Como o senhor avalia o sucesso dos programas de desenvolvimento econômico?

Judging the success of economic development programs may be complex and multifaceted. In my view, below aspects can be used to measure the effectiveness of economic development programs: Economic impact: GDP growth, improvement in employment rate, improvement in median household income. Evaluating inclusivity: Reduction of income inequality, inclusive labour participation (gender, social strata) Innovation: More R&D spend, Diversification of economic growth drivers Resilience: The programs should enable the economy to become more resilient to potential shocks which have traditionally held the economy down. Social upliftment: Improved literacy, inclusivity and harmony. Transparency: Improved information exchange and accountability

In a nutshell, there are mainly two parameters of measurement economic development of a nation: 1. Per capita income 2. Social Development Many more parameters can be mentioned but these two are possible only when the political stability and peace exist in the country.

Behavioral patterns play a critical role in policy design, a point extensively covered by Chetty (R. Chetty, 2015). Using the Danish pension system as a case, Chetty demonstrates that many individuals adhere to the default option. Whether automatically enrolled or not in the pension scheme, a significant number showcase behavioral inertia, preferring the default over making an active choice. This highlights a flaw in the traditional approach to boost retirement savings through subsidies in retirement accounts. Due to this innate default behavior, the effectiveness of such a strategy might be compromised. Therefore, for any economic program's success, it's vital to ask: "Is this program aligned with the default behavior?"

While assessing indicators like GDP growth, employment, and poverty reduction, we must remain vigilant about measurements driven by political agenda. It is crucial to recognize the complexity of contextual factors and the unpredictability of external influences. Integrating behavioral economics and contextual insights, fostering multi-stakeholder collaboration, adopting an ecosystem approach, promoting good governance principles, capacity building, adaptive learning, and a culture of shared learning can enhance program design and evaluation. Employing these perspectives provides a comprehensive understanding of the impact of economic development programs, paving the way for informed decision-making.

As I alluded to in my previous point, tapping into the knowledge from previous empirical research about those logic models can be extremely helpful. One should be critical about this and really sift the economic journals. AER, QJE, and Econometrica are good examples of such. If you are relying on a theoretical model solely, try to verify them with empirical literature. Meticulous literature reviews can be helpful as well to quantify the impact.

One point to make is that researchers should be cognizant of the existent biases in the evaluation methods and try to avoid them. To name a few examples of such biases: Response Bias, Selection Bias, Leading Questions, Confirmation Bias, Placement Bias, and Attrition Bias among others.

When focusing on the results, don't be urged to p-hack them just to have a study in your hands. P-hacking, in short, is the questionable practice of repeatedly analyzing data or trying multiple statistical techniques until a statistically significant result is found. This can lead to false positives and undermines the integrity of research. Stick to the initial goals of the research. Benchmarking is an important part of this process. However, I believe that it is more often the case that those results will be benchmarked, not against international institutions, but rather other researchers like you. Of course, it depends on the economic field.