# data[].attributes.attachment_link.readable_text
The Australian dollar has been in a tailspin lately, particularly against the US dollar.

This week, the Australian dollar sank to as low as US63.63c, the lowest since last November. Excluding that dip, and a sharper one during the early Covid pandemic panic - when it was buying only about US57c - the dollar hasn't been this weak against the greenback since the global financial crisis in 2009.

It's a similar tale for the dollar against the euro. It was buying less than 59 euro cents on Friday, which - if you exclude the Covid dip - was also the lowest since the GFC.

British visitors in Australia for the World Cup will be enjoying almost $2 for every pound. Beyond the Covid slump, the last time the dollar was this weak against the pound was during the Brexit poll when Britain voted to leave the European Union.

But the US dollar is the comparison currency Australians typically hear about. Although only a small portion of bilateral trade is done with the US, most of Australia's exports are US dollar-denominated commodities, so the exchange rate matters.

Value of Australian dollar falling against the euro, USD and GBP

What's causing the recent slide?

Two main factors are at play. The US economy's surprising strength has led to expectations that the federal reserve will raise its key interest rate again beyond the July increase to a range of 5.25%-5.5%.

By contrast, the Reserve Bank of Australia has left its cash rate unchanged at 4.1% since June. Two batches of weak data - from modest wage increases to an uptick in the jobless rate - have reinforced expectations lately that the RBA's work is done.

The difference in those two interest rates - which might yet widen further - will steer investors to buy US dollars and sell Australian dollars.

The other big influence is China's surprisingly weak economic rebound from its severe Covid lockdowns. Deflation in July - contrary to the price issues mostly elsewhere - has only stoked those concerns.

The Australian and New Zealand dollar are often seen by investors as a proxy for China because of the economy's trade exposure. Australia's exports to China (including Hong Kong) are about as large as the next four partners combined.

The RBA cited "China's uneven recovery from Covid-19 restrictions" as the top of its list of "key domestic uncertainties" in its latest quarterly statement of monetary policy.

'Doom loops'

Ray Attrill, head of foreign exchange strategy for NAB, says forex markets "can be very fickle and very dramatic".

His team is currently revising its view that the Australian dollar would be about 70 US cents by the year's end, an outcome looking "increasingly untenable". Instead, the so-called psychological level of 60 US cents is "getting a lot of airplay" but that isn't the NAB's prediction - yet.

A "doom loop" is under way as Japan, China and South Korea in turn let their currencies weaken to shore up their export industries. Attrill says it remains "an ongoing function of the Bank of Japan's policy setting" to keep the yen weak against the US dollar.

"It's making the monetary authorities in other parts of the world - no more so than in China - to not want to have Japan steal a march in terms of export competitiveness," he says. "So they're becoming tolerant of weakness in their own currency."

skip past newsletter promotion

Sign up to Afternoon Update

Our Australian afternoon update breaks down the key stories of the day, telling you what's happening and why it matters
Privacy Notice: Newsletters may contain info about charities, online ads, and content funded by outside parties. For more information see our Privacy Policy. We use Google reCaptcha to protect our website and the Google Privacy Policy and Terms of Service apply.
after newsletter promotion

A weaker yen has also delivered Japan higher levels of inflation after decades of deflationary risk. It's an outcome that might now appeal to China too.

The link between commodity prices and the Australian dollar also appears to have become less reliable. Traditionally, if commodity prices went up, so did the dollar, and vice versa.

More recently, the US dollar and commodities such as oil have traded in the same direction. Attrill attributes the shift in part to supply mayhem, particularly in the wake of Russia's invasion of Ukraine, which have masked the actual supply-demand balance.

What does it mean for Australia?

Nobody knows where the Australian dollar will end up in 2023, but the trajectory of Shanghai's top 300 companies share index might be one proxy to watch, Attrill says.

In the meantime, "all our export clients [are] pretty happy bunnies" as they enjoy "a windfall when they're translating mostly US-dollar earnings back into Australian dollars", Attrill says. Many overseas visitors, including for the World Cup, will also have been more inclined to spend.

He says importers will be suffering "a world of pain" if they are bringing in, say, ceramic tiles from Italy, but less so if they are importing from China, where the currency has been moving in tandem with Australia's.

Whether they pass on the cost to consumers will depend on their pricing power, Attrill says.

The RBA noted in its recent statement that the dollar was "broadly around its levels in May on a trade-weighted basis" but had "depreciated of late".

A tumbling dollar would act as an automatic stabiliser for the economy by supporting growth through more exports and reduced imports.

NAB is alone among the big four commercial banks to forecast another RBA interest rate rise, and a weaker dollar may force its hand.

"With the currency doing what it's doing, it's got to add to the risks that they will be minded to raise rates further," Attrill says. "And, obviously, if that happens, that would be a supportive influence for the currency."

# data[].attributes.attachment_link.title
Why is the Australian dollar falling so fast - and what does it mean?

# data[].attributes.attachment_link.description
Some analysts believe the dollar may go below US60c, bringing joy to exporters and tourists, but encouraging further interest rate rises

# data[].attributes.attachment_link.description
Abandonment of years-long policy follows removal of central bank chief Godwin Emefiele

# data[].attributes.attachment_link.readable_text
In the ever-evolving landscape of work, there's a buzzword that's been making waves: the "gig economy." But what exactly is it, and why should high school students care? Let's dive in. The gig economy is [...]

# data[].attributes.attachment_link.readable_text
In the ever-evolving landscape of work, there's a buzzword that's been making waves: the "gig economy." But what exactly is it, and why should high school students care? Let's dive in. The gig economy is [...]

# data[].attributes.attachment_link.readable_text
In the ever-evolving landscape of work, there's a buzzword that's been making waves: the "gig economy." But what exactly is it, and why should high school students care? Let's dive in. The gig economy is [...]

# data[].attributes.attachment_link.readable_text
China's consumer price index rebounded in August as the world's second-largest economy emerged from deflation, official data released on Saturday suggested, despite sluggish domestic consumption that is complicating its post-Covid economic recovery.

Last month's CPI, the main gauge of inflation, rose 0.1% year on year, the national statistics bureau said.

China briefly slipped into deflation in July for the first time in more than two years, with prices falling 0.3% year on year.

Analysts surveyed by Bloomberg had anticipated a rebound in August of 0.2% year on year.

Deflation indicates falling prices of goods and services, the opposite of inflation.

China experienced a short period of deflation at the end of 2020 and early 2021, due largely to a collapse in the price of pork, its most widely consumed meat. Prior to that, the last deflationary period was in 2009.

Despite the August rebound, many analysts do not rule out a relapse into deflation in coming months, as China's main growth engines stall and after youth unemployment reached a record level of more than 20% in June.

Authorities suspended the publication of official data on youth unemployment in August.

A crisis in the real estate sector - long providing around a quarter of China's GDP - presents a major obstacle to recovery, while exports - traditionally a main growth engine - are also flagging.

Exports fell in August by 8.8%, year on year, for the fourth consecutive month of decline, according to official statistics, as weak overseas demand continued to take its toll. The downturn has a direct impact on employment, as China's tens of thousands of export firms bear the brunt.

The producer price index contracted again in August by 3%, marking the eleventh consecutive month of decline, according to official data.

The PPI measures the cost of goods leaving factories and gives an overview of the general health of the economy.

Falling producer prices mean reduced margins for companies.

Analysts polled by Bloomberg had anticipated this decline following the 4.4% drop recorded in July.

# data[].attributes.attachment_link.title
Chinese economy out of deflation but faces threat of relapse

# data[].attributes.attachment_link.description
Slow domestic consumption, high youth unemployment and decline in exports among factors dragging on post-Covid recovery

# data[].attributes.attachment_link.readable_text
03 Jan 2022 21:35
COORDINATING MINISTRY FOR ECONOMIC AFFAIRS
REPUBLIC OF INDONESIA
PRESS RELEASE
HM.4.6/4/SET.M.EKON.3/1/2022
During The Increase of Global Inflation, Indonesia's Inflation Rate in 2021 Remains Under Control, Low and Stable Jakarta, January 3rd, 2022
During the pandemic, Indonesia's inflation was relatively under control compared to several countries which continued to experiencing rising inflation due to the supply-demand imbalance and energy crisis, for example, Singapore at 3.8% (YoY), the Euro Area at 4.9% (YoY) and the United States. by 6.8% (YoY) in November 2021.

Amid inflationary pressures in various developed countries, Indonesia's inflation rate in 2021 is still under control at a low and stable level and is below the target range of 3±1% (YoY) that has been set. Inflation realization in 2021 was recorded at 1.87% (yoy) or increased from the realization in 2020 which was 1.68% (YoY). The results of this control are inseparable from the strong coordination between the Central Government, Regional Governments, and Bank Indonesia in maintaining price stability.

​Low and stable inflation is a prerequisite for sustainable economic growth which will ultimately provide benefits for improving people's welfare. "The achievement of inflation realization in 2021 is supported by volatile food (VF) inflation which is still under control amid the rising of administered prices (AP) inflation and limited core inflation," said Coordinating Minister for the Economy Airlangga Hartarto, in Jakarta, Monday (3/01).

On a monthly basis, inflation in December 2021 increased in accordance with seasonal trends with a realization of 0.57% (MtM), which was influenced by movements in all inflation components and was the highest figure throughout 2021. The VF component in December 2021 experienced inflation of 2.32% (MtM) or 3.20% (YoY) with a share of 0.38%. Several VF commodities which dominantly contributed to inflation in December 2021 include cayenne pepper, cooking oil, broiled chicken eggs, broiled chicken meat, and red chilies. On an annual basis, VF inflation was maintained within the target range agreed at the High Level Meeting of the Central Inflation Control Team (HLM TPIP) on February 11, 2021, in the range of 3% to 5% (YoY).

In particular, the price of cayenne pepper in December 2021 increased by 85.98% (MtM) with a contribution to inflation of 0.11%. The increase in the price of cayenne pepper was caused by non-optimal production, resulting in limited supply amid rising public demand in line with the loosening of PPKM in various regions. The suboptimal production was caused, among other things, due to the attack of the patek pest in the Garut area, flooding in Pontianak, and the end of the harvest period in some areas of cayenne pepper production centers, such as in Cianjur, Magelang, and Blitar.

Another commodity that plays an important role in contributing to national inflation in December 2021 is cooking oil. Throughout 2021, the total share of cooking oil to general inflation is 0.31%. Since July 2020, cooking oil has shown a price increase of 46.32%. According to data from the Strategic Food Price Information Center (PIHPS) as of December 31, 2021, the price of cooking oil has reached Rp19,900.00/liter.

"The current increase in CPO prices does have an impact on consumers, namely the increase in the price of cooking oil as one of its derivatives. But on the other hand, it also provides incentives for the welfare of farmers, which can be seen from the increase in the People's Plantation Farmers' Exchange Rate (NTPR)," said Coordinating Minister Airlangga.

The Government continues to push for price stabilization measures in order to reduce the prices of several foodstuffs which are experiencing an upward trend towards the end of the year. One of these steps was carried out by the Government together with cooking oil producers and retailers, namely through a program of providing 11 million liters of simple packaged cooking oil at Rp14,000.00 per liter which was carried out through a market operation scheme, especially ahead of the Christmas 2021 and New Year 2022 periods.

In the midst of an upward trend in cooking oil prices and high fluctuations in the prices of various chili peppers, rice commodity was observed to be stable in the range of Rp11,650-Rp11,800.00/kg. This can be seen from the rice commodity that is no longer included in the top 20 commodities that dominantly contribute to national inflation.

The policy of easing PPKM ahead of Christmas 2021 and New Year's 2022 also boosted inflation in the AP component. The AP component experienced inflation of 0.45%, mainly driven by an increase in air freight rates which contributed to inflation in December 2021 of 0.06%.

Throughout 2021, air transport fares contributed to national inflation of 0.08%. Commodities in the AP component which also contribute dominantly to national inflation are various types of cigarettes. Throughout 2021, filtered kretek cigarettes and white cigarettes contributed to national inflation of 0.08% and 0.04%, respectively. The increase in the price of filtered kretek cigarettes and other types itself is in line with the increase in tobacco excise rates (CHT) as regulated in the Minister of Finance Regulation Number 198/PMK.010/2020 concerning Tobacco Products Excise Tariffs which have been in effect since January 1st 2020.

Meanwhile, the core component experienced inflation of 0.16% (MtM) or 1.56% (YoY) with a share of 0.11%. Core inflation in December 2021 was mainly driven by an increase in the price of fresh fish and powder/liquid detergent soap with a share of 0.02% and 0.01%, respectively. Throughout 2021, the dominant core component commodity that contributed to national inflation was rice with side dishes which contributed 0.05%. The increase in the price of rice with side dishes is usually driven by an increase in prices for several VF commodities.

On the other hand, the consistent decline in Covid-19 cases has allowed the Government to continue to enforce the relaxation of mobility restrictions. This condition encourages the smooth running of economic activity, including in the manufacturing sector. As a result, there was an increase in domestic and foreign demand so it also boosted the level of production. Indonesia's Manufacturing Purchasing Managers' Index (PMI) in December 2021 was recorded at 53.5 or still at an expansive level. Indonesia's PMI level is still higher than several ASEAN countries, namely Malaysia (52.8), the Philippines (51.8), and Myanmar (49.0).

In the future, the inflation rate in 2022 is expected to increase compared to the achievement in 2021. Domestic demand that is increasingly recovering in line with vigorous economic activity is expected to encourage an increase in inflation.

"The government will also continue to monitor imported inflation in line with the upward trend in global commodity prices and the normalization of monetary policy by the world's central banks. In the midst of various challenges that will be faced in 2022, the commitment and synergy with all parties, including the Central Government, Regional Governments and Bank Indonesia to strengthen coordination of inflation control strategy policies is the key to keep inflation under control," concluded Coordinating Minister Airlangga. (ltg/alr/tam/fsr)

***

Bureau of Communications, Information Services, and Meetings
Coordinating Ministry for Economic Affairs of the Republic of Indonesia
Haryo Limanseto

Website: www.ekon.go.id
Twitter, Instagram, Facebook, & Youtube: @PerekonomianRI
Email: humas@ekon.go.id
LinkedIn: Coordinating Ministry for Economic Affairs of the Republic of Indonesia

# data[].attributes.attachment_link.keywords[]
indonesia's inflation rate in 2021 remains under control

# data[].attributes.attachment_link.title
During The Increase of Global Inflation, Indonesia's Inflation Rate in 2021 Remains Under Control, Low and Stable - Kementerian Koordinator Bidang Perekonomian Republik Indonesia

# data[].attributes.attachment_link.description
During The Increase of Global Inflation, Indonesia's Inflation Rate in 2021 Remains Under Control, Low and Stable

# data[].attributes.body
https://www.nst.com.my/news/nation/2023/02/881110/malaysias-national-debt-passes-rm15-trillion-mark-cause-alarm-warns

# data[].attributes.attachment_link.title
Malaysia's national debt passes RM1.5 trillion mark, cause for alarm, warns Johari | New Straits Times

# data[].attributes.attachment_link.description
KUALA LUMPUR: Malaysia's rising level of national debt should not be taken lightly and it can no longer rely on oil revenue to keep the nation's economy afloat.

# data[].attributes.headline
imperfect relationship between income per capita and a range of development outcomes

# data[].attributes.subject
imperfect relationship between income per capita and a range of development outcomes

# data[].attributes.body
https://www.oecd-ilibrary.org/sites/a46067bf-en/index.html?itemId=/content/component/a46067bf-en

# data[].attributes.attachment_link.readable_text
This chapter illustrates the weakness of income per capita as a measure of development in Latin America and the Caribbean. Trends in income per capita may not fully reflect changes in other dimensions of development. Countries with similar levels of income per capita display very different development outcomes. This is especially true for those that are middle- and upper middle-income, such as most Latin American and Caribbean countries. This chapter compares current and long-term trends in income per capita with other well-being indicators at regional, national and sub-national levels. It also discusses the importance of developing adapted statistics that better reflect people's living standards to improve the design, implementation and monitoring of public policy for development.

This chapter provides evidence of the imperfect relationship between income per capita and a range of development outcomes through a series of exploratory analyses. This evidence shows clearly that income per capita and other development outcomes do not always move in sympathy.

There is no standard definition of development. Different actors have continuously argued about their preferred development objectives, such as economic growth, social welfare, political participation, freedom, national independence and environmental integrity. While theorists have favoured some objectives over others at different periods, development strategies have increasingly come to embrace all of them (De Janvry and Sadoulet, 2014).

Development paradigms are the result of external factors and accumulated knowledge. External factors have indeed played a major role in shifting paradigms. The era of economic planning in the 1960s, when economic development was treated as a precise science, demonstrated that development was more than just about the economy. Already in the 1970s, the need to look beyond gross domestic product (GDP) was brought to the fore of development thinking and practice (Seers, 1969). In 1972, the Stockholm Conference on the Human Environment was an important milestone in environmental policy making at the global level, while the 1995 World Summit on Social Development was pivotal on the social side. Both strands were reflected in the 1987 Brundtland Report and the 1992 Earth Summit.

Economic structure and its transformation matter. It was commonly thought that developing countries would have to follow a different path from previously industrialising ones. This view was advocated by the dependency school, for example (Prebisch, 1949). But the oil crisis in 1973 and the debt crises in Latin America a few years later placed macro-stability front and centre for the next two decades.

Today's theorists have built on a vast array of earlier development thinking. They have come up with more holistic approaches, including addressing environmental and climate issues in ways that reflect local conditions, and addressing people's needs and wants (OECD, 2018a).

A consensus is emerging that development should deliver improvements in people's lives. Over 70 years, economic and societal objectives have come and gone. Most have now been included in the policy commitments of the 2030 United Nations Agenda for Sustainable Development through its 17 Sustainable Development Goals to end poverty, protect the planet, and ensure peace and prosperity for all (UN, 2015; EU, 2017; OECD, 2018a).

In this perspective, development is about enlarging people's choices, which requires progress in both material and non-material aspects of people's living standards. Development, in turn, requires inclusive growth. Such a "people-centred growth model" combines productivity growth and structural change with inclusiveness and lower inequalities. It improves potential for the masses through better health, education, workplace conditions, digital access, social mobility, trust in government, political participation, entrepreneurship and environmental quality (OECD, 2018b).

If people are at the centre of the development agenda, improving their well-being is the end-line of such a route.

Around the world, countries with similar levels of income per capita display very different well-being outcomes. This is especially true for those that are middle- and upper middle-income. Indeed, the relationship between GDP per capita and well-being is not constant across the income ladder. As economies grow, other dimensions of people's well-being come to the fore. Moreover, countries' progress in terms of education, health, security, political stability, human rights, environmental protection, employment and equity can differ from that achieved for GDP per capita. This divergence between GDP and well-being, and more importantly between well-being gains and GDP growth, reflects the multi-dimensionality of development.

Most Latin America and the Caribbean (LAC) countries today are middle-income countries, with high heterogeneities across different development indicators. Cross-country disparities in well-being outcomes at a given level of GDP per capita are glaring in LAC. As in other emerging economies around the world, LAC countries still face high inequalities in terms of income and access to public services, both across all people within a country and across subnational regions, a pattern that has persisted despite the positive per capita GDP performance in the past decade.

The large income gaps between the very wealthy and the rest of society create relative deprivation and rising aspirations that affect people's assessments of their well-being (Graham, 2005). In fact, most well-being indicators vary considerably among LAC countries. Some LAC countries perform worse than low-income economies worldwide in several well-being aspects. These include satisfaction with living standards, share of non-vulnerable jobs, housing facilities, personal security or perceived honesty of public officials. Additionally, within LAC countries, subnational regions present considerable disparities. These disparities can be measured in terms of income per capita, as well as poverty, access to formal employment, education, health and security.

Although large improvements have been made, development data beyond GDP for LAC remain limited. Large data gaps persist especially for certain dimensions such as subjective well-being, governance and inequalities at subnational level. Even when data exist, there are comparability problems. Overcoming these limits will require investment and joint efforts, and a common understanding across countries on the key challenges confronting the region.

This chapter illustrates the imperfect relationship between income per capita and development throughout LAC countries. First, it summarises the seminal literature on the need to go beyond GDP to assess development. Second, it illustrates the wide heterogeneity, both across and within countries, in development outcomes that income per capita might hide, based on the limited comparative information that is available. Third, based on historical estimates, it analyses how the relationship between income per capita and well-being outcomes changes as countries' income per capita increases. Fourth, it analyses income per capita and well-being variations across time. Fifth, it argues that much progress needs to be achieved in the availability of reliable measures for a broad range of well-being outcomes, based on a framework adapted to the specific context of Latin America and its member countries. Finally, it summarises the main messages and insights on how to assess development in the region.

The pros and cons of GDP per capita as a measure of the success of a development process have been widely discussed in the literature. Until the 1970s, GDP per capita growth was widely viewed as a good proxy for more general development in a country. The general consensus was that economic development should provide the means to improve individual living standards and that GDP could adequately reflect it. Additionally, GDP per capita was a convenient proxy indicator to benchmark human development for two reasons. Apart from its established methodology, economic growth was implicitly linked to changes in more direct measures of well-being (i.e. employment or household consumption). Yet even Kuznets, one of the main originators of GDP, warned against using it as a measure of welfare (Kuznets, 1962; Costanza et al., 2009).

Although GDP growth is a key condition for development, increases in the volume of economic production alone do not necessarily translate into sustained improvements in well-being. Focusing exclusively on GDP implies ignoring distributional issues, as well as the contribution of non-market goods, services and activities such as health, education, security, governance and the environment.

Attention to other non-income dimensions of well-being is warranted because they enhance opportunities for participating in economic and social life (Stiglitz, Sen and Fitoussi, 2009). For instance, good education and good health improve people's well-being. At the same time, they are a pre-condition for participating in the labour market and benefiting from social relationships. When individuals are well integrated into the job market, their sense of accomplishment contributes to life over and above financial rewards (OECD, 2017).

Likewise, measuring development solely through GDP per capita has proven to be a flawed approach to identify what drives positive and negative changes in people's lives and to guide policy makers. Essentially, GDP per capita measures market transactions without accounting for social and environmental costs, income inequality or environmental sustainability (Stiglitz, Sen and Fitoussi, 2009).

The Stiglitz, Sen and Fitoussi Commission (hereafter "the Commission") argued that GDP cannot be used as a measure of success. For the Commission, GDP fails to encompass the multi-dimensionality of development, as well as the structural changes that have characterised the evolution of modern economies. It called for a broader understanding of development and well-being. Moving beyond GDP metrics as the sole indicator of development requires focus on a range of well-being outcomes, on the distribution of these outcomes across the population and on the resources needed for development to last (OECD, 2011).

A single universal path to development does not exist. Development processes are not marked by a succession of stages characterised by linear increases in per capita GDP, homogeneous elements and similar policies. Instead, development is the process that enlarges people's choices by expanding human capabilities (Sen, 1999). In this perspective, development is inherently more complex and multi-dimensional than income per capita. It also requires analysis of the "dynamics of the development process in the small", i.e. at local level, to determine policy priorities (Hirschman, 1961).

Development encompasses access to the resources needed for a decent standard of living. These include political, social, economic and cultural freedom, a sense of community, opportunities for being creative and productive, self-respect and human rights. It is more than just achieving these capabilities; the process of pursuing them, in a way that is equitable, participatory, productive and sustainable, matters as much as the final results (Sen, 1999).

The notion of well-being is close to that of human development promoted by Sen (1999), among others. It focuses on outcomes and opportunities that are intrinsically important to people (an end) rather than only as an instrument to achieve something else (a means); on the diversity of these outcomes; and on their irreducibility to a single aspect (e.g. no amount of income can offset the lack of basic freedom) (OECD, 2018a).

The complexity of development problems highlights the need to move from a single aggregative yardstick such as GDP (Seers, 1969). In fact, the Commission called for moving from measuring economic production as the sole metric of success towards consideration of outcomes for people. It also stressed the importance of combining GDP with broader metrics of household economic well-being, quality of life and inequality, as well as the sustainability of these outcomes over time (Stiglitz, Sen and Fitoussi, 2009). Since then, several international organisations and other institutions have played a central role in moving this agenda forward by regularly analysing a range of multi-dimensional measures through, for instance, the Human Development Index (UN), the OECD Well-being Framework (OECD), and the Structural Gap approach (ECLAC) (see Chapter 4).

This section looks at well-being outcomes among countries with similar income per capita levels, as well within each country. Lack of data constrains a full analysis of the distribution of well-being. However, even incomplete data show that significant disparities across well-being outcomes persist both within countries and across the region.

Key well-being dimensions have mixed outcomes compared to those warranted by GDP levels

GDP growth has not always translated into similar well-being gains in LAC. Despite countries moving up the income ladder, the region presents a mixed picture in terms of well-being outcomes. Performance in each individual dimension of well-being varies significantly. LAC has better outcomes than warranted by its level of per capita GDP in terms of life expectancy, employment, health services, social connection, the environment and overall life satisfaction. Still, other aspects of well-being are underperforming relative to the GDP per capita of the region. These include quality of education, governance, corruption and, especially, inequality, informality and security ( Figure 2.1).

Figure 2.1. Well-being in Latin America and the Caribbean, selected indicators

Comparison of actual and predicted well-being outcomes given GDP per capita

Note: This figure is based on a cross-country bivariate regression of each well-being indicator against GDP per capita across all countries in the world with population above 1 million. The coefficient of this regression, alongside the GDP per capita of each country and region, allows computing the expected value that each well-being variable should attain given the GDP per capita of the region or country. Actual values for each variable are compared to expected ones with the difference standardised by the standard deviation of the indicator. In this way, the figure highlights areas of well-being where the region (or individual countries within it) is performing better or worse. GNI is used as a proxy for household income. Mean reading score from the Programme for International Student Assessment is used as a proxy of quality of education. LAC countries include Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Trinidad and Tobago, Uruguay and Venezuela. NEET=Not in education, employment or training. GINI coefficient measures the inequality of levels of income.

Source: Own calculations based on OECD (2015); Gallup (2017); UNDP (2017); UNESCO (2018); UNODC (2018); World Bank (2018).

There are also significant cross-country differences within the LAC region for each well-being dimension. Gaps between worst and best regional performer values are much larger in the share of people with no health problems, satisfaction with the education system and affected by homicides.

Heterogeneity within income-groups in Latin America and the Caribbean

LAC countries with higher income per capita do not always perform better in terms of development outcomes than countries in the region with lower income per capita. Development challenges persist even when countries cross a given income level. Development performance tends to be higher among LAC countries with the highest income per capita. However, high income per capita alone does not guarantee high performance across all development indicators.

Using an average income measure, such as GDP or gross national income (GNI) per capita, can hide strong disparities across countries in different essential aspects of people's lives. At the international level, countries are usually classified in four income groups: low-income, lower middle-income, upper middle-income and high-income according to their level of GNI per capita. The OECD Development Assistance Committee list of official development assistance (ODA) recipients is defined through the GNI per capita.

Most LAC countries are upper middle-income, including Brazil, Colombia, Costa Rica, Dominican Republic, Ecuador, Jamaica, Mexico and Peru. Other countries, such as Argentina, Chile, Panama, Trinidad and Tobago, and Uruguay, are classified as high-income countries, while only Haiti is a low-income country ( Figure 2.2). Although LAC countries in each group share common characteristics, belonging to one of these groups does not necessarily entail similar outcomes across the multiple dimensions of development.

Figure 2.2. Selected Latin American and Caribbean countries by GNI per capita

GNI per capita, Atlas method (current USD), 2017

Note: Only countries with more than 1 million population were included. GNI data for Aruba, Cuba, Curacao, St. Martin (French part), Sint. Marteen (Dutch part), Turks and Caicos Islands, Venezuela, British Virgin Islands and Virgin Islands (US) are missing. Antigua and Barbuda, Bahamas, Barbados, Belize, Dominica, Grenada, Guyana, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines and Suriname were not included because their population is below 1 million inhabitants. See Chapter 6 for an analysis on the Caribbean small states.

Source: World Bank (2018), World Development Indicators.

Overall, LAC countries perform poorly in selected development outcomes compared to their worldwide income category peers. This is particularly evident for higher-income countries. For instance, high-income countries in LAC lag behind in terms of homicide rates, inequality, vulnerable employment and satisfaction with healthcare compared to high-income countries worldwide ( Figure 2.3, Panels A, B, C and D). At the same time, on average, all three LAC income groups have higher homicide rates than their corresponding worldwide income groups ( Figure 2.3, Panel A).

Figure 2.3. Selected development indicators by country income groups

Note: Simple averages are used both for LAC and world averages. LAC lower middle-income countries include Bolivia, El Salvador, Honduras and Nicaragua. LAC upper middle-income countries include Belize, Brazil, Colombia, Costa Rica, Cuba, Ecuador, Grenada, Guatemala, Guyana, Jamaica, Mexico, Paraguay and Peru. LAC high-income countries include Argentina, Bahamas, Barbados, Chile, Panama, Puerto Rico, Trinidad and Tobago and Uruguay.

Source: Own calculations based on World Bank (2018), UNODC (2018) and Gallup (2017).

Among the LAC countries with available data, development outcomes are mixed across income groups. High-income countries do not always perform better than middle-income or lower-income countries. In fact, with respect to income inequality and satisfaction with healthcare, lower-income countries perform better than upper middle-income countries ( Figure 2.3, Panels B and D).

The mixed development outcomes occur partially because LAC countries' individual performance across selected indicators varies considerably among countries belonging to the same income group. For instance, the homicide rate of El Salvador (105 deaths per 100 000 inhabitants) is 17 times the homicide rate of Bolivia (6 deaths per 100 000 inhabitants), while both countries are lower middle-income economies ( Figure 2.3, Panel A). Similarly, vulnerable employment is 40.6 percentage points higher between the best performing and worst performing upper middle-income country in LAC (49.7% in Peru and 10.3% in Cuba) ( Figure 2.3, Panel C). The share of people satisfied with the healthcare system varies considerably among LAC high-income countries, from 67% in Uruguay to 33% in Chile ( Figure 2.3, Panel D).

Heterogeneity is so large among income groups that in several development outcomes lower-income countries in LAC perform better than middle-income and even high-income countries. For instance, Trinidad and Tobago, and Uruguay, both high-income countries, present homicide rates greater than Bolivia, the best performer of the lower middle-income group, as well as for the average for all three income groups ( Figure 2.3, Panel A). Similarly, inequality in Panama, the worst-performing country of the high-income group, is higher than in Mexico and El Salvador, the best-performing countries of the upper middle-income group and lower middle-income group, respectively. Meanwhile, the share of people satisfied with their healthcare system in Nicaragua (63%), the best performer of the lower middle-income group, is greater than in Brazil (36%) and Chile (33%), the worst-performing countries of the upper middle-income and high-income groups, respectively.

All in all, the income level group for a country does not provide a full picture of its performance across all development dimensions nor its challenges.

Subnational heterogeneity in development outcomes

Subnational regions and cities in LAC display considerable differences in well-being outcomes relative to national averages. National averages generally hide large diversity across subnational regions in all continents, but the pattern is especially pronounced in LAC (OECD, 2016a). The need for a regional lens when considering well-being outcomes is key to better understanding differences in the region and in better designing public policies to address them.

Subnational differences in LAC characterise all well-being outcomes and are evident when considering GDP per capita. LAC's territorial disparities in GDP per capita are striking, and much larger than in OECD member countries. Subnational differences in GDP per capita (as measured by the Gini coefficient in average GDP per capita across regions) in OECD member countries is around 16%. However, in some LAC countries such as Brazil, Chile, Colombia, Mexico and Peru, subnational differences are close to or higher than 30% (OECD, 2016a). Moreover, GDP per capita does not truly reflect average differences in GNI since regions that rely on oil and other commodities can have both high GDP and low average national income.

Subnational regions with high income per capita coexist with lower income per capita regions in most LAC countries ( Figure 2.4). This is particularly evident in Colombia, Mexico and Peru where high-income subnational regions coexist with others with upper and lower middle-income ( Figure 2.4, Panels C and E). When states and provinces within each country are evaluated based on a proxy of GNI 1, the conventional ODA graduation criterion, their disparities can be measured as "years to graduation". In Mexico, for instance, Ciudad de Mexico became high-income more than 13 years ago when it achieved a GNI per capita of USD 12 056; while Chiapas will achieve high-income status in 60 years assuming the growth rate of its GNI per capita remains constant. Likewise, Chile became a high-income country in 2013, but half of its regions, where one-third of the country's population live, still have a GNI per capita below the graduation threshold of USD 12 056. Similarly, the capital cities of Brazil, Colombia and Peru have a per capita GNI of more than twice that of half the provinces of their country.

There are also large gaps within LAC countries with respect to other well-being measures. Well-being inequality in LAC countries is closely linked to where people live and work. The "urban advantage" remains strong when looking at other development measures, especially in high-income countries such as Argentina and Chile. Large LAC cities, especially capitals, have better education, employment and health, and lower poverty outcomes than most small cities and rural areas. Many more children in rural areas die before the age of 1 year due to lack of access to basic healthcare than in urban areas in LAC. For example, infant mortality in Boca del Toro, a rural province in Panama with a share of 1% of the country's GDP, is more than twice that of Panama City, which is responsible for 75% of the country's output. Likewise, on average, youth in states and provinces with large rural areas leave school earlier than those in regions with large urban areas. Conversely, crime and violence are higher in the regions with larger cities.

Territorial disparities are largest for poverty and informality rates. Colombia and Mexico have the largest regional differences in national income poverty. In Mexico, only 14.2% of the population in Nuevo Leon live under the national income poverty line; while 77.1% of the population do so in Chiapas (INEGI, 2018). Likewise, in Colombia, 12.4% of the population of Bogota live below the poverty line, while the poverty rate of Chocó is 58.7%. Regional gaps in terms of informality are also wide across most LAC countries analysed in this section, ranging from 12.6 percentage points in Costa Rica to 45.1 percentage points in Mexico. Most countries have a gap of around 35 percentage points ( Figure 2.5).

Figure 2.5. Regional disparities across selected development indicators, selected Latin American and Caribbean countries

Note: Each marker represents a province, state or region within a country. Countries ordered by level of GDP per capita. For the employment and informality indicators, working-age population refers to people aged 14 or older in Argentina and Peru, 15 or older in Costa Rica and Chile, 16 or older in Brazil, 12 or older in urban areas of Colombia and 10 or older in rural areas of Colombia. The informality figures for Brazil are based on authors' calculations with data from IBGE (2018): they include independent workers not registered in National Registry of Legal Entities (CNPJ-Cadastro Nacional da Pessoa Jurídica) and dependent workers without a signed work contract.

Source: Centre for the Study and Analysis of Crimes in Chile (2018), CONAPO (2018), CONEVAL (2016), DANE (2018a, 2018b, 2018c), IBGE (2017, 2016, 2015), IGARAPE (2018a, 2018b, 2018c), INDEC (2018, 2017a, 2017b, 2017c), INE (2017, 2015), INEC (2016, 2017a, 2017b, 2017c), INEGI (2017, 2015), INEI (2018a, 2018b, 2015a, 2015b), Ministry of ICT of Colombia (2018), Ministry of Labour and Employment Promotion of Peru (2015), Ministry of Social Development of Chile (2017), OECD (2016b), RIMISP (2018).

Territorial disparities in infant mortality are also wide. For example, in Colombia, the infant mortality of Vichada is almost three times that of Antioquia (DANE, 2018a); in Peru, the infant mortality of Tumbes is over three times higher than that observed in Puno (INEI, 2015a).

In terms of homicide rate, territorial differences vary between countries in LAC. Brazil, Colombia, Mexico and Peru have large gaps in homicide across regions ( Figure 2.5). These gaps are much lower in Costa Rica and almost non-existent in Argentina and Chile ( Figure 2.5).

The benefits of education are also spread unevenly across regions. Territorial-related differences are less marked than for other development outcomes in most countries except Colombia. Secondary enrolment rates are 10 percentage points higher in the region with the highest share of youth enrolled in secondary education than in the one with the lowest share in Brazil and Costa Rica. The gap in Colombia exceeds 50 percentage points ( Figure 2.5).

Addressing regional disparities should be a key element of any development strategy in LAC. The factors that most influence peoples' well-being are local, such as employment, access to health services and education, and security. Policy responses must also be locally targeted. Policies that take better account of regional problems and needs may have a greater impact in terms of improving well-being for the country as a whole by tackling the sources of inequality more directly. But to target policies effectively, governments need the tools to fully understand local conditions and the expectations of their citizens (OECD, 2016a).

Large cross-country differences in domestic resource mobilisation

Despite moving to high-income or upper middle status, some LAC countries are still unable to meet their financial needs due to low tax revenues. Country groupings based on GDP per capita hide disparities in countries' abilities to mobilise domestic resources to face development challenges. A high level of GDP per capita is not always associated with greater tax capacity.

LAC countries show a mixed performance in terms of their tax capacities; differences are only weakly related to their GDP per capita level. For instance, in all high-income economies in LAC, the level of taxes mobilised to fund development is below the world average of high-income countries ( Figure 2.6). Similarly, several LAC high- or middle-income economies might be unable to meet their future financial needs given their tax-to-GDP ratios are similar to or below those of lower middle-income economies (see Chapter 4 for an analysis on domestic resource mobilisation).

Figure 2.6. Tax-to-GDP ratios in LAC, OECD and world average by income

Total tax revenue as percentage of GDP, 2016

Note: Bars represent the world average across the 80 countries covered in the OECD Global Revenue Statistics (25 in LAC, 18 in Africa, 35 in the OECD and 4 in Asia). In LAC, high-income economies include Argentina, Bahamas, Barbados, Chile, Panama, Trinidad and Tobago, and Uruguay. Lower middle-income economies include Bolivia, El Salvador, Honduras and Nicaragua, and upper middle-income economies include Belize, Brazil, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, Guatemala, Jamaica, Mexico, Paraguay, Peru and Venezuela. Countries are classified by income groups according to World Bank methodology. The flat line represents the simple average of LAC economies depending on their income group ( https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups).

Source: Own calculations based on OECD (2018c).

In the LAC region, the relationship between levels of GDP per capita and well-being outcomes is not uniform across the income ladder. In other words, high-income countries may feature worse development outcomes than upper middle-income countries. For their part, upper middle-income countries may confront development challenges as serious as those encountered by low-income ones. A similar phenomenon emerges when comparing changes in GDP per capita to changes in well-being outcomes. Availability of comparable time-series for key well-being outcomes for LAC countries is even more limited than for recent estimates. However, analysis in this section shows that trends in real GDP per capita do not fully reflect changes in other dimensions of well-being such as life expectancy, education, security or income inequality.

The analysis rests on estimates by a group of economic historians charting long-term developments in GDP and in a range of well-being outcomes across the world (van Zanden et al., 2014). Based on these estimates, this section looks at the relationship between a composite well-being measure and the seven individual series included in this composite, on one side, and GDP per capita on the other over time. While historical research must contend with many data limitations, this section aims to investigate the strength of the association between GDP per capita and well-being, as well as to understand changes in this association as countries' GDP grows.

Although the value of considering multiple indicators of well-being is clear, a composite measure can provide valuable information about well-being when all the indicators are considered at the same time. While policies require looking at each individual variable, a composite allows assessment of how countries compensate for a bad performance in one aspect of well-being with a good performance in another.

Aggregating multiple indicators into one composite is not without problems. Trade-offs exist in every composite indicator and all approaches to constructing a composite indicator have advantages and disadvantages (Ravallion, 2011). The composite indicator used in this section ( Box 2.1) is constructed through a latent variable model similar to that used in How Was Life? (van Zanden et al., 2014). This approach allows dealing with missing data and accounting for the uncertainty that arises. Yet this inevitably comes at a price in terms of transparency (van Zanden et al., 2014).

GDP per capita is strongly associated with the composite well-being measure for countries with low level of GDP per capita. Up to a level of around USD 2 000 (2011 PPP), every additional standard deviation of GDP per capita increases the composite well-being measure by around eight standard deviations ( Figure 2.7).

copy the link link copied!

Figure 2.7. The link between GDP per capita and well-being weakens as income per capita increases

Coefficient of rolling panel fixed-effect regression of a composite well-being measure and GDP per capita (USD 2011 PPP)

Note: Beta coefficients of a rolling fixed-effects panel regression across the income ladder (see Box 2.2).

Source: Own calculations based on https://www.clio-infra.eu/ and Rijpma (2017).
StatLink https://doi.org/10.1787/888933936615

GDP per capita and well-being outcomes gradually delink as countries become richer in terms of GDP per capita. The association between GDP per capita and the composite well-being measure (as well as with the individual well-being variables that make up this composite) become weaker when moving further up the income ladder. In other words, the link between GDP per capita and well-being weakens among richer countries. In fact, the association between the composite well-being measure and GDP per capita is more than twice as large for low-income countries than for upper middle-income countries, and almost three times stronger than for high-income economies. For upper middle-income countries, those with a GDP per capita of around USD 7 250 (2011 PPP), an increase of one standard deviation in GDP per capita increases the composite well-being measure by only four standard deviations. At income levels of USD 11 750 (2011 PPP) and more, on average, an increase of one standard deviation in GDP per capita increases the composite well-being measure by only three standard deviations ( Box 2.2).

(1)

In a second step, this exercise uses the residuals from the regression as dependent variable to run a rolling panel country-fixed effects regression against the different well-being dimensions shown in Table 2.1 (with a similarly sized window and evolution). The results of this second exercise are presented in Annex 2.A1. The model assumes that well-being can be characterised according to the following equation:

(2)

The same delinking between GDP per capita and the composite well-being measure also holds when looking at individual measures included in the composite. This pattern is most pronounced for life expectancy, educational attainment and real wages ( Figure 2.8). Although at a slower pace, homicide rate, income inequality and height delink from GDP per capita as countries become wealthier. This is especially the case when countries surpass the upper middle-income threshold of GDP per capita of USD 7 250, 2011 PPP.

copy the link link copied!

Figure 2.8. GDP per capita and selected development variables by level of income per capita

Coefficient (standard deviation) of rolling panel fixed-effect regression of selected well-being indicators and GDP per capita (USD 2011 PPP)

Note: Beta coefficients of a rolling fixed-effects panel regression across the income ladder (see Box 2.2).

Source: Own calculations based on www.clio-infra.eu/ and Rijpma (2017).
StatLink https://doi.org/10.1787/888933936634

As LAC economies grow, several development dimensions other than GDP per capita become more important in improving people's lives. As countries climb the income ladder, the association between life expectancy, education, personal security and democratic stability, and the residual of the regression between GDP per capita and the well-being composite indicator (the error term in the previous analysis), gains strength and significance. This relationship is most evident in the case of life expectancy (See Annex 2.A1.).

The relationship between various dimensions of well-being and GDP per capita has also changed over time. At the global level, there was no additional well-being accrued beyond that explained by higher per capita GDP for most well-being indicators in the 19th century. This changed, however, in the 20th century as some well-being indicators began delinking from GDP per capita (OECD, 2018a). While the LAC region had higher GDP per capita than several other world regions in the past century, it did not always achieve better well-being outcomes.

This section compares the LAC average performance for seven well-being variables to the one expected given its GDP per capita from 1950 to 2010 (for methodology see Box 2.3). Evidence shows that GDP per capita has not always been a good predictor of the various dimensions of well-being in LAC. While some well-being outcomes increased more rapidly than implied by GDP growth alone, others increased at a slower pace.

The coefficient is used to obtain a predicted or expected level for all well-being indicators according to the level of GDP per capita. Finally, ten-year averages are computed to present the results over time.

Real wages in LAC are lower than those expected based on GDP per capita. Although GDP per capita is strongly correlated with real wages, the difference between the expected and the observed levels are large and statistically significant for most of the period covered by available data (from 1960 up to 2005, the difference is at least at the 10% level). Since 1950, the region's real wages have consistently lagged behind the level observed in countries with similar levels of GDP per capita ( Figure 2.9, Panel A).

copy the link link copied!

Figure 2.9. Actual and expected performance for selected well-being outcomes in Latin America and the Caribbean over time

Expected performance based on GDP per capita

Note: * Real wages are measured as the number of consumption baskets purchased with the real wages of a male unskilled worker in the building industry.

Expected values are calculated with a panel dataset composed of 183 countries worldwide from 1900 to 2010. Actual values not statistically significant, at least at the 10% level, were signalled in blue. In the case of the Gini coefficient, the series was expanded for 2005 and 2010 using growth rates from the CEPALSTAT Gini series for LAC. The LAC average includes all countries in the Americas except Canada and the United States.

Source: Own calculations based on www.clio-infra.eu/ and CEPALSTAT.
StatLink https://doi.org/10.1787/888933936653

Informality is one of the main obstacles to higher wages and to a more inclusive labour market. A large share of the working-age population encounters labour-market difficulties due to insufficient work-related skills, lack of quality jobs and territorial disparities. Improving job quality, reducing informality and increasing employment levels - especially for women and youth - are key challenges for improving material conditions and equity (OECD/CAF/ECLAC, 2016).

Income inequality has been higher in LAC than expected based on GDP per capita. Although income inequality has declined since the 2000s in most LAC countries, it has slowly increased since the 1980s at a regional level. Both current and expected levels of income inequality are only weakly related to countries' GDP per capita. The differences between the two are statistically significant since 1960 for every period where data are available (at least at the 10% level).

Income inequality is the dimension of well-being, from the ones analysed in this section, that best illustrated the problem of using GDP per capita as a sufficient condition for development. GDP growth is much needed for countries to improve people's well-being, but is not enough ( Figure 2.9, Panel B). Lowering income inequalities in the region requires more than higher GDP.

Life expectancy in LAC tracked closely its predicted value between the 1950s and 1980s. Since the 1980s, the region's life expectancy delinked from GDP per capita and exceeded the level expected. A statistically significant gap remained between the two (at least at the 10% level) ( Figure 2.9, Panel C).

LAC's educational attainment exceeds the level expected given the region's GDP per capita. Average years of education of the population of LAC were below the levels expected between 1950 and 1985. Since the 1990s, LAC's performance in education exceeded expectations as a result of policies to expand education coverage, especially at primary level ( Figure 2.9, Panel D).

Yet education quality remains a pressing concern throughout the region. More than half of 15-year-old Latin Americans enrolled in school do not have basic proficiency in reading, mathematics and science (OECD, 2016b). Fewer than 1% of LAC students perform at the highest levels of proficiency in mathematics, reading or science. Lack of proficiency in these areas is an obstacle to developing more specific skills later in life and may hamper innovation and entrepreneurship. This is a major challenge for LAC countries as they try to transition into knowledge-based economies where citizens need to innovate, adapt and leverage advanced human capital (OECD/CAF/ECLAC, 2016).

Personal security in LAC is much worse than expected based on economic performance. Homicide rates fell significantly between the 1960s and the 1980s, but increased sharply from 1985 onwards. The rates far exceeded predicted levels, with a statistically significant difference since 1995 (at least at the 10% level) ( Figure 2.9, Panel E).

Democratic stability has been strengthening in the region in recent decades. Since the beginning of the 20th century, democratic stability in LAC has been a winding path. It was hindered sizably between 1960 and 1970 when most LAC countries were ruled by military dictatorships. However, since the 1980s, when the region experienced the third wave of democratisation (Huntington, 1991), democratic stability has been strengthening, surpassing that of other countries with similar levels of GDP per capita. Since 1990, the difference between expected and observed is statistically significant (at least at the 10% level) ( Figure 2.9, Panel F).

Over the last decades, LAC's GDP performance has not rendered into the better real wages, lower inequality and greater security expected. Income thresholds ignore the complex nature of development and the diversity and heterogeneity of countries in transition. Development must be conceived as a multifaceted process consistent with facing the structural challenges of a particular country rather than as a one-size-fits-all approach based on grouping the countries according to their income levels.

A broader concept of development requires a different approach to measurement. Moving beyond GDP metrics as the sole indicator of success requires measures for a broad range of development outcomes. This includes using data on how well-being outcomes are distributed across a population and local areas, as well as on sustainability.

To go beyond GDP, countries should focus more on people's well-being and societal progress. They should look not only at the functioning of the economic system, but also at the diverse experiences and at living conditions of people. These kinds of measurements are key to understanding what drives well-being of people and nations, and what is needed to achieve greater progress for all.

To assess development outcomes beyond GDP, LAC countries need first to identify the dimensions of life that matter most for people and the resources needed to ensure sustainability. Concurrently, LAC countries should operationalise all these dimensions through a set of indicators, as well as collect data on country averages and both vertical and horizontal inequalities.

Beyond statistical capacity and co-ordination of national initiatives, developing indicators that can capture key concerns for LAC requires building consensus around the most relevant issues and challenges confronted by national governments in the region. To be sustainable and to provide the basis for policy dialogue and co-operation within the region, a wide range of stakeholders must take part in the process of consensus-building.

After having identified the most relevant development outcomes for the region, a significant data challenge remains: most LAC countries lack comparable data for many key well-being domains shaping people's lives. These domains range from households' material conditions to more qualitative aspects such as job quality, trust in other people and in institutions, people's self-reports of their own lives, social connections or pressures on natural resources. Such data challenges are all the more important as monitoring development outcomes to inform policy requires data that look beyond averages. Specifically, data should consider inequalities in all life dimensions and assess the conditions of different population groups (e.g. by gender, age, race and ethnicity, place or living). Co-ordinated efforts are therefore needed to reach agreement on a well-being framework for the region, to develop the capacity needed to fill data gaps, and to improve comparability and disaggregation of selected measures.

In response to the challenge, the European Union, the OECD and the UN Economic Commission for Latin America and the Caribbean are launching a project to develop improved metrics of well-being and multi-dimensional development for the region. The project, "New metrics for development: A well-being approach to improving people's lives in Latin America", is part of a broader Regional Facility for Development in Transition in Latin America and the Caribbean.

Over three years (2018-21), the project has three aims:

to develop a well-being framework adapted to the realities and priorities of Latin American and Caribbean countries

to populate this framework with higher quality and more granular data than those available and used in this chapter, to be developed in co-operation with national statistical offices in the region

to support policy makers in identifying development priorities and designing policies and programmes to achieve them, based on the metrics developed in the context of the project.

Despite considerable progress in the past century, GDP growth has not always translated to similar well-being gains for Latin American people. As LAC countries moved up the income ladder, violence and income inequality grew. At the same time, informality has become a persistent problem. Real wages have increased at a slower pace than in other countries in the world with similar income per capita. Though school enrolment has increased, access to higher education and quality secondary school are still limited to a privileged group. While Latin Americans live, on average, almost as long as citizens of OECD countries, they face more health problems. In sum, economic gains have improved certain areas of development, but not other longstanding challenges. They have also raised new problems (OECD, 2018a).

Policy makers need to re-conceptualise development and to rethink both domestic policies and international co-operation to "leave no-one behind" and fulfil the goals of the 2030 Agenda (see Chapters 4 and 5). In this context, it is essential to acknowledge the heterogeneity across countries in terms of their development challenges, which are often independent of their income. This is particularly important for LAC countries that are transitioning to higher-income levels, but still lack capacity to compete and narrow their economic and social gaps relative to more advanced developed countries (Barcena, Manservisi and Pezzini, 2017).

Income per capita can provide a ballpark idea of the development challenges confronting each country in the region. However, it fails to draw the detailed print that policy makers need as a roadmap to achieve sustainable development. Indeed, the relationship between GDP per capita and well-being varies across the income ladder. Furthermore, as economies grow, other dimensions of welfare beyond GDP take over as co-determinants of well-being.

Looking at development through a multi-dimensional lens serves as a good compass to design, implement, monitor and evaluate policies to improve people's lives. The evidence in this chapter confirms that material living conditions, and especially income, matter. But it also shows that income per capita is not the only feature shaping people's lives and well-being. Quality jobs, health, education, democracy, personal security and inequality are important as well. And they are especially important for well-being as countries grow.

Flawed measurement tools will distort policy making (Stiglitz, Sen and Fitoussi, 2009). LAC countries should invest in better data collection to measure and monitor those well-being dimensions that are most important for the region across their territory and population groups. In this process, consultation with different stakeholders is crucial to build a shared understanding of what well-being dimensions have been acclaimed to matter the most. Efforts should be made to produce accessible, timely and disaggregated data. This is a remarkable challenge for all countries, and especially so for those in transition. Achieving these goals through building national statistical capacity is critical to establish where each country stands, to prepare long-term development plans and to monitor progress along the way.

Annex 2.A1. The link between various well-being dimensions and what GDP per capita cannot explain in well-being

As economies grow in terms of GDP per capita, other dimensions of welfare, such as life expectancy, education, personal security and democratic stability take over as co-determinants of well-being.

This exercise is based on the empirical analysis described in Box 2.2 that uses the following regression:

(1)

This exercise uses the residuals from the regression as dependent variable to run a rolling panel country fixed effects against the different well-being dimensions shown in Table 2.1 (with a similarly sized window and evolution). The model assumes that well-being can be characterised according to the following equation:

(2)

Overall, this exercise aims to find which dimensions of welfare explain people's well-being given that GDP per capita does not fully translate into quality of life.

Results show that the relationship between the portion of well-being that cannot be explained by GDP per capita (the error term in the previous analysis) and the life expectancy of countries strengthens at higher levels of income (Annex Figure 2.A1.1, Panel A). In fact, at low level of income, an additional standard deviation in life expectancy is to increase by 0.4 standard deviation the portion of well-being unexplained by GDP per capita. At higher level of income, this impact increases up to one standard.

Additionally, education stands out as another key dimension of well-being. At all income levels, education plays an important role in explaining the part of well-being that GDP per capita fails to do. Having basic notions of reading or mathematics makes everyday life easier for transactions, for example. As well, knowledge can be an intrinsic pleasure for individuals (OECD, 2011). Besides, education can foster economic development (Romer, 1990) or increase political stability (Alesina and Perotti, 1996), which indirectly affects well-being. Although the results are relatively constant across country levels of GDP per capita, the association between years of education and well-being shows a slight increase around USD 8 500 (2011 PPP) (Annex Figure 2.A1.1, Panel B).

Personal security and democratic stability are also positively associated with higher levels of well-being. As in the case of life expectancy and education, the analysis looks at the relationship between the portion of well-being unexplained by GDP per capita and the level of personal security. This is measured by the homicide rate at different levels of country income. The correlation becomes positive at around USD 10 000 (2011 PPP). This means that, after this threshold, homicide rate starts explaining some portion of well-being that GDP per capita failed to explain. (Annex Figure 2.A1.1, Panel C). On the other hand, the correlation with political stability and democracy is positive at all levels of income, and remains relatively stable (Annex Figure 2.A1.1, Panel D). 2

References

Alesina, A. and Perotti, R (1996), "Income distribution, political instability, and investment", European Economic Review, Vol. 40, pp. 1 203-1 228.

Barcena, A., S. Manservisi and M. Pezzini (11 July 2017), "Development in transition", OECD Development Matters blog, https://oecd-development-matters.org/2017/07/11/development-in-transition/.

Centro de Estudios y Analisis del Delito (2018), "Tasa de Homicidio Cada 100 000 Habitantes" [Homicide Rate per 100 000 Inhabitants], Crime Statistics (database), Centre for the Study and Analysis of Crime, Chile www.cead.spd.gov.cl/estadisticas-delictuales/#descargarExcel (accessed 27 September 2018).

Chakravarty, S.R. (2003), "A generalized human development index", Review of Development Economics, Vol. 7/1, Wiley Online Library, pp. 99-114.

CONAPO (2018), Indicatores demograficos de Mexico 2017 (database) [Mexico's Demographic Indicators 2017], Mexico, https://www.gob.mx/conapo (accessed 24 September 2018).

CONEVAL (2016), "Cuadro resumen evolucion nacional y por entitad federativa" [Summary table of evolution at national and federative level], Medicion de la Pobreza 2008-2016, Mexico www.coneval.org.mx/Medicion/Paginas/Pobreza_2008-2016.aspx (accessed 26 September 2018).

Costanza, R. et al. (2009), "Beyond GDP: The need for new measures of progress", The Pardee Papers, No. 4, The Frederick S. Pardee Center for the Study of the Longer-Range Future, Boston University, www.bu.edu/pardee/files/documents/PP-004-GDP.pdf.

DANE (2018a), "Estimaciones Tasa de mortalidad infantil nacional, departamental y municipal, período 2005 -16" [Estimates of the infant mortality rate at national, departmental and municipal level, period 2005-16], Estadísticas vitales nacimientos y defunciones [Vital Statistics, Births and Deaths], (database), www.dane.gov.co/files/investigaciones/poblacion/vitales/Cert_TMI_Mpal_Deptal_WEB_2005_2016.xls (accessed 4 October 2018).

DANE (2018b), "Tabla 1. Indicadores de mercado laboral 2016-2017" [Table 1. Labour market indicators 2016-2017], Gran Encuesta Integrada de Hogares 2017 [General Integrated Household Survey 2017], www.dane.gov.co/files/investigaciones/boletines/ech/ml_depto/Boletin_dep_17.pdf (accessed 4 October 2018)

DANE (2018c), Sistema Estadistico Nacional [National Statistics System] (database), www.dane.gov.co/index.php/sistema-estadistico-nacional-sen (accessed 1 September 2018).

De Janvry, A. and E. Sadoulet (2014), "Sixty years of development economics: What have we learned for ecconomic development?", Revue d'économie de développement, Vol. 22, pp. 9- 19, https://www.cairn.info/revue-d-economie-du-developpement-2014-HS01-page-9.htm

EU (2017), "The new European consensus on development: Our world, our dignity, our future", Joint Statement by the Council and the Representatives of the Governments of the Member States meeting within the Council, the European Parliament and the European Commission, 9 June 2017, Brussels, https://ec.europa.eu/europeaid/sites/devco/files/european-consensus-on-development-final-20170626_en.pdf.

Gallup (2017), World Poll (database), www.gallup.com/services/170945/world-poll.aspx (accessed 1 May 2018).

Graham, C. (2005), "Insights on development from the economics of happiness", The World Bank Research Observer, Vol. 20/2, pp. 201-231, Oxford University Press, Oxford.

Hirschman, A. (1961), The Strategy of Economic Development, Yale University Press, New Haven.

Huntington, S.P. (1991), "Democracy's third wave", Journal of Democracy, Vol. 2/2, National Endowment for Democracy, Washington, DC, pp. 12-34.

IBGE (2017), "Indicadores de Desenvolvimento Sustentável, Tabela 3834 - Taxa de mortalidade infantile" [Sustainable Development Indicators, Table 3834 - Infant Mortality Rate], Summary of Social Indicators (database), https://sidra.ibge.gov.br/tabela/3834 (accessed 25 September 2018).

IBGE (2016), "Trabalho, Tabela 1.5" [Labour, Table 1.5], Summary of Social Indicators (database), www.ibge.gov.br/en/np-statistics/social/labor/18704-summary-of-social-indicators.html?=&t=resultados (accessed 20 September 2018).

IBGE (2015), "Educação, Tabela 3.12 - Taxa de escolarização das pessoas de 4 anos ou mais de idade" [Education, Table 3.12 - Enrolment Rate of People Aged 4 years or More], Pesquisa Nacional por Amostra de Domicílios 2014-2015, Summary of Social Indicators (database), www.ibge.gov.br/estatisticas-novoportal/sociais/educacao/19897-sintese-de-indicadores-pnad2.html?edicao=9129&t=resultados (accessed 21 September 2018).

IGARAPE (2018a), "Argentina", Homicide Monitor (database), https://homicide.igarape.org.br/ (accessed 27 September 2018).

IGARAPE (2018b), "Brazil", Homicide Monitor (database), https://homicide.igarape.org.br/ (accessed 27 September 2018).

IGARAPE (2018c), "Costa Rica", Homicide Monitor (database), https://homicide.igarape.org.br/ (accessed 27 September 2018).

INDEC (2018), "Mercado de trabajo. Tasas e indicadores socioeconómicos" [Labour market. Rates and Socioeconomic Indicators], Informes Técnicos Vol. 2/178, Ministry of Housing, Argentina www.indec.gob.ar/uploads/informesdeprensa/mercado_trabajo_eph_2trim18.pdf (accessed 21 September 2018).

INDEC (2017a), "Población de 6 a 11 y de 12 a 17 años de edad que asiste a un establecimiento educativo según provincia. Total del país. Años 2001 y 2010" [Population aged 6 to 11 years and 12 to 17 years that attends an educational establishment by province. Total of the Country. Years 2001 and 2010], Sistema Intregado de Estadísticas Sociodemográficas [Integrated System of Sociodemographic Statistics], Socio-demographic Indicators (database), www.indec.gob.ar/indicadores-sociodemograficos.asp#top (accessed 21 September 2018).

INDEC (2017b), "Tasa de mortalidad infantil por mil nacidos vivos, según provincia de residencia de la madre. Total del país. Años 1980-2014" [Infant Mortality Rate for a Thousand Live Births, by Province of Residence of the Mother. Total of the Country. Years 1980-2014], Sistema Intregado de Estadísticas Sociodemográficas [Integrated System of Sociodemographic Statistics], Socio-demographic Indicators (database), www.indec.gob.ar/indicadores-sociodemograficos.asp#top (accessed 21 September 2018).

INDEC (2017c), "Incidencia de la pobreza y la indigencia en 31 aglomerados urbanos" [Incidence of poverty and destitution in 31 urban conglomerates], Condiciones de vida. Vol. 2, nº 4, Argentina www.indec.gob.ar/uploads/informesdeprensa/eph_pobreza_02_17.pdf (accessed 21 September 2018).

INE (2017), "Tasa de Ocupacion, primero trimestre 2017" [Employment-to-Population Ratio, First Trimester 2017], Banco de datos de la Encuesta Nacional de Empleo [Databank of the National Survey on Employment] (database), http://bancodatosene.ine.cl/Default.aspx (accessed 19 September 2018).

INE (2015), "Tabulados vitales 2015" [Vital Statistics 2015] (database), www.ine.cl/estadisticas/demograficas-y-vitales (accessed 20 September 2018).

INEC (2017a), "Asistencia a educación regular y nivel educativo de la población según zona y región de planificación" [Enrolment in Formal Education and Educational Level of the Population by Area and Planning Region], Encuesta Nacional de Hogares julio 2017, Education (database), www.inec.go.cr/educacion (accessed 20 September 2018).

INEC (2017b), "Sinopsis de la condición de actividad de las regiones de planificación" [Synopsis of the Condition of Activity in the Planning Regions], Encuesta Continua de Empleo II Trimestre 2017 (database), www.inec.go.cr/documento/ece-iii-trimestre-2017-sinopsis-de-la-condicion-de-actividad-de-las-regiones-de (accessed 20 September 2018).

INEC (2017c), "Cuadro 16 Estimaciones de la variabilidad de las personas según región de planificación y nivel de pobreza" [Table 16. Estimations of the variability of the people by planning region and level of poverty], Encuesta Nacional de Hogares julio 2016 y julio 2017, www.inec.go.cr/pobreza-y-desigualdad/pobreza-por-linea-de-ingreso (accessed 20 September 2018).

INEC (2016), "Cuadros y gráficos del Boletín de mortalidad infantil y su evolución reciente" [Tables and Graphs of the Infant Mortality Bulletin and its Recent Evolution], Vital Statistics (database), www.inec.go.cr/estadisticas-vitales (accessed 20 September 2018).

INEGI (2017), "Tasa de Homicidios por cada 100 000 habitantes por entidad federativa según año de registro" [Homicide rate per 100 000 inhabitants per federative entity according to registration year], Comunicado de Prensa num. 298/17, Mexico www.inegi.org.mx/saladeprensa/boletines/2017/homicidios/homicidios201707.pdf (accessed 27 September 2018).

INEGI (2015), "Educacion" [Education], Encuesta Intercensal 2015 [Intercensal Survey 2015] (database), www.beta.inegi.org.mx/proyectos/enchogares/especiales/intercensal/ (accessed 24 September 2018).

INEI (2018a), "Cuadro N°III.1 Evolucion de la Incidencia de la Pobreza" [Table III.1 Evolution of the poverty rate], Evolucion de la Pobreza Monetaria, Encuesta Nacional de los Hogares [Evolution of Income Poverty, National Household Survey] www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1533/index.html (accessed 21 September 2018).

INEI (2018b), "Tasa de Homicidios segun Departamento 2017" [Homicide rate by department 2017], Sistema Integrado de Estadisticas de la Criminalidad y Seguridad Ciudadana (database), http://criminalidad.inei.gob.pe/panel/mapa (accessed 2 October 2018).

INEI (2015a), "Defunciones, Encuesta Demográfica y de Salud Familiar" [Deaths, Survey on Demographics and Family Health], Social Indicators (database), www.inei.gob.pe/estadisticas/indice-tematico/sociales/ (accessed 21 September 2018).

INEI (2015b), "Perú: Indicadores de Educación por Departamentos, 2004-2014" [Peru: Educational Indicators by Department, 2004-2014] (database), www.inei.gob.pe/media/MenuRecursivo/publicaciones_digitales/Est/Lib1293/index.html (accessed 21 September 2018).

Kuznets, S. (1962) "The sources of economic growth, Challenge, Vol. 10/7, Taylor & Francis Journals, April, pp. 44-46.

Ministerio de Desarrollo Social (2017), "Tabela N2.2 Personas en situacion de pobreza por ingresos segun region y pais" [Table 2.2 People in income poverty by region and country], Informe de Desarrollo Social 2017 [Report on Social Development 2017], Encuesta CASEN 2015, Ministry of Social Development, Chile http://www.ministeriodesarrollosocial.gob.cl/pdf/upload/IDS2017.pdf (accessed 26 September 2018).

Ministerio de Tecnologías de la Información y las Comunicaciones (2018), "Estadisticas en Educacion Basica por Departamento" [Statistics on Basic Education for Department], Datos Abiertos Gobierno Digital (database), Ministry of ICT, Colombia, www.datos.gov.co/Educaci-n/ESTADISTICAS-EN-EDUCACION-BASICA-POR-DEPARTAMENTO/ji8i-4anb/data (accessed 8 October 2018).

Ministerio de Trabajo y Promocion del Empleo (2015), "Grafico 3.6, Peru: Tasa de ocupacion por departamentos, 2014" [Graph 3.6, Peru: Employment-to-Population Ratio by Department], Informe Annual del Empleo en el Peru 2014 [Annual Report on Employment in Peru], Ministry of Labour and Employment Promotion, Peru www.trabajo.gob.pe/archivos/file/estadisticas/peel/enaho/INFORME_ANUAL_EMPLEO_ENAHO_2014.pdf (accessed 28 September 2018).

OECD (2018a), Income Distribution, OECD Social and Welfare Statistics (database), https://doi.org/10.1787/data-00654-en (accessed 1 May 2018).

OECD (2018b), Opportunities for All: A Framework for Policy Action on Inclusive Growth, OECD Publishing, Paris, https://doi.org/10.1787/9789264301665-en.

OECD (2018c), Global Revenue Statistics (database), www.oecd.org/tax/tax-policy/global-revenue-statistics-database.htm (accessed 1 September 2018).

OECD (2018d), Perspectives on Global Development 2019: Rethinking Development Strategies, OECD Publishing, Paris, https://doi.org/10.1787/persp_glob_dev-2019-en.

OECD (2018e), Regional Well-Being (database), https://stats.oecd.org/Index.aspx?DataSetCode=RWB (accessed 25 September 2018).

OECD (2017), How's Life? 2017: Measuring Well-being, OECD Publishing, Paris, https://doi.org/10.1787/how_life-2017-en.

OECD (2016a), OECD Regions at a Glance 2016, OECD Publishing, Paris, https://doi.org/10.1787/reg_glance-2016-en.

OECD (2016b), Education at a Glance 2016: OECD Indicators (database), OECD Publishing, Paris, https://doi.org/10.1787/eag-2016-en (accessed 25 May 2018).

OECD (2015), PISA Products (database), www.oecd.org/pisa/pisaproducts/ (accessed 1 September 2018).

OECD (2011), How's Life?: Measuring Well-being, OECD Publishing, Paris, https://doi.org/10.1787/9789264121164-en.

OECD/CAF/UN ECLAC (2016), Latin American Economic Outlook 2017: Youth, Skills and Entrepreneurship, OECD Publishing, Paris, https://doi.org/10.1787/leo-2017-en.

Prebisch, R. (1949), The Economic Development of Latin America and its Principal Problems, E/CN.12/89, United Nations, New York.

Ravallion, M. (2011), "Mashup indices of development", The World Bank Research Observer, Vol. 27/1, pp. 1-32, https://doi.org/10.1093/wbro/lkr009.

Rijpma, A. (2017), "What can't money buy? Well-being and GDP since 1820", CGEH Working Paper Series, No. 78, Centre for Global Economic History, University of Utrecht, The Netherlands, www.cgeh.nl/working-paper-series/.

RIMISP (2018), "3. Educación: Chile - Base Provincial", www.rimisp.org/contenido/date/ (accessed 17 October 2018).

Romer, P. (1990), "Endogenous Technological Change", Journal of Political Economy, Vol. 98, No. 5, Part 2, pp. S71-S102.

Seers, D. (1969), The Meaning of Development, IDS Communication 44, Institute of Development Studies, Brighton, United Kingdom.

Sen, A. (1999), Development as Freedom, Oxford University Press, Oxford.

Stiglitz, J.E., A. Sen and J.P. Fitoussi (2009), Report by the Commission on the Measurement of Economic Performance and Social Progress, www.stiglitz-sen-fitoussi.fr/documents/rapport_anglais.pdf.

UN (2015), Transforming Our World: The 2030 Agenda for Sustainable Development 2015, United Nations, https://sustainabledevelopment.un.org/post2015/transformingourworld.

UNDP (2017), International Human Development Indicators (database), http://hdr.undp.org/en/data (accessed 25 September 2018).

UNESCO (2018), UIS Data Centre (database), http://data.uis.unesco.org (accessed 25 September 2018).

UNODC (2018), Drugs and Crime Indicators (database), www.unodc.org (accessed 25 September 2018).

van Zanden, J. et al. (eds.) (2014), How Was Life?: Global Well-being since 1820, OECD Publishing, Paris, https://doi.org/10.1787/9789264214262-en.

World Bank (2018), World Bank World Development Indicators (database), http://data.worldbank.org/ (accessed 1 May 2018).

Notes

← 1. Regional GNIs were calculated based on a three-step calculation under the assumption that GDP and GNI follow the same regional distribution. First, the difference between the national GDP and GNI of each country is calculated. Second, each region's share of this difference is calculated based on its share of national GDP. Third, each region's share of the difference between national GDP and GNI is subtracted from its regional GDP.

← 2. Real wages, environmental quality, and income inequality were not included in the analysis as they did not appear to have significant association with the portion of well-being not explained by GDP per capita.

Metadata, Legal and Rights

This document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. Extracts from publications may be subject to additional disclaimers, which are set out in the complete version of the publication, available at the link provided.

https://doi.org/10.1787/g2g9ff18-en

© OECD/UNITED NATIONS/CAF/EU 2019

Chapter 2. Changing the lens: Development beyond income

# data[].attributes.attachment_link.description
OECD's dissemination platform for all published content - books, podcasts, serials and statistics

# data[].attributes.headline
India’s infrastructure boost creates jobs 

# data[].attributes.subject
India’s infrastructure boost creates jobs 

# data[].attributes.body
https://www.cnbc.com/2023/03/01/indias-infrastructure-boost-isnt-going-to-solve-its-job-problem-economists.html

# data[].attributes.headline
Unemployment rates for Black and Hispanic Women in US

# data[].attributes.subject
Unemployment rates for Black and Hispanic Women in US

# data[].attributes.body
https://www.cnbc.com/2023/03/10/unemployment-for-black-and-hispanic-women-rose-in-february.html

# data[].attributes.body
https://en.antaranews.com/news/184122/president-calls-for-price-ceiling-of-pcr-test-at-rp550000

# data[].attributes.attachment_link.title
President calls for price ceiling of PCR test at Rp550,000

# data[].attributes.attachment_link.description
President Joko Widodo has instructed the Health Ministry to set the price ceiling for polymerase chain reaction (PCR) test at Rp550 thousand. “I have…

# data[].attributes.headline
Japan Subsidise Chip and battery projects 

# data[].attributes.subject
Japan Subsidise Chip and battery projects 

# data[].attributes.body
https://www.cnbc.com/2023/04/28/japan-1point8-billion-in-subsidies-for-storage-battery-chip-projects.html?&qsearchterm=subsidies

# data[].attributes.body
https://profit.pakistantoday.com.pk/2023/03/28/pakistan-faces-dilemma-as-major-allies-join-hands-in-emerging-crude-bloc/

# data[].attributes.headline
UK bans cartoon on unhealthy food (framing)

# data[].attributes.subject
UK bans cartoon on unhealthy food (framing)

# data[].attributes.body
https://www.bbc.com/news/health-44298135

# data[].attributes.attachment_link.title
'Ban cartoon characters' on unhealthy food, MPs say

# data[].attributes.attachment_link.description
Tony the Tiger and the Milky Bar Kid should no longer be used to promote items to children, MPs say.

# data[].attributes.body
https://www.reuters.com/technology/indonesian-govt-agency-scrutinise-gojek-tokopedia-merger-avoid-tech-monopoly-2021-05-20/

# data[].attributes.attachment_link.readable_text
JAKARTA, May 20 (Reuters) - Indonesia's anti-trust agency (KPPU) on Thursday said it would scrutinise the multi-billion dollar merger of the country's startups Gojek and Tokopedia to check for potential monopolistic behaviour.

Ride-hailing and payments firm Gojek and e-commerce leader Tokopedia announced their merge earlier this week into a tech company GoTo in Indonesia's largest-ever deal. read more

The combined entity, which will span online shopping, courier services, ride-hailing, food delivery and other services in Southeast Asia's largest economy, will be the biggest privately held technology firm in the region.

Gojek and Tokopedia did not immediately respond to requests for comment.

The KPPU said neither company has given the agency notice about the merger, but they are allowed to notify authorities 30 days after the transaction.

The agency will then review the transaction "to focus on various relevant markets in the GoTo Group ecosystem, as well as the potential for monopolistic practices or unfair business competition that can arise after the transaction," it said, noting that a transaction in the digital market usually requires a complex network effect analysis.

The KPPU also urged other business owners and members of the public to report any violation of regulations on business competition after the formation of the GoTo group.

If the KPPU assessment found that the merger could result in unhealthy business competition, the company will be told to make adjustments to their operation to maintain fair competition, but it does not rule out mergers.

The day's top news in your inbox. We cover the world, from front lines to boardrooms. (This includes the Reuters Weekend Briefing.) Sign up here.

Reporting by Bernadette Christina Munthe; Writing by Gayatri Suroyo, Editing by Alexandra Hudson

Our Standards: The Thomson Reuters Trust Principles., opens new tab

# data[].attributes.attachment_link.title
Indonesian govt agency to scrutinise Gojek-Tokopedia merger to avoid tech monopoly

# data[].attributes.attachment_link.description
Indonesia's anti-trust agency (KPPU) on Thursday said it would scrutinise the multi-billion dollar merger of the country's startups Gojek and Tokopedia to check for potential monopolistic behaviour.

# data[].attributes.body
https://theconversation.com/a-little-nudge-goes-a-long-way-in-increasing-organ-donor-registrations-115051

# data[].attributes.attachment_link.readable_text
Each year, hundreds of Canadians die waiting for organ transplants. At the end of 2017, for example, 4,333 people were waiting for transplants; 242 of them died.

Many of these deaths could be prevented if people signed their organ donor registration cards. And even though the vast majority of Canadians support organ donation, less than 23 per cent have made plans to donate.

With one organ donor having the potential to benefit more than 75 people and save up to eight lives, is there a way to encourage more people to sign an organ donor card? Our research reveals there is.

Behavioural science tells us that there are some key psychological barriers that prevent people from becoming organ donors, including what's known as the status quo bias - meaning we tend to stick with the status quo, or current state of affairs, even when making a change would better align with our personal beliefs and goals.

Developing interventions to overcome these barriers has the potential to close the gap between the 81 per cent of Canadians who indicate they're willing to donate their organs after death and the mere 23 per cent who are registered.

In fact, during an eight-week trial conducted with the Ontario government at one Service Ontario centre, such interventions led to a dramatic increase in organ donor registrations.

Nudging people into action

Those who haven't given the idea much thought are sometimes caught off guard by the question of whether they want to donate their organs after death. And when people are asked to make a decision that they don't feel they've put adequate time and effort into considering, they choose not to decide. They put it off.

In many jurisdictions, the decision to donate organs happens most often when people renew their driver's licences or health cards. In Ontario, for example, 85 per cent of organ donor registrations occur at Service Ontario centres via a prompted-consent system. Customer service representatives ask if people are willing to register to be an organ donor.

To improve the process at Service Ontario centres and avoid surprising people with the question, a number of interventions were tested, from simplifying the organ donor registration form to providing the form earlier in the process and giving key information at optimal points in time.

Another change: At the top of each consent form, several statements in bold text were tested: "If you needed a transplant, would you have one?" And: "How would you feel if you or someone you loved needed a transplant and couldn't get one?"

Such "nudge" statements are designed to motivate people to take action while maintaining their freedom of choice. These nudge statements helped people imagine themselves in the position of someone needing a transplant.

The most successful changes from this Ontario pilot project led to a 143 per cent increase in organ donor registrations. If rates were to rise similarly across the province, the Ontario government estimates it could increase organ donor registrants by more than 450,000 a year - up from the current number of approximately 200,000.

The drawbacks of presumed consent

Additional methods of increasing organ donor registrations have also been investigated in some jurisdictions. Presumed consent is one example - a system whereby individuals are automatically registered as donors unless they explicitly opt out.

Read more: Organ donation: does presumed consent work?

In April, Nova Scotia became the first jurisdiction in North America to adopt presumed consent for organ donations.

While there's been some indication that countries with presumed consent have 60 per cent more of the population registered, there is also evidence that it may be ineffective. Chile saw a decrease in organ donations when it implemented the system, as did France and Brazil.

Part of the reason for the decline is that families have the last word. When you make the decision to become an organ donor, often your family is aware of your intent. But when the government assumes you want to donate your organs without your explicit consent, your family may overturn the decision after your death.

For these reasons it will be interesting to see the impact of Nova Scotia's decision.

Meanwhile in Ontario, many of the insights uncovered by the pilot project conducted with Ontario's Behavioural Insights Unit -including the nudge statements - are now used on Ontario's organ and tissue donor registration consent form, and data is being collected to determine how things have improved.

It's a great example of how the government is using evidence-based decision-making to better serve its citizens, and how research into consumer behaviour can benefit society.
Nicole Robitaille conducted this research with Nina Mazar, Claire Tsai and Elizabeth Hardy in partnership with the the Ontario Government's Behavioural Insights Unit and the Behavioural Economics in Action research centre at the Rotman School of Management (BEAR).

# data[].attributes.attachment_link.title
A little nudge goes a long way in increasing organ donor registrations

# data[].attributes.attachment_link.description
Most Canadians support organ donation after death, but fewer than 25 per cent have registered to donate their organs. What can be done to encourage more registrations?

# data[].attributes.headline
Command & control regulation and legislation

# data[].attributes.subject
Command & control regulation and legislation

# data[].attributes.body
https://www.cnbc.com/2023/03/29/with-chatgpt-hype-swirling-uk-government-urges-regulators-to-come-up-with-rules-for-ai.html?&qsearchterm=government%20regulation

# data[].attributes.body
https://www.forbes.com/sites/jonhartley/2016/05/31/indias-price-ceiling-on-uber-rides-hurts-riders-drivers-and-the-economy/?sh=4437db192e0e

# data[].attributes.attachment_link.readable_text
Recent regulatory blowback against Uber, Lyft and other ridesharing services around the world have reignited the ongoing regulatory policy debate surrounding the ride service in India, Uber's second biggest market in terms of cities where it operates. Most prominently, India's government recently enacted legislation that allows major cities to arbitrarily set limits how much ride service companies such as Uber can charge riders during peak times.

As surge pricing often results in fares above the new price ceilings set by the Indian government resulting in supply-demand problems, a potential shortage of rides problem now arises during times of peak demand. Meanwhile, opponents against surge pricing in India argue that the fare price ceiling helps to maintain competitive fares for current cab drivers.
Price ceilings on Uber fares will create shortages of available drivers, longer wait times and deadweight loss
If India really cared for its drivers and riders, it would remove the price ceiling for surge pricing during peak times in order for the supply-demand of rides to reach what economists call an equilibrium. There are examples throughout history that when a government sets these sort of price controls on goods, it interferes with the natural supply and demand equilibrium price and amount of those goods, creating shortages and surpluses. For example, long lines formed at gas stations during the 1970s when the U.S. government put a price ceiling on gasoline, stopping the price from going high enough to reach the equilibrium market clearing price between consumers and producers, creating a shortage. Consumers were incentivized to consume more of the good than they would have consumed under normal market conditions and producers were incentivized to produce less of the good because they couldn't sell at a price high enough to recoup costs and make profits.

India's current price ceiling and the predictable shortages for rides during peak times is bad for both the riders, drivers, society, and leads to a deadweight loss because of the misallocation of time, money, human capital and human potential.
New academic research highlights how surge pricing is necessary to prevent shortages of Uber rides
A new paper by researchers at the University of Chicago Booth School of Business the economics of Uber's surge pricing demonstrates that in the absence of surge pricing at peak times wait times for rides increased from the average of less than three minutes for those willing to pay the surge pricing, to over fifteen minutes across the board.

The graphs below illustrate Uber's surge pricing mechanism behind incentivizing more drivers to come out during peak times, and what happens when this mechanism fails, in order to have a constant and ready supply of drivers for riders willing to pay. Based on the first graph, we can see that Uber incentivizes its drivers with a price high enough for them to outnumber ride requests most of the time. This helps to make sure the supply of drivers is ready for riders at any given time and keeps the riders happy. In the second graph, the Booth School paper talked about how because of the surge price outage, the price of a ride couldn't go high enough to signal to Uber drivers to come out, thus resulting in longer wait times.

In the case of India, because the signal of the higher surge price isn't there under the price ceiling and because the drivers' opportunity costs of going out and driving around are higher than the money they hope to make, they aren't willing to come out to pick up those who value the ride more and who would be willing to pay higher than the price ceiling. This is money that the driver would otherwise be making if there were no price ceiling and it thus leads to less producer surplus because the driver isn't producing the ride. It also leads to a decrease in consumer surplus because those who want rides and are willing to pay the higher prices can't get rides. This decrease of consumer and producer surplus leads to deadweight loss, which is economically inefficient.
How India's Uber price ceiling could widen a mismatch of demand and supply for rides
To give an idea of the supply of and demand gap for rides in India, Mumbai has only has a third of the amount of taxis as New York despite having four times the population. Because India's price ceiling doesn't incentivize as many drivers to come out at peak times as would be needed for the market to clear, people are left hailing rides longer than necessary. This wastes time that they could be using to be more productive, used to build human capital, or put more of their time toward other projects. A recent Washington Post article hypothesized that the amount of time people waste while waiting in commutes in just the U.S. in one year alone could have been used to build hundreds of the pyramids of Giza. Imagine what India can do with all of that human potential being put to better use and all the private and public projects they can accomplish.

Furthermore, if someone is in a hurry or valued their time more by being somewhere else, say somewhere where they are more productive, and is wiling to pay to get there but can't get there because there aren't enough rides during peak times, this leads to a misallocation of human resources to their most preferred ends.

In 2015, India was the second most populated country in the world and was Uber's second biggest market. That is why repealing the price ceiling is so important for both India and for the ride-hailing industry there. In order for India's economy to move forward and compete on a global scale, they must aim for legislation that will allow resources like their citizens' time, money, human capital, and human potential to go toward to their most preferred and efficient uses in order to maximize their country's utility so they can progress.

# data[].attributes.attachment_link.title
India's Price Ceiling On Uber Rides Hurts Riders, Drivers And The Economy

# data[].attributes.attachment_link.description
Co-authored by Jon Hartley and Nawaphon Sittisawassakul Cellphone hailing Uber car (Photo credit: Andrew Caballero-Reynolds/Getty Images) Recent regulatory blowback against Uber, Lyft and other ridesharing services around the world have reignited the ongoing regulatory policy debate surrounding the ride service in India, Uber's second biggest market in terms of cities where it [...]

# data[].attributes.body
https://www.bbc.com/news/business-64792718

# data[].attributes.attachment_link.title
US attaches childcare strings to chip-maker subsidies

# data[].attributes.attachment_link.description
The White House has been frustrated in its efforts to expand national support for childcare.

# data[].attributes.body
https://www.cnbc.com/2022/07/25/indias-bold-gst-reform-expands-tax-base-but-too-soon-to-celebrate.html?&qsearchterm=indirect%20tax

# data[].attributes.body
https://www.cnbc.com/2022/12/29/gst-increase-singapore-to-raise-goods-and-services-tax-in-january.html?&qsearchterm=indirect%20tax

# data[].attributes.body
https://qz.com/how-scotland-cut-alcohol-related-deaths-by-13-in-less-1850247506

# data[].attributes.attachment_link.title
How Scotland cut alcohol-related deaths by 13% in less than three years

# data[].attributes.attachment_link.description
A study has found that minimum alcohol pricing likely saved 156 lives annually

# data[].attributes.body
https://www.cnbc.com/2023/01/01/these-states-will-raise-their-minimum-wages-in-2023.html?&qsearchterm=minimum%20price

# data[].attributes.body
https://www.washingtonpost.com/health/2022/08/08/insulin-price-cap-diabetes-senate-republicans/

# data[].attributes.attachment_link.readable_text
Despite support from seven Senate Republicans, an amendment to include a $35 insulin price cap in the Inflation Reduction Act failed to get 60 votes on Aug. 7. (Video: The Washington Post)

Share Comment Save
The Senate passed a sweeping budget package Sunday intended to bring financial relief to Americans, but not before Republican senators voted to strip a proposal that would have capped the price of insulin at $35 per month for many patients.

A proposal that limits the monthly cost of insulin to $35 for Medicare patients was left untouched. But using a parliamentary rule, GOP lawmakers were able to jettison the part of the proposal that would apply to privately insured patients.

How the Inflation Reduction Act might impact you - and change the U.S.

Seven Republicans joined Democrats in supporting the broader price cap, but that wasn't enough for passage. A number of Republican senators who voted for the proposal to be removed come from states with some of the highest mortality rates for diabetes, according to data from the Centers for Disease Control and Prevention, including Arkansas, Oklahoma and Tennessee.

Lowering the price of drugs such as insulin, which is used by diabetics to manage their blood sugar levels, is broadly popular with voters, polling shows. Senate Democrats denounced Republicans for voting against relief for Americans with private insurance struggling to pay for the lifesaving drug. Senate Majority Leader Charles E. Schumer (D-N.Y.) pledged on NPR Monday to bring the issue back in the fall "because there's going to be huge heat on Republicans."

More than 30 million Americans have diabetes, and about 7 million require insulin daily to manage their blood sugar levels.

Here's what we know about how Americans would be affected by the Senate vote:

Republicans block cap on insulin costs for millions of patients

What would the insulin price cap do?

The insulin price cap, part of a larger package of proposals to cut prescription drug and other health-care costs, was intended tolimit out-of-pocket monthly insulin costs to $35 for most Americans who use insulin.

More than 1 in 5 insulin users on private medical insurance pay more than $35 per month for the medicine, according to a recent analysis from the Kaiser Family Foundation. The same analysis found that the median monthly savings for those people would range from $19 to $27, depending on their type of insurance market.

The average Medicare patient using insulin paid $54 for prescriptions, according to KFF, an increase of nearly 40 percent since 2007.

With the Republican vote to strip the provision, only Medicare recipients would be eligible for the cap. The legislation still must pass the House.

Why is insulin so expensive?

Insulin was discovered in Canada in the 1920s, and the researchers, who won the Nobel Prize, sold their patent to the University of Toronto for $3. Since then, the drug has become a major commercial enterprise.

The global insulin market is dominated by U.S.-based Eli Lilly, the French company Sanofi and the Danish firm Novo Nordisk. A report released in December by Democrats on the House Oversight and Reform Committee accused the drugmakers of repeatedly raising their prices in lockstep and working to "maintain monopoly pricing," allegations the companies have denied.

In a statement, Novo Nordisk said the complexities of the U.S. health-care system influence the insulin market and that "many factors" determine what a person pays out of pocket for insulin. The company said net prices for its products have "continued to decline over the past 5 consecutive years." A Sanofi spokesperson said in a statement that "despite rhetoric about insulin prices," the net price of its insulin has fallen for seven straight years, "making our insulins significantly less expensive for insurance companies."

Eli Lilly did not immediately respond to requests for comment.

A generic insulin is slated to come on the market in 2024 and could help drive down prices.

Researchers also blame issues such as increasingly complicated supply chains for the dramatic rise in drug prices over the past decade. U.S. insulin prices are well above the average price paid in other developed countries, according to a government report.

A Yale University study found insulin is an "extreme financial burden" for more than 14 percent of Americans who use it. These people are spending more than 40 percent of their income after food and housing costs on the medicine.

What does this mean for uninsured patients and Medicaid recipients?

The legislation doesn't limit the cost of insulin for uninsured patients, despite last-minute lobbying from some House lawmakers to add in such protections. Uninsured Americans with diabetes are more likely to be using less costly formulations of insulin compared with those on private insurance or Medicaid, yet they have a higher tendency to pay full price for the lifesaving medication, according to a 2020 report from the Commonwealth Fund, a health-care think tank.

For those on Medicaid, many don't have co-pays for insulin, though some states may have modest amounts beneficiaries must pay, such as $2 for a standard prescription, according to Sherry Glied, dean of the Robert F. Wagner Graduate School of Public Service at New York University.

But in general, costs for those with diabetes can vary widely from person to person, except for those on Medicaid.

"There's no average person with diabetes, right, and so no two people are managing their diabetes in the exact same way," said Aaron Turner-Phifer, advocacy director for JDRF, an organization funding research into Type 1 diabetes. "Folks are taking different types of insulin, they're taking them via pens, they're taking them via pumps, some are using different devices. ... The amount of insulin that they're taking varies from person to person"

What are Republicans saying about the insulin price cap?

Many Republicans have opposed the $35 cap, saying the measure did not address the root problem of skyrocketing insulin prices. Instead, they said, it would force insurance companies to pass on the cost through premiums.

The cap would have also been a major win for Democrats ahead of the midterm elections in November, possibly feeding GOP opposition to the proposal.

Still, other Republicans decried what they have called "socialist" government interference in the free market. "Today it's the government fixing the price on insulin," said Rep. Cathy McMorris Rodgers (R-Wash.), the top Republican on the House Energy and Commerce Committee. "What's next, gas? Food?"

Did President Donald Trump lower the price of insulin?

In 2020, PresidentDonald Trump claimed that he had drastically lowered the price of insulin: "Insulin, it's going to - it was destroying families, destroying people. The cost," Trump said in a debate. "I'm getting it for so cheap it's like water." His statement drew criticism from patient advocates and people still struggling to afford their medication.

In 2020, drugmakers reduced the cost of insulin for some patients who lost jobs, health insurance or both as a result of the pandemic.

Trump signed an executive order to lower the price of insulin as one of his final health-care acts in office. The ruling was narrow, experts said, and would have lowered the cost of insulin for certain patients who go to certain federally qualified health centers.

It was rescinded by the Biden administration. Health officials said at the time that the rule would have imposed "excessive administrative costs and burdens" on health centers - and reduced resources for other health services.

Where have Democrats and Republicans stood historically on insulin prices?

Both Democrats and Republicans have blasted the high price of insulin, including in congressional hearings and in bipartisan investigations. But they've taken different approaches toward curbing the cost of the medicine.

Republicans have long proposed alternatives to Democrats' drug-pricing measures. In the House, key GOP lawmakers have released plans to place a monthly $50 cap on insulin and its supplies for those in Medicare's drug benefit after seniors hit their deductibles. In the Senate, top-ranking Republicans have crafted a bill to make permanent an existing temporary pilot project that gives those on Medicare the option to get a voluntary prescription drug plan where insulin costs $35 per month.

Meanwhile, a bipartisan pair of senators unveiled legislation in June aimed at tackling the cost of insulin, which was the result of months of work to forge a compromise. But the legislation hasn't come up for a vote and faces daunting political odds in its quest to obtain 10 Republican votes to pass the bill in the Senate.
Evan Halper, Bryan Pietsch and Tony Romm contributed to this report.

# data[].attributes.attachment_link.title
The GOP blocked an insulin price cap: What it means for diabetics

# data[].attributes.attachment_link.description
Another proposal in the Inflation Reduction Act that caps insulin at $35 per month for Medicare patients was left untouched.

# data[].attributes.body
https://theicct.org/asean-indonesia-evs-mar23/

# data[].attributes.attachment_link.provider_name
International Council on Clean Transportation

# data[].attributes.attachment_link.readable_text
In December 2022, Indonesia's Minister of Industry, Agus Gumiwang, announced the government's plan to provide purchase subsidies for electric cars, hybrid cars, and electric motorbikes, and to subsidize the cost of converting combustion-engine motorbikes to electric. Under the scheme, a subsidy of IDR 80,000,000 (~US$ 5,130) will be given for purchasing a new battery-electric vehicle and half that amount for purchasing a conventional hybrid. New electric motorbikes will receive a purchase subsidy of IDR 8,000,000 (~US$ 520), and the government will pay IDR 5,000,000 (~US$ 320) for converting an ICE two-wheeler to an electric two-wheeler.

Before announcing the subsidy, the Government of Indonesia studied similar policies in other countries to evaluate their effectiveness in spurring demand for electric vehicles. ICCT's studies of policies to accelerate EV adoption in global markets tend to support incentive plans. For example, China adopted a very structured monetary incentive program for passenger cars, buses, and trucks that linked the value of the incentive to battery chemistry and capacity. France, the Netherlands, and the U.S. also offer monetary incentives to EV customers.

The Indonesian government's main interest in offering these subsidies is developing domestic EV manufacturing and related supply chains. Electrification of the transport sector can also ease the cost burden of fuel subsidies. In addition, this transition to EVs can help ensure that national net-zero emissions can be achieved by 2060, the target date set by the government.

Transport decarbonization requires a comprehensive approach that facilitates access to battery electric technology not only for private owners but also for public transit operators and users. Fortunately, Indonesian President Joko Widodo recently announced subsidies for electric public transport vehicles that are produced domestically or meet minimum local content requirements. The magnitude of the incentives for battery electric bus purchases has not been made public.

These incentives are designed to close the purchase price gap between EVs and conventional vehicles. However, the cost of owning and operating a vehicle includes a wider range of factors that affect this value, including taxes.

We recently published a paper examining the impact of taxation on the consumer cost of EVs and internal combustion engine (ICE) vehicles in Indonesia. We show there that cost parity between two popular vehicles belonging to the same market segment, the Hyundai Kona EV and the Honda HR-V, cannot be achieved within six years of ownership with the existing taxation system in Indonesia. This is because the base price of the Hyundai Kona EV is higher. and incentives such as luxury tax and transfer tax exemptions alone cannot sufficiently reduce the upfront cost differential. The chart below illustrates this.

Even the generous direct incentives proposed by the government are insufficient to close the price gap between conventional and electric vehicles. The showroom price for a Hyundai Kona EV in Jakarta is ~US$ 51,000, while the price for the comparable Honda HR-V is ~US$ 26,500. The proposed subsidy of ~US$ 5,130 will lower the EV price by 10 percent but will only close about one-fifth of the purchase price gap.

Our analysis shows that to close the consumer cost of ownership gap between electric and conventional vehicles, additional actions are required on top of generous monetary incentives and the current tax reductions for EVs. A purchase subsidy to help lower the upfront cost of EVs could work better if paired with mandatory fuel efficiency or CO ₂ emission standards. Passenger vehicle fuel efficiency or CO ₂ emissions standards drive technology innovation and position the battery electric option as a viable solution to meet the standards.

Instead of direct purchase subsidies for EVs and hybrids, it could be even more effective for the government to introduce a feebate program and structure it based on fuel consumption or CO ₂ emission levels. In such a program, the least-efficient vehicles or those with the highest CO ₂ emissions (for example, SUVs) are taxed most heavily, and financial rebates are given for the most-efficient models, especially EVs. This type of program can be designed to be revenue neutral: highly polluting vehicles, which tend to be purchased by wealthy individuals, pay for the incentives to zero-emission and low-polluting vehicles. Because of the revenue-neutral nature of these programs, they can be run for many years, which addresses the challenge of direct monetary subsidy schemes.

Limiting the subsidy to vehicles below a certain price cap (e.g., US$ 50,000) and allocating more subsidies for electric two-wheeler and electric public transport would also improve the effectiveness in reducing carbon emissions from transportation.

Indonesia is the second-largest automotive producer in the Southeast Asian region, behind only Thailand, with 900,000 passenger vehicles produced in 2021. The plan to provide purchase subsidies for EVs and hybrids is ambitious and will likely contribute to the growth of EV production in the country. With its large nickel deposits, Indonesia could be an important player in the global EV supply chain if it nurtures its domestic EV industries.

The transition to electric vehicles can positively impact Indonesia's future carbon emissions and help it achieve its industrial development goals. The plan to subsidize electric vehicles for both private and public transit users aligns with global practices on EV policy but may not be able to deliver the best results without other policies targeting fuel efficiency or CO ₂ emission standards and feebates.

# data[].attributes.attachment_link.title
Will Indonesia's ambitious plan to subsidize EVs and hybrids benefit everyone? - International Council on Clean Transportation

# data[].attributes.attachment_link.description
In December 2022, Indonesia's Minister of Industry, Agus Gumiwang, announced the government's plan to provide purchase subsidies for electric cars, hybrid cars, and electric motorbikes, and to subsidize the cost of converting combustion-engine motorbikes to electric. What does this mean for consumers?

# data[].attributes.headline
Isle of Man ban sale of vape for those under 18

# data[].attributes.subject
Isle of Man ban sale of vape for those under 18

# data[].attributes.body
https://www.bbc.com/news/world-europe-isle-of-man-63558809

# data[].attributes.attachment_link.title
Isle of Man to ban sale of vapes to those aged under 18

# data[].attributes.attachment_link.description
The island has fallen "well behind" the UK in restricting vape use, the cabinet office minister says.