What does an economist do?

In short, economists collect and analyse data to find the answers to difficult questions in a variety of fields. On a day-to-day basis, an economist performs tasks such as gathering data, writing up reports and preparing presentations for stakeholders.

Economists analyse a range of issues, including topics related to education, healthcare, development, and the environment. Regardless of the field of study, economic analysis is at the core of an economist’s work.

This page provides an overview of two key responsibilities of an economist: presenting data effectively and conducting economic analysis using quantitative modelling tools.

Additionally, this page provides insight into the day-to-day activities of an economist.

Presenting Data

A key responsibility of economists includes presenting their data and findings in a clear way – a way in which allows non-economists to make informed decisions. Sometimes this is done by simply taking publicly available data and presenting it clearly and concisely. When more detailed datasets are used, cleaning and sorting of the data is needed before it can be presented. Either way the end result is the same – an easily read chart or table that summarises the key trends.

As economists often produce analysis that is later used by non-economists, how the data is presented matters for both decision-making and public opinion. Data that is poorly presented can result in misunderstandings. For someone that is not used to studying data on a daily basis, a quick look at a chart with poorly presented data can easily result in incorrect interpretations of the data.

For instance, in the chart below it seems like output per hour has risen substantially since 1998. This increase also seems to be driven by strong growth in some years, rather than by a steady increase over time.

Figure 1: Output per hour in Scotland for the years 1998-2019

Source: ONS

However, in the chart above, the vertical axis starts at 85. Adjusting the scale so that the y-axis instead starts at 0 produces a graph that looks completely different. In the chart below, it appears that output per hour in Scotland has followed a very steady trend since 1998, with no major fluctuations. In other words, the change in output per hour has been much less dramatic than what it seems in figure 1.

Figure 2: Output per hour in Scotland for the years 1998-2019

Source: ONS

Depending on which one of these two charts is used, one would probably have a different understanding of how productivity in Scotland has changed over the past few years.

Similarly, bar charts with different scales are also prone to varying interpretations of the information presented.

For example, the chart below displays overall poverty rates in the UK and 19 EU27 countries in 2017. In this chart, it seems like the UK has a rather low poverty rate compared with most of the other countries. For instance, the bar for the UK is almost half the size of Luxembourg, which could be interpreted as the UK having a poverty rate half the size of that in Luxembourg. This is an incorrect interpretation, caused by the vertical axis starting at 15% rather than at 0.

Figure 3: Overall poverty rates in the UK and 19 other EU28 countries in 2017

Source: ONS

With the y-axis adjusted to start from 0, it is clear that the difference in poverty rates between the UK and the other countries is far smaller than the chart above indicates. The poverty rate in the UK does not appear to be half the size of the poverty rate in Luxembourg anymore. Instead, there seems to be a difference of only a few percentage points. Figure 4.

Figure 4: Overall poverty rates in the UK and 19 other EU28 countries in 2017

Source: ONS

These two examples demonstrate the importance of presenting data accurately. While both examples focus on the impact of having different scales on the y-axis, there are of course many other components of a chart that influence how data is presented and interpreted.

Using appropriate data, the right type of chart, a comprehensive colour scheme, and accurate labelling are all important considerations for presenting data well and minimising the risk of incorrect interpretations.

With economic analysis and data frequently being used to inform policy, it is important that all presentation of data is entirely transparent and easy to understand for policy-makers. Otherwise there is a risk that money is misused for the sake of implementing ineffective policies, or that policies that would be necessary are disregarded based on incorrectly presented information.

For instance, Figure 3 makes it seem like overall poverty in the UK is much lower than most EU27 countries, which might cause policy-makers to divert spending away from poverty alleviating programmes. However, Figure 4 clearly shows that the poverty rate in the UK is not that different from the EU27 countries.

Economic Analysis

Economists regularly employ both quantitative and qualitative analysis when addressing difficult policy questions. For some of the economic analysis work, specialist software and mathematical modelling techniques are required. Econometric modelling, computable general equilibrium (CGE), and input-output modelling are three particularly common forms of applied economic analysis.

Econometric Modelling

Econometric modelling can be used to provide some statistical evidence surrounding the relationships of various economic, and non-economic, indicators. For instance, an econometric model can be applied to estimate the relationship between democracy and economic growth.

Economists often use econometric models to forecast future developments in the economy, such as economic growth, unemployment levels, and prices. Econometric models can also be used to predict developments in the present, the very near future, or the very recent past, referred to as ‘nowcasting’. Nowcasting makes it possible to estimate important economic indicators earlier than official estimates.

For instance, the first official estimate of Gross Domestic Product (GDP) in Quarter 4 is published in April the following year. However, nowcasting methodologies use data released prior to this to estimate Q4 GDP earlier, often before or at the end of the quarter. Furthermore, nowcasting estimates are typically updated as new information and data becomes available. For example, Nowcasting Scotland publish and update nowcasts of the Scottish economy, such as estimates of quarterly GDP.

Economists also use econometric modelling to estimate the effect of a specific intervention or treatment, such as a passage of law, execution of policy, or large-scale programme implementation. This type of modelling is called difference-in-differences (DID) estimation. DID employs observational data from two different groups, one that is subject to the intervention or treatment and one that is not. If one only considered the group that is subject to the treatment, it would be difficult to identify causality – i.e. it would be difficult to estimate if the intervention had any effect on those exposed to it.

For instance, if one wanted to evaluate the impact on test scores of an initiative providing free school lunches, one might simply measure overall test scores one year before the free school lunch initiative was implemented and compare this with the scores one year after the initiative was introduced. While the free school lunches initiative probably would drive some difference between the two overall test scores – as healthier children are expected to perform better in school – a range of other variables could have had an impact too.

For example, suppose there was a major international sporting event during the week of exams in the year before the free school lunches initiative was implemented, but not in the year after. This would also impact the difference in test scores. Thereby, using only data from the group affected by the treatment makes it difficult, if not impossible, to distinguish the causal effect of the intervention.

Assuming that there is another group which is not subject to the treatment group but is similar in terms of relevant characteristics, it is possible to evaluate the causal impact generated by the specific intervention using a DID model. The group that is subject to treatment is often called the ‘treatment group’ while the other group is referred to as ‘control group’.

In the example of the free school lunches, one could include a group of schools which are not receiving the school lunches as a control group, whereas the schools ‘treated’ by the initiative would be the treatment group. The DID model relies on the assumption that these two groups would follow parallel trends over time without treatment, and that when the intervention is put into effect, the treatment group will follow a new trend which is not parallel with the control group trend. This is illustrated in the chart below, where the treatment group clearly changes path when the intervention is implemented.

Figure 5: Difference-in-differences explained in a graph

Source: Columbia University (Mailman School of Public Health)

The difference in the outcome variable between the treatment group and control group pre-intervention is the constant difference in outcome, caused solely by external factors and not related to the intervention. However, comparing the two groups post-intervention indicates the difference in outcomes which is caused both by these external factors as well as the intervention. This means that the impact of the intervention can be identified by taking the difference between the post-intervention difference in outcomes and the pre-intervention difference in outcomes. Hence the name, difference-in-differences.

In the example of free school lunches, the impact of the programme would be determined by measuring the difference in overall test scores between the two towns – one year after implementation of free lunches and one year before. After that, taking the difference in the pre- and post-programme outcomes between the two towns would indicate the causal effect of the free school lunches programme on overall test scores.

DID models can be used to study a range of variables and their use has contributed to extremely influential work in the field of economics. The two American economists Card and Kreuger used a DID model to estimate the effect of a law introduced in 1992, raising the minimum wage in the US state of New Jersey from $4.25 to $5.05 an hour. Neighbouring eastern Pennsylvania kept their minimum wage at $4.25 an hour, making it a perfect control group.

The two economists evaluated the impact of the new law by surveying 410 fast food restaurants in New Jersey, the treatment group, and eastern Pennsylvania, the control group, before and after the rise in minimum wage. Using their DID model, Card and Kreuger found that a higher minimum wage had no or a very small positive effect on the level of employment in the relevant industries. This study caused a shift in the debate around minimum wages and employment, causing several states and counties to follow suit and raise the minimum wage.

Computable General Equilibrium Modelling

Economists also employ computable general equilibrium (CGE) modelling for economic analysis. CGE models are large numerical models which combine economic theory with real economic data in order to computationally derive the impacts of policies or shocks in the economy. While econometric models such as the difference-in-differences model focus on the impact of a policy on a specific outcome variable, CGE models go a few steps further and evaluates how a policy impacts multiple components of the economy.

CGE models fit economic data to a set of equations which aim to capture the structure of the economy and behavioural response of economic agents (i.e. firms, households, government). Through this framework it is then possible to simulate policy changes or shocks and trace the effects on multiple key economic variables. The impact of the policy or shock is estimated by comparing the economy before and after the shock.

CGE modelling is used internationally by governments, research organisations, academics and private consultancies. CGE models can be used to model fiscal policy scenarios, the impacts of changes in the level and profile of government spending, and the effects of economic shocks such as changes in investment, productivity, export demand and labour supply. For instance, the Scottish Government used their own developed CGE model to calculate the economic impact of EU migration into Scotland. EU migration was modelled as an increase in labour supply, which expands the productivity capacity of the Scottish economy and ultimately leads to a new economy. The main findings in the report are presented in the figure below.

Figure 6: Contributions of EU citizens working in Scotland

Source: Scottish Government

£4.42 billion: Total contribution by EU citizens working in Scotland

£34,000: The average additional contribution to GDP per additional EU citizen working in

£10,40: Contribution to Government Revenue per each additional EU citizen working in Scotland

Input-Output Modelling

Input-output (IO) modelling is another type of model widely used by economists. IO models focus on the inter-industry linkages within an economy, such as how one industry trades goods with another. IO models are commonly used to estimate the effects of positive or negative economic shocks throughout the economy.

IO models rely on a foundation of input-output tables; a series of rows and columns of data that quantify the supply chain for all sectors of an economy. These tables can then be studied and analysed to estimate the effects throughout the economy of an economic shock, such as a government VAT cut, job losses within an industry, or changes in expenditure by a specific industry or organisation.

The estimated effects of such shocks are depicted by three impacts: direct, indirect and induced. For instance, if a construction company decides to build 10,000 new build homes then:

· The direct effect is the economic impact of the construction firm’s spend on labour and materials;

· The indirect effect is stimulated by the purchases of goods and services made by the suppliers to meet the construction firm’s demand; and,

· The induced effect is the economic impact of the increase in economic activity driven by the wages paid to build these 10,000 new homes.

For example, the Fraser of Allander Institute conducted an impact assessment, using IO tables, to estimate the economic contribution of Glasgow Housing Association to the Scottish economy through its day-to-day operating spend and capital expenditure. They found that the economic impact of overall activities in 2016-17 amounted to over £150m contributed to Scottish GDP and 2,575 additional jobs supported, while the economic impact of capital expenditure since 2003 corresponded to a contribution of £2bn to the Scottish economy and an average of 2,425 full-time equivalent jobs supported annually.

Figure 7: Results of the economic impact assessment of Glasgow Housing Association

Source: Fraser of Allander Institute

Economic impact of activities 2016/17

GDP : £153 million contributed to the Scottish economy

JOBS : 2,575 additional jobs supported in the Scottish economy

Economic impact of capital expenditure 2003/04 to 2017/18

GDP: Around £2 billion contributed to the Scottish economy since 2003

JOBS: On average 2,425 FTE jobs supported annually since 2003

In order to estimate the losses to the economy caused by the loss of an organisation or industry from the economy, a hypothetical extraction method can be applied to the input-output tables. This method allows for an extraction of the purchases and sales made by a sector or organisation from the model of the Scottish economy, which results in a reduction in economy activity across the whole economy. After extraction, the total output in the economy is smaller due to both loss within the extracted sector, but also its purchases and sales to other sectors, as well as other losses in forward and backward linkages.

In a study published by The Royal Society in 2019, a hypothetical extraction method was applied to regional IO tables in order to assess the economic impacts of IT service shutdown during the York flooding in 2015. The study found that the three-day shutdown of IT services had generated total economic losses of £3.24m. The IT services industry suffered the largest part of almost 60%, with the remaining 40% of the economic losses resulting from indirect and induced effects. The top 10 service industries suffering the greatest indirect economic losses in York in 2015, as published in the study, are presented in the figure below.

Figure 8: The top 10 service industries suffering indirect economic losses in York 2015

Source: Xia et al., 2019

Economic impact of capital expenditure 2003/04 to 2017/18

GDP: Around £2 billion contributed to the Scottish economy since 2003

JOBS: On average 2,425 FTE jobs supported annually since 2003

A Typical Day as an Economist

Economic Futures ran a survey at the end of 2020 of UK economists, asking them questions about life as an economist. As part of the survey, respondents were asked what a typical day looks like in their job -

Project, time and team management

  • Project management.
    • Balancing short term and longer-term projects.
  • Checking emails and regularly managing workload for the day/week.
    • Typically, respondents said mornings were for ad-hoc work while afternoons were dedicated to a particular project.
  • Managing a team of economists and analysts, ensuring every one knows what they are doing and are on track to meet their deadlines.

Economic Analysis

  • Meetings to discuss projects with clients.
    • Meetings with clients: face-to-face and over the phone and via webcam.
  • Literature reviews and background analysis for a project.
  • Data collection and analysis.
  • Economic modelling: input-output modelling, computable general equilibrium modelling, forecasting, microsimulation models, econometric modeling, etc.
  • Writing reports, articles, economic commentary.

Policy analysis

  • Writing briefing notes.
  • Corresponding with policy colleagues, ministers and analysts.

Academia and further learning

  • Lecturing/Guest Lecturing and presenting report findings to clients and stakeholders.
  • Career events for high school and university students.
  • Attend training, workshops, events and webinars.
  • Journal editing.

Social media, engaging with your audience and dealing with the press

  • Speaking with different organisations and building relationships and partnerships with various bodies.
  • Dealing with the press after publishing of projects, articles and economic commentary.
  • Podcasting.
  • Blogs.
  • Social media to build profile.


As explained throughout this article, economists do a range of things. Economists are found almost everywhere, analysing issues within a range of fields.

On a day-to-day basis, economists gather material, conduct economic analysis, and prepare data for presentation. Some of the typical tasks of an economist are quite technical, while some are more focused on data visualisation and clear writing.

But, there is also a side of economics which involves dealing with the media and increasing readership and audience engagement through podcasting, blogs and events.

The day-to-day activities of an economist can therefore be quite varied depending on what career path the economist is in.

Typically, economists produce results that are later on used by the public and policy-makers alike, and economic analysis often form the basis for discussion, decision, and implementation of policy. This means that economists have a responsibility to present their findings clear and well, so that someone without a background in economics easily can understand.

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