By Rohith Muthuvelu
Illustration by Keo Morakod Ung
What is Economics? Officially defined as studying “production, consumption, and the transfer of wealth,” it broadly assesses resource interaction in society. Each of these quantifiable variables can be measured, and thus, it is the role of the Economist to represent the computed data utilising models, to affect change. For instance, Unemployment at time: tx, to influence Interest Rates at time: tx+1. More simply, to use quantitative measures to optimise strategy based on theoretical predictions. However, humans are unpredictable and emotional – examining numerical trends alone during decision-making is insufficient, and thus, the importance of qualitativeresearch becomes clear.
Qualitative Research refers to investigations about individual circumstances as opposed to trends or averages, usually by interviewing sampling units; it is measured not by numerical data, but by explanations from interviews, although it may make use of numbers. For instance, a qualitative interview can ascertain a 30% shift in producer risk-aversion, although it is not concerned with representing this graphically. On the other hand, Quantitative Research is concerned with estimating the average effect of a given cause, considering statistical likelihood and probability to generate trends. Crucially, the former studies “causes-of-effects” whereas the latter studies “effects-of-causes.”
Principally, quantitative research generates an inherently limited output space, the high probability set, to prove causes for economic effects. Therefore, every “necessary and sufficient” cause for a given effect cannot be identified, preventing Economists from better optimising their strategies, as discussed in our introductory definition. Consider price determination; relying upon market forces and modal market prices alone can be inadequate – these emotionless numbers are statistically summarised, and thus, smaller strata can be lost as “anomalous” or “misrepresentative.” Therefore, quantitative methods can provide a less exhaustive insight, in this case, hindering producer profits. Moreover, macroeconomically, quantitative methods’ dependence on statistical frequency leads counterfactual claims to be assumed. Historically, high tariffs have increased GDP. However, that is not sufficient evidence that increasing tariffs in a given circumstance will increase GDP at time: tx+1. Similarly, it does not indicate that if GDP rises, high tariffs are the cause – although quantitatively based studies would suggest this. This is reinforced as quantitative research places equal weight on all data entries, and thus, only common effects of generalised causes are noted, as opposed to all effects of specific causes, adapted for “equifinality” (distinct causes of one effect). This “effects-of-causes” approach is intrinsically reactive not proactive, and therefore cannot explain certain outcomes, particularly with unprecedented causes, such as the Covid-19 pandemic. Therefore, qualitative methods often have better predictive power.
Although, the analysis of group trends present in quantitative methods is consistent with macroeconomic judgement. Macroeconomics measures large-scale variables, such as growth, inflation, and unemployment; therefore, analysis of individual “cause-of-effect” flows are both unviable and insignificant. Let us consider minimum wage imposition in firms. Conducting interviews for several firms about wage motivations is impossible, whereas numerical surveys can represent comparable trends simply. Moreover, as macroeconomics concerns large bodies of capital and people, individual, “anomalous” sub-strata trends occupy a smaller percentage of effects across the whole economy, and therefore, quantitative research excluding their behaviour is, by nature, representative. For instance, if 3% of a population are rate-cut indifferent, something only qualitative research would expose, GDP will still rise if the consensus indicates shifts in that direction. Therefore, qualitative data can be both unhelpful and unviable in macroeconomics. However, “statistically insignificant strata” need not be insignificant in reality, as quantitative research considers all sampling units to be of equal significance – we know this to be untrue, and the aforementioned 3% could be disproportionately influential on inflation, for instance. This highlights the need for qualitative research in particular. Yet, quantitative “effect-of-cause” thinking allows Economists to distinguish between correlation and causation, a far greater challenge by qualitative methods. By measuring independent and dependent variables, Economists can isolate causation, allowing models to be constructed and policy to be influenced, our criteria of success. Notably, cities with more police have more crime – quantitative research would demonstrate that as police force increases, crime reduces. However, working backwards qualitatively and inductively could even suggest that more police creates more crime, blurring lines of causation. This feeds defective information to policymakers, supporting the use of quantitative research instead.
Although, qualitative research alone provides the “why” for quantitative models, revealing deeper insights into decision-making, applicable to additional contexts. While isolating variables can prove causal trends, qualitative research explains why such trends exist. By personalising the problem, qualitative research exposes what drives humans to behave as they do, a profound bridge between Economics and Psychology. For example, evidence indicates that gender wage inequality exists – do women deserve less? No. Are they less capable? No. Does systemic discrimination exist? Potentially; quantitative research merely exposes the dilemmas for qualitative investigators to solve – in this case, working to minimise societal inequality and increase wellbeing, un-modellable factors. Moreover, qualitative research acts to dictate predictive models – however, regardless of data trends and measured probabilities, people are quintessentially irrational and unpredictable. Therefore, in the absence of humanised investigation guiding us toward constant modifications, new data can stray too greatly from static quantitative models, leaving them redundant.
However, whilst it comprehensively identifies data patterns and why they form, qualitative research is invariably constrained to a small sample size, limiting causal pathways to be recognised. This is because categorising spoken reasoning is harder than numerical data, increasingly so with sample size. Therefore, whilst crucial, qualitative research is critically restricted, deeming it unviable for larger scale policy modelling, our earlier criteria. However, the breadth of insight provided by qualitative research increases its “effective sample size” beyond the merely numerical one, further complicating this dilemma.
Evidently, quantitative methods are becoming obsolete for matters concerning nuanced populations and models assuming rationality or predictability from humans. This points researchers toward qualitative methods, although their contingency upon small sample sizes and backwards induction deems their application limited, a significant disadvantage. Therefore, combined analysis presenting quantitative data explained by qualitative findings ensures the humanisation, thoroughness, and representative trend perception required to influence policymakers.
UCL Rethinking Economics 