Traditional approaches to occupational gender segregation that rely on employment data and occasionally on indices that ensure invariance to labor market participation rates often depict a skewed representation of segregation levels in the population. The methodology is universally applicable to any segregation index and necessitates a representative sample comprising detailed gender-per-occupation and participation frequency data. A key aspect of this method is that it tackles endogenous selection in participation decisions and the consequential nonignorability in gender-per-occupation frequencies via a full maximum-likelihood estimation approach. I illustrate the approach with Eurostat data.

The HBS, with nearly 24,000 dwellings in its sample, provides annual information on the nature and destination of household consumption expenses in Spain and the distribution of said expenditure among the different ECOICOP consumption divisions. The ICT (Information and Communication Technology) expenditures and equipment purchases are already included. The analysis is carried out using panel-data techniques with Stata, including descriptive and graphical analysis of the variables. The Hausman test to determine whether fixed-effects or random-effects models should be used accordingly with the influence of the heteroskedasticity affecting individuals along the time and the impact of errors. The panel data only examines individuals over two periods, so the study will focus on the trend evolution rather than the individual evolution.

Stata’s meta command offers full support for meta-analysis from computing various effect sizes and producing basic meta-analytic summaries to performing tests for small-study effects. Stata 18 introduced support for meta-analysis of one proportion, meaning you can now use standard meta-analysis features such as forest plots and funnel plots with one-sample binary data. Stata 18 also introduced two new commands, meta meregress and meta multilevel, for performing multilevel meta-analysis. These commands allow you to analyze results from multiple studies in which the reported effect sizes are nested within higher-level groupings such as regions or schools. By properly accounting for the dependence among the effect sizes, we can produce more accurate inference.

In this presentation, I will demonstrate how to perform meta-analysis of proportions and multilevel meta-analysis in Stata 18. I will provide a brief introduction to meta-analysis and discuss effect sizes and confidence intervals relevant to prevalence data. For multilevel data, we will see how to include random intercepts and coefficients at different levels of hierarchy, perform sensitivity analysis, and assess the variability among the effect sizes at different levels of the hierarchy.

To further empower Stata users, I introduce a versatile and user-friendly ADO (Advanced Do-file) designed to streamline the presentation of regression results. This innovative tool, specifically tailored for Stata, transforms the output of multiple regressions or multinomial regressions into a comprehensive table of marginal effects, complete with standard errors and significance indicators. This presentation aims to showcase the potential of this Stata ADO by offering a detailed overview of its features and capabilities. With just a few simple commands, researchers can now effortlessly generate publication-ready tables that provide a deeper understanding of their regression analyses. No more manually extracting and formatting results—this ADO automates the process, saving valuable time and reducing the risk of human error.

Key features of the ADO include the following abilities:

1. Calculate marginal effects: easily compute marginal effects for categorical, binary, or continuous predictors, making it simple to interpret the impact of variables on your outcome of interest.
2. Display standard errors: ensure the robustness of your results by including standard errors, enabling users to assess the precision of the estimated effects.
3. Show significance indicators: highlight significant findings with user-defined asterisks or other symbols, enhancing the visual clarity of your tables.

In this presentation, we introduce a novel approach to enhance the analytical potential of graphs by incorporating interactivity. Our proposed method focuses on solving multiple regressions and selecting coefficients with a significant positive relationship using weighted mean contrasts. By employing this approach, we generate graphs that highlight categories with predicted proportions or means significantly greater than those of the population, thus providing valuable insights into the analyzed elements. Additionally, to further enhance their analytic power, our graphs offer interactive features, allowing users to filter elements based on their size or attributes and explore the most central and strongest links within the network.

We will showcase an advanced Stata ado-program that enables the creation of these interactive graphs, offering a diverse range of examples to illustrate their applications. Participants will gain practical knowledge on implementing this methodology and leveraging Stata's capabilities to visualize and analyze complex networks.

Designing tables in a standardized format for reports or slides is time-consuming. The new version of Stata 18 includes a couple of commands long awaited by the scientific community: collect and dtable.

The dtable command creates tables of descriptive statistics, commonly known as a “Table 1”. Up to today, to build a Table 1, researchers had to combine several Stata commands or use a community-contributed command such as baselinetable or table1. Although these commands are useful, they do not have all the capabilities of the new Stata command.

The collect command set creates custom tables, allowing researchers to design their own presentation styles. During this presentation, we will demonstrate how we have improved our lives with the dtable and collect commands in a heavily work-loaded Biostatistics Unit. We will present a Stata ado-program that allows the creation of Table 1 of a research study in an automated fashion.

The success of policies relies on the degree of prisoners' reintegration. The cost of public services is transferred to taxpayers. Hence, governments and regional authorities aim to minimize costs by pleading for larger prisons (Titan prisons) rather than smaller ones without losing security and safety controls (National Audit Office, 2013). Evidence shows a higher engagement in small-size establishments. The prison population is rising worldwide to question the need for an upper limit of inmates assuring the effectiveness of internal policies such as humane incarceration for reintegration. Opportunities for reintegration start with education, for example, prisoners' engagement in educational or other activities during the imprisonment. I apply data envelopment analysis (DEA) for panel data (2018–2022) in the UK, accounting for fixed effects of the prisoners' background and regional characteristics. The study assesses the effectiveness of the internal reintegration policies according to the efficiency in using prison's public resources influenced by the convicted's background. The expected results are that a more structured family and economic and social background increase the likelihood of engagement during prison time, hence their reintegration.

Keywords: DEA; prisons; quality service; engagement; reintegration

ChatGPT enhances programming productivity by automating routine tasks, offering real-time troubleshooting, and suggesting coding approaches. Case studies illustrating how ChatGPT optimizes Stata programming tasks will be presented, yielding benefits in time efficiency and analytical accuracy. However, instances where ChatGPT may fall short will also be addressed, giving a comprehensive view of its functionality in real-world applications.

(Written using AI technology.)

Tables allow us to summarize our data, present estimation results, and highlight patterns and relationships. With this in mind, the goal is to demonstrate Stata's features that can be used to best present your data in a tabular fashion with your preferred style. With these features, you can create tables with summary statistics, results of hypothesis tests, regression results, or results from any other Stata command. You can also create tables with any combination of these types of results. Additionally, you can export them to Microsoft Word, Excel, LaTeX, PDF, HTML, Markdown, and more; this allows you to share your work with others, regardless of which format you prefer. In this presentation, I will introduce the essential commands used to create tables in Stata, highlight ways to customize them, and demonstrate how to create a standard style.