variables for sustained growth 2015 index

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Variables for sustained growth 2015 index January 2016

Contents

Page 1 Executive summary

Introduction

Page 4 The VSG Index 2015 ranking

Page 6 Why the VSGs matter

Page 12 Regional performance

Page 16 Focus on institutional strength

Page 18 Focus on technology readiness

Page 19 Conclusion and how the VSGs can be used

Page 20 Appendix 1: Methodology

Page 24 Appendix 2: VSG Index performance by pillar

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© 2016 KPMG LLP, a UK limited liability partnership and a member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved

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Executive summary The Variables for Sustained Growth (VSG) Index was developed in order to compare the productivity potential of different countries across a broad range of areas. It forms part of a set of models that are used to assess countries’ long term economic growth potential.

There are numerous areas that influence countries’ productivity performance. The VSG Index aims to capture the most significant ones, and offers policy makers and investors an insight into countries’ performance and how it compares to peers and those best in class in each category. Improvements in areas covered by the VSGs, including transport infrastructure, education, and the strength of public institutions, can have significant impact on future economic growth and wealth, as illustrated in this report.

The top performances in this year’s VSG Index are dominated by Western Europe, with Singapore, New Zealand, and Hong Kong the only non-European countries to make it into the top 10.

Productivity potential among the major developing economies in Asia ranged from relatively strong performance in Malaysia to poor results in Afghanistan, which recorded only just over a third of Malaysia’s Index level. Latin America saw less progress towards better productivity enablers over the past five years than developing Asia, with the biggest improvements in the region enjoyed by Ecuador and Costa Rica.

Our research found that of the five pillars of the VSG Index, Institutions’ strength has the greatest influence on productivity, with government effectiveness particularly important. It is therefore not surprising that the majority of countries that form the top 10 in this category are also among the top overall performers in the index. Taking Nigeria as an example, our analysis suggests that strong improvements to the strength of public institutions could see GDP per capita 31% higher by 2050.

Technology readiness, another important driver of productivity in our VSG Index, has a relatively uneven distribution of results among the 181 countries covered by the VSG Index. It highlights how a comparatively small group of countries has managed to leap far ahead, while a significant number of countries are far behind, with limited internet access and only basic telecom infrastructure, making it harder for them to catch up with more advanced economies.

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Introduction Economic theory tells us that countries’ performance, in terms of GDP growth, is driven primarily by three factors: growth in employment (Labour), growth in capital stock, and improvements to Total Factor Productivity (TFP). Productivity therefore has a major role to play in determining the level of wealth that countries will attain.

Numerous factors are likely to influence productivity in each country, but for public policy makers and investors it is important to understand how some of the major elements evolve over time and how each country’s performance compares to its peers, in order to gain better understanding of the economic growth potential of their country and how its future course could be improved.

The Variables for Sustained Growth (VSG) Index was developed in order to compare the productivity potential of different countries across a broad range of areas. It forms part of a set of models that are used to assess countries’ long term economic growth.

The VSG Index comprises 21 series, which were selected based on academic studies and business survey results to assess countries’ productivity performance. The importance of each category in the index, as captured by the weights used for each series, was determined by econometric analysis, as well as by primary research.1

There is substantial amount of correlation between the individual series making up the index. This is consistent with a balanced development path, where progress across economic infrastructure goes hand-in hand with improvements in the institutional framework.

The VSG Index is divided into five pillars:

■ Macroeconomic stability

■ Openness to catch up in best practice

■ Infrastructure quality

■ Human capital

■ Strength of public institutions

1 See Appendix 1 for further discussion of the methodology used to create the VSG Index.

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Each pillar is represented by a number of series and sub-series, which capture key areas associated with productivity performance, as highlighted in Table 1 below:

Table 1: Components of the VSG Index

Pillars Series Sub-series

Macro stability • Government deficit • Government debt

Open to catch up • FDI stock • Total trade

Infrastructure • Quality of transport • Technology readiness • Financial institutions – availability of

financial services

• Roads • Rail • Ports • Air • Mobile users • Internet users • Secure internet server

Human capital • Education – average years of schooling and estimated rate of return

• Life expectancy

Institutions’ strength

• Regulatory quality • Judicial independence • Transparency of government

policymaking • Government effectiveness • Corruption • Business rights

• Property rights • Intellectual property rights

Source: KPMG Macroeconomics

The VSG Index was originally developed in 2013 by members of the KPMG macroeconomics team in collaboration with external advisors. It covers 181 countries and tracks their performance across the productivity drivers since 1997.

In the next section we outline the index’s overall ranking results for 2015. We then provide examples of how improvements to productivity drivers could raise long term GDP growth. This is followed by a discussion of the main VSG Index results by region, and a focus on the two areas with most weight in the index; institutions’ strength and technology readiness. Detailed performance by pillar is provided in Appendix 2.

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The VSG index 2015 ranking Rank Country Overall

score Rank Country Overall



score 1 Luxembourg 8.4 23 Korea, South 7.0 45 Seychelles 5.5 67 Kazakhstan 4.8

2 Netherlands 8.4 24 United Arab Emirates

7.0 46 Panama 5.5 68 Botswana 4.8

3 Switzerland 8.4 25 France 6.7 47 Uruguay 5.4 69 Romania 4.8

4 Singapore 8.4 26 Czech Republic

6.6 48 Hungary 5.4 70 Kuwait 4.8

5 New Zealand 8.3 27 Israel 6.4 49 South Africa 5.4 71 China 4.8

6 Norway 8.3 28 Qatar 6.4 50 Costa Rica 5.4 72 Namibia 4.8

7 Finland 8.2 29 Malaysia 6.4 51 Oman 5.4 73 Saint Vincent and the Grenadines

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8 Hong Kong 8.1 30 Slovenia 6.3 52 Macedonia FYR

5.4 74 Azerbaijan 4.7

9 Sweden 8.1 31 Chile 6.3 53 Georgia 5.3 75 Mexico 4.7

10 Ireland 8.1 32 Latvia 6.2 54 Jordan 5.2 76 Armenia 4.7

11 Denmark 8.0 33 Cyprus 6.2 55 Trinidad and Tobago

5.2 77 Saint Lucia 4.6

12 Germany 8.0 34 Lithuania 6.0 56 Croatia 5.1 78 Russia 4.6

13 Canada 7.9 35 Bahamas 6.0 57 Montenegro 5.1 79 Albania 4.6

14 Australia 7.9 36 Barbados 5.9 58 Belize 5.1 80 Cabo Verde 4.6

15 Iceland 7.8 37 Bahrain 5.9 59 Thailand 5.0 81 Vietnam 4.5

16 United Kingdom

7.8 38 Spain 5.9 60 Bulgaria 5.0 82 Philippines 4.5

17 United States

7.6 39 Portugal 5.7 61 Greece 5.0 83 Tunisia 4.5

18 Estonia 7.4 40 Slovakia 5.7 62 Italy 5.0 84 Bhutan 4.5

19 Austria 7.3 41 Mauritius 5.7 63 Turkey 5.0 85 Morocco 4.5

20 Malta 7.3 42 Saudi Arabia 5.6 64 Jamaica 5.0 86 Belarus 4.5

21 Belgium 7.2 43 Poland 5.6 65 Sri Lanka 4.9 87 Rwanda 4.5

22 Japan 7.1 44 Brunei 5.5 66 Antigua and Barbuda

4.9 88 Serbia 4.4

89 Fiji 4.4 112 Mongolia 4.0 135 Gambia 3.5 158 Mozambique 2.8

90 Lebanon 4.4 113 Zambia 3.9 136 Ethiopia 3.4 159 Burkina Faso 2.7

91 Grenada 4.4 114 Guatemala 3.9 137 Uganda 3.4 160 Togo 2.7

92 El Salvador 4.4 115 Bolivia 3.9 138 Pakistan 3.4 161 Myanmar 2.7

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93 Peru 4.3 116 Bosnia and Herzegovina

3.9 139 Nepal 3.4 162 Djibouti 2.7

94 Dominican Republic

4.3 117 Paraguay 3.9 140 Tanzania 3.3 163 Angola 2.6

95 Colombia 4.3 118 Gabon 3.9 141 Bangladesh 3.3 164 Comoros 2.6

96 Ecuador 4.3 119 Egypt 3.9 142 Cameroon 3.3 165 Sierra Leone 2.6

97 Samoa 4.3 120 Kyrgyzstan 3.9 143 Benin 3.3 166 Haiti 2.5

98 Honduras 4.3 121 Senegal 3.9 144 Equatorial Guinea

3.2 167 Iraq 2.5

99 Maldives 4.3 122 Swaziland 3.8 145 Malawi 3.2 168 Niger 2.4

100 Indonesia 4.3 123 India 3.8 146 Zimbabwe 3.1 169 Syria 2.3

101 Brazil 4.2 124 Vanuatu 3.8 147 Sao Tome and Principe

3.1 170 Guinea 2.3

102 Moldova 4.2 125 Cote d'Ivoire 3.8 148 Nigeria 3.0 171 Chad 2.3

103 Ukraine 4.1 126 Cambodia 3.8 149 Solomon Islands

3.0 172 Burundi 2.3

104 Ghana 4.1 127 Micronesia 3.8 150 Madagascar 3.0 173 Afghanistan 2.3

105 Iran 4.1 128 Laos 3.6 151 Mali 3.0 174 Timor-Leste 2.3

106 Suriname 4.1 129 Lesotho 3.6 152 Libya 2.9 175 Yemen 2.1

107 Argentina 4.1 130 Liberia 3.6 153 Turkmenistan 2.9 176 Guinea-Bissau

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108 Tonga 4.1 131 Nicaragua 3.5 154 Congo 2.9 177 Sudan 2.0

109 Guyana 4.1 132 Uzbekistan 3.5 155 Papua New Guinea

2.8 178 Eritrea 1.9

110 Kenya 4.1 133 Algeria 3.5 156 Venezuela 2.8 179 Congo, Dem. Rep

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111 Tajikistan 4.0 134 Kiribati 3.5 157 Mauritania 2.8 180 South Sudan 1.8

181 Central African Republic

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Why the VSGs matter Improvements to infrastructure, governance, or the other productivity drivers captured by the VSGs can have significant impact on countries’ future economic growth, as well as on the future income of their people. We use our VSG Index together with our long term growth model to assess the wider economic benefits of individual policies and broader policy objectives2.

In this section, we look at a number of examples how improvements in different areas of the VSGs could translate into stronger economic performance. Better performance in individual areas represented by the VSGs leads to higher GDP growth, and higher GDP per capita increases the future value of the VSGs Index in our model.

Improvements in specific areas of the VSG Index will therefore gradually increase the performance across other areas captured by the VSGs through the income effect. However, some direct relationships between the series are also likely, with improvements in transport infrastructure, for example, likely to have a direct impact on openness to trade and FDI, or more effective governance directly impacting the quality of infrastructure. Our economic impact projections are therefore likely to underestimate the full effects of policy changes, but they provide a good indicator of some of the broader benefits.

Bangladesh: Better transport infrastructure

Rising wage costs in China should benefit countries in its vicinity like Bangladesh, with companies looking to move production to lower cost locations. But the ability to attract more FDI will also depend on the quality of essential infrastructure on offer. Despite making some progress in infrastructure quality, Bangladesh remains significantly behind the world’s developed economies. The 2015 VSG Index rates Bangladesh transport infrastructure at 3.52, which ranks it 111 out of 138 countries for which data is available.

Chart 1: Effect of better transport infrastructure in Bangladesh


2 See ‘The global economic impact of anti-microbial resistance’, KPMG 2014, for a description of our long term growth model, and an example how it was used together with the VSG Index to estimate the economic costs of no new antibiotics coming to market. kpmg.com/UK/economicoutlook.

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A significant improvement in transport infrastructure should lift productivity and generate stronger economic growth. The higher income achieved thanks to improved transport infrastructure could then have additional positive effects on other productivity drivers, via increased investment in health and education, for example. Other impacts, generated through direct links between transport quality and openness to catch up and other productivity drivers are also likely, but are not captured by our model. Similarly, the financing method used to fund such infrastructure improvements may be significant, as it may cause public finances to deteriorate, affecting the macroeconomic stability pillar, and putting downward pressure on growth.

We used our VSG Index and our long term growth model to estimate the impact better infrastructure could have on long term economic performance in Bangladesh. We compared our baseline scenario for the economy with a scenario in which the transport infrastructure of Bangladesh improves to the value of the best performer in our sample, UAE at 9.40.

An improvement of this scale would see the growth rate of real GDP in Bangladesh rise by around 0.9% per annum according to our model in the years 2020-25 (see Chart 1 above). The lift to GDP growth is expected to slowly subside thereafter due to the catch-up mechanism, as the country develops the pace of new growth slows down, but the overall result is still a significant improvement to economic performance.

The effect of this change would see GDP per capita rise from US$9,844 to US$11,369 by 2050 (measured in 2014 USD).

Mexico: Improvements to technology readiness

Technology readiness, which assesses countries’ quality of telecommunication infrastructure together with the level of ICT use, is an important productivity driver. It is measured in the VSG Index through three sub-series: the number of mobile phones and internet users per 100 people, and the number of secured internet servers.

Mexico’s latest score for technology readiness is relatively low at 1.97 in the VSG Index, compared to the top score of 9.83 enjoyed by Luxembourg. Recent reforms to promote greater competition in the telecom sector in the country should broaden the availability and use of ICT, allowing Mexico’s performance in this area to reach levels more in line with its peers.

In order to gauge the potential impact further improvements could have on the economy, we looked at a scenario where Mexico experiences a significant increase in its VSG score for technology readiness, rising to a similar level of Luxembourg’s 9.83, which ranks highest on this measure.

US $9,844 US $11,369

Baseline scenario

Better transport infrastructure scenario

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As highlighted in Chart 2 below, annual GDP growth rises by an average of 0.3 percentage points between 2020 and 2050 thanks to improved technology readiness, representing just under 8% higher growth each year on average than without the improvement in technology readiness.

Chart 2: GDP growth under alternative technology readiness scenarios for Mexico


Higher GDP growth rates, thanks to better technology readiness, would lift Mexico’s GDP per capita to US$34,497 (in 2014 USD) by 2050, compared to only US$31,072 GDP per capita in our main scenario for the country.

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Baseline scenario

Improved telecom technology scenario

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Nigeria: Stronger public institutions

President Buhari’s election victory on the back of popular demand to reduce corruption and strengthen some of Nigeria’s public institutions highlights the burden lack of developed public institutions represents for the economy.

Even though the country has achieved above-average GDP growth in recent years, economic performance was still below the potential, while recent lower oil prices have put further pressure on the government to deliver public services and investment with fewer resources.

In order to estimate how far the Nigerian economy is held back by its weak public institutions, we looked at the difference in economic growth a rise in this part of the VSG Index could generate. To do so, we raised Nigeria’s current score of 3.4 in the VSG category for institutions’ strength to 9.1, which is equivalent to the VSG value of Finland, the highest performer.

Chart 3 below outlines the growth path for Nigeria under the two scenarios. Average annual GDP growth is expected to be 1.4% higher in 2020-25 in a scenario of stronger public institutions, with the difference in annual GDP growth gradually narrowing to just over 0.4% by 2045-50.

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Chart 3: The effect of stronger public institutions in Nigeria


The higher economic performance should see GDP per capita in 2050 increasing from US$12,301 in our baseline scenario to US$16,1223 in the alternative stronger institution scenario.

Whilst we necessarily apply our experience and expertise to our analysis, the realisation of our assessment of the future economic outlook is dependent on the continuing validity of the assumptions on which they are based. Since the projections we outline in this part of the report relate to the future, actual results are likely to be different from the projected results because events and circumstances frequently do not occur as expected, and the differences may be material. We accept no responsibility for the realisation of the projections.

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Baseline Stronger institutions scenario

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Baseline scenario

Stronger institutions scenario

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Regional performance The top performers in this year’s Variables for Sustained Growth Index are dominated by Western Europe, with Singapore, New Zealand, and Hong Kong the only non-European countries to make it to the top 10.

Although Western Europe has dominated the top of the index since its creation, performance among economies in the region has varied, with countries such as Spain and Italy showing minimal overall improvement over the past ten years, while countries like Ireland benefiting from a significant uplift thanks to sizeable progress in technology readiness and a strong rise in FDI stock, which more than compensated for the deterioration in the macroeconomic environment (see Chart 4 below).

Elsewhere in Europe, the turmoil in Greece has taken its toll on the country’s productivity potential, with higher government debt and slightly weaker public institutions causing its VSG Index score to stagnate between 2005 and 2015. The UK performed relatively well, with its VSG Index score rising by the same amount as Switzerland and Luxembourg over the same period, thanks in particular to improvements in technology readiness.

Chart 4: VSG performance of selected European countries, 2005 versus 2015


Productivity potential among the major developing economies in Asia ranged from relatively strong performance in Malaysia to comparatively poor results in Afghanistan, recording only just over a third of Malaysia’s Index level (see Chart 5 below).

Most countries saw improvement in performance over the past five years, however, the perception of weaker public institutions, largely due to a lower assessment of property rights, saw India’s score fall in 2015 compared to 2005.

Among the largest economies in the region, China’s performance lagged behind that of some of its peers due to its more limited openness to catch up, as measured in the VSG Index by FDI stock and total trade compared to the size of the economy.

The low level of the workforce’s education in Indonesia was one of the key areas that kept the country’s VSG Index score behind its regional peers, as did the quality of its transport infrastructure.

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Chart 5: Performance of selected developing Asian countries, 2010 versus 2015


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Latin America saw less progress towards better productivity enablers over the past five years than developing Asia, with the biggest improvements enjoyed by Ecuador and Costa Rica (see Chart 6 below). Venezuela saw a near 10% decline in its VSG Index score over the past five years, due to a deteriorating macroeconomic environment, reduced openness to catch up, and weaker public institutions in 2015 compared with 2010.

Chile remains the highest performer in the region, despite no progress in the ranking over the past five years, thanks to relatively strong public institutions, high human development score, good road and air transport infrastructure, and low government debt.

Chart 6: Performance of selected Latin American countries, 2010 versus 2015


Latin America experienced a relative improvement in its average VSG values compared to the world average between 2007 and 2013, but the gap has widen again since then (see Chart 7 below).

Chart 7: Selected regional performance relative to world’s VSG average, 1997-2015


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Even poorer regions such as Africa saw an improvement in their VSG Index over the medium term, with the average score for Africa rising by nearly 30% between 1997 and 2015. However, the gap in absolute terms between the average African VSG Index score and the world average rose over the period unlike the gap between the world average and developing countries overall, pointing at the limited success to raise productivity among African countries on average compared to other developing country peers (see Chart 7 above).

Among African countries, Nigeria experienced important improvements in its VSG Index score between 1997 and 2015, while South Africa saw a more modest rise over the same period, with the gap between its VSG Index score and world’s average rising in absolute terms.

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Focus on institutional strength Our research shows that of the five pillars, Institutions’ strength has the greatest influence on productivity, with government effectiveness particularly important. It is therefore not surprising that the majority of countries that form the top 10 in this category are also among the top overall performers (see Table 2 below).

Japan and the UK are the only two countries whose relatively strong public institutions are not sufficient to give them a place within the overall top 10. Workforce education and openness to catch up, compared with some of the smaller economies, are the main areas to hold the UK back. While for Japan, it is macroeconomic stability and openness to catch up that primarily held it back.

Table 2: Best and worst performers in the institutional strength pillar of the VSG Index, 2015

Rank Country 2005 2010 2015 1 Finland 9.1 9.1 9.1

2 Switzerland 9.1 8.6 9.0

3 New Zealand 8.6 8.7 8.9

4 Singapore 9.0 8.8 8.9

5 Norway 8.6 8.6 8.7

6 Hong Kong 7.9 8.5 8.6

7 Luxembourg 8.6 8.3 8.6

8 Netherlands 9.2 8.5 8.6

9 Japan 7.5 8.0 8.5

10 United Kingdom 8.4 8.3 8.5

172 Haiti 2.4 2.1 2.1

173 Congo, Dem. Rep 1.3 2.2 2.0

174 Syria 3.0 2.3 2.0

175 Guinea-Bissau 2.7 2.1 2.0

176 Sudan 2.8 2.0 1.9

177 Equatorial Guinea 2.0 1.8 1.9

178 Central African Republic 2.5 2.2 1.8

179 Libya 3.0 2.8 1.6

180 Venezuela 3.7 1.8 1.6

181 South Sudan N/A 1.9 1.2


Among the worst performers within the institutional strength category, Libya and Venezuela experienced the sharpest deterioration in the past ten years, while the Democratic Republic of Congo was the only one to experience an improvement over the same period, although from a very low base (see Table 2 above).

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Focus on technology readiness

The second most significant pillar in the VSG Index is infrastructure, which comprises a number of areas, ranging from transport to technology and finance. Within this pillar, technology is given the biggest weight, with an emphasis on measures that focus on the more recent technological advances, such as the availability of secure internet servers and internet penetration.

The distribution of technology readiness scores among the 181 countries covered by the VSG Index is relatively uneven, suggesting that a comparatively small group of countries has managed to leap far ahead of the majority in terms of its technology readiness. Unfortunately, a significant number of countries congregate at the bottom of the scale, making it harder for them to catch up with faster growing economies (see Chart 8 below).

Chart 8: Distribution of technology readiness series, VSG Index 2015


Ensuring better technology coverage, in particular among the less wealthy economies, could provide significant boost to productivity and quality of life in those countries through wider access to finance, health, and information.

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Conclusion and how the VSGs can be used Performance in the 2015 VSG Index ranged from 8.4 in Luxembourg to only 1.8 in the Central African Republic. Such a wide range represents the challenges and opportunities faced by many countries to up their game and provide better prospects for their people.

While developed economies fared relatively well in our 2015 index, some developing countries, such as Malaysia and Chile, were not far behind, illustrating how economies with lower levels of wealth could reach relatively high productivity potential.

There are numerous areas that influence countries’ productivity performance. The VSG Index aims to capture the major parts, and offers policy makers and investors an insight into countries’ performance and how it compares to peers and those best in class in each category.

Performance in the VSG Index, together with changes to the future labour and capital stocks, is also used to ascertain countries’ long-term growth prospects.

Improvements in areas covered by the VSGs, including transport infrastructure, technology readiness, and the strength of public institutions can have significant impact on future economic growth and wealth, as illustrated in this report.

How the VSG Index can be used

It is important for policy makers to understand how different policy options would impact economic performance.

The VSG Index is used in conjunction with our long term growth model to translate different policy options into alternative economic scenarios.

Alternative policy scenarios are created through:

■ benchmarking analysis – the selection of a benchmark country whose performance a government wishes to emulate in one area; and

■ policy change simulation – the estimation of the impact on the VSG Index from a specific policy option.

Investors can use the same methodology to demonstrate how improvements in areas such as infrastructure, health, or education originated by their investment would impact economic growth in the countries where they plan to invest.

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APPENDIX 1: METHODOLOGY

The VSG Index

The VSG Index comprises 21 series that were selected to assess countries’ productivity performance, based on relevant academic studies and business survey results. The index covers 181 countries and tracks their performance since 1997.

For each series, a fixed floor and ceiling value were established and the series score in the range of 0-10 was calculated from the value of the underlying variable. For all series a higher value of the index denotes a strictly better outcome for the country.

The values for the floor and ceiling were chosen to be reasonable maxima and minima for the data available. For series with defined ranges, these values were used instead. Scores for values below the floor or above the ceiling were truncated at zero and ten respectively. This has the effect of reducing the influence of outliers in terms of the distribution of the underlying variable.

Weights used to aggregate the series, sub-series and pillars were derived using the results of our econometric analysis in conjunction with results of previous studies and business surveys output. The weights are fixed between different countries and over time.

During the aggregation stage of sub-series to series, pillars and eventually the final index we make an allowance for the possibility of missing data. If a single measurement is not available we allow the weighting of the index to take this into account and aggregate only over the remaining available data.

Our aggregate series are weighted by the real GDP of the individual countries, that is larger economies’ scores have a larger weigh in the aggregate series.

Historical TFP was calculated from the output function of our long term growth model and analysed against the results of our VSG Index. The relationship between the overall VSG Index and TFP was statistically significant in both the cross-sectional dimension (in terms of variation between countries at each point in time, as shown in the Chart 9 below) and in the time-series dimensions (in terms of changes in the VSG index and in TFP for individual countries over time).

Chart 9: TFP versus VSG relative to the US, 2011


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The data sources used to compile the index are listed in Table 3 below. For all of the series selected for this analysis, significant care has been put to verify the accuracy and the measurement fidelity of the sources, although we cannot guarantee the absolute correctness of the underlying data.

A number of the data sources used to make up the index do not cover the earliest years in our database. In these cases we produced our own estimates of the series measured. To do this, we looked for data on proxy series that are available. We then found the average country specific correlations between the series in the VSG and the proxy, and based on the correlation used the predicted value of the VSG series as our estimate for those years where the actual VSG estimate is not available.

Table 3: Data sources – VSG Index

Series Sources Government deficit • International Monetary Fund

Government debt • International Monetary Fund

FDI stock • UNCTADStat

Total trade • The World Bank

Quality of transport - Roads • World Economic Forum, Executive Opinion Survey • IRF Geneva, World Road Statistics WRS

Quality of transport - Rail • World Economic Forum, Executive Opinion Survey • The World Bank

Quality of transport - Air • World Economic Forum, Executive Opinion Survey • The World Bank

Quality of transport - Ports • World Economic Forum, Executive Opinion Survey • UNCTADStat

Technological readiness - Mobile users World Development Indicators, The World Bank

Technological readiness - Internet users World Development Indicators, The World Bank

Technological readiness - Secures internet servers

• World Development Indicators, The World Bank • Netcraft

Financial institutions - availability of financial services

• World Economic Forum, Executive Opinion Survey • World Development Indicators, The World Bank

Life expectancy World Development Indicators, The World Bank

Education – average years of schooling and estimated rate of return

• Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table" forthcoming American Economic Review, available for download at www.ggdc.net/pwt

• UNESCO Institute for Statistics (UIS) • African Development Bank, Economic and Social Statistics

Division Regulatory quality • Worldwide Governance Indicators (www.govindicators.org)

Judicial Independence • World Economic Forum, Executive Opinion Survey • Worldwide Governance Indicators (www.govindicators.org)

Transparency of government policymaking • World Economic Forum, Executive Opinion Survey • Worldwide Governance Indicators (www.govindicators.org)

Government effectiveness • Worldwide Governance Indicators (www.govindicators.org)

Corruption • Worldwide Governance Indicators (www.govindicators.org)

Business rights - Property rights • World Economic Forum, Executive Opinion Survey • Worldwide Governance Indicators (www.govindicators.org)

Business rights - Intellectual property rights • World Economic Forum, Executive Opinion Survey • W.G Park, 2005, International Patent Protection, Research

Policy 37 (2008)

http://www.rug.nl/research/ggdc/data/pwt/v81/the_next_generation_of_the_penn_world_table.pdf

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The long term growth model

Our long term growth model links the VSG Index to the long run growth potential of each country. In our model, GDP is generated by labour (L) and capital (K), which have a Total Factor Productivity (TFP) given by At. These are combined by a usual Cobb-Douglas Production function:

𝐺𝐺𝐺𝐺𝐺𝐺𝑡𝑡 = 𝐴𝐴𝑡𝑡𝐾𝐾𝑡𝑡1/3𝐿𝐿𝑡𝑡

2/3

To estimate the value of L, we use the interpolated estimates provided by the UN Department of Economic and Social Affairs for the number of people between the ages of 15 and 64.

We estimate the current levels of K by using estimates of the capital stock from the 8.1 edition of the Penn World Tables. For the evolution of the capital stock, we assume that the ratio between output and the capital stock is roughly steady at recent historical levels.

Total factor productivity (TFP) follows a catch-up process, with poorer and less productive countries growing faster than countries closer to the technological frontier. Our approach mirrors similar work by Goldman Sachs (2003, 2011) and the OECD’s long run model (2013). The equation for the growth rate of TFP for countries other than the US is as follows:

(1)

The first term on the right hand side is the growth rate of the technology frontier, which in this case is the US. Our projections take into account the long-run growth rate in the potential of the US economy of 2.1%. There is substantial uncertainty about this figure as the future path of the US economy is uncertain, especially the distant future. The Federal Open Market Committee (FOMC) projections put a range of 1.8% to 2.7%4 on this figure, and our assumption is in line with this.

The second term on the right hand side of equation describes the catch-up to the technology frontier. This is dependent on the level of the country’s productivity relative to the US. The speed of catch-up is controlled by a constant, α, multiplying the level of VSGs relative to the US.

When the country has caught up with the US completely, the second term in the equation becomes 1, and the level of TFP grows at the same rate as the US from then onwards. If the level of TFP in a country is higher than the US, then the catch up term becomes less than one and the country’s progress slows down towards the US level.

The power term: 𝛼𝛼 𝑉𝑉𝑉𝑉𝑉𝑉𝑖𝑖,𝑡𝑡𝑉𝑉𝑉𝑉𝑉𝑉𝑈𝑈𝑈𝑈𝑈𝑈,𝑡𝑡

; determines the speed of catch up to the technology frontier. Higher VSG

values will always result in faster catch-up, but crucially this should not affect economies whose TFP has reached the US level. This illustrate the notion that higher VSGs will lead to faster growth, unless they have already completed the catch-up process.

A key parameter here is the value of α. A higher value of this parameter will mean a faster rate of convergence among the world’s developing countries and therefore a higher level of output in 2050.

This parameter was estimated from historic data. Firstly, rearranging the equation by taking g to the LHS and taking logs of both sides:

𝑙𝑙𝑙𝑙 �𝐴𝐴𝑖𝑖,𝑡𝑡+1𝐴𝐴𝑖𝑖,𝑡𝑡

− 𝑔𝑔� = 𝛼𝛼 𝑉𝑉𝑉𝑉𝑉𝑉𝑖𝑖,𝑡𝑡𝑉𝑉𝑉𝑉𝑉𝑉𝑈𝑈𝑈𝑈,𝑡𝑡

𝑙𝑙𝑙𝑙 �𝐴𝐴𝑈𝑈𝑈𝑈,𝑡𝑡𝐴𝐴𝑖𝑖,𝑡𝑡

�

4 Economic Projections of Federal Reserve Board Members and Federal Reserve Bank Presidents, March 2015

∆𝐴𝐴𝑖𝑖,𝑡𝑡+1𝐴𝐴𝑖𝑖,𝑡𝑡

= 𝑔𝑔 + �𝐴𝐴𝑈𝑈𝑈𝑈,𝑡𝑡𝐴𝐴𝑖𝑖,𝑡𝑡

�𝛼𝛼

𝑉𝑉𝑈𝑈𝑉𝑉𝑖𝑖,𝑡𝑡𝑉𝑉𝑈𝑈𝑉𝑉𝑈𝑈𝑈𝑈,𝑡𝑡-1

23


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Rearranging, gives:

𝑙𝑙𝑙𝑙�𝑈𝑈𝑖𝑖,𝑡𝑡+1𝑈𝑈𝑖𝑖,𝑡𝑡

−𝑔𝑔�

𝑙𝑙𝑙𝑙�𝑈𝑈𝑈𝑈𝑈𝑈,𝑡𝑡𝑈𝑈𝑖𝑖,𝑡𝑡

�= 𝛼𝛼 𝑉𝑉𝑉𝑉𝑉𝑉𝑖𝑖,𝑡𝑡

𝑉𝑉𝑉𝑉𝑉𝑉𝑈𝑈𝑈𝑈,𝑡𝑡

We use this relation to calculate the value of α from recent historical data.

The speed of convergence also crucially depends on the evolution of the VSG Index in the future. We allow the value of the index to evolve in line with the historical dependence on GDP per capita. We therefore estimate the following equation for the countries in our sample:

%𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺ℎ 𝑖𝑖𝑙𝑙 𝑉𝑉𝑉𝑉𝐺𝐺𝑡𝑡,𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1𝑙𝑙𝑙𝑙 �𝑉𝑉𝐺𝐺𝐺𝐺𝑡𝑡−1,𝑖𝑖

𝐺𝐺𝑃𝑃𝑃𝑃𝑃𝑃𝑙𝑙𝑃𝑃𝑡𝑡𝑖𝑖𝑃𝑃𝑙𝑙𝑡𝑡−1,𝑖𝑖� + 𝜀𝜀𝑡𝑡,𝑖𝑖 (2)

Our regression results show that the VSG Index tends to grow over time at a rate of 1.2% per year (𝛽𝛽0) (standard error, s.e. of 0.39%), with a slowdown of 0.1% (s.e. 0.04%) for a 1% increase in GDP per capita (𝛽𝛽1). The slowdown occurs because the economy exhausts the pool of “easy wins” and requires difficult reforms to be undertaken to improve its growth potential.

Our model links the growth rates of the VSG Index and GDP per capita, which grows as a result of improving productivity. The equation specified above does not imply that the causality runs from income to the VSG Index, but rather that we expect both of these to improve over time as part of the long run development path of the economy.

Table 4: Data sources – 2050 Model

Series Sources Capital

Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table" forthcoming American Economic Review, available for download at www.ggdc.net/pwt

GDP International Monetary Fund

Oil exports and imports UNCTADStat

Population United Nations, Department of Economic and Social Affairs, Population Division (2015). World Population Prospects: The 2015 Revision, DVD Edition.

24


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Appendix 2: VSG index performance by pillar

Rank Country Overall score

Macro stability

Open to catch up Infrastructure

Human capital

Institutions' strength

1 Luxembourg 8.4 7.7 10.0 8.9 7.1 8.6

2 Netherlands 8.4 5.1 9.0 9.2 7.6 8.6

3 Switzerland 8.4 6.4 6.3 9.1 7.2 9.0

4 Singapore 8.4 3.3 10.0 9.3 6.9 8.9

5 New Zealand 8.3 7.3 2.5 8.5 8.4 8.9

6 Norway 8.3 7.7 3.2 8.7 8.1 8.7

7 Finland 8.2 5.3 3.9 9.2 7.0 9.1

8 Hong Kong 8.1 9.1 10.0 7.4 7.5 8.6

9 Sweden 8.1 6.4 4.6 8.8 7.8 8.4

10 Ireland 8.1 3.3 10.0 8.3 7.9 8.3

11 Denmark 8.0 6.2 5.3 8.8 7.0 8.4

12 Germany 8.0 5.1 4.1 8.8 7.9 8.2

13 Canada 7.9 3.7 3.0 8.6 8.0 8.4

14 Australia 7.9 6.8 1.6 8.5 8.2 8.2

15 Iceland 7.8 5.0 5.4 8.7 7.6 7.8

16 United Kingdom 7.8 3.7 2.9 8.9 6.9 8.5

17 United States 7.6 2.9 2.1 8.9 8.4 7.6

18 Estonia 7.4 8.3 9.0 6.9 7.6 7.3

19 Austria 7.3 4.0 5.6 7.9 6.9 7.8

20 Malta 7.3 5.2 10.0 8.0 7.3 6.7

21 Belgium 7.2 2.9 9.3 7.0 7.3 7.6

22 Japan 7.1 0.6 0.2 6.9 8.0 8.5

23 Korea, South 7.0 6.7 4.7 7.8 8.1 6.1

24 United Arab Emirates 7.0 7.6 8.4 6.3 6.2 7.6

25 France 6.7 3.3 2.5 6.4 7.5 7.4

26 Czech Republic 6.6 6.6 8.8 6.1 7.7 6.1

27 Israel 6.4 5.1 2.8 5.5 7.7 7.0

28 Qatar 6.4 7.6 1.0 5.8 5.8 7.6

29 Malaysia 6.4 5.7 8.5 5.1 6.8 6.9

30 Slovenia 6.3 4.3 8.1 5.7 7.8 5.9

31 Chile 6.3 7.8 3.7 4.8 7.1 7.1

32 Latvia 6.2 6.8 6.6 5.6 6.8 6.2

33 Cyprus 6.2 3.0 7.3 5.3 7.1 6.6

34 Lithuania 6.0 6.8 8.4 4.9 7.0 6.0

35 Bahamas 6.0 5.5 7.1 4.1 7.3 6.7

36 Barbados 5.9 3.0 8.1 4.1 6.5 7.2

37 Bahrain 5.9 4.4 3.7 5.5 6.6 6.4

38 Spain 5.9 3.2 3.0 5.6 7.4 5.9

39 Portugal 5.7 1.8 4.1 5.2 6.3 6.5

25


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40 Slovakia 5.7 5.9 8.7 4.7 7.2 5.3

41 Mauritius 5.7 5.6 6.4 4.1 5.8 6.7

42 Saudi Arabia 5.6 7.6 4.0 4.4 6.2 6.2

43 Poland 5.6 6.0 3.0 4.8 6.7 5.9

44 Brunei 5.5 7.8 2.8 3.2 6.4 6.9

45 Seychelles 5.5 5.6 10.0 4.2 6.4 5.4

46 Panama 5.5 6.2 5.4 5.0 6.7 5.1

47 Uruguay 5.4 5.3 2.1 3.7 6.3 6.7

48 Hungary 5.4 4.8 4.8 4.7 7.4 5.0

49 South Africa 5.4 6.2 3.1 4.8 4.7 6.3

50 Costa Rica 5.4 6.3 3.7 3.8 6.6 6.1

51 Oman 5.4 7.5 1.6 4.7 5.7 5.9

52 Macedonia FYR 5.4 6.7 6.2 3.9 7.4 5.2

53 Georgia 5.3 6.4 6.0 3.4 6.9 5.8

54 Jordan 5.2 3.9 6.7 3.6 6.3 5.7

55 Trinidad and Tobago 5.2 6.2 6.7 4.2 6.2 5.0

56 Croatia 5.1 3.8 4.8 4.5 6.4 5.2

57 Montenegro 5.1 4.5 6.2 3.7 7.0 5.0

58 Belize 5.1 4.4 7.0 5.4 6.4 4.0

59 Thailand 5.0 6.5 8.2 3.8 5.6 5.1

60 Bulgaria 5.0 7.3 8.3 3.9 6.5 4.4

61 Greece 5.0 1.0 2.9 4.2 7.5 5.1

62 Italy 5.0 1.5 2.2 4.6 7.0 5.1

63 Turkey 5.0 7.3 2.5 4.4 5.5 5.1

64 Jamaica 5.0 2.4 6.4 3.6 6.4 5.4

65 Sri Lanka 4.9 4.5 2.0 3.5 7.1 5.2

66 Antigua and Barbuda 4.9 2.5 7.0 3.4 6.0 5.4

67 Kazakhstan 4.8 7.7 4.2 3.5 6.4 4.8

68 Botswana 4.8 7.7 4.7 2.8 4.6 6.1

69 Romania 4.8 6.6 4.2 3.0 6.8 5.0

70 Kuwait 4.8 7.7 0.6 4.5 5.0 5.1

71 China 4.8 6.4 1.2 3.7 6.0 5.3

72 Namibia 4.8 7.2 5.4 3.3 4.2 5.9

73 Saint Vincent and the Grenadines 4.7 4.4 5.8 2.6 6.1 5.6

74 Azerbaijan 4.7 7.3 3.2 3.9 6.6 4.2

75 Mexico 4.7 6.0 3.0 3.5 6.5 4.7

76 Armenia 4.7 6.2 4.4 3.2 6.8 4.5

77 Saint Lucia 4.6 4.3 6.7 2.6 6.1 5.3

78 Russia 4.6 7.5 1.4 3.9 7.0 4.0

79 Albania 4.6 4.7 4.8 3.7 7.0 4.0

80 Cabo Verde 4.6 1.8 5.2 3.2 5.7 5.3

81 Vietnam 4.5 5.2 8.7 3.4 5.1 4.5

82 Philippines 4.5 7.1 2.5 3.1 5.8 4.9

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83 Tunisia 4.5 5.7 4.3 3.2 5.6 4.9

84 Bhutan 4.5 2.4 5.2 2.7 4.9 5.9

85 Morocco 4.5 5.2 4.2 3.9 4.2 5.0

86 Belarus 4.5 6.7 6.1 2.7 7.2 4.0

87 Rwanda 4.5 7.0 1.5 2.7 3.2 6.5

88 Serbia 4.4 4.6 5.6 3.2 6.3 4.2

89 Fiji 4.4 5.9 7.9 2.1 6.5 4.5

90 Lebanon 4.4 1.4 8.0 3.8 5.9 4.0

91 Grenada 4.4 4.0 5.1 2.5 5.7 5.1

92 El Salvador 4.4 5.5 2.2 3.4 5.7 4.5

93 Peru 4.3 7.7 1.9 3.1 6.2 4.2

94 Dominican Republic 4.3 7.3 2.6 3.7 5.4 4.1

95 Colombia 4.3 6.1 1.3 3.4 5.8 4.5

96 Ecuador 4.3 6.6 2.4 3.8 6.1 3.8

97 Samoa 4.3 6.0 2.3 1.2 6.4 5.7

98 Honduras 4.3 6.1 6.4 3.0 5.5 4.2

99 Maldives 4.3 4.3 9.1 2.7 5.2 4.4

100 Indonesia 4.3 7.4 1.9 3.0 4.6 5.0

101 Brazil 4.2 5.0 0.5 3.5 5.6 4.5

102 Moldova 4.2 6.3 6.8 2.9 6.1 3.6

103 Ukraine 4.1 3.7 5.6 3.1 6.9 3.4

104 Ghana 4.1 4.9 4.8 2.6 4.5 4.9

105 Iran 4.1 7.9 0.7 2.7 6.1 4.2

106 Suriname 4.1 6.4 1.2 2.3 6.6 4.3

107 Argentina 4.1 5.9 0.6 3.5 6.5 3.6

108 Tonga 4.1 5.0 4.2 1.7 6.3 4.6

109 Guyana 4.1 4.7 4.2 2.8 5.5 4.2

110 Kenya 4.1 5.4 1.7 3.4 4.2 4.6

111 Tajikistan 4.0 7.0 1.3 2.5 5.8 4.3

112 Mongolia 4.0 2.6 7.4 2.3 5.5 4.2

113 Zambia 3.9 6.3 4.3 2.3 4.0 4.9

114 Guatemala 3.9 7.6 2.3 3.1 4.3 4.1

115 Bolivia 3.9 6.5 2.2 2.6 6.1 3.7

116 Bosnia and Herzegovina 3.9 6.4 2.7 2.7 5.6 3.8

117 Paraguay 3.9 7.7 4.2 2.6 6.0 3.4

118 Gabon 3.9 6.7 4.4 2.2 5.2 4.1

119 Egypt 3.9 3.6 1.3 3.0 5.1 4.3

120 Kyrgyzstan 3.9 5.5 7.0 2.3 5.8 3.5

121 Senegal 3.9 5.7 3.4 2.5 3.7 4.8

122 Swaziland 3.8 7.8 1.5 2.8 3.9 4.4

123 India 3.8 5.1 1.7 2.8 3.9 4.6

124 Vanuatu 3.8 7.4 4.1 1.1 5.2 4.6

125 Cote d'Ivoire 3.8 6.9 4.0 3.1 2.5 4.5

126 Cambodia 3.8 6.9 8.7 2.5 4.1 3.5

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127 Micronesia 3.8 7.7 1.3 4.6 4.7

128 Laos 3.6 5.1 4.6 2.3 4.0 4.1

129 Lesotho 3.6 5.8 1.8 1.7 3.4 5.1

130 Liberia 3.6 6.0 7.3 1.9 3.5 4.0

131 Nicaragua 3.5 7.2 5.8 2.5 4.5 3.2

132 Uzbekistan 3.5 8.3 2.4 1.8 5.9 3.2

133 Algeria 3.5 7.4 2.5 2.1 4.1 4.0

134 Kiribati 3.5 8.2 6.3 0.5 4.4 4.3

135 Gambia 3.5 2.9 2.6 2.9 2.7 4.4

136 Ethiopia 3.4 7.6 1.2 2.1 3.7 4.1

137 Uganda 3.4 6.9 2.0 2.0 3.5 4.2

138 Pakistan 3.4 5.3 0.6 2.6 4.1 3.8

139 Nepal 3.4 7.8 1.8 1.9 3.6 3.9

140 Tanzania 3.3 6.5 2.0 1.8 3.8 3.9

141 Bangladesh 3.3 7.0 1.4 2.0 4.6 3.4

142 Cameroon 3.3 6.9 1.8 2.0 3.6 3.8

143 Benin 3.3 6.9 2.1 1.8 3.1 4.2

144 Equatorial Guinea 3.2 7.8 9.3 2.5 4.3 1.9

145 Malawi 3.2 4.1 5.2 1.5 2.9 4.3

146 Zimbabwe 3.1 5.0 4.0 2.3 4.5 2.6

147 Sao Tome and Principe 3.1 3.2 3.6 1.2 4.4 3.7

148 Nigeria 3.0 8.1 0.5 2.6 2.2 3.4

149 Solomon Islands 3.0 7.4 4.5 0.7 4.4 3.3

150 Madagascar 3.0 6.8 3.9 1.5 4.1 3.0

151 Mali 3.0 6.4 1.8 2.2 1.9 3.8

152 Libya 2.9 5.1 5.2 1.5 6.3 1.6

153 Turkmenistan 2.9 8.7 3.6 1.3 3.8 2.8

154 Congo 2.9 5.1 10.0 0.9 3.8 2.7

155 Papua New Guinea 2.8 6.8 1.6 0.6 4.0 3.6

156 Venezuela 2.8 5.0 0.4 2.7 5.5 1.6

157 Mauritania 2.8 4.2 7.4 1.6 3.3 2.6

158 Mozambique 2.8 5.2 4.6 1.8 1.4 3.6

159 Burkina Faso 2.7 7.0 2.7 0.8 2.0 3.9

160 Togo 2.7 5.2 8.5 0.6 3.5 2.8

161 Myanmar 2.7 6.9 1.7 1.3 3.6 2.9

162 Djibouti 2.7 5.3 6.3 0.6 2.7 3.4

163 Angola 2.6 5.6 4.9 1.1 3.9 2.6

164 Comoros 2.6 7.6 3.4 0.6 4.5 2.5

165 Sierra Leone 2.6 6.1 4.1 1.6 1.7 3.2

166 Haiti 2.5 7.3 2.8 1.6 3.3 2.1

167 Iraq 2.5 3.7 0.8 1.0 4.5 2.6

168 Niger 2.4 6.0 2.8 0.4 1.9 3.6

169 Syria 2.3 2.3 1.3 4.5 2.0

170 Guinea 2.3 6.3 4.0 1.3 2.0 2.5

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171 Chad 2.3 7.5 4.0 1.3 1.7 2.6

172 Burundi 2.3 6.7 1.1 1.4 2.3 2.6

173 Afghanistan 2.3 4.9 1.7 0.7 4.0 2.4

174 Timor-Leste 2.3 0.4 0.4 4.0 3.0

175 Yemen 2.1 5.0 0.6 1.1 3.1 2.2

176 Guinea-Bissau 2.0 6.0 1.1 1.3 2.4 2.0

177 Sudan 2.0 4.8 0.5 1.2 2.8 1.9

178 Eritrea 1.9 0.4 1.4 0.2 4.6 2.1

179 Congo, Dem. Rep 1.9 8.0 3.3 0.4 1.9 2.0

180 South Sudan 1.8 5.2 4.2 1.6 1.9 1.2

181 Central African Republic 1.8 6.3 1.7 0.8 2.1 1.8


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30


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Contact

Yael Selfin

Head of Macroeconomics

KPMG

Phone: +44(0)20 7311 2074

Email: [email protected]

kpmg.com/UK/economicoutlook

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