Ryoko Sato (World Bank)
Income tax collection and non-compliance in Ghana (joint with Edward Asiedu, Chuqiao Bi, Dan Pavelesku and Tomomi Tanaka)
Ghana’s tax collection is very low compared with other lower middle-income countries. The revenue from income tax is particularly low, lower than the average of low-income countries. Non-compliance of tax payments is an urgent issue in Ghana, as the government has been suffering from a widening fiscal deficit and a rising debt burden. This paper combines data from household surveys, the business census, and administrative income tax data, and examines the scale of non-compliance and potential revenue gains from the enforcement of tax collection in Ghana. Business census data suggests the actual number of formal sector workers is higher than the number of formal sector workers reported both in the household survey and the administrative income tax data in Ghana. The income tax files do not include the individuals who work for formal sector firms which failed to file PAYE (Pay As You Earn), whereas the household data suffers from under-sampling and under-reporting of high-wage income earners. By combining the household survey data and the administrative income tax data, and adjusting the number of formal sector workers using the business census, we reconstruct the distribution of wage earners in the formal sector who are subject to income tax, and estimate potential income tax revenue gain from the enforcement of tax collection. We find the income tax revenue could have been higher by 582 million Cedi (equivalent to 0.5 percent of the GDP) if everyone who filed income tax in 2014 had paid the full amounts of income tax due. If all formal sector firms and organizations, regardless of whether they actually filed income tax or not in 2016 paid the full amounts of PAYE for all their employees, the income tax revenue could have been higher by 1.2 billion Cedi (equivalent to 1.4 percent of GDP). In 2016, we observe a further reduction of income tax revenue. The total income tax revenue could have been higher by 3.6 billion Cedi (2.2 percent of GDP) if all formal sector firms and organizations, regardless of whether they actually filed income tax or not in 2016, paid the full amounts of PAYE for all their employees.
Tomomi Tanaka (World Bank)
Monetary and non-monetary poverty in urban slums in Accra: Combining geospatial data and machine learning to study urban poverty (joint with Ryan Engstrom, Dan Pavelesku and Ayago Wambile)
As Sub-Saharan Africa continues to urbanize, slum populations are growing at 4.5 percent per year. Providing housing to slum dwellers, protecting them from natural disasters and diseases, and connecting them to jobs and services through improved infrastructure are urgent policy issues in many Sub-Saharan African cities. Identifying the location and living conditions of slums is a critical step toward designing effective urban policies. By combining household survey data and census data with high spatial resolution satellite imagery and other geospatial data using multiple methodologies, including machine learning, we attempt to define slums objectively within the city of Accra. Within these defined slum areas, the patterns of monetary and non-monetary poverty are assessed. Poverty rates are estimated at the neighborhood level and indicate that living in slums is strongly correlated with higher monetary poverty, higher fertility among women, and lower school attendance among children. Poverty is more prevalent in communities in areas of lower elevation, which in Accra are generally flood-prone areas. Ethnic, religious, and regional ties are important reasons people live in slums for long periods of time. People born in the community and ethnic majorities are more likely to get jobs in the manufacturing sector, while ethnic minorities, and new migrants tend to get jobs in the wholesale sector in poorer slum communities. Overall, the results indicate a wide range in economic opportunities between slum communities. These results have important policy implications and are crucial to understand the impact of social networks and how they generate economic opportunities in slums so that effective urban policies can be designed.