Process development of new complex reactions in the pharmaceutical and fine chemicals industries is challenging, and expensive. The field is beginning to see a bridging between fundamental first-principles investigations, and utilisation of data-driven statistical methods, such as machine learning. Nonetheless, process development and optimisation in these industries is mostly driven by trial-and-error, and experience. Approaches that move beyond these are limited to the well-developed optimisation of continuous variables, and often do not yield physical insights. This thesis describes several new methods developed to address research questions related to this challenge. First, we investigated whether utilising physical knowledge could aid statistics-guided self-optimisation of a C-H activation reaction, in which the optimisation variables were continuous. We then considered algorithmic treatment of the more challenging discrete variables, focussing on solvents. We parametrised a library of 459 solvents with physically meaningful molecular descriptors. Our case study was a homogeneous Rh-catalysed asymmetric hydrogenation to produce a chiral γ-lactam, with conversion and diastereoselectivity as objectives. We adapted a state-of-the-art multi-objective machine learning algorithm, based on Gaussian processes, to utilise the descriptors as inputs, and to create a surrogate model for each objective. The aim of the algorithm was to determine a set of Pareto solutions with a minimum experimental budget, whilst simultaneously addressing model uncertainty. We found that descriptors are a valuable tool for Design of Experiments, and can produce predictive and interpretable surrogate models. Subsequently, a physical investigation of this reaction led to the discovery of an efficient catalyst-ligand system, which we studied by operando NMR, and identified a parametrised kinetic model. Turning the focus then to ligands for asymmetric hydrogenation, we calculated versatile empirical descriptors based on the similarity of atomic environments, for 102 chiral ligands, to predict diastereoselectivity. Whilst the model fit was good, it failed to accurately predict the performance of an unseen ligand family, due to analogue bias. Physical knowledge has then guided the selection of symmetrised physico-chemical descriptors. This produced more accurate predictive models for diastereoselectivity, including for an unseen ligand family. The contribution of this thesis is a development of novel and effective workflows and methodologies for process development. These open the door for process chemists to save time and resources, freeing them up from routine work, to focus instead on creatively designing new chemistry for future real-world applications.

The dissertation focuses on the different determinants of education, their effects on the educational outcome, and the overall effect of education on the lifetime consequences. The first chapter focuses on the inequality of educational opportunity across different demographic factors. This chapter employs a broader set of social factors to provide fresh insights into the inequality situation in the USA relative to those of the extant literature. The chapter employs polynomial trends for the effects of social factors to identify long-term trends in the determinants of the differences in attainment of each of four achievements (high school graduation, some college, college graduation, and post-college work) across different endogenous social groups. Using the Panel Study of Income Dynamics (PSID) data for the years of 1968-2013, we show how inequality of educational opportunity and its determinants have evolved over the years. The chapter utilizes the machine-learning process and logistic regression model to identify inequality of opportunity. The second chapter examines the age demographic distribution of graduates across cohorts from 1940 until 1990. Using the PSID data, the paper explored the first and second moment of the age of graduating from high school and college across the US. To deal with the data deficiencies, a large part of the chapter dealt with data preparation. The chapter provides a unique method of extracting information on the graduating age of the individuals both from high school and from college. The results show a large dispersion across the full sample. The data truncated to a standard length, however, provides a much smaller dispersion and much smaller moments. The chapter concludes that as the time passes, people tend to attain education at a younger age. The third chapter investigates the trends of the contribution of different factors of income starting from 1910 cohort. Following Mincer (1974), a wave of papers studied how various factors contribute to the earnings of individuals. This paper contributes to that literature in three ways: (i) using the PSID data, it computes the actual working experience of the individuals, (ii) it studies the cohorts who were born in 1910 or afterwards, unlike the existing papers, and (iii) it adds two variables—technological progress and the occupation with which individuals start their careers—to an extended Mincerian equation. The results re-emphasize the importance of education in lifetime earnings. The results also show that while some of the determinants of income have become more important over the years, other factors have not changed much in importance. / PHD / The reason for choosing the theme ‘Evolution of the Determinants of Educational Attainment and its Consequences’ was to investigate the different determinants of education, their effects on the educational outcome, and the overall effect of education on the lifetime consequences. Education is considered as one of the tools to eradicate poverty. Yet, countries with high educational coverage keeps suffering from poverty, a reason for which is higher inequality of opportunity. In the first chapter, entitled ‘Inequality in Educational Opportunity in the United States’, opportunity inequality in education is illustrated. Much inequality stems from differences in educational attainment. A lack of educational attainment puts an individual behind in the career race, even before the race has started. While individuals are responsible for some of the differences in educational attainment, there are factors outside the control of individuals that play substantial roles. The inequality that arises from these factors is known as inequality of opportunity. This paper focuses on inequality of educational opportunity across socioeconomic background, race, and sex. The factors that are analyzed for their contributions to inequality of educational opportunity are father’s education, father’s occupation, mother’s education, and economic status of the individual’s family. The results show that inequality of opportunity has seen a consistent decline for high school completion. The inequality of opportunity (IO) declines for obtaining some college education for the bottom two social groups and remained persistent for the relatively more advantaged group. For college/post-college education, the IO is much lower and, in general, remained persistent across the social strata. Although the females were behind the males – given the equal opportunity – regardless of the race and socioeconomic status during the beginning and the mid twentieth century, the scenario reversed in the late twentieth century. In terms of educational disparity among races, African Americans trail their White counterparts along all the years. The second chapter ‘First and Second Moments of the Age Distributions of Graduates’ looks into the age characteristics (mean and variance) in graduating from high school and college across the cohorts from 1940s to 1990s. The idea of the paper largely came from the first chapter of the dissertation as we assumed the lack of opportunity at the earlier age could delay the attainment of education. The paper intends to find out the average age of graduation over the years. In the process, the paper put forward a method to extract the information of age of graduation from the Panel Study of Income Dynamics (PSID) data, as the database does not readily avail the information. The chapter concludes that as the time passes, people tend to attain education at a much younger age. Titled as ‘Factors Affecting Income: Education, Experience, and Beyond’, the third chapter investigates the contribution of different factors – education, experience, parental endowments, and labor market conditions – in the returns to education using the PSID data and compare the more recent scenarios with the past. This paper focuses on the trend of the rate of return to different factors of income across the two cohorts – those born between 1910 and 1950, and those born after 1950 – while identifying the changes in the returns for the same education level over time. The paper aims to find out how the contribution of the different factors of earning has changed in the USA over the years. The paper also intends to find out the role of technological progress in reducing the earning gaps across the different social groups. The results re-emphasize the importance of education in lifetime earnings. Experience has become a more important factor of income over the years. The chapter also suggests that income of an individual is a monotonic function of socioeconomic endowments and better endowments resulted in higher returns. Lastly, the chapter finds that the technological investment is progressive in manner.