Against the background of Nigeria’s substantial rise in oil income, under three distinct chapters, this thesis analysed poverty mobility, inequality, and developments in oil industry. Given that the rate of progress against poverty is considered to be an increasing function of growth (Ravallion and Chen, 1997; Ravallion, 2001; Dollar and Kraay, 2002, 2016); and based on documented evidence on the negative effect of oil intensity on growth, it is hypothesized that: (a) ‘resource-curse’ makes it harder to grow the economy (e.g Gelb (1988) and associates, Sachs and Warner (1995, 1997, 2001), Auty (2001); Gylfason (2011; 2001), and Sala-i-Martin and Subranmanian (2013)); (b) inequality is growth-impeding and also make it harder for the growth that occurs to help poor people (Ravallion, 2007). Therefore, the presence of ‘resource-curse’ and the high level of inequality in Nigeria could explain the country’s inability to address its rising poverty incidence, given its enournous oil wealth. In other words growth drives poverty reduction. Growth is crowded-out by ‘resource-curse’ (or oil intensity). Therefore oil intensity harms poverty reduction. To provide the contexts for these hypotheses, we open the thesis by clearly laying out, in Chapters 1 and 2, the groundwork for the analyses that follow: respectively, general introduction, and review of relevant literature. We analyse the evolution of welfare of households according to the demographics of the household heads in Chapter 3. In Chapters 4, 5 and 6, we asked the following questions and try to address the questions using original survey data on Nigeria and aggregate-level data on oil-related variables. What is the current extent of poverty and inequality in Nigeria? How have these evolved through the years? Is there micro-based evidence of the ‘curse-effect’ of oil on the average living standards of households in the country? Since a static poverty profile understates the extent of poverty, in Chapter 3, we construct and used a synthetic panel to measure poverty dynamics (i.e. the rates of poverty transitions, movement in and out of poverty or individual poverty experiences through time) in the country. In addition, we model the determinants of poverty dynamics using Censored Least Absolute Deviation (CLAD) estimator. The chapter produces evidence that there were more transitions [into] than exits from poverty over 1980-2010; and, as a result, absolute poverty incidence has risen by nearly four-fold over the period. We also find evidence that much of the observed poverty in Nigeria is chronic than transient and the determinants of transient and chronic poverty are not congruent. For instance, the dummy coefficient for households living in oil producing states indicate stronger impact on transient than chronic poverty.Finally, drawing on six sweeps of household surveys of Nigeria that together span 1980–2010 with a pooled sample size of about 97,000 households and data on Nigeria’s age-gender-specific life expectancy from the World Health Organization, this paper shows that about 72 percent to 91 percent of Nigeria’s poor are at risk of spending their entire life below the poverty line. To show this, I estimate the duration of poverty spells and link this to the average age of the poor and to the life expectancy. I find that the poor are expected to escape poverty at the age of 85.46 years on average. However, there is heterogeneity in the exit time, with the transient poor averaging 3–7 years below the poverty line and the chronically poor averaging 37 years or more. Given these exit times and life expectancy, the mean age of the poor at their expected time of escaping poverty exceeds the average life expectancy, meaning some of the poor are not guaranteed to escape poverty in their remaining lifetime. The implication is that growth in Nigeria has not been sufficient nor has it demonstrated the potential to help the poor break free from poverty. However, like Brazil, Nigeria can significantly reduce poverty without absolute reliance on economic growth by reducing its high inflation rate and substantially expanding its social security and social assistance transfers.In Chapter 5, using household surveys of Nigeria, we link and analyse the evolution of poverty in Nigeria to the response of poverty to growth. In particular, we test two hypotheses [put forward by earlier studies]: (i) "Growth is still good for the poor" - (Dollar, Kleineberg, & Kraay, 2016); (ii)" Inequality is bad for the poor" - (Ravallion and Chan, 2007). In a two-fold aim, we estimate the various measures of distribution in order to see how inequality has evolved over 1980-2010 on one hand, and link this evolution to the response of poverty to growth, on the other. Based on the findings, our measures of distribution are all in agreement that Nigeria is less unequal in 2010 than it was in 1980. This decline in inequality, we found, was partly driven by contractions in average living standards, 'pro-poor' growth during 1996-2004 and redistribution of welfare among the non-poor rather than, as expected, redistribution between the non-poor and the poor. Also, we found that the changing pattern of inequality has mitigated the impact of contraction on the poor and in another period, countervailed the gains of growth that should have accrued to the poor. We investigate in Chapter 6, at a micro-level, the hypothesis that the abundance of natural resources (e.g. oil) exerts a depressing effect on growth. Instead of growth in GDP per capita, growth in PCE was used as the LHS variable in the growth regression. Because the surveys in Nigeria are not panel, we follow Deaton (1985) to construct a pseudo panel for the above exercise. This chapter did not find – as far as our leading measures of oil intensity are concerned – negative effects of changes in oil intensity on changes in household consumption. However, growth in the country’s oil revenue is found to be growth-impeding in household consumption. For instance, based on our POLS (FE) results, a 1% rise in real oil revenue is associated with decline in per capita expenditure of households by 0.35%. The impact of the variable that measures oil output (in barrels) per person per day, is negative both for POLS and FE estimations and significant at 1% levels. This result has two implications. First, the country’s population size has been growing at a rate faster than the output from oil, i.e the country’s major source of revenue. More clearly, the more Nigerians there are, for every barrel of crude produced per day, the slower the growth in household welfare. Precisely, if the number of citizens for every barrel of crude produced per day grows by 100%, household welfare will decline by 52%. We provide general policy conclusion in Chapter 7.