This folder contains data used in chapter 4 of the thesis. Various data sources are used. Data on trade openness, services sector employment, education, and financial development are sourced from the World Development Indicators Database of the World Bank. The data on digital infrastructure captures Internet access, fixed telephone subscriptions (per 100 people), and mobile cellular subscriptions (per 100 people). Data on institutional quality and inflation comes World Governance Indicators and while data on International Monetary Fund (IMF) database respectively. For missing observations, besides the institutional quality variable, we impute these missing observations using their growth trend. However, for the variable of institutional quality, data points for the years 1997, 1999, and 2001 are not available. We use the averages of the two periods before and after to impute them. The final sample contains data on 45 Sub-Saharan African countries for the period 1996–2017. The analysis was implemented in stata. We use Fixed-Effects Method for the baseline estimates. Subsequently, we address endogeneity by employing the Fixed Effect IV (FEIV) method and the Lewbel (2012) Fixed Effect IV (FE-IV LB) approach.

The extent to which fossil fuel prices paid by consumers do not reflect the fuels' full financial and social costs, expressed as an aggregate value for each fuel and economy.

Undercharging for fuels is disaggregated into explicit and implicit subsidies. Explicit subsidies measure the amount that the financial cost to supply a fuel (i.e., the supply cost) exceeds the price paid by the fuel user. Implicit subsidies measure the difference between a fuel's full social cost and the price paid by the fuel user, exclusive of any explicit subsidy. A fuel's full social cost includes both supply costs and negative externalities, which are costs imposed on society due to consuming the fuel and primarily include local air pollution, climate change, and broader externalities related to driving.

It should be noted that the concept of "subsidies" used here differs from the definition of subsidies in macroeconomic statistics.

For further details, please refer to https://climatedata.imf.org/datasets/d48cfd2124954fb0900cef95f2db2724_0/about

The study investigates how transport infrastructure and quality of institutions impacts inter-regional trade. The focus is on inter-regional trade in Sub-Saharan Africa (SSA), specifically, trade between East African Community (EAC) and three other regional economic communities in SSA, namely, SADC, ECOWAS and ECCAS. The data was mainly collected from WDI of the World Bank (data for macro variables), exports data from DOTS of IMF, transport infrastructure data from AfDB, distance and other dummy variables were collected from CEPII database. Description and labeling of the variables used in the regression has been done in in sheet 4 of the excel file attached. The data is in 3 excel sheets, for EAC-SADC, EAC-ECOWAS and EAC-ECCAS respectively. The results indicate positive impact of transport infrastructure and quality of institutions to inter-EAC exports.

It is produced based on the INFORM Risk with IMF staff calculations.Climate-driven Hazard & Exposure reflects the probability of physical exposure associated with specific climate-driven hazards. Vulnerability represents economic, political and social characteristics of the community that can be destabilized in case of a hazard event.

Lack of coping capacity relates to the ability of a country to cope with disasters in terms of formal, organized activities and the effort of the country’s government as well as the existing infrastructure which contribute to the reduction of disaster risk. The data are updated annually based on August data release by INFORM Risk.Sources: Disaster Risk Management Knowledge Centre (DRMKC). 2022. INFORM Risk Index. European Commission. https://drmkc.jrc.ec.europa.eu/inform-index/ INFORM-Risk; IMF staff calculations.Category: AdaptationData series: Climate-driven INFORM Risk Indicator Climate-driven Hazard & Exposure Lack of coping capacity Vulnerability Methodology:Climate-driven INFORM Risk Indicator is produced based on the INFORM Risk with IMF staff adjustments to focus on climate-driven risks only. INFORM Risk has three dimensions: hazard & exposure, vulnerability and lack of coping capacity. The hazard & exposure dimension has been adjusted by IMF staff to include climate related components only (i.e., flood, tropical cyclone, and drought). This results in a Climate-driven INFORM Risk Indicator. Vulnerability and lack of coping capacity dimensions are based on the INFORM Risk with no adjustments.

Climate-driven INFORM Risk Indicator helps assess risk for climate-driven hazards. It can support decisions about prevention, preparedness, and response. It has three dimensions: climate-driven hazard & exposure, vulnerability, and lack of coping capacity. It is produced based on the INFORM Risk with IMF staff calculations.

Climate-driven Hazard & Exposure reflects the probability of physical exposure associated with specific climate-driven hazards. Vulnerability represents economic, political and social characteristics of the community that can be destabilized in case of a hazard event. Lack of coping capacity relates to the ability of a country to cope with disasters in terms of formal, organized activities and the effort of the country’s government as well as the existing infrastructure which contribute to the reduction of disaster risk.

The data are updated annually based on August data release by INFORM Risk.