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This DALA Assessment report collects and integrates relevant data for the authorities of the government of the Bahamas, on the effects on the different social, productive, services and infrastructure sectors, which have been caused in the country for the period 2020 -2023 due to the impact generated by the Covid-19 pandemic in the Bahamas. The COVID-19 pandemic has disrupted the world's economic order and global public health. Social distancing measures to curb the spread of the virus created significant challenges for global economic activity. Sectors such as tourism which depend on the movement of people virtually shut down. The economy of The Bahamas is heavily reliant on tourism, which accounts for approximately 50% of the country's Gross Domestic Product (GDP). The pandemic also highlighted the country's socio-economic vulnerability as businesses and citizens felt the crunch These impacts highlighted the need for comprehensive Disaster Risk Management (DRM) and Health Risk Management (HRM) among other strategies and instruments to serve the country better. The Inter-American Development Bank (IDB) is proud of its long-standing partnership with the Government, businesses, and people of The Bahamas towards improving lives and future outcomes. Given the impact of COVID-19 on the health of citizens and the main productive sectors of the economy, the IDB responded to support recovery. The IDB formed a pilot team Preparedness, Recovery and Reconstruction Country Team (P2RCT) for the coordination of DRM and HRM matters in The Bahamas. P2RCT is supporting the country as it improves its response and resilience to natural disasters and other disaster events with the potential to negatively impact the country's already compromised social and economic development. Through a partnership with the Economic Commission for Latin America and the Caribbean (ECLAC), the Bank deployed a team of local and international research experts to the evaluate and collect pertinent data to p

We estimate a Cobb-Douglas production function distinguishing between a domestic and a foreign capital stock built from data of imported machinery and transport equipment for Brazil. The preferred regression uses log levels estimated by GMM-HAC. Results are that the elasticity of production of foreign capital is about 40% of that of domestic capital, the function has constant returns to scale in capital and labour variables, and human capital and technical change are also highly productive.

The effects of COVID-19 have been stronger in service-related subsectors, where supply and/or demand were constrained by lockdowns and social distancing measures. The losses in these subsectors have had direct impacts-through their weight in countries GDP-and indirect impacts through their effect on other sectors. In Latin America, effects on the three most affected sectors-wholesale, retail, and hospitality services; construction; and manufacturing-add up to a 4.9 percent hit to economy-wide labor productivity through direct and indirect channels. Large productivity improvements in infrastructure may be needed to fully compensate for the negative productivity losses traceable to COVID-19.

This paper examines sectoral productivity shocks of the COVID-19 pandemic, their aggregate impact, and the possible compensatory effects of improving productivity in infrastructure-related sectors. We employ the KLEMS annual dataset for a group of OECD and Latin America and the Caribbean countries, complemented with high-frequency data for 2020. First, we estimate a panel vector autoregression of growth rates in sector level labor productivity to specify the nature and size of sectoral shocks using the historical data. We then run impulse-response simulations of one standard deviation shocks in the sectors that were most affected by COVID 19. We estimate that the pandemic cut economy-wide labor productivity by 4.9 percent in Latin America, and by 3.5 percent for the entire sample. Finally, by modeling the long-run relationship between productivity shocks in the sectors most affected by COVID 19, we find that large productivity improvements in infrastructure--equivalent to at least three times the historical rates of productivity gains--may be needed to fully compensate for the negative productivity losses traceable to COVID 19.

Guarantees to back state-owned enterprise (SOE) borrowing are one of the most significant contingent liabilities that the public sector has had to face. Particularly in times of crisis, credit guarantees are an important solution for financing SOEs. It is, however, essential to fully measure their risks to adequately manage the public sector balance sheet. This note examines this question and presents a simple credit guarantee valuation methodology as a first step toward sound management of the financial risks linked to SOEs.

We propose a new family of easy-to-implement realized volatility based forecasting models. The models exploit the asymptotic theory for high-frequency realized volatility estimation to improve the accuracy of the forecasts. By allowing the parameters of the models to vary explicitly with the (estimated) degree of measurement error, the models exhibit stronger persistence, and in turn generate more responsive forecasts, when the measurement error is relatively low. Implementing the new class of models for the S&P 500 equity index and the individual constituents of the Dow Jones Industrial Average, we document significant improvements in the accuracy of the resulting forecasts compared to the forecasts from some of the most popular existing models that implicitly ignore the temporal variation in the magnitude of the realized volatility measurement errors.

Ex-post economic impact evaluations are standard requirements for loans and grants from multilateral international development institutions. In many cases, however, lack of sufficient baseline or historical data, or the very nature of the investment itself renders orthodox economic impact evaluation approaches unviable. Nonetheless, evaluations are required to provide an indication of the benefits generated by the investment and insights for future program design. Addressing this challenge, this paper develops an ecosystem service, retrospective stated preferences approach to assess the benefits of a coastal infrastructure investment in Barbados. Results show that the investment generated cultural and aesthetic ecosystem service benefits for tourists and residents, and that local businesses derived value and avoided some damage costs from the enhancement of regulatory ecosystem services. The approach is versatile facing data constraints and generates policy-relevant information to support decisions to scale up interventions, catalyze additional investment, and provide data on user preferences that can be incorporated in the design of future interventions.

Sovereign contingent liabilities materialize when uncertain future events, which are largely beyond the State's control, occur. They can represent a significant burden for public finances and jeopardize public debt management and sustainability. This paper presents a toolkit for the identification and systematization, measurement, monitoring, and reporting of these liabilities. Based on the proposal of a general methodology, the toolkit develops specific estimation methodologies for different categories of sovereign contingent liabilities. These methodologies are then applied in hypothetical exercises, complemented with illustrations taken from the international context. Furthermore, to illustrate how the toolkit might be applied, the paper provides examples that test sensitivity to changes in the variables andparameters that affect contingent liabilities. The study demonstrates that the toolkit can help to achieve improved and better-informed management of public debt and sovereign contingent liabilities, as well as of their associated financial and fiscal risks.

The applied social sciences have witnessed a growing use of sensitive survey techniques (SSTs) to study the relationship between facets of an individuals background and his propensity to engage in sensitive behavior. The rationale undergirding the use of these techniques is the assumption that the rate of misrepresentation and/or nonresponse under direct questioning among individuals bearing the sensitive trait would be so high as to make the use of direct questioning infeasible. But is this indeed the case? Presently available methodological tools do not provide an answer. The current paper presents a survey questioning strategy and corresponding statistical framework that simultaneously addresses the question of whether or not the use of a SST is required to study a given sensitive behavior, provides an estimate of the prevalence of the sensitive behavior in the population of interest, and, in its extended form, describes how individual characteristics relate to the likelihood of engaging in the behavior.

This paper estimates the impact of industrial agglomeration on firm-level productivity in Chinese manufacturing sectors. To account for spatial autocorrelation across regions, we formulate a hierarchical spatial model at the firm level and develop a Bayesian estimation algorithm. A Bayesian instrumental-variables approach is used to address endogeneity bias of agglomeration. Robust to these potential biases, we find that agglomeration of the same industry (i.e. localization) has a productivity-boosting effect, but agglomeration of urban population (i.e. urbanization) has no such effects. Additionally, the localization effects increase with educational levels of employees and the share of intermediate inputs in gross output. These results may suggest that agglomeration externalities occur through knowledge spillovers and input sharing among firms producing similar manufactures.

This paper makes three contributions to the existing literature. First, it provides descriptive evidence on gender differentials by education level in the US labor market over the last twenty years. Second, it uses the structural estimation of a search model of the labor market to identify and quantify the impact of employers' prejudice on labor market gender differentials. Third, it connects both the descriptive and the analytical findings to recent policy interventions in the US labor market and presents some policy experiments. The results show that prejudice may still have a role in explaining the evidence on gender differentials and there is at least one scenario where the possibility of the presence of prejudiced employers in the labor market has substantial effects. In particular, it is responsible for the reversal of the returns to schooling ranking in recent years and it may explain up to 44% of the gender wage gap of the top education group (Master and PhD) in 2005. Since prejudice is still important, policy interventions may be effective in attaining both efficiency and welfare gains. The paper is in favor of implementing an affirmative action policy because it is frequently able to close the gender gap without reducing overall welfare and because it is effective in targeting the group that should take center stage in the future debate about gender differentials: high-skilled, high-earners workers, who also have family responsibilities.