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Following the COVID-19 outbreak, there are concerns whether economies are becoming farther from equality and competency. While this matters to every economy, it is more crucial for developing ones who already suffer from income inequalities and lack of competency. The purpose of this paper is to address this issue.

This study uses an administrative data from the Iran's Social Security Organization (ISSO) that provides insurance to workers entitled to the Labor Law of Iran. The data contain more than 7,000,000 workers. The authors assess heterogeneous impact of the first wave of the pandemic by firms' size and average payment.

The authors’ estimation results indicate that, following the initiation of the pandemic, the workers whose corresponding firms are smaller, overall, are more prone to the pandemic and are more likely to submit a request for unemployment benefits. However, the relation is neither homogeneous across sectors nor linear among micro-sized firms. Few sectors indicate a positive relationship between size and likelihood of request submission, including cultural activity, shoemaking and clothing sectors. Besides the size, the authors investigate whether pay grades could explain the probability of becoming unemployed after the pandemic. Results show that workers whose corresponding firms pay less are more likely to submit a request. This is robust within different sectors.

The ISSO dataset is not a panel, so the authors cannot employ methods of causal inferences. The authors’ results should be seen as correlation; however, due to exogeneity and sharpness of the pandemic the result infers to some degree of causality. The data does not cover the informal sector, so the estimates are at lower boundary.

Administrative data on unemployment benefits during COVID-19 show that the pandemic interferes with competition by forcing low-paid workers and small firms to exit the market. This is an alarm for the competition in every economy, specially developing ones.

Does the long-term growth rate of a firm increase by exporting? If yes, how large is that increase in a developing economy? The paper aims to discuss this issue.

The authors incorporate data from the manufacturing plants in Iran as a developing economy for 2003–2011 to address this question. Using fixed effect panel and propensity score matching method, the authors examine whether exportation can affect a firm’s growth rate to test for the learning to grow hypothesis.

The findings document that: not only the exporters are larger and more productive than non-exporters, but they also grow faster in size and productivity measures as well. Additionally, the authors find that the rise in the growth rate is a short-term phenomenon and it disappears in the second year; meaning that exportation does not have a permanent growth effect. The findings are consistent with a spot effect of learning, compared to a permanent growth engine. Results are robust to different analysis tests.

The authors investigate the learning effect of exporting within recently released firm-level data of a developing country.

The main purpose of this paper is to predicate financial distress using the worst-practice-frontier data envelopment analysis (WPF-DEA) model and artificial neural network.

In this study, a neural network technique was used to forecast inputs and outputs in the future time-period. Using a WPF-DEA model, financially distressed companies were identified based on the worst performance, and an improvement solution was provided for those decision-making units.

This study’s findings show that dynamic WPF-DEA has high predictability in corporate financial distress, and it can be used with high confidence. Based on the future time-period results, JOUSH & OXYGEN was predicted to be a financially distressed company in the two future time-periods.

In recent decades, globalization, technological changes and a competitive space have increased uncertainty in the economic environment. In such circumstances, economic growth certainly depends on correct decision-making and optimal allocation of resources. It can be done by introducing appropriate tools and models for assessing corporate financial conditions, including financial distress and bankruptcy.