Lecture 1: Welfare and Economic Growth Trends and Cycles PDF

Lecture 1. Welfare and economic growth: trends and cycles INTRODUCTION TO ECONOMICS OF THE EU Bachelor of European Studies Universidad Complutense de Madrid Outline 1. The relative position of the EU countries in the Index of Human Development. 2. The measurement of welfare and the GDP: supply, de...

Lecture 1. Welfare and economic growth: trends and cycles INTRODUCTION TO ECONOMICS OF THE EU Bachelor of European Studies Universidad Complutense de Madrid Outline 1. The relative position of the EU countries in the Index of Human Development. 2. The measurement of welfare and the GDP: supply, demand and income. Comparing GDP. 3. Measuring long-term growth, short-term-growth and recessions in the EU. 4. Explanatory factors of long-term growth: productivity and the role of innovation. 2 1. The relative position of the EU countries in the Index of Human Development. 3 The Index of Human Development. Motivation • The Human Development Index (HDI) was first elaborated by the United Nations in 1990. • Its objective was to extend the statistical base that measures living quality in a country. • It is based on the idea, proposed by Amartya Sen, of measuring development of countries by using not only their production or income levels, but also their capabilities (for example, human capital). • The HDI has been created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not just economic growth. • Expanding human choices should be the ultimate criteria for assessing development results. Economic growth is a mean to that process but not an end in itself. 4 The Index of Human Development. Definition • United Nations defines the Human Development Index (HDI) as a summary measure of average achievement in key dimensions of human development: 1. A long and healthy life (Health). 2. Being knowledgeable (Education). 3. Have a decent standard of living (Income). • The HDI is the geometric mean of normalised indices for each of these three dimensions. 5 The Index of Human Development. Measurement • The health dimension is assessed by life expectancy at birth. • The education dimension is measured by: (i) Expected years of schooling for children of school entering age. (ii) Mean of years of schooling for adults aged 25 years and more. • Both variables give a proxy for the stock of human capital available in each country. • The standard of living dimension is measured by gross national income per capita: income generated by primary factors of production (labour and capital), owned by resident citizens, measured in international dollars, i.e. national currencies converted to USD using purchasing power parity (PPP) rates, and divided by midyear population. 6 Purchasing power parities • Purchasing power parities (PPPs) are indicators of price level differences across countries. • They indicate how many currency units a particular quantity of goods and services costs in different countries. • PPPs can be used as currency conversion rates to convert expenditures expressed in national currencies into an artificial common currency (the Purchasing Power Standard in the case of Eurostat, PPS), thus eliminating the effect of price level differences across countries. • The idea is to correctly identify purchasing power of economies when comparing standards of living across countries having different currencies. • Short term exchange rates do not reflect purchasing power, they are subject to frequent variations motivated by the availability of each currency in currency exchange markets at each moment of time. 7 The Index of Human Development. Measurement Source: United Nations. https://hdr.undp.org/data-center/human-development-index#/indicies/HDI 8 The Index of Human Development. Methodology • HDI is defined as the geometric mean of the three partial indices: health, education and income. Partial indices, I, for health and educations are calculated as follows: min(𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣; 𝑚𝑚𝑚𝑚𝑚𝑚 ∗ ) − 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 𝐼𝐼𝑗𝑗 = 𝑗𝑗ϵ ℎ𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒, 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 − 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 • The index for education is calculated as the average value of the indices resulting form evaluating the above expression for (i) Expected years of schooling and (ii) Mean years of schooling. 9 The Index of Human Development. Methodology • In the case of income, per capita GNI is evaluated in natural logarithms, to reflect the diminishing importance of income in the HDI with increasing gross national income. The index is calculated as follows: 𝐼𝐼𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 ln(min 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣; 75,000 ) − ln(100) = ln(75,000) − ln(100) • The following Table shows the predetermined maximum and minimum values for each of the required variables. Dimension Health Education Education Standard of living Indicator Life expectancy at birth (years) Expected years of schooling (years) Mean years of schooling (years) GNI per capita (2017 PPP$) Minimum 20 0 0 100 Maximum (max*) 85 18 15 75,000 10 The Index of Human Development. Methodology • The scores for the three HDI dimension indices are then aggregated into a composite index using geometric mean. 𝐻𝐻𝐻𝐻𝐻𝐻 = 1/3 𝐼𝐼𝐻𝐻𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 � 1/3 𝐼𝐼𝐸𝐸𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 � 1/3 𝐼𝐼𝐼𝐼𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 • In calculating the HDI, all variables are normalised to values within the interval [0,1]. 11 A simple example Country HDI Wonderland 0.950 Life expectancy at birth Expected years of schooling (years) (years) 82 19 Mean years of schooling (years) Gross national income (GNI) per capita (2017 PPP $) 12 77,000.00 HDI calculation for Wonderland Health index Expected years of schooling index Mean years of schooling Education index Income index Human Development Index min(82;85) − 20 = 0.954 85 − 20 min(19;18) − 0 𝐼𝐼𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 = =1 18 − 0 min(12;15) − 0 𝐼𝐼𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 = = 0.8 15 − 0 𝐼𝐼𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 + 𝐼𝐼𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 1 + 0.8 = 𝐼𝐼𝐸𝐸𝐸𝐸𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 = = 0.9 2 2 ln(min(77,000;75,000)/100) 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 = =1 ln(75,000/100) 𝐼𝐼𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻 = 𝑯𝑯𝑯𝑯𝑯𝑯 = 𝟎𝟎. 𝟗𝟗𝟗𝟗𝟗𝟗 � 𝟎𝟎. 𝟗𝟗 � 𝟏𝟏 𝟏𝟏/𝟑𝟑 = 𝟎𝟎. 𝟗𝟗𝟗𝟗𝟗𝟗 12 The Index of Human Development. Four levels • Countries are classified into four levels of human development according to their HDI score: (i) Very high human development: HDI ≥ 0.80. (ii) High human development: 0.70 ≤ HDI < 0.80. (iii) Medium human development: 0.55 ≤ HDI < 0.70. (iv) Low human development: HDI < 0.55. • Data and documentation available at: https://hdr.undp.org/data-center/documentation-and-downloads • Last available report: Human Development Report 2021-22. Uncertain Times, Unsettled Lives: Shaping our Future in a Transforming World. https://hdr.undp.org/reports-and-publications 13 Human Development Index. 2021 14 Top 5 EU countries in terms of HDI HDI rank 2021 (1 – 191) Country HDI 2021 6 7 8 9 10 Denmark Sweden Ireland Germany Netherlands 0.948 0.947 0.945 0.942 0.941 Expected Mean years Gross national Life expectancy years of of income per capita at birth (years) schooling schooling (years) (years) (2017 PPP $) 81.4 18.7 13.0 60,365 83.0 19.4 12.6 54,489 82.0 18.9 11.6 76,169 80.6 17.0 14.1 54,534 81.7 18.7 12.6 55,979 GNI per capita rank minus HDI rank 6 9 -3 6 3 Source: United nations. https://hdr.undp.org/sites/default/files/2021-22_HDR/HDR21-22_Statistical_Annex_HDI_Table.xlsx 15 5 EU countries with lower HDI HDI rank 2021 (1 – 191) 40 45 46 53 68 Country Croatia Slovakia Hungary Romania Bulgaria HDI 2021 0.858 0.848 0.846 0.821 0.795 Expected Mean years Gross national Life expectancy years of of income per capita at birth (years) schooling schooling (years) (years) (2017 PPP $) 77.6 15.1 12.2 30,132 74.9 14.5 12.9 30,690 74.5 15.0 12.2 32,789 74.2 14.2 11.3 30,027 71.8 13.9 11.4 23,079 GNI per capita rank minus HDI rank 8 1 -2 -4 -8 Source: United nations. https://hdr.undp.org/sites/default/files/2021-22_HDR/HDR21-22_Statistical_Annex_HDI_Table.xlsx 16 Mean values of Human Development Index. EU27, 4 segments of human development (HD), and the world Group EU27 Very high HD High HD Medium HD Low HD World HDI 0.896 0.896 0.754 0.636 0.518 0.732 Mean years of Life expectancy Expected years of schooling at birth (years) schooling (years) (years) 79.4 78.5 74.7 67.4 61.3 71.4 16.8 16.5 14.2 11.9 9.5 12.8 12.3 12.3 8.3 6.9 4.9 8.6 Gross national income per capita (2017 PPP $) 43,516.17 43,751.60 15,167.25 6,353.49 3,009.12 16,752.1 Source: United nations. https://hdr.undp.org/sites/default/files/2021-22_HDR/HDR21-22_Statistical_Annex_HDI_Table.xlsx 17 HDI. Some facts • All 27 EU countries, except Bulgaria, are classified under the highest level of human development. • Nonetheless, Bulgaria has a HDI = 0.795. • EU27 rank ranges from 6 to 68 out of 191 countries worldwide. • HDI of EU27 countries averages up to 0.896, the same as the average HDI for very high human development countries. • Life expectancy at birth above 79 years, higher than the average for the very high human development countries. 18 Human Development Index. EU27 1990-2021 1 0.95 0.9 0.85 0.8 0.75 0.7 0.65 0.6 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Source: From All composite indices and components time series (1990-2021): https://hdr.undp.org/data-center/documentation-and-downloads 19 HDI and components. Mean values. EU27 1990-2021 HDI Life expectancy at birth (years) Expected years of schooling (years) Mean years of schooling (years) GNI per capita (2017 PPP$) 1990 (25 of actual EU27) 0.759 73.82 12.59 8.44 26,734.75 2000 0.818 75.89 14.86 10.25 31,295.29 2010 0.869 78.56 15.99 11.56 36,492.65 2021 0.896 79.39 16.75 12.30 43,516.17 HDI has increased 18 per cent over the last 31 years of available time horizon. Life expectancy is almost 6 years longer. Expected and mean years of schooling have increased by 4 years. HDI of EU27 countries averages up to 0.896, the same as the average HDI for very high human development countries. • GNI growth is almost 1.6 per cent and per year (cumulative annual growth rate): 1/𝑛𝑛 𝑦𝑦𝑛𝑛 𝑟𝑟 = −1 𝑦𝑦0 • • • • 20 2. The measurement of welfare and the GDP: supply, demand and income. Comparing GDP. 21 Gross Domestic Product (GDP) • GDP measures the productive capacity of an economy. • It is defined as the flow of final goods and services produced by the economy along a given period of time (year, quarter). • The System of National Accounts measures nominal gross domestic product (current prices) at market prices (GDPmp) in monetary units, i.e. million euros. • GDP es estimated by three different approaches: (i) The expenditure approach. (ii) The income approach. (iii) The output approach. 22 The expenditure approach • GDPmp measured by the flow of final goods and services, i.e. the sum of final demands of the economy, which are measured at market prices (purchasers’ prices). • We want to measure domestic and external demand satisfied with domestic production: GDPmp = FCE + GKF + X – M • FCE: final consumption expenditure. • GKF: gross capital formation. • X: exports of goods and services. • M: imports of goods and services. • Domestic demand = FCE + GKF. • External demand = X. • Domestic and external demand may be partially satisfied by production from the rest of the world, i.e. through imports of goods and services. For this reason M must be subtracted from domestic and external demand in the expenditure equation of GDPmp. 23 The income approach • Gross value added (GVA) at basic prices: GVAbp = Obp – ICpp • Obp: output at basic prices. • ICpp: intermediate consumption at purchasers’ prices. For simplicity, net taxes are excluded from the following analysis • Total production costs include intermediate consumption and compensation of employees (CE): TC = IC + CE ⇒ IC = TC - CE • Thus GVA can be expressed as a function of total costs and compensation of employees: GVA = O – TC + CE 24 The income approach • Capital income = O – TC. Operating surplus, gross/Mixed income, gross. • Labour income = CE. • Mixed income: Income generated by individual workers, a mixture of labour and capital income. • Thus gross value added is an indicator of total income: ∴ GVAfc = Operating surplus, gross + gross/Mixed income, gross + CE • In this case, gross value added is measured at factor costs, i.e. the costs associated to the use of the basic factors of production: labour and capital. • If measured for the total of the economy, we have gross domestic product at factor cost (GDPfc). 25 The output approach • Gross domestic product at basic prices is obtained by the sum of the gross value added generated by the N different sectors of economic activity. 𝑁𝑁 𝐺𝐺𝐺𝐺𝐺𝐺𝑏𝑏𝑏𝑏 = � 𝐺𝐺𝐺𝐺𝐺𝐺𝑏𝑏𝑏𝑏,𝑖𝑖 𝑖𝑖=1 • In order to transform GDPbp into GDPmp, we need to add to GDPbp, net taxes on products. Net taxes refer to the difference between taxes and corresponding subsidies. These taxes convert output at basic prices into output at purchasers’ prices. GDPmp = GDPbp + Net taxes on products 26 Taxes and corresponding subsidies Taxes Taxes on production and imports = = Taxes on products + + Other taxes on production Net taxes Taxes less subsidies on production and imports = = Taxes less subsidies on products + + Other taxes less subsidies on production Subsidies Subsidies = = Subsidies on products + + Other subsides on production Also identified as… Net taxes on production and imports = = Net taxes on products + + Other net taxes on production 27 Taxes and subsidies • Basic prices: prices faced by producers. • Purchasers’ prices: prices faced by final consumers. • Net taxes on products allow conversion from basic prices to market prices: GDPmp = GDPbp + Net taxes on products • Other net taxes on production allow conversion from factor cost to basic prices: GDPbp = GDPfc + Other net taxes on production • Net taxes on production and imports allow conversion from factor cost to market prices: GDPmp = GDPfc + Net taxes on products + Other net taxes on production GDPmp = GDPfc + Net taxes on production and imports 28 Nominal vs real. Current prices vs constant prices • Consider an economy producing n goods and services, whose quantities and prices are denoted by qt and pt, respectively. Economic variable expressed in nominal terms • The value of production at current prices is computed by: 𝑛𝑛 � 𝑝𝑝𝑖𝑖,𝑡𝑡 𝑞𝑞𝑖𝑖,𝑡𝑡 𝑖𝑖=1 Economic variable expressed in real terms • The value of production at constant prices of a given reference year, denoted by t = 0, is computed by: 𝑛𝑛 � 𝑝𝑝𝑖𝑖,0 𝑞𝑞𝑖𝑖,𝑡𝑡 𝑖𝑖=1 29 Real terms • When doing intertemporal comparisons of a given variable expressed in monetary units, it is desirable to consider the evolution of the variable in real terms, i.e. once inflation has been removed from the time series of the variable. • National accounts provide gross domestic product at market prices expressed by means of chain-linked volume indices. • Those chain-linked volume indices measure volume variations through Laspeyres indices defined on quantities: 𝑛𝑛 ∑ 𝑖𝑖=1 𝑝𝑝𝑖𝑖,0 𝑞𝑞𝑖𝑖,𝑡𝑡 𝑡𝑡 𝑉𝑉𝑉𝑉0 = 𝑛𝑛 ∑𝑖𝑖=1 𝑝𝑝𝑖𝑖,0 𝑞𝑞𝑖𝑖,0 30 Resident and non-resident units; total economy and rest of the world. ESA 2010 p. 12 • The total economy is defined in terms of resident units. A unit is a resident unit of a country when it has a centre of predominant economic interest on the economic territory of that country — that is, when it engages for an extended period (one year or more) in economic activities on this territory. • The institutional sectors (non-financial corporations, financial corporations, general government, households, non-profit institutions serving households) are groups of resident institutional units. • Resident units engage in transactions with non-resident units (that is, units which are resident in other economies). These transactions are the external transactions of the economy and are grouped in the rest of the world account. So the rest of the world plays a role similar to that of an institutional sector, although non-resident units are included only in so far as they are engaged in transactions with resident institutional units. 31 From GDP to GNI and vice versa. Allocation of primary income account Uses Resources Operating surplus, gross/Mixed income, gross Compensation of employees (domestic) Net taxes on production and imports Compensation of resident employees by non-resident employers - Compensation of non-resident employees by resident employers Balance of primary incomes, gross/Gross National Income Property income received from the rest of the world - Property income payed to the rest of the world 32 From GDP to GNI and vice versa. Allocation of primary income account Uses Labour income generated by resident units: Compensation of employees (national) Income generated by capital owned by resident units Balance of primary incomes, gross/Gross National Income Resources Operating surplus, gross/Mixed income, gross Compensation of employees (domestic) Net taxes on production and imports Compensation of resident employees by non-resident employers - Compensation of non-resident employees by resident employers Property income received from the rest of the world - Property income payed to the rest of the world 33 From GDP to GNI and vice versa Resident employees [3] [1] Resident employers [2] Non-resident employers Domestic: economic territory National: residence units Non-resident employees Compensation of employees (domestic) = [1] + [2], payed by resident employers. Generated inside economic territory. Compensation of employees (national) = [1] + [3], received by resident employees. 34 GDP figures for the EU 27 Code BE BG CZ DK DE EE IE EL ES FR HR IT CY LV Country codes and names Name Code Name Belgium LT Lithuania Bulgaria LU Luxembourg Czechia HU Hungary Denmark MT Malta Germany NL Netherlands Estonia AT Austria Ireland PL Poland Greece PT Portugal Spain RO Romania France SI Slovenia Croatia SK Slovakia Italy FI Finland Cyprus SE Sweden Latvia Source: Key figures on Europe. 2022 Edition. Eurostat 35 Economic size distribution for the EU 27 • The economic size of the countries in terms of GDP is uneven. • Eight countries (Germany, France, Italy, Spain, the Netherlands, Poland, Sweden, Belgium) account for almost 80% of total EU27 GDP. • Baldwin and Wyplosz (2012) classify remaining nations as small, tiny or miniscule. (i) Small: an economy that accounts for between 1% and 3% of EU27 GDP: Ireland, Austria, Denmark, Finland, Czech Republic, Romania, Portugal, Greece, Hungary. (ii) Tiny: less than 1% of the total: Slovakia, Luxembourg, Bulgaria, Slovenia, Croatia, Lithuania, Latvia, Estonia, Cyprus. (iii) Miniscule: accounts for less than 0.1% of total GDP: Malta. 36 3. Measuring long-term growth, short-term-growth and recessions in the EU. 37 Long term growth estimation. Data • Tow sources of information. (i) World Bank: long time series (1990-2021 for most EU27 countries) of per capita real GDP based on purchasing power parity (PPP) and expressed in constant 2017 international dollars. (ii) Eurostat: chain linked volumes (2010) in euros per capita for European countries and EU27. • GDP is converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as the US dollar has in the United States. • Estimation of time trend to approximate long term growth. 38 Long term growth estimation. Methodology • Annual economic time series usually have a trend (𝑇𝑇𝑡𝑡 ), a cycle (𝐶𝐶𝑡𝑡 ) and an irregular component (𝜀𝜀𝑡𝑡 ): 𝑦𝑦𝑡𝑡 = 𝑇𝑇𝑡𝑡 + 𝐶𝐶𝑡𝑡 + 𝜀𝜀𝑡𝑡 • 𝑦𝑦𝑡𝑡 is the natural logarithm of per capita GDP. • In order to estimate 𝑇𝑇𝑡𝑡 , we run an Ordinary Least Squares regression of 𝑦𝑦𝑡𝑡 on years 𝑡𝑡. 𝑦𝑦𝑡𝑡 = 𝛼𝛼 + 𝛽𝛽 � 𝑡𝑡 + 𝜀𝜀𝑡𝑡 𝑇𝑇𝑡𝑡 = 𝛼𝛼� + 𝛽𝛽̂ � 𝑡𝑡 • We shall demonstrate that parameter 𝛽𝛽 is the approximate cumulative annual rate of growth of per capita GDP. • The cycle component can be derived by subtracting from per capita GDP, the estimated trend. 𝑦𝑦𝑡𝑡 − 𝑇𝑇𝑡𝑡 = 𝐶𝐶𝑡𝑡 + 𝜀𝜀𝑡𝑡 39 𝑻𝑻𝒕𝒕 , 𝒚𝒚𝒕𝒕 EU27 natural logarithm of real per capita volumes (2010) in Euros per capita. 2000 – 2021. 𝒚𝒚𝒕𝒕 and 𝑻𝑻𝒕𝒕 . Eurostat 10.3 𝑻𝑻𝒕𝒕 = 𝜶𝜶 + 𝜷𝜷 � 𝒕𝒕 + 𝜺𝜺𝒕𝒕 10.25 𝒚𝒚𝒕𝒕 𝑻𝑻𝒕𝒕 = −𝟖𝟖. 𝟑𝟑𝟑𝟑𝟑𝟑𝟑𝟑 + 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎 � 𝒕𝒕 (1.5183) (0.0008) 10.2 10.15 10.1 𝑻𝑻𝒕𝒕 10.05 10 2000 2002 2004 2006 0.92% cumulative annual growth rate of per capita real GDP 2008 2010 lnGDPpc 2012 2014 2016 2018 2020 Estimated Source: https://ec.europa.eu/eurostat/databrowser/view/sdg_08_10/default/table 40 EU27 cycle 2000 – 2021. 𝑪𝑪𝒕𝒕 . Eurostat 0.06 𝑪𝑪𝒕𝒕 𝑪𝑪𝒕𝒕 0.05 0.04 𝒚𝒚𝒕𝒕 − 𝑻𝑻𝒕𝒕 = 𝑪𝑪𝒕𝒕 + 𝜺𝜺𝒕𝒕 0.03 0.02 0.01 0 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 -0.01 -0.02 -0.03 -0.04 -0.05 Source: https://ec.europa.eu/eurostat/databrowser/view/sdg_08_10/default/table 41 Long term growth OLS estimates for EU27 countries, 1990 – 2021 (32 observations). The World Bank Country Austria Belgium Bulgaria Cyprus Czechia Germany Denmark Spain Estonia Mean SD Max Min R2 0.8866 0.9126 0.8794 0.7267 0.9469 0.9717 0.8756 0.7160 0.8919 0.0223 0.0129 0.0497 0.0028 Obs 32 32 32 32 32 32 32 32 27 Growth 0.0127*** 0.0125*** 0.0290*** 0.0142*** 0.0230*** 0.0124*** 0.0113*** 0.0112*** 0.0379*** Country Finland France Greece Croatia Hungary Ireland Italy Lithuania Luxembourg R2 0.8024 0.8781 0.1617 0.8368 0.9517 0.9019 0.1858 0.9628 0.8035 Obs 32 32 32 27 31 32 32 27 32 Growth 0.0165*** 0.0097*** 0.0057** 0.0218*** 0.0249*** 0.0375*** 0.0028** 0.0497*** 0.0158*** Country Latvia Malta Netherlands Poland Portugal Romania Slovak Republic Slovenia Sweden R2 0.9077 0.9701 0.8863 0.9912 0.7615 0.9334 0.9676 0.8665 0.9256 Obs 27 32 32 32 32 32 30 27 32 Growth 0.0452*** 0.0331*** 0.0141*** 0.0395*** 0.0100*** 0.0358*** 0.0369*** 0.0205*** 0.0172*** *** coefficient is statistically significant at the 99% confidence interval. ** coefficient is statistically significant at the 95% confidence interval. * coefficient is statistically significant at the 90% confidence interval. Source: Estimated from per capita real PPP GDP expressed in constant 2017 international dollars. https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD 42 Short-term growth and recessions in the EU • We use chain linked volumes, index 2010 = 100, for GDP at market prices, quarterly data, seasonally and calendar adjusted data to measure short-term growth. • Quarter-on-quarter growth rates are used to identify recessions and expansion periods of the economy. • Quarterly data must be cleaned from the seasonal and calendar components in order to be able to identify short-term fluctuations. • The seasonal and calendar components correspond to intra-year fluctuations that are more or less stable over time, with respect to timing, direction and magnitude. 43 Quarter-on-quarter growth rates of chain linked volumes, index 2010 = 100, for GDP at market prices. Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data). EU27 0.12 Growth rates in Q2 and Q4 are mostly positive 0.1 0.08 0.06 0.04 0.02 2022-Q2 2021-Q4 2021-Q2 2020-Q4 2020-Q2 2019-Q4 2019-Q2 2018-Q4 2018-Q2 2017-Q4 2017-Q2 2016-Q4 2016-Q2 2015-Q4 2015-Q2 2014-Q4 2014-Q2 2013-Q4 2013-Q2 2012-Q4 2012-Q2 2011-Q4 2011-Q2 2010-Q4 2010-Q2 2009-Q4 2009-Q2 2008-Q4 2008-Q2 2007-Q4 2007-Q2 2006-Q4 2006-Q2 2005-Q4 2005-Q2 2004-Q4 2004-Q2 2003-Q4 2003-Q2 2002-Q4 2002-Q2 2001-Q4 2001-Q2 2000-Q4 2000-Q2 1999-Q4 1999-Q2 1998-Q4 1998-Q2 1997-Q4 1997-Q2 1996-Q4 1996-Q2 1995-Q4 -0.02 1995-Q2 0 -0.04 -0.06 -0.08 -0.1 Growth rates in Q1 and Q3 are mostly negative Source: https://ec.europa.eu/eurostat/databrowser/view/NAMQ_10_GDP__custom_4440975/default/table?lang=en 44 Short-term growth and recessions in the EU • Taking quarter-on-quarter growth rates removes the trend component of the time series. • When taken over unadjusted data, we identify the seasonal and calendar components. • In order to remove the noise generated by the seasonal and calendar components, we could take year-on-year growth rates over unadjusted data. 45 -0.02 -0.04 Source: https://ec.europa.eu/eurostat/databrowser/view/NAMQ_10_GDP__custom_4440975/default/table?lang=en 2022-Q3 2022-Q1 2021-Q3 2021-Q1 2020-Q3 2020-Q1 2019-Q3 2019-Q1 2018-Q3 2018-Q1 2017-Q3 2017-Q1 2016-Q3 2016-Q1 2015-Q3 2015-Q1 2014-Q3 2014-Q1 2013-Q3 2013-Q1 2012-Q3 2012-Q1 2011-Q3 2011-Q1 2010-Q3 2010-Q1 2009-Q3 2009-Q1 2008-Q3 2008-Q1 2007-Q3 2007-Q1 2006-Q3 2006-Q1 2005-Q3 2005-Q1 2004-Q3 2004-Q1 2003-Q3 2003-Q1 2002-Q3 2002-Q1 2001-Q3 2001-Q1 2000-Q3 2000-Q1 1999-Q3 1999-Q1 1998-Q3 1998-Q1 1997-Q3 1997-Q1 1996-Q3 1996-Q1 Year-on-year growth rates of chain linked volumes, index 2010 = 100, for GDP at market prices, quarterly data. Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data). EU27 0.16 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 -0.06 -0.08 -0.1 -0.12 -0.14 -0.16 46 -0.02 Source: https://ec.europa.eu/eurostat/databrowser/view/NAMQ_10_GDP__custom_4440975/default/table?lang=en 2022-Q2 2021-Q4 2021-Q2 2020-Q4 2020-Q2 2019-Q4 2019-Q2 2018-Q4 2018-Q2 2017-Q4 2017-Q2 2016-Q4 2016-Q2 2015-Q4 2015-Q2 2014-Q4 2014-Q2 2013-Q4 2013-Q2 2012-Q4 2012-Q2 2011-Q4 2011-Q2 2010-Q4 2010-Q2 2009-Q4 2009-Q2 2008-Q4 2008-Q2 2007-Q4 2007-Q2 2006-Q4 2006-Q2 2005-Q4 2005-Q2 2004-Q4 2004-Q2 2003-Q4 2003-Q2 2002-Q4 2002-Q2 2001-Q4 2001-Q2 2000-Q4 2000-Q2 1999-Q4 1999-Q2 1998-Q4 1998-Q2 1997-Q4 1997-Q2 1996-Q4 1996-Q2 1995-Q4 1995-Q2 Short-term growth and recessions in the EU • Alternatively, the cycle can be identify by taking quarter-on-quarter growth rates over seasonally and calendar adjusted data. 0.13 0.08 0.03 -0.07 -0.12 47 Quarter-on-quarter growth rates of chain linked volumes, index 2010 = 100, for GDP at market prices. Seasonally and calendar adjusted data. EU27. 1995-Q2 to 2019-Q4 (Pre-COVID-19) 0.015 0.01 0.005 0 -0.005 -0.01 -0.015 -0.02 -0.025 -0.03 Source: https://ec.europa.eu/eurostat/databrowser/view/NAMQ_10_GDP__custom_4440975/default/table?lang=en 48 Short-term growth and recessions in the EU • There is not full consensus on what a recession means. One the most widely accepted definition was proposed by the National Bureau of Economic Research (NBER), in the US. • It defines the existence of a recession as two or more consecutive quarters of negative real GDP growth. Though, other criteria are also used, this is one of the most popular. • Application of this criterion over quarter-on-quarter growth rates of chain linked volumes, index 2010 = 100, for GDP at market prices, seasonally and calendar adjusted data, we obtain three recessions for the EU27 along the period 1995-Q1 – 2022-Q3. 49 Quarter-on-quarter growth rates of chain linked volumes, index 2010 = 100, for GDP at market prices. Seasonally and calendar adjusted data. EU27. 1995-Q2 to 2022-Q3 Great Lockdown Great Recession 2020-Q1 – 2020-Q2 (2 quarters) 0.13 2011-Q4 – 2013-Q1 (6 quarters) 0.08 2022-Q2 2021-Q4 2021-Q2 2020-Q4 2020-Q2 2019-Q4 2019-Q2 2018-Q4 2018-Q2 2017-Q4 2017-Q2 2016-Q4 2016-Q2 2015-Q4 2015-Q2 2014-Q4 2014-Q2 2013-Q4 2013-Q2 2012-Q4 2012-Q2 2011-Q4 2011-Q2 2010-Q4 2010-Q2 2009-Q4 2009-Q2 2008-Q4 2008-Q2 2007-Q4 2007-Q2 2006-Q4 2005-Q4 2005-Q2 2004-Q4 2004-Q2 2003-Q4 2003-Q2 2002-Q4 2002-Q2 2001-Q4 2001-Q2 2000-Q4 2000-Q2 1999-Q4 1999-Q2 1998-Q4 1998-Q2 1997-Q4 1997-Q2 1996-Q4 1996-Q2 1995-Q4 1995-Q2 -0.02 2006-Q2 2008-Q2 – 2009-Q2 (5 quarters) 0.03 -0.07 -0.12 Source: https://ec.europa.eu/eurostat/databrowser/view/NAMQ_10_GDP__custom_4440975/default/table?lang=en 50 Quarter-on-quarter growth rates of chain linked volumes, index 2010 = 100, for GDP at market prices. Seasonally and calendar adjusted data. EU27. 1995Q2 to 2019-Q4 (Pre-COVID-19) Great Recession 0.015 0.01 0.005 0 -0.005 -0.01 -0.015 -0.02 -0.025 -0.03 Source: https://ec.europa.eu/eurostat/databrowser/view/NAMQ_10_GDP__custom_4440975/default/table?lang=en 51 4. Explanatory factors of long-term growth: productivity and the role of innovation. 52 Explanatory factors of long-term growth: productivity and the role of innovation “Productivity isn’t everything, but in the long run it is almost everything. A country’s ability to improve its standard of living over time depends almost entirely on its ability to raise its output per worker.” Paul Krugman, 1997. "The Age of Diminished Expectations, 3rd Edition: U.S. Economic Policy in the 1990s," MIT Press Books, The MIT Press. 53 Explanatory factors of long-term growth: productivity and the role of innovation • Previous analysis is now completed by assessing some factors that explain longrun growth. • We begin by considering the following identity: Per capita income = Labour productivity ∙ Employment rate ∙ Demographic factor GDP/population = GDP/employment ∙ Employment /Population aged 16-64 years old ∙ Population aged 16-64 years old/Population • Per capita income = GDP/population. • Labour productivity = GDP/employment. • Employment rate = Employment /Population aged 16-64 years old. • Demographic factor = Population aged 16-64 years old/Population. 54 Explanatory factors of long-term growth: EU27 DATA • GDP: Chain linked volumes (2010), million euro: Eurostat. National accounts indicator (ESA 2010) [NAMA_10_GDP__custom_4468430]. • AMECO: https://economy-finance.ec.europa.eu/economic-research-and-databases/economicdatabases/ameco-database_en • Total population, thousands: AMECO. Total population (national accounts) (NPTD). • Population 15 to 64 years, thousands: AMECO. (NPAN). Population 15 to 64 years (NPAN). • Employment, thousands: AMECO. Employment Total economy, domestic (NETD). • • • • → Per capita income = GDP/population. → Labour productivity = GDP/employment. → Employment rate = Employment /Population aged 16-64 years old. → Demographic factor = Population aged 16-64 years old/Population. 55 Explanatory factors of long-term growth: EU27: 2001 - 2021 Per capita Labour Empoyment income productivity rate EU27: 2001-2021 Proportional growth rate 0.2176 Demographic factor 0.1461 0.1225 -0.0536 Contribution (percentage points) 0.1479 0.1239 -0.0542 Contribution as a percentage of per capita income growth (%) 67.96 56.97 -24.93 Per capita Labour Empoyment income productivity rate EU27: 2001-2021 Cumulative annual growth rate 0.0099 Demographic factor 0.0068 0.0058 -0.0028 Contribution (percentage points) 0.0068 0.0058 -0.0028 Contribution as a percentage of per capita income growth (%) 69.22 58.61 -27.82 Source: AMECO and Eurostat. 56 Explanatory factors of long-term growth: EU27: 2001 - 2021 • Poor real per capita income growth along the considered time horizon. Less than 1 per cent in annual terms. • Labour productivity growth below the .7 per cent. • Labour productivity growth contributes to the greatest extent to real per capita income growth. • Almost 70 per cent of per capita income growth is due to labour productivity growth. • The demographic factor decreases due to population aging, and thus contributes negatively to per capita income growth. 57 Explanatory factors of long-term growth: productivity and the role of innovation Helpman, Elhanan, 2004. The Mystery of Economic Growth, Harvard University Press, p. 34. “We have seen that living standards vary greatly across countries, and that rich countries have higher levels of income per capita because they have more educated workers, and higher levels of TFP (Total Factor Productivity). Importantly, more than half of the variation in income per capita (levels) results from differences in TFP (levels) . And the same applies to differences in growth rates of income per capita: more than half of the variation results form variations in TFP growth. Students of economic growth have concluded from this evidence that, in order to understand the growth of nations, it is necessary to develop a better understanding of the forces that shape total factor productivity” • Conclusion: approximately, half of the differences in income per capita among countries is explained by differences in the TFP level. The same applies to differences in terms of income per capita growth. 58 Application of the elasticity concept. An example • Robert Solow (1957) applies the framework presented in detail in Appendix D.1 Explanatory factors of long-term growth: productivity and the role of innovation, to measure the sources of long run growth of the US economy throughout the period 1909-1949. • Annual growth rate of labour productivity (output per hour): 1.8% [𝑔𝑔𝑦𝑦 ]. • Annual growth of the stock of capital per worked hour: 0.7% [𝑔𝑔𝑘𝑘 ]. • Labour productivity elasticity of capital per worker: α =1/3. 59 Application of the elasticity concept. An example • Technical change, Solow residual, total factor productivity is defined as: 𝑔𝑔𝐴𝐴 = 𝑔𝑔𝑦𝑦 − 𝛼𝛼𝑔𝑔𝑘𝑘 • The excess growth of output per worker (labour productivity) that cannot be attributed to the growth of capital per worker • This implies a contribution of technical progress to labour productivity growth: • 1.8 - 1/3*0.7 = 1.8 - 0.233 = 1.567. • Therefore 87% of labour productivity growth is explained by technological change. 60 Total factor productivity growth 1961-2021 0.1500 0.1000 0.0500 0.0000 1961 1971 1981 1991 2001 2011 2021 -0.0500 -0.1000 Germany Spain France Italy Source: Total factor productivity time series. AMECO. Eurostat 61 Total factor productivity growth 1961-1981 0.1200 Evident slowdown in TFP growth along this period 0.1000 0.0800 0.0600 0.0400 0.0200 0.0000 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 -0.0200 -0.0400 -0.0600 Germany Spain France Source: Total factor productivity time series. AMECO. Eurostat Italy 62 Total factor productivity growth 1981-2021 0.0600 TFP growth time series appear stationary since 1981 0.0400 0.0200 0.0000 1981 1986 1991 1996 2001 2006 2011 2016 2021 -0.0200 -0.0400 -0.0600 -0.0800 -0.1000 Germany Spain France Source: Total factor productivity time series. AMECO. Eurostat Italy 63 Total factor productivity growth 1996-2021. EU27 Stationary TFP growth. Average growth .59 per cent 0.0400 0.0300 0.0200 0.0100 0.0000 1996 2001 2006 2011 2016 2021 -0.0100 -0.0200 -0.0300 -0.0400 -0.0500 -0.0600 Source: Total factor productivity time series. AMECO. Eurostat 64 Technical change and labour productivity growth • The most striking feature of these graphs is the stagnation of the TFP since the early 1980s. • According to the Solow analysis the reduction in the rate of growth of labour productivity is mainly due to a lower technical progress. • This reduction in the rate of technological progress is probably the most relevant problem that the European economies face in terms of their long-run growth. • A set of factors have been suggested to explain productivity slowdown. These factors are under discussion. I mention three of them (for further reading: A. Haldane (2017), A. Banerjee and E. Duflo (2019)). 65 Suggested factors to explain productivity slowdown • Mismeasurement: Fairly widespread perception that official statistics underestimate economic activity to some significant degree (new services produced in digital markets which are difficult to measure). Nonetheless, some of these mismeasurement issues already existed long before productivity started slowing. • Slowing innovation. Technological progress behind productivity growth over the past two centuries may not continue at the same pace in the future. Two arguments: (i) the current wave of innovations, based on ICT, does not have the same potential as past innovations (electricity and all its spin-offs; internal combustion engines, etc.). (ii) the ICT revolution is already quite mature and that future progress is likely to be slower. • Slowing technological diffusion. An alternative explanation of slower productivity growth is that it arises, not from slower innovation, but from slower rate of diffusion of innovation to other companies. The OECD has highlighted this possibility showing an increasing distance between those firms operating in the technological frontier and the rest of firms. Solid growth at the global productivity frontier but spillovers have slowed down. 66 References • Baldwin, R. and Wyplosz , C., 2012. The Economics of European Integration. McGraw Hill, London, 4th edition, pp. 560. • Abhijit Banerjee and Esther Duflo, 2019. Good Economics for Hard Times. Better Answers to Our Biggest Problems. Penguin. • Eurostat. https://ec.europa.eu/eurostat/publications/key-figures • Andrew G. Haldane, 2017. Productivity Puzzles, https://www.bankofengland.co.uk/speech/2017/productivity-puzzles • Helpman, Elhanan, 2004. The Mystery of Economic Growth, Harvard University Press. • Human Development Reports. https://hdr.undp.org/reports-andpublications • Solow, R., 1957. Technical Change and the Aggregate Production Function. Review of Economics and Statistics, pp. 312-320. 67 Data • AMECO. Eurostat. https://economy-finance.ec.europa.eu/economicresearch-and-databases/economic-databases/amecodatabase_en#database • Eurostat data browser. https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes • Key figures in Europe. https://ec.europa.eu/eurostat/cache/digpub/keyfigures/ • United Nations Development reports and data. https://hdr.undp.org/data-center • World Bank open data. https://data.worldbank.org/ 68