Journal of Policy Modeling最新文献

South Africa runs a primary fiscal deficit and the long-term interest rate on government borrowing, r, is greater than the long-term economic growth rate, g. Without intervention, debt will continue to rise until there is a disorderly fiscal stop. Reforms to raise growth have not materialised, leaving fiscal consolidation as the second-best solution. Using a medium-sized, open-economy, fiscal DSGE model of South Africa, we show that the least cost policy is to impose a time-consistent fiscal policy rule with debt-to-GDP as the fiscal anchor and a pre-announced path for government consumption spending as the intermediate operational objective. This result obtains with and without explicit policy coordination between the fiscal and monetary authorities.

The declaration by the World Health Organization and government-initiated actions by different countries for the COVID-19 vaccine have led to the rapid evolution of sentiments on various social media platforms. Real-time data related to vaccination has grown the need to anticipate the changes in vaccine uptake. Using Twitter dataset, the study models different emotions and their associated word. The emotions are majorly classified into hesitancy and willingness for vaccination. The study categorizes the tweets into pre-launch, post-launch, and booster doses of the COVID-19 vaccine. Based on comparative analysis, most sentiments were related to hesitancy for vaccination during pre-launch. In post-launch, the majority of sentiments were oriented towards willingness for vaccination. However, during the booster dose, the sentiments were oriented toward happy, adequate, and free emotions. Over the time period, the willingness of the COVID-19 vaccine has improved. The practitioners and policymakers can obtain real-time sentiments based on this approach and strategize the long-term vaccination policy for COVID-19 and other vaccination programs.

Evaluating the relevance of development policies is a complex task as many different dimensions must be considered. By focusing on one particular, quantifiable aspect of relevance, this study proposes a new method for generating data-driven evidence that can be used both for assessing the relevance of international development policies ex post, and for guiding decisions about future strategic priorities. For this purpose, relevance is defined as the match between (i) the types and scopes of support provided and (ii) the types and scopes of support most needed in a given context, according to measurable indicators (proxies). The latter is operationalized using a multidimensional vulnerability score constructed based on a set of empirical indicators commonly argued to proxy vulnerability to shocks. Comparing the vulnerability score with the sector-specific allocation of support yields two empirical measures of relevance, one at the country level and one at the sector level within each country. The proposed method is then applied to evaluate the relevance of the World Bank Group’s early response to Covid-19 across 133 countries. At the same time, many of the modeling insights are more broadly applicable and may also be useful in informing evaluations of development policies beyond the specific application considered here.

The energy issue in both developed and developing countries remains vital. However, in developing countries, energy pressure is much greater. Despite this, few studies have examined credit, entrepreneurship, and energy transition in developing countries. We addressed this research gap by using the data collected from six regions in Burkina Faso. Employing the multivariate probit and the endogenous switching probit model, three scenarios are explored. We find that entrepreneurship promotes energy transition while access to credit is not useful to the energy transition. However, the joint effect of access to credit and entrepreneurship is 4 times higher for solar PV use and 3 times for biogas adoption. Access to credit promotes the energy transition when credit is devolved to the entrepreneur supporting the trinity rather than the trilemma.

US international trade policy under both the Trump and Biden administrations has been increasingly protectionist. This paper considers two policy scenarios under active discussion: (1) an across-the-board increase in all US tariffs by 10 percentage points, and (2) a severe escalation of the US trade war with China. The scenarios are analyzed using a multi-country computable general equilibrium (CGE) simulation model of the global economy. Trade wars or policy regimes of widespread protection will increase tariffs in many sectors simultaneously and include both final goods and intermediate inputs. The impacts are complex, with a web of direct and indirect forces coming into play across domestic and international markets. The global CGE model captures these mechanisms, including both short and long-run effects, with and without retaliation by partner countries. In a world economy where the US accounts for only 10 % of global trade and potentially rival trade bloc have emerged in Europe and E&SE Asia, the US is no longer hegemonic in global markets. We find that across-the-board tariffs do not protect manufacturing jobs because the cost of imported intermediate goods increases, raising costs in manufacturing production. The US trade war with China leads to a dramatic fall in bilateral trade. Other countries expand their trade to China and the US with the exception of closely linked partners (e.g. Canada and Mexico and all countries in E&SE Asia). We find that the world economy can adjust to US trade wars, diverting trade around the US.