GDP, Real vs Nominal, Labor Markets, and the Business Cycle — Lecture Notes

News and policy context

  • Fed meeting expected to implement expansionary monetary policy due to weak labor market data from prior week.
  • Expansionary policy implies actions that increase the money supply to stimulate the economy; typically lowers interest rates indirectly via increased lending capacity for banks.
  • Explanation analogies used:
    • Money supply increase leads to cheaper loans, analogous to more avocados in the economy lowering avocado prices.
    • Medical metaphor: prescribing medication to the economy; lowers rates but can have inflation side effects.
  • Market implications:
    • Market activities already price-in anticipated Fed actions; changes in press conference details could move markets.
    • Core message: lower interest rates likely, but communication about underlying economic weakness could shift sentiment.
  • Core tension: policy aims to balance unemployment/real activity with inflation risk; expansionary policy can boost activity but risks higher inflation.
  • Conceptual note: real GDP vs nominal GDP; price level dynamics matter for interpreting growth and policy.

Nominal vs Real GDP; base-year methodology

  • Nominal GDP includes current price levels; it can rise due to higher prices (inflation) even if output (volumes) stays the same.

  • Real GDP controls for price level changes to measure true output changes over time.

  • Why real GDP matters for business cycles: we want to compare output across years using constant prices.

  • Base-year approach:

    • Choose a base year (e.g., 2012) and value all years using prices from the base year.
    • Real GDP for year t: RealGDP_t =

    \sumi P^{\text{base}}i \; Q{t,i} where Pbase</em>iP^{\text{base}}</em>i is the base-year price of good i and Qt,iQ_{t,i} is the quantity of good i in year t.

  • Alternative expression: the GDP deflator links nominal and real GDP:

    • Deflator<em>t=NominalGDP</em>tRealGDPt×100Deflator<em>t = \frac{NominalGDP</em>t}{RealGDP_t} \times 100
    • RealGDP<em>t=NominalGDP</em>tDeflatort×100RealGDP<em>t = \frac{NominalGDP</em>t}{Deflator_t} \times 100

  • Practical example used in lecture:

    • Base year selected near present (e.g., 2012) to keep price levels intuitive for students.
    • 1930 real per capita GDP around 8{,}200

    and 2019 real per capita GDP around 58{,}000, illustrating large real gains per person over time.

  • Real per capita GDP definition:

    • RealPerCapitaGDPt = \frac{RealGDPt}{Population_t}

  • Key takeaway: real GDP per capita is the standard metric to assess changes in living standards over time, while nominal GDP can be distorted by price level changes.

The business cycle: structure and terminology

  • Real GDP is plotted on the vertical axis; time on the horizontal axis.
  • Two components of change:
    • Trend line (potential real GDP): the long-run growth path.
    • The cycle: fluctuations around the trend (upward growth with alternating expansions and contractions).
  • Phases of the business cycle (objective terms):
    • Peak: high point, temporary.
    • Contraction: output shrinking.
    • Trough: low point, temporary.
    • Expansion: output rising again; can be a recovery or boom.
  • Expansion breakdown:
    • Recovery: return to normal after a trough.
    • Boom: expansion above the trend (above potential).
  • Recession vs Depression:
    • Recession: two consecutive quarters of negative real GDP growth (historical rule).
    • Official dating by the NBER (National Bureau of Economic Research) can differ and uses broader indicators beyond quarterly GDP changes.
    • COVID-19 period challenged the traditional six-month rule; the economy contracted rapidly but for a relatively short time, prompting discussion about the rule’s applicability.
    • Depression: no objective standard; historically refers to the Great Depression (1929–1933).
  • Data caveats and historical context:
    • The six-month rule historically applied to define recessions; post-2020 debates about continuing that rule.
    • Pandemics and policy—COVID-19—introduced unusual dynamics not captured by prior norms.

Per capita GDP, standard of living, and cross-country comparisons

  • Per capita GDP = Real GDP per person; better proxy for living standards than aggregate GDP because it accounts for population size.
  • Why not rely on GDP alone? Distribution matters; two countries can have the same per capita GDP but very different income distributions.
  • Real vs nominal per capita: use real values to compare across time; nominal figures can reflect price level changes.
  • Base-year selection rationale: pick a year close to the present so price levels are familiar when interpreting index values.
  • Historical pattern (1930 to 2019, real per capita):
    • Real per capita GDP rose from about 8{,}200in1930toaboutin 1930 to about58{,}000 in 2019, about sevenfold growth.
  • International rankings and PPP vs current exchange rates:
    • Under current exchange rates, the US and China are the two largest economies; India often cited as third by population but varies by measure.
    • With Purchasing Power Parity (PPP), China can appear as the largest economy due to cost-of-living adjustments.
  • Population effects:
    • India > China > US by population; larger populations can yield larger aggregate GDPs even if per-capita incomes differ.
  • Per capita income distribution and elite sectors:
    • In small, wealthy countries (Luxembourg, Liechtenstein, Qatar, Monaco), a large share of per-capita income concentrates in certain sectors (e.g., finance, natural resources) and may not reflect the typical citizen's living standard.
    • Macau is extremely rich per capita due to gambling/tourism, but many residents do not directly benefit from that sector.
  • Disposable income as a closer proxy for household living standards:
    • In the lecture, US disposable income is cited around 62{,}000; Luxembourg often ranks high in per-capita GDP but disposable income for households can be lower due to taxes elsewhere.
    • Disposable income ranking example includes: US highest in the discussion after taxes and transfers, with Luxembourg, Germany, Australia, Austria, Belgium, Netherlands, UK following.
  • Non-market production and underground economy caveats:
    • GDP misses non-market activities (home production like growing food, childcare by family members, mowing the lawn).
    • Underground/shadow economy funds (e.g., illicit activities, unreported tips, unrecorded work) are not captured; estimates vary (roughly up to ~10% of GDP in the US; Peru anecdote around 61%; measurement challenges remain).
    • The underground economy is often measured indirectly; in the US, the use of USD as the global currency can aid measurement but still leaves important gaps.
  • Leisure time and happiness:
    • Some countries with shorter work weeks or higher leisure time report higher happiness indices, suggesting that more leisure does not necessarily reduce well-being.
    • Countries like Japan and South Korea have high productivity but higher stress and suicide rates, suggesting work intensity and social factors influence happiness.
  • Environmental and social costs of growth:
    • Manufacturing economies historically increased output and living standards but often produced higher air and water pollution; richer countries face environmental degradation trade-offs along with economic gains.
    • Crime and social problems can accompany urbanization and concentration of population in productive sectors.
  • Income distribution and policy implications:
    • GDP per capita growth can disproportionately benefit a minority if income gains accrue to the top earners (historical Versailles example illustrating a large share of GDP going to one project/sector).
    • This motivates looking beyond GDP to measures of income distribution and social welfare.

Labor markets: measurement, surveys, and key rates

  • Two main sources of labor-market data:
    • Household survey: surveys individuals about employment status; yields headline unemployment rate and other indicators.
    • Establishment survey: surveys businesses about payrolls; estimates job additions and payroll changes; often revised after initial release.
  • Survey scopes and samples:
    • Household survey: ~60,000 households monthly via phone and other methods; extrapolates to population of >100 million households.
    • Establishment survey: ~147,000 businesses/government sites; focuses on hires, losses, and net changes.
  • Why both surveys matter: the household survey captures non-market workers and unemployment status; the establishment survey captures payroll trends across the business sector.
  • Definitions and population concepts:
    • Civilian working-age population: individuals aged 16+ who are not in the military.
    • Labor force: those who are employed or unemployed and actively seeking work.
    • Not in the labor force: not employed and not looking for work (e.g., full-time students, retirees, some disabled).
  • Employment status:
    • Employed: worked at least one hour in the past week.
    • Unemployed: not employed but looked for work in the past four weeks (and are available for work).
    • Not in labor force: not employed and not looking for work in the last four weeks.
  • Key labor-market metrics and formulas:
    • Unemployment rate: u = \frac{U}{LF}wherewhereUisunemployedandis unemployed andLF is the labor force (employed + unemployed).
    • Employment-population ratio: EPR = \frac{E}{WAP}wherewhereEisemployedandis employed andWAP is the civilian working-age population.
    • Employment-population ratio interpretation: if the working-age population grows faster than employment, the ratio falls, signaling underutilization of new entrants.
    • Labor force participation rate: LFP = \frac{LF}{WAP} where LF is the labor force and WAP is the civilian working-age population.
  • Historical patterns of participation:
    • Post-WWII rise in female labor-force participation from about 33% in 1948 to around 60% by 2018.
    • Male participation declined from about 90% in 1948 toward about 70% in recent decades.
    • The divergence reflects structural shifts (service-oriented economies, educational attainment, household roles, and demographics).
  • Interpretation and implications:
    • Participation rate changes affect potential output; rising female participation has boosted labor supply, while rising retirements and long-term male declines have reduced potential output.
    • Participation trends interact with educational attainment and cultural factors; policy debates around work, childcare, and retirement influence labor supply.

Data specifics, examples, and implications for policy

  • Sample sizes and recent data points cited in the lecture:
    • Household survey sample: about 60,000 households per month.
    • Establishment survey sample: about 147,000 businesses/government sites per month.
    • Notable recent data points mentioned: August 2022 unemployment around 6 million; 22,000 jobs added in August; 911,000 downward revision to prior year payrolls.
    • Employment-population ratio example given: ~60.1% (illustrative).
    • Female labor-force participation in 1948 ~33%, rising to ~60% by 2018; male participation from ~90% in 1948 down toward ~70% in later years.
    • Disposable income in the US discussed as around 62{,}000, with Luxembourg often cited as high in GDP per capita but disposable income differences due to taxes and transfers.
  • Conceptual takeaways from data:
    • The headline unemployment rate reflects short-term slack but may be impacted by labor-force composition and discouraged workers.
    • The employment-population ratio and labor-force participation rate provide complementary views of labor-market slack and potential.
    • Economic policy relies on these indicators to gauge whether to stimulate demand via monetary or fiscal channels.

Non-market production, the underground economy, and broader limitations of GDP

  • Non-market production gaps:
    • GDP misses activities not exchanged in markets (e.g., home production like cooking, childcare, mowing, farming done for household use).
    • Increases in market production (hiring services) raise GDP, but declines in home production can lead to higher measured GDP even if well-being changes differently.
  • Underground (shadow) economy:
    • Encompasses unreported income from illegal activities (drugs, prostitution) and legitimate activities that go unrecorded (tips, cash-only services).
    • Estimates suggest the underground economy could be around 10% of GDP in the US; much higher in some countries like Peru (often cited around 61%), with significant measurement challenges.
    • The underground economy complicates GDP comparisons across countries and over time; it tends to be somewhat easier to approximate in USD-denominated economies due to currency tracking, but remains imprecise.
  • Leisure, work hours, and happiness:
    • Leisure time is a valued good; shorter work weeks can correlate with higher happiness in many contexts, though not universally (e.g., Japan/S Korea show high productivity yet higher stress/suicide rates).
    • Quality of life depends not only on output but also on work conditions, social factors, and distribution of income.
  • Environmental and social costs:
    • Manufacturing-led growth raises output and living standards but often correlates with pollution and environmental degradation.
    • Urbanization and concentration of population can be linked to crime and social tensions, highlighting the trade-offs of productivity-driven growth.
  • Distribution of income and the meaning of GDP per capita:
    • GDP per capita does not reveal how income is distributed; growth can be captured by a small elite while many see little improvement.
    • The Versailles example (Louis XIV) illustrates how large investments can raise GDP but not necessarily improve average living standards.
    • Policies and institutions that promote broad-based productivity and fair distribution influence overall living standards more than aggregate GDP alone.

Measuring standard of living: disposable income and other indicators

  • Disposable income (DPI) as a practical proxy for household well-being:
    • DPI = Personal Income − Personal Taxes + Government Transfers
    • In the lecture, US disposable income is highlighted as a more direct reflection of household welfare than GDP per capita alone.
    • Cross-country comparisons show that while GDP per capita can be high in some small, wealthy economies, disposable income for households may tell a different story due to tax structures and transfers.
  • Country comparisons and policy implications:
    • Countries with high per-capita GDP and heavy taxation may deliver less disposable income to households than countries with lighter tax burdens.
    • Large finance sectors in small countries (e.g., Luxembourg, Liechtenstein) can raise per-capita GDP figures even if many residents do not directly participate in those sectors; DPI can help adjust for this difference.
    • Macau’s high per-capita GDP is driven by tourism and gambling, affecting national GDP aggregates but not necessarily average household welfare for all residents.
  • Takeaway on measuring standard of living:
    • Use real GDP per capita for long-run productivity insights, but supplement with disposable income and distributions to gauge actual living standards and welfare.

Summary: key concepts, formulas, and takeaways

  • Real vs nominal GDP:
    • Real GDP isolates quantity changes by using base-year prices.
    • Nominal GDP includes current prices; subject to inflation/deflation effects.
  • Base-year technique and real per capita GDP:
    • Real GDPt = Σi P^{base}i Q{t,i}; Real per capita GDPt = RealGDPt / Population_t.
  • GDP deflator relationships:
    • Deflatort = (NominalGDPt / RealGDP_t) × 100.
  • Labor market metrics:
    • Unemployment rate: u = \frac{U}{LF}
    • Employment-population ratio: EPR = \frac{E}{WAP}
    • Labor force participation rate: LFP = \frac{LF}{WAP}
    • Employed = worked at least one hour in the past week; Unemployed = not employed but looked for work in last 4 weeks.
  • Business cycle terminology:
    • Peak, Contraction, Trough, Expansion; Expansion includes Recovery and Boom.
    • Recession: two consecutive quarters of negative real GDP growth; Depression: no objective standard.
  • Distribution and standard of living:
    • GDP per capita is informative but not sufficient; consider DPI and income distribution.
    • Non-market production and underground economy bias GDP measures; cross-country comparisons are imperfect.
  • Real-world examples cited in lecture:
    • 1930 real per capita GDP ≈ 8{,}200;2019≈; 2019 ≈58{,}000 (growth ~7×).
    • US disposable income ≈ 62{,}000$$; Luxembourg often high in GDP per capita but DPI varies.
    • Underground economy in US ~ up to 10% of GDP; Peru example ~61% (illustrative; measurement varies).
    • Population order: India, China, United States as the top three by population; PPP vs current exchange rates affects ranking of the largest economies.
  • Final takeaway:
    • Macro indicators (GDP, unemployment, inflation) provide essential insights, but each has limitations and should be interpreted alongside broader welfare measures, distributional data, and non-market activities to form a complete view of economic well-being.