the European debt crisis. However, surpluses also constitute an issue as a deficit in any country must be financed through a surplus in another country. In 2013, Germany, now the world’s largest surplus economy, registered a record high US$273 billion surplus. This paper looks at what accounts for Germany’s surplus, revealing that the major driving factors include strong global demand for quality German exports, domestic wage restraint, an undervalued single currency, high domestic savings rate and interest rate convergence in the euro area. This paper echoes the US Treasury’s view that a persistent German surplus makes it harder for the eurozone as a whole and the southern peripheral economies in particular to recover from the current financial crisis by imposing a Europe-wide “deflationary bias” through pushing up the exchange rate of the euro, exporting feeble German inflation and projecting its ultra-tight macroeconomic policies onto crisis economies. This paper contends that Germany’s trade surplus is likely to endure as Germany and other eurozone countries uphold diverging views on the nature of the surplus engage in a blame-game amidst a sluggish rebalancing process. Prizing the surplus as a reflection of hard work and economic competitiveness, German authorities urge their southern eurozone colleagues to undertake bold structural reforms to correct the imbalance, while the hand-tied governments in crisis-stricken economies call on Germany to do its “homework” by boosting German demands for European goods and services.

Asian Middle Classes - Drivers of Political Change? Asia Policy Brief 2014/06, November 2014

Asia watchers have been kept exceptionally busy by recent political developments in the region. An unprecedented landslide victory in India’s general elections, pro-democracy protests in Hong Kong, close elections in Indonesia, a coup in Thailand – the list goes on. As unrelated as these events appear, analysts may find a missing link among a social group that is currently exploding in numbers: Asia’s middle classes. Often discussed simply in terms of its economic potential, Asia’s middle-income population is also flexing its political muscle. A closer look at its influence throughout the region in recent months seems to confirm for the field of politics what economists have known for some time: The rise of the Asian middle classes constitutes one of the most fundamental transformations of our time. The consequences remain to be seen.

TEXDOC: Stata module to create a LaTeX document including Stata output

texdoc provides tools to create a LaTeX document from within Stata in a weaving fashion. This is especially useful if you want to produce a LaTeX document that contains Stata output, such as, e.g., a Stata Journal article or solutions to statistics homework assignments.

TABLETUTORIAL: Stata module to provide tutorial on automated table generation and reporting

-tabletutorial- illustrates how Stata can be used to export statistical results and generate customized reports. Part 1 explains how results from Stata routines can be accessed and how they can be exported using the -file- comand or a wrapper such as, e.g., -mat2txt-. Part 2 shows how model estimation results can be archived using -estwrite- and how models can be tabulated and exported to LaTeX, MS Excel, or MS Word using -estout-. Part 3 illustrates how to set up automatic reports in LaTeX or MS Word. The tutorial is based on a talk given at CEPS/INSTEAD in Luxembourg in October 2008. After install, type -help tabletutorial- to start the tutorial (in Stata 8, type -whelp tabletutorial-). The -mat2txt-, -estwrite-, and -estout- packages, also available from SSC, are required to run the examples.

PSPLINE: Stata module providing a penalized spline scatterplot smoother based on linear mixed model technology

Pspline uses xtmixed to fit a penalized spline regression and plots the smoothed function. Additional covariates can be specified to adjust the smooth and plot partial residuals.

NLCHECK: Stata module to check linearity assumption after model estimation

nlcheck is a simple diagnostic tool that can be used after fitting a model to quickly check the linearity assumption for a given predictor. nlcheck categorizes the predictor into bins, refits the model including dummy variables for the bins, and then performs a joint Wald test for the added parameters. Alternative, nlcheck uses linear splines for the adaptive model. Support for discrete variables is also provided. Optionally, nlcheck also displays a graph of the adjusted linear predictions from the original model and the adaptive model

LOGITCPRPLOT: Stata module to graph component-plus-residual plot for logistic regression

logitcprplot can be used after logistic regression for graphing a component-plus-residual plot (a.k.a. partial residual plot) for a given predictor, including a lowess, local polynomial, restricted cubic spline, fractional polynomial, penalized spline, regression spline, running line, or adaptive variable span running line smooth

RRREG: Stata module to estimate linear probability model for randomized response data

rrreg fits a linear probability model for randomized response data

OAXACA: Stata module to compute the Blinder-Oaxaca decomposition

-oaxaca- computes the so-called Blinder-Oaxaca decomposition, which is often used to analyze wage gaps by sex or race. Older versions of this routine are available as -oaxaca9- and -oaxaca8-.

DIGDIS: Stata module to analyze the distribution of digits

digdis tabulates the distribution of digits of the specified variables, performs goodness-of-fit tests against a reference distribution and, optionally, graphs the distributions. The default is to tabulate the first (nonzero) digit and to test against Benford's law. The moremata package and the mgof package, also available from SSC, are required.

MGOF: Stata module to perform goodness-of-fit tests for multinomial data

mgof computes goodness-of-fit tests for the distribution of a discrete (categorical, multinomial) variable. The default is to perform classical large sample chi-squared approximation tests based on Pearson's X2 statistic and the log likelihood ratio (G2) statistic or a statistic from the Cressie-Read family. Alternatively, mgof computes exact tests using Monte Carlo methods or exhaustive enumeration. A Kolmogorov-Smirnov test for discrete data is also provided. The moremata package, also available from SSC, is required.

ADOLIST: Stata module to manage lists of ado packages

-adolist- creates, installs and uninstalls lists of user ado packages.

ANOGI: Stata module to generate Analysis of Gini

anogi computes the "Analysis of Gini" for population sub-groups proposed by Frick et al. (2006; Sociological Methods and Research 34/4).

GSAMPLE: Stata module to draw a random sample

gsample draws a random sample from the data in memory. Simple random sampling (SRS) is supported, as well as unequal probability sampling (UPS), of which sampling with probabilities proportional to size (PPS) is a special case. Both methods, SRS and UPS/PPS, provide sampling with replacement and sampling without replacement. Furthermore, stratified sampling and cluster sampling is supported.