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on Informal and Underground Economics |
| By: | Anders Jensen; Pierre Bachas; Lucie Gadenne |
| Abstract: | Can taxes on consumption redistribute in developing countries? Contrary to consensus, we show that taxing consumption is progressive once we account for informal consumption. Using household expenditure surveys in 32 countries we proxy for informal consumption using the type of store where purchases occur. We find that the budget share spent in informal stores steeply declines with income, so that the effective tax rate of a broad consumption tax rises with income. Our findings imply that the widespread policy of exempting food from taxation cannot be justified on equity grounds in low-income-countries. |
| Keywords: | Budget Surveys, Inequality, Informality, Redistribution, Taxes |
| JEL: | E26 H21 H23 |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:cid:wpfacu:407&r= |
| By: | Hofmann, Christine.; Zelenka, Markéta.; Savadogo, Boubakar.; Akinyi Okolo, Wendy Lynn. |
| Abstract: | This paper undertakes a meta study on informal apprenticeship in developing countries. It compares the findings of country-level research conducted by the ILO and others in the past 15 years to shed more light on apprenticeship systems in the informal economy. It discusses the features and practices of informal apprenticeship systems, their responsiveness to rights at work, and the effectiveness of such systems along criteria such as dropouts, training quality, and transitions to employment. The analysis is complemented by a selected number of country case studies that describe and assess the policies and programmes that were introduced during past years to strengthen and upgrade apprenticeship systems in the informal economy. The findings aim to improve understanding of this complex, heterogenous, yet self-sustained training system in the informal economy for evidence-based discussions and policy dialogue between ILO constituents and beyond. |
| Keywords: | apprenticeship, skills development, informal economy |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ilo:ilowps:995168891602676&r= |
| By: | Lourenço S. Paz |
| Abstract: | Illicit financial flows have recently attracted the attention of academia, practitioners, and multilateral organizations who consider them harmful to economic development. Some observers suggest that many of these flows occur via the misinvoicing of international trade transactions. This study develops a novel methodology based on the gravity model of international trade to estimate illicit financial flows using publicly available product-level international trade data. |
| Keywords: | Gravity model, Illicit financial flows, International trade, Misinvoicing, Transportation cost |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:unu:wpaper:wp-2022-24&r= |
| By: | Rivera Moreno, Pablo Nebbi; Triana Montaño, Karol Lorena |
| Abstract: | Central Bank Digital Currency (CBDC) has been in the center of discussion of many monetary policy research agendas. We explore how the business cycle behavior of a developing economy is affected by the introduction of this type of money as a second monetary policy tool. We emphasize on the characteristic dual formal and informal labor markets that are present in most developing economies, given its relevance on explaining the business cycle dynamics. Our main contribution is the building of a model that encompasses such characteristics and features the relevance of monetary balances to macroeconomic fluctuations. We find that CBDC has the ability to improve the monetary policy effectiveness, and the response of relevant variables may be amplified or dampened, depending on the nature of the shock. Also the magnitude of the new dynamics introduced by CBDC are also profoundly dependant on its structural parameters. The main transmission mechanisms that are affected by CBDC are the dynamics of distortions generated by transaction costs. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:cpm:dynare:074&r= |
| By: | Pim Pinitjitsamut; Wisarut Suwanprasert |
| Abstract: | This paper examines informal loans in Thailand using household survey data covering 4,800 individuals in 12 provinces across Thailand’s six regions. We proceed in three steps. First, we establish stylized facts about informal loans. Second, we estimate the effects of household characteristics on the decision to take out an informal loan and the amount of informal loan. We find that age, the number of household members, their savings, and the amount of existing formal loans are the main factors that drive the decision to take out an informal loan. The main determinations of the amount of informal loan are the interest rate, savings, the amount of existing formal loans, the number of household members, and personal income. Third, we train three machine learning models, namely K–Nearest Neighbors, Random Forest, and Gradient Boosting, to predict whether an individual will take out an informal loan and the amount an individual has borrowed through informal loans. We find that the Gradient Boosting technique with the top 15 most important features has the highest prediction rate of 76.46 percent, making it the best model for data classification. Generally, Random Forest outperforms the other two algorithms in both classifying data and predicting the amount of informal loans. |
| Keywords: | Informal Loans; Machine Learning; Shadow Economy; Thailand; Loan Sharks |
| JEL: | E26 G51 O16 O17 |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:pui:dpaper:173&r= |