This chapter presents an overview of well-being outcomes in the four main urban agglomerations of the province of Córdoba (Argentina): Gran Córdoba, Río Cuarto-Las Higueras, Villa María-Villa Nueva and San Francisco. Using around 30 statistical indicators, the report analyses the performance of the Córdoba agglomerations in 12 well‑being dimensions in comparison with 391 TL2 regions (first administrative tier of subnational government) of 36 OECD countries and 98 TL2 regions of Brazil, Peru, Colombia and Costa Rica. Adopting a more local perspective, the report also presents the well-being inequalities between Córdoba’s four agglomerations.
How's Life in the Province of Córdoba, Argentina?
OECD Regional Development Studies
Chapter 2. Overview of regional well-being in Córdoba
Abstract
The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law.
Introduction: Indicators for measuring well-being
As a result of the first policy dialogue on regional development between the province of Córdoba and the OECD (OECD, 2016b), the General Secretariat of Government, through its Statistics and Censuses Directorate (Dirección General de Estadística y Censos, DGEyC), decided to use the OECD framework to measure well-being in the province’s four largest urban agglomerations (Gran Córdoba, Río Cuarto-Las Higueras, Villa María-Villa Nueva and San Francisco). The province of Córdoba asked the OECD to assist in modernising and, above all, strengthening the provincial statistical infrastructure “by developing a multidimensional well-being framework aligned with the OECD regional well-being framework and the Sustainable Development Goals, and by producing the indicators needed to assess well-being at the regional level”.
The OECD regional well-being framework consists of 12 well-being dimensions that are measured through different relevant indicators – mainly objective indicators (e.g., exposure to PM2.5), but also through subjective measures (e.g., self-reported life satisfaction) that contribute to a more complete understanding of well-being. The framework typically uses 13 baseline indicators for all OECD large regions, but it can be adapted to the territorial specificities of any country, region or city. For example, this framework was used to assess well-being in Mexico in 2015 at the regional level (OECD, 2015c), in Denmark in 2016 at the city level (OECD, 2016a), and most recently in the province of Córdoba in 2018 at the level of Agglomeration.
Jointly with the OECD, the General Secretariat of Government carried out a multi-stakeholder process to adapt the OECD regional well-being framework to the province’s needs. After several roundtables with different stakeholders (government, private sector, academia and civil society) held in the city of Córdoba (from the 13 to 17 of November, 2017), the DGEyC and the OECD agreed to measure well-being in the four agglomerations of Córdoba through 30 indicators that can be disaggregated by gender when relevant (Table 2.1). For that purpose, some methodologies had to be adjusted, statistical definitions adapted and new indicators had to be produced; for example, 2 of the 13 OECD baseline indicators were replaced (only for the purpose of this report) and 17 new indicators were added (see Chapter 1 for a detailed description of the OECD well‑being framework).
Table 2.1. Selected indicators for measuring well-being in Córdoba
* Available for most OECD regions and countries.
** Available only for OECD country averages (in this report).
Notes: Baseline indicators are in bold. With the exception of the indicator of Air pollution (that was estimated by the OECD), all the indicators of this table that refer to the Córdoba agglomerations were provided by the Directorate of Statistics and Censuses (DGEyC) of the Province of Córdoba.
The following section starts by providing a general overview of well-being in the agglomerations of Córdoba focusing mainly on 13 baseline indicators (see Table 2.1 and Figure 2.1), while the subsequent sections of this chapter also make use of the 17 complementary indicators to review in more detail the performance of Córdoba’s agglomerations in each of the OECD’s 12 well-being dimensions: income, housing, jobs, education, work-life balance, health, environment, personal security, civic engagement and governance, access to services, community and social support, and life satisfaction. All the indicators that come from the DGEyC’s Well‑being survey correspond to the second semester of 2018, with the exception of household gross income which for methodological reasons (the deflator was available only at an annual time scale) was estimated as an annual indicator of 2018. It is worth noting that a technical workshop between the OECD and the DGEyC took place from the 27 to 29 of June 2018 in the province of Córdoba to define and compute preliminary well-being indicators using the available microdata from the Well-being Survey. Finally, under the request of the DGEyC to ensure the quality of the indicators used for this report, the OECD also estimated the well-being indicators for the Córdoba agglomerations using the microdata of the Well-being Survey 2018 and verified that its results coincide to the indicators provided by the DGEyC.
General overview of well-being in the Córdoba agglomerations
Applying the OECD methodology to measure and compare well-being across regions and cities (Box 2.1), Figure 2.1 shows the results (normalised scores from 0 to 10, where 10 represents the best possible outcome) for each well-being dimension for the Córdoba agglomerations compared to 391 OECD (TL2) large regions (Box 2.2). The comparison is based on the 13 OECD classic indicators of the OECD Regional Well-being tool (www.oecdregionalwellbeing.org) – with the exception of household disposable income, even if this is the recommended OECD indicator, that was replaced with household gross income due to data availability in the Córdoba agglomerations, and the standardised mortality rate that was substituted for infant mortality rate due to the relevance of the latter indicator in the context of Córdoba since the adoption of the Millennium Development Goals (MDG) in 2000 (when Argentina set the objective of reducing infant mortality rate to 8.5 deaths per 1 000 live births by 2015). These 13 indicators are hereafter also referred as “baseline indicators” for the Córdoba agglomerations.
Figure 2.1. Performance of the Córdoba agglomerations by well-being dimension
Notes: This chart has been prepared using the indicators of household income, rooms per person, employment rate, unemployment rate, educational attainment of the labour force, life expectancy, infant mortality rate, air pollution, homicide rate, voter turnout, households with internet access, perceived social support network and life satisfaction. It uses data on the OECD’s 391 large (TL2) regions (first administrative tier of sub-national government). The average of Chilean and Mexican regions includes 15 large regions of Chile and 32 large regions of Mexico.
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en; OECD (2018a), OECD Environment Statistics (database), https://doi.org/10.1787/96171c76-en.
Córdoba agglomerations have one of the highest performances in the well-being dimensions of community and social support, and life satisfaction, ranking higher than 95% and 80%, respectively, of the 391 OECD regions studied. In the dimensions of civic engagement and governance, education, safety, jobs, access to services, and environment on the other hand, the results are similar to the OECD average (i.e., simple average of OECD large regions with available data), and – with the exception of jobs – higher than the average levels observed in most of the regions of Chile and Mexico. In the income dimension, there are still household income level gaps to be closed with respect to the OECD regions, given that the Córdoba agglomerations are among the 40% bottom regions in this dimension. Lastly, regarding housing and health the Córdoba agglomerations have one of the lowest performances compared to the OECD regions.
Although the OECD framework for measuring well-being serves to identify, in simplified terms, a country, city or region’s relative performance in certain dimensions that are crucial to people’s well-being, each dimension needs to be examined in more detail to identify more precisely the particular features that have generated that performance. In what follows, the results of the indicators used to measure the performance of the agglomerations of Córdoba (also referred as Córdoba agglomerations) in each well-being dimension are discussed in greater detail.
The general overview of the Córdoba agglomerations in the material dimensions of housing and income reveals a performance below the average of the OECD and below that of most large (TL2) regions. Nevertheless, when going beyond levels and looking at distributions, Córdoba is among the best-performing regions in income equality. The results in housing space (rooms per person), quality (dwellings without a private indoor toilet that is connected to sewer lines or to a septic tank)1 and affordability (housing expenditure) are adverse if compared with the levels observed in the OECD. Household income is also below the OECD average, although it is higher than that observed in all the regions of Mexico and Chile. On the other hand, if we examine the distribution of income in the Córdoba agglomerations, we find one of the best performances of all the OECD regions, and well above that of its Latin American peers. More specifically, the Córdoba agglomerations have achieved an exclusion rate based on income2 of 23.76% (top 15% of TL2 regions) and a Gini index of 0.37 (ninth best performance among the OECD regions).
The performance of the Córdoba agglomerations in the jobs dimension is fairly similar to the OECD average, but there still are important gender gaps. The employment and unemployment rates in the agglomerations of Córdoba are very close to the observed average for the TL2 regions in the OECD. The situation changes, however, if we explore the differences in labour market participation by gender group. The gender gaps in employment and unemployment are considerably greater in Córdoba than in most OECD regions. This inequality is even more relevant considering that in the education dimension the proportion of women with at least upper secondary education is considerably greater than that of men. In other words, although the female population has higher skills and abilities in terms of educational attainment than the male population, this relative advantage is not reflected in women’s integration in the labour market.
Although the Córdoba agglomerations have education levels slightly below the OECD average, their performance in this dimension is better than that of most Latin American countries and regions here studied. The level of educational attainment of adults in the agglomerations of Córdoba is higher than the country averages of Chile, Colombia, Brazil, Costa Rica and Mexico. Educational attainment of the labour force in Córdoba is also in the top 1% of the regions in these countries (only slightly below the Chilean region of Antofagasta). These positive results in education are largely attributable to having a female population that is relatively highly educated compared with Córdoba’s male population.
In terms of personal security, Córdoba presents homicide rates higher than the 75% of the OECD regions; nevertheless, compared to the regions of Mexico, Colombia, Peru and Chile, Córdoba agglomerations display a favourable performance (top 5% of the 105 regions in these countries for which data is available) – only behind two regions in Peru and one in Mexico.
Although air pollution has been steadily decreasing in the province of Córdoba, more efforts have to be done to reach the levels suggested by the World Health Organisation (WHO). Cordobans’ exposure to air pollution from PM2.5 particles has been decreasing over the last 22 years. From 1995 to 2017 the exposure to PM2.5 particles fell by around 20% to reach a level of 15.2 micrograms per cubic meter (μg/m3), which is still above the World Health Organization’s recommended limit of 10 μg/m3. The level of PM2.5 in the Córdoba agglomerations is slightly above the average for the OECD (13 μg/m3) and the value for Argentina (14.2 μg/m3) and it is the median value if compared to Argentina’s 23 provinces and the city of Buenos Aires. Analysing performance within the province, it can be observed that Córdoba’s 26 departments are above the WHO’s recommended levels, in particular the departments of Presidente Roque S. P. and Capital with the worst exposure to PM2.5 air pollution (of around the 16 μg/m3). Marcos Juarez, Sobremonte, Minas and Pocho are the departments with the lowest levels of exposure to PM2.5, with values around the 13 μg/m3.
As regards the indicators of community and social support and life satisfaction, the Córdoba agglomerations show very good results compared to the 391 OECD large regions. With around 97% of the population reporting that they have someone they can rely on in the event of difficulties and with an average life satisfaction of 7.5 (where 10 is the highest satisfaction), the agglomerations are in the top 5 and 15% of OECD regions in these two dimensions, respectively. Similarly, Cordobans’ perceived health status is also very high: with nearly 83% of people reporting good or very good health, the Córdoba agglomerations rank higher than 31 OECD countries (out of 36 countries). This result suggests a mixed performance for the agglomerations in the health dimension, as other measures of health such as infant mortality and life expectancy show lower outcomes for Córdoba when compared to OECD regions. Although infant mortality has been declining in Córdoba in the past 25 years reaching better outcomes than in 80% of the Chilean, Mexican and Peruvian regions, its levels are still within the bottom 20% of OECD regions.
Another dimension with contrasting results, depending on the indicator that is analysed, is that of civic engagement and governance. The Córdoba agglomerations show high levels of citizen participation through voting (which is mandatory), with a 78% voter turnout, well above the OECD average of 68% and outperforming 75% of OECD regions. However, the volunteering indicator reveals that only 12% of people aged 18 to 64 are involved in volunteering activities (e.g. charity work, NGOs, unions, cooperative schools, etc.). The Córdoba agglomerations thus rank below 19 OECD countries (out of 28 countries recorded) in this indicator.
Lastly, the Córdoba agglomerations are outperformed by most OECD regions in the dimensions of work-life balance and access to services. With around 16% of Cordoban employees reporting that they work 50 hours or more per week, the agglomerations’ performance is below that of 32 OECD countries (out of the 36 observed). On the other hand, while in the OECD regions (on average) almost 74% of households have internet access, the corresponding figure for the Córdoba agglomerations is 68%. This result places Córdoba among the 26% of TL2 regions with the lowest accessibility to internet.
Box 2.1. Calculation of scores for well-being dimensions
Well-being indicators are expressed in different units depending on their nature. For example, household gross income is expressed in USD PPA, whereas life expectancy and voter turnout are expressed in years and as a percentage of registered voters (on the electoral register), respectively. To compare well-being indicators on the same scale, the OECD normalises them using the min-max method. This statistical formula transforms the value of the indicator into a score from 0 to 10 (where 10 is the highest score possible for a normalised indicator).
To transform the value of an indicator into a well-being score (0-10) there are three concrete steps to be taken:
1. To identify, for each well-being indicator, the regional minimum and the regional maximum values (using a sample without extreme values).
2. To normalise the indicators by applying the min-max formula (see below).
3. To calculate the arithmetic mean of the normalised indicators within the same well‑being dimension.
Before applying these steps and with the purpose of reducing skewness, regions are ranked from the lowest to the highest value, for each indicator, and outliers are excluded from the normalisation process. More specifically, (for each indicator) the regions below the 4th percentile and above the 96th percentile are excluded from the application of the first two steps. In the case of the homicide rate – an indicator that is highly skewed by certain regions with extreme values – the regions below the 10th percentile and above the 90th percentile are excluded from the computations. These criteria generate more evenly distributed scores from 0 to 10. For example, if this cut-off was not applied for the homicide rate indicator, most regions would have scores between 9 and 10 in the personal security dimension, and only a few regions with extreme values would have a score of zero.
After this initial consideration, scores are calculated for the regions that were not excluded from the sample. Formula is used for indicators with a positive sense (e.g. employment, life satisfaction) and formula for indicators with a negative sense (e.g. unemployment, air pollution). After calculating these values, the regions with extreme values are factored back in and assigned the corresponding score of 0 or 10.
Finally, based on the third step, when a well-being dimension is measured by more than one indicator (e.g., jobs that is composed of employment and unemployment rates, or health that uses life expectancy and mortality rates), the score of the well-being dimension is defined by the arithmetic mean of the normalised indicators within the same dimension.
Source: OECD (2014), How’s Life in Your Region?: Measuring Regional and Local Well-being for Policy Making, https://doi.org/10.1787/9789264217416-en.
Box 2.2. How are the TL2 regions defined?
To increase international comparability, the OECD classifies regions on two territorial levels, reflecting the administrative organisation of countries. The large OECD regions (TL2) represent the first administrative tier of sub-national government, for example, provinces in Canada, comunidades autónomas in Spain, régions in France or states in the United States of America. The well-being indicators presented in this chapter have been developed for the 391 OECD large regions. Data on these large regions also provide information on inter-regional disparities in the various well-being dimensions, showing that in some cases disparities within countries are larger than across countries. Because the large administrative regions include local governments and many areas with different economic functions (e.g. cities and rural areas), the OECD has also established a common classification of “smaller regions”; these are subdivisions of the larger regions, and generally correspond to administrative units, with the exception of those in Australia, Canada, Germany and the United States. For these countries, the small regions refer to statistical or economic divisions established by countries and used for data collection. Relying on the criteria of population density, the share of people living in rural communities, the size of urban areas and the distance from urban centres, the OECD rural-urban typology classifies the small regions as “predominantly rural remote”, “predominantly rural close to a city”, “intermediate” and “predominantly urban” (Brezzi et al., 2011). Most OECD and non-OECD countries have a national definition of rural and urban regions whose criteria are the same as those used in the OECD rural-urban typology, although the thresholds chosen may differ.
Source: OECD (2015a), How’s Life? 2015: Measuring Well-being, http://dx.doi.org/10.1787/how_life-2015-en; Brezzi, M., L. Dijkstra and V. Ruiz (2011), “OECD Extended Regional Typology: The Economic Performance of Remote Rural Regions”, https://doi.org/10.1787/5kg6z83tw7f4-en.
Income
Although the OECD framework for measuring well-being sustains the idea that income (sometimes inaccurately proxied with GDP per capita and its growth) is not the only or the most important factor for measuring well-being, it recognises that monetary conditions can strongly contribute to people’s quality of life. Household income is typically crucial for meeting people’s material needs, such as food and housing. Income can also help people develop intellectually. For example, by investing in their education and health or in cultural activities. Income provides households with security, not only in material terms but also to make decisions about their lives and inter-personal relationships (e.g. starting a business or having children).
To cover the well-being dimension of income in the Córdoba agglomerations, the following indicators are considered:
Gross household income (before taxes and transfers) per unit of consumption (or equivalised) – baseline indicator.
Percentage of people with gross income below 60% of the median.
Gini index of gross income (0 for perfect equality, 1 for perfect inequality).
Ratio between average gross income of top and bottom quintiles.
Income levels in the Córdoba agglomerations are below the OECD average but above their Latin American peer regions. According to the definition of household income per unit of consumption (or household equivalised income) used for this analysis (Box 2.3), average annual household income in the Córdoba agglomerations for 2018 was of USD 12 756 PPP (at 2010 prices), equivalent to 80% of the OECD average, and lower than in 60% (191 out of 319) of OECD regions. In the Latin American context, however, the household equivalised income of the Córdoba agglomerations is above the levels displayed by all the regions of Mexico and Chile – although only slightly above the levels of Santiago and Antofagasta, the Latin American OECD regions with the highest values in this indicator (Figure 2.2).
Figure 2.2. Household income
Notes: “Córdoba agglomerations” corresponds to the weighted average of the four main urban agglomerations in the province of Córdoba, Argentina: Gran Córdoba, Río Cuarto-Las Higueras, Villa María-Villa Nueva and San Francisco (this note applies to all the figures in this chapter). The indicator of household gross income should be read with caution as it does not fully reflect the real available income of households after transfers and taxes; for this reason, when available, the OECD recommends the use of disposable income.
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database): http://dx.doi.org/10.1787/region-data-en.
To avoid limiting the analysis of income to a single indicator that shows average levels, the province of Córdoba has included in its well-being statistics the indicators of exclusion based on income, income inequality according to the Gini coefficient, income quintile share ratio (S80/S20) and income by gender. The distribution of income within a society may be as or more important for well-being than the average level of income. High levels of income inequality may have negative repercussions not only for a society’s economic growth (OECD, 2015b), but also for the sense of belonging (community dimension), cooperation between people, civic engagement and trust in institutions. Thus, it is very important to observe income differences between population groups (e.g. by gender, ethnic origin, nationality, etc.).
Box 2.3. Income measurement and income distribution
The indicators for measuring the income dimension are defined as follows:
Equivalised household gross income: this is the income reported by households before adding transfers and discounting taxes. This indicator is adjusted by the units of consumption in the household (“equivalised”). More precisely, household gross income is divided by the square root of the number of members of the household. It is stated at 2010 prices (using the implicit price deflator of household final consumption expenditure at national level) and in US dollars (USD) adjusted using purchasing power parities (PPPs) for actual individual consumption (using the World Bank 2010 PPP conversion factor). The OECD typically suggests the use of household disposable (after transfers and taxes) income to measure this well-being dimension. However, due to data availability for the Córdoba agglomerations and to maintain comparability with OECD regions, it was decided to use the household gross income from household surveys (contrary to using national accounts) for this analysis.
As part of its statistical agenda to improve the measurement of material conditions of people, the DGEyC should aim at estimating equivalised household disposable income in the Córdoba agglomerations. In the OECD, household disposable income is obtained by adding to people’s gross income (earnings, self-employment and capital income, as well as current monetary transfers received from other sectors) the social transfers in-kind that households receive from government (such as education and health care services), and then subtracting taxes on income and wealth as well as the social security contributions paid by households. This indicator, which is mainly drawn from the OECD national accounts, also takes into account the depreciation of capital goods consumed by households.
Exclusion based on income: this is a measure of inequality based on a headcount ratio of people below a relative poverty line (sometimes referred to as relative poverty, as opposed to absolute poverty). It is presented as the percentage of people who receive less than 60% of the median equivalised gross income of a given country or region.
Gini index (income inequality): this is a summary measure of income inequality. It is computed based on microdata (collected from household surveys) for equivalised gross household income. The Gini index, which is more sensitive to changes in the middle of the distribution, ranges from zero (where everyone has the same mean level of income) to one (where all the income goes to a single person). A change of one “gini point” means a change of 0.01 on this scale from 0 to 1 (OECD, 2015a).
Income quintile share ratio (S80/S20): this is a measure of income inequality that is more sensitive to changes in the extremes of the distribution; it refers to the share of all income received by the richest 20% of the population, divided by the share of all income received by the poorest 20%. It is computed based on measures of equivalised gross household income.
Sources: OECD (2018c), “Income distribution”, https://doi.org/10.1787/data-00654-en; OECD (2015a), How’s Life? 2015: Measuring Well-being, http://dx.doi.org/10.1787/how_life-2015-en; Piacentini, M. (2014), “Measuring Income Inequality and Poverty at the Regional Level in OECD Countries”, https://doi.org/10.1787/5jxzf5khtg9t-en.
Although their income levels are not particularly high, the Córdoba agglomerations have low levels of relative monetary exclusion. Whereas, on average, OECD regions display around one third of their population living with incomes below the relative exclusion line (60% of the median regional income), in the Córdoba agglomerations only 23.76% of the population is living below that line (Figure 2.3). The agglomerations perform better in this indicator than 87% (268 out of 307) of OECD regions. Moreover, if inequality is measured in terms of the Gini coefficient (where higher values represent greater inequality), the Córdoba agglomerations show very low levels of inequality. With a Gini coefficient of 0.37, 10 gini points below the OECD average of 0.47 (where one gini point represents one hundredth in terms of the Gini index), the Córdoba agglomerations have the ninth lowest level of observed inequality in all the OECD regions (only two regions in Australia and seven regions in Turkey perform better than Córdoba in this indicator). Comparing to Latin American regions, the positive performance of the Córdoba agglomerations is even more pronounced, as Córdoba’s Gini coefficient is 13 gini points below the average observed in Mexico and Chile (of around 0.5) (Figure 2.4).
Figure 2.3. Exclusion rate based on income
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
The differences in annual household income between agglomerations are relatively small (Table 2.2). Household income in San Francisco is only 9% higher than household income in Gran Córdoba, the agglomerations with the highest and lowest household income respectively. This difference is less than half of the gap in income generated by gender inequalities.
Figure 2.4. Income inequality
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
Table 2.2. Income in the Córdoba agglomerations
Source: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar.
In the Córdoba agglomerations, households headed by a man tend to have incomes 20% higher than households headed by a woman. This inequality is very similar across the four agglomerations (Table 2.2). For example, while in Villa María-Villa Nueva – the agglomeration with the smallest gap – households with a male breadwinner have 18% more income than those with a female breadwinner, in Gran Córdoba – the agglomeration with the largest gap – the gender gap rises up to 20.6%.
The measures of income distribution show consistent results across agglomerations. Whereas the indicators of relative exclusion rate, the Gini index and the quintile share ratio reveal that Gran Córdoba is the agglomeration with the highest challenges in terms of economic equality, Río Cuarto-Las Higueras is the agglomeration with the best performance in terms of income distribution based on these three indicators (Table 2.2).
Housing
Housing conditions can play a fundamental role in well-being. Housing not only provides shelter and personal security, but also a space for people to carry out their daily activities, including studying and meals, and build healthy family relationships. Housing can also increase a family’s financial security; the home often serves as guarantee for loans, thus facilitating investment and entrepreneurship.
To cover the well-being dimension of housing in the agglomerations of Córdoba, the following indicators are considered:
Average number of rooms per person (excluding bathroom, toilet, kitchen, utility room and garage) – baseline indicator.
Percentage of people without private access to an indoor flushing toilet connected to sewer lines or to a septic tank.
Percentage of total household gross income spent on rent (only households that rent).
Percentage of households that own their home.
Housing space in terms of rooms per person is below the levels observed in the OECD. Figure 2.5 shows that the average number of rooms per person in households in the Córdoba agglomerations is 1.36, below the OECD average of 1.8 and less than the outcomes observed in 76% (316 out of 413) of the regions with available data. The average number of rooms in the Córdoba agglomerations is very similar to the country average of Turkey and higher than in all the regions of Hungary, Israel, Estonia, Mexico, the Slovak Republic and Poland.
Housing quality in the agglomerations of Córdoba is lower than in other OECD countries, with the exception of Chile and Latvia. An indicator used by the OECD to cover this aspect is the percentage of the population who live in dwellings without private access to an indoor flushing toilet connected to sewer lines or to a septic tank (only available at national level for OECD countries). It is worth noting that, although practically all the households in the Córdoba agglomerations have a toilet (99.9%), not all of these toilets are indoors, or of private use, or connected to sewer lines or to a septic tank. According to Figure 2.6, the percentage of people who live in dwellings without an indoor flushing toilet is 9.2% in the Córdoba agglomerations, against 2.1% for the OECD as a whole.
Figure 2.5. Rooms per person, circa 2016
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
Figure 2.6. Dwellings without basic facilities, circa 2015
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2017), How’s Life? 2017: Measuring Well-being, https://doi.org/10.1787/how_life-2017-en.
In Córdoba, housing expenditure (rental expenditure only) accounts for around 30% of total household gross income, whereas in the OECD average region (where, besides rent, housing expenditure includes services) it accounts for around one-fifth of total household disposable income (since the comparability of the housing expenditure indicator between Córdoba agglomerations and the OECD is limited, the comparative results here discussed should be seen only as a first approximation). Housing expenditure in the Córdoba agglomerations is relatively higher than for 86% (159 out of 185) of the TL2 regions observed. Although the Córdoba agglomerations represent the urban part of the province, and the share of housing expenditure could be expected to be higher than in regions that include large rural areas (as is the case for many OECD TL2 regions), Figure 2.7 shows that even in regions that are mainly urban, such as Greater London, Vienna or Auckland, housing expenditure can be lower than one quarter of the total household disposable income.
Figure 2.7. Housing expenditure, circa 2015
Note: For the OECD regions, housing expenditure includes, besides rent, expenditure on electricity, gas and water; and it is expressed as a share of disposable income. For this reason, the comparability of this indicator between OECD regions and the Córdoba agglomerations is limited. In this graph, the value for Córdoba should be seen only as an initial approximation of total housing expenditure.
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
Major inequalities are observed between the Córdoba agglomerations, mainly in the indicator of dwellings without basic facilities, although differences are also observed in other housing indicators. Even if the availability of a toilet is practically universal across the four agglomerations of Córdoba, the quality of these toilets is not the same between agglomerations. Whereas in Río Cuarto-Las Higueras only 2% of people lives in dwellings without access to a private indoor flushing toilet connected to sewer lines or to a septic tank (similar to the OECD average), in San Francisco the equivalent figure goes up to 11.4% (mainly due to the existence of numerous dwellings that are not connected to sewer lines or to a septic tank) (Table 2.3). On the other hand, San Francisco is the best performing agglomeration in terms of housing space, home ownership and housing expenditure.
Table 2.3. Housing in the Córdoba agglomerations
Note: The indicator of households that rent their dwelling (%) was added to contextualise the Housing expenditure (%) indicator, which for Córdoba agglomerations corresponds only to expenditure in rent. However, it should be noted that the indicator of percent of households that rent their dwelling was not selected as one of the main indicators to measure well-being in the Córdoba agglomerations.
Source: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar.
Jobs
Having a well-paid job that satisfies personal interests and development goals is decisive in achieving high well-being outcomes. A good job not only provides a source of income to satisfy material needs such as food, housing and services but also fosters a person’s intellectual development, provides new skills and expands social support networks. In contrast, unemployment can have a very negative effect on the physical and mental health of the unemployed person and the people around him or her. Long-term unemployment tends to lead to a loss of working skills and abilities, creating further obstacles to return to employment, and representing a loss of individual well-being and human capital in society. A society that guarantees opportunities for all requires an inclusive labour market, with gender-equal labour force participation.
To cover the well-being dimension of jobs in the Córdoba agglomerations, the following indicators are used:
Number of employed people as a percentage of the population (aged 15 to 64) – baseline indicator.
Number of unemployed people as a percentage of the labour force (aged 15 to 64) – baseline indicator.
Percentage of the labour force unemployed for more than one year (aged 15 to 64).
Number of unemployed people as a percentage of the labour force (aged 15 to 24).
Workers without a retirement plan as a percentage of employed people (aged 15 to 64).
The employment rate in the Córdoba agglomerations is similar to the average of the OECD regions and Brazil. Figure 2.8 shows that the employment rate in the agglomerations of Córdoba is 61%, which is similar to the OECD average of 64% and higher than the employment rate of all the regions of Greece and Turkey. However, it is lower than that of 244 out of 386 OECD regions. In comparison with the countries of Latin America for which data is available, the employment rate in the Córdoba agglomerations is very similar to the country average of Mexico, Chile, and Brazil, and below the average for Peru and Colombia.
Figure 2.8. Employment rate
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
The labour market participation of women in the Córdoba agglomerations is lower than that of men. Figure 2.9 shows the difference in percentage points between the male and female employment rates. The gender gap in the employment rate in the Córdoba agglomerations is 23.5 percentage points, which is 10 percentage points above the OECD average and greater than in 83% (320 out of 386) of the observed OECD regions. Only some regions of Greece, Italy, Chile, Peru, Brazil and Colombia and most of the regions of Mexico and Turkey have a larger employment gender gap than the Córdoba agglomerations.
The unemployment rate in the Córdoba agglomerations is 8.4%, slightly above the OECD average, but lower than the rate observed in all the regions of Greece, Spain, Portugal and Latvia. The long-term unemployment rate in the Córdoba agglomerations (3.3%) is also very similar to the OECD average (3%). Even so, long-term unemployment in the Córdoba agglomerations is higher than in 71% of the OECD regions (Figure 2.10 and Figure 2.11).
Figure 2.9. Gender gap in the employment rate
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
Figure 2.10. Unemployment rate, circa 2016
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
Figure 2.11. Long-term unemployment rate, circa 2016
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
In the Córdoba agglomerations, unemployment affects certain population groups more severely for reasons of age and gender. Figure 2.12 explores the youth unemployment rate (age 15 to 24), while Figure 2.13 documents the gender gap in unemployment rate. Youth unemployment in the Córdoba agglomerations is around the 25%, a level above the OECD average (17.6%). Only 18% (68 out of 375) of the OECD regions have a higher youth unemployment rate than the Córdoba agglomerations. Youth unemployment in the Córdoba agglomerations is lower than in all the regions of Spain, Portugal, Greece and Italy (with the exception of four Italian regions), all of which are regions from countries that have been deeply affected by the euro zone crisis. Figure 2.13 shows the difference between the unemployment rates of women and men (higher values indicate a greater difficulty for women to join the labour market than for men). The gender gap in the unemployment rate is 3.6 percentage points for the agglomerations of Córdoba, above the OECD average of 1.7 percentage points. This gap is higher than that observed in all the regions of 33 countries (out of 39 available). Only in some regions of Brazil, Colombia, Spain, Italy, Greece and Turkey is the observed gender gap more severe than in the Córdoba agglomerations.
Figure 2.12. Youth unemployment rate
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
Although all the agglomerations need to improve labour market participation for women, in the Gran Córdoba the rigidities faced by women, relative to men, are the greatest (with an employment gender gap above the 24 percentage points). Table 2.4 shows that the gender gap in the employment rate is very high and close to 20 percentage points or more in all the agglomerations. The exception is San Francisco, where it is 16 percentage points, although still above the OECD average. Moreover, there are clear disparities between agglomerations in the unemployment gender gap. Whereas in San Francisco and Río Cuarto-Las Higueras the gap is close to zero, in Gran Córdoba the unemployment rate is 4.7 percentage points higher for women than for men.
Major disparities between agglomerations are also observed in youth unemployment and the informality rate (percentage of employees without a retirement scheme). For example, whereas in Villa María-Villa Nueva the youth unemployment rate is close to 11%, in Gran Córdoba it reaches 27%. On the other hand, Villa María-Villa Nueva is the agglomeration that performs worst in informality, with around 40% of its employees lacking any sort of contribution pension plan, 10 percentage points more than in Gran Córdoba, which is the agglomeration that performs best in this indicator.
Figure 2.13. Gender gap in unemployment rate
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
Table 2.4. Jobs in the Córdoba agglomerations
Source: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar.
Education
Education is not only crucial for people to find a well-paid job that helps them meet their material needs, but is also a means to developing the intellect and skills and individual needs to achieve their personal life goals. By providing opportunities for all from a very early age, education can boost people’s social mobility (i.e. help them move out of poverty) and reduce inequality. The OECD also uses the educational attainment of the labour force as an indicator providing information on the skills level of the labour force. A higher skilled labour force should be able to find a decent job more easily. Moreover, high levels of education among the same labour force should result in greater productivity and innovation in companies. Education also contributes to creating more democratic and functional societies; high levels of education are associated with citizens being more engaged and committed to society.
To cover the well-being dimension of education in the Córdoba agglomerations, the following indicators were selected:
Percentage of the labour force (aged 15 to 64) with at least upper secondary education – baseline indicator.
Percentage of the population (aged 25 to 64) with at least upper secondary education.
The performance of the Córdoba agglomerations with regards to education is below the OECD average, although it is noteworthy that there is a higher percentage of highly qualified women compared to the male population. With 73% of their adult population (25‑64 year-olds) with at least upper secondary education, the Córdoba agglomerations are below the OECD average (78%) but exceed the averages of Chile, Italy, Spain, Colombia, Brazil, Portugal, Costa Rica, Turkey and Mexico – all below 65% – and Argentina (59%) (Figure 2.14). Figure 2.15 shows that the gap between the educational attainment of women and men in the Córdoba agglomerations (of 5.1 percentage points) is, alongside Portugal, Estonia, Latvia, Ireland and Lithuania, among the highest in the OECD, and higher than in 33 of the 39 countries studied.
In the agglomerations of Córdoba, 75% of the labour force has achieved at least upper secondary education; the same level as the OECD average, but below the level displayed by 63% of the OECD regions (243 out of 386). Nevertheless, compared to the regions of Latin America, Córdoba lies within the top 1% of regions of Chile, Colombia, Brazil, Costa Rica and Mexico (only slightly below the Chilean region of Antofagasta) (Figure 2.16).
The intra-provincial differences in outcomes related to education (not broken down by gender) are relatively small. The largest difference between the agglomerations in educational attainment for the adult population is of around 6 percentage points (between Gran Córdoba and Río Cuarto-Las Higueras) and 6.5 percentage points when looking at the labour force’s educational attainment (between Gran Córdoba and Villa María-Villa Nueva). However, there are large disparities in the gender gap in educational attainment between the adult population and the labour force, and across agglomerations. While the educational attainment gender gap (difference between outcomes for women versus men) is around 5 percentage points for the adult population, this difference increases to 11 percentage points for the labour force. The education gender gap for the labour force varies between agglomerations; while San Francisco has a gender gap of 9 percentage points, Río Cuarto-Las Higueras has a gap of around 18 percentage points (Figure 2.17; Table 2.5).
Figure 2.14. Adult education
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018d), Education at a Glance 2018: OECD Indicators, https://doi.org/10.1787/eag-2018-en.
Figure 2.15. Gender gap in adult education
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018d), Education at a Glance 2018: OECD Indicators, https://doi.org/10.1787/eag-2018-en.
Figure 2.16. Education of labour force
Note: It should be considered that upper secondary education in Argentina is unlikely to be completed at 15 or 16 years old, which could generate a negative bias in the performance of Córdoba agglomerations in this indicator.
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
Figure 2.17. Intra-provincial differences in educational attainment of the labour force, 2018
Source: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar.
Table 2.5. Education in the Córdoba agglomerations
Source: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar.
Work-life balance
Having free time is crucial for people’s well-being; although this is often easier said than done, especially when one has a full-time job: since working is the activity that employed people spend the largest proportion of their time doing. While having a job is essential to meet certain material and personal needs, it is also important that individuals dedicate time to other activities that benefit their health and lives. Sport, cultural activities, family time, and having enough rest after work are fundamental to a healthy and well-balanced life. Achieving a good work-life balance is crucial not only for individuals, but also for the people around them; for example, the well-being of children may be heavily affected by the time their parents spend with them.
To measure the well-being dimension of work-life balance in the Córdoba agglomerations, the following indicators were selected:
Percentage of employed people (aged 15 to 64) whose usual hours of work per week are 50 hours or more.
Percentage of the employed population (aged 15 to 64) who travel to work in a municipality other than the municipality of residence.
Percentage of the employed population (aged 15 to 64) who take 30 minutes or more to get to their main place of employment.
Percentage of the employed population (aged 15 to 64) who use a vehicle or motorcycle to get to their main place of employment.
Percentage of the employed population (aged 15 to 64) who use urban or suburban public transport to get to their main place of employment.
To measure work-life balance, the DGEyC decided to include an indicator that captures the percentage of “Employees working very long hours”, which corresponds to workers aged 15 to 64 who have stated that they work 50 hours or more per week (see Box 2.4 for further details on the OECD definition). This indicator, which is still not available for the OECD’s TL2 regions (except for the states of Mexico; see OECD, 2015c), is compared with the country averages in Figure 2.18. The graph shows that around 16% of workers in Córdoba work 50 hours or more a week, around 4 percentage points above the OECD average. Compared to the Córdoba agglomerations, only Turkey, Mexico, Japan and Korea have a higher share of workers spending 50 hours or more a week at work. Figure 2.19 shows that the shares of employees working long hours are higher in all Mexican regions than those observed in Córdoba, with the exception of Jalisco, which performs similarly to the Córdoba agglomerations.
Box 2.4. Measuring work-life balance
The two work-life balance indicators used by the OECD are defined below:
Employees working very long hours: it corresponds to the employees working 50 hours or more per week as a percentage of total workers of the same age (for the agglomerations of Córdoba it was applied using the population aged 15 to 64). The limit is set at 50 hours because, factoring in commuting time, unpaid work and essential needs (such as sleeping and eating), it is likely that workers who regularly spend more than 50 hours a week at work have very few hours (one or two per week) for other activities. Furthermore, in countries with laws regulating maximum working hours, the limit tends to be at 48 hours per week. Figures are obtained from national labour force surveys and are generally comparable across countries.
Time devoted to leisure and personal care for a normal day and for people in full-time employment. For the sole purpose of improving comparability between countries with differing employment rates, the information is collected through national time-use surveys, which involves respondents keeping track of their activities over one or more normal days over a certain period. The activities considered in the definition of “time devoted to leisure and personal care” include: sleeping, eating, hygiene, exercising, spending time with family and friends, and travelling for leisure. These surveys can be difficult to compare for certain countries and some specific type of activity. The data used by the OECD come from the Harmonised European Time Use Survey: the Eurostat database on time use, microdata and charts from the time-use surveys published by the various national offices of statistics.
The two indicators here presented provide both direct and indirect measures of time available for non-work activities that improve personal and family well-being. Nevertheless, measuring work-life balance is a more difficult task. First, how people spend their time depends on their needs, personal preferences and cultural, social and family context. In other words, what one person deems to be “balanced” might not be the same for another individual People who run their own businesses may have an extra incentive to work longer hours each week, which is why they are excluded when calculating this indicator. However, this could affect the results if the self-employed make up a significant share of the total workforce. Second, as the indicators covered in this report only refer to the amount of time spent on different tasks, they do not provide insights into the quality of time spent outside work and therefore neither into each individual’s enjoyment of life. Third, the time-use surveys in most OECD countries are only conducted very specifically or infrequently (i.e., every 5 or 10 years), resulting in indicators that are generally outdated and thus irrelevant.
Source: OECD (2015a), How’s Life? 2015: Measuring Well-being, https://doi.org/10.1787/9789264240735-es.
Figure 2.18. Employees working very long hours in OECD countries
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018e), OECD Employment and Labour Market Statistics (database), http://dx.doi.org/10.1787/lfs-lfs-data-en.
Figure 2.19. Employees working long hours in Mexico’s regions
Note: While the indicator for the Córdoba agglomerations corresponds to the population aged 15 to 64, for the Regions of México it corresponds to the populations aged 14 to 98.
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; INEGI (2018), National Survey of Occupation and Employment (ENOE), https://www.inegi.org.mx/programas/enoe/15ymas/.
There are some differences in work-life balance between agglomerations. With the objective to deepen their understanding of how time and transport services are used in the agglomerations, the DGEyC introduced the indicators shown in Table 2.6 (along with the indicator of working long hours). While only 15.5% of workers in Gran Córdoba declares working long hours, this figure rises to 22% in Villa María-Villa Nueva. Almost 14% of Villa María-Villa Nueva’s population have to travel out of their municipality to work, while only 4.7% of Gran Córdoba’s inhabitants have to. That said, travel times in the latter agglomeration are longer. It can be seen that the percentage of people taking more than 30 minutes to reach their place of work and the percentage of people using public transport to get to work increase with the population size of the agglomeration. For instance, while 39% of Gran Córdoba’s inhabitants take over 30 minutes to get to their main place of work, only 4% of San Francisco’s inhabitants experience this (Figure 2.20).
Table 2.6. Work-life balance in the Córdoba agglomerations
Source: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar.
Figure 2.20. Intra-provincial differences in work-life balance
Source: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar.
Health
Having a long, healthy (i.e. illness-free) life is one the most valuable things for people. As well as its intrinsic value, physical and mental health is crucial to other aspects of well‑being such as employment, education and work-life balance. Poor health tends to lead to physical pain and negative emotions, which translate into lower levels of life satisfaction.
To cover the well-being dimension of health in the Córdoba agglomerations, the following indicators are considered:
Number of years a newborn can expect to live – baseline indicator.
Number of deaths of children younger than one year old per 1 000 live births – baseline indicator.
Percentage of the population (aged 18 or more) who report good or very good health.
The average infant mortality rate in the Córdoba agglomerations from 2014 to 2016 was of 8.1 infant deaths per 1 000 live births. This value is 60% higher than the average of OECD regions in 2015 (of 4.9 infant deaths per 1 000 live births). Furthermore, only 60 out of 391 OECD regions have a higher infant mortality rate than that registered for the Córdoba agglomerations (Figure 2.21). The Córdoba agglomerations’ performance in this indicator is better when compared to the Latin American regions for which data is available. The infant mortality rate in Córdoba agglomerations is lower than that displayed in all the regions of Peru and Mexico, and in two (out of fifteen) Chilean regions. It is worth noting that infant mortality rate in Córdoba has decreased from 21.4 infant deaths per 1 000 live births in 1990-1992 to 8.1 in 2014-2016.
Figure 2.21. Infant mortality rates, circa 2015
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
Figure 2.22. Life expectancy at birth
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
Life expectancy at birth in the agglomerations of Córdoba is 76 years, some 4 years shorter than the OECD average and below that of 85% (371 out of 444) of all TL2 regions shown in Figure 2.22. Córdoba agglomerations outperform 65% (68 out of 101) of the regions in Chile, Colombia, Mexico and Peru. The gender gap in life expectancy (difference between the life expectancy of women and of men) in the agglomerations is very similar to the OECD average (of around 5.5 years) and smaller than that of a third of the regions (112 out of 338) (Figure 2.23).
In contrast to the results for objective health indicators, the subjective indicator of perceived health shows that close to 83% of people over the age of 18 in the Córdoba agglomerations claim they are in good or very good health. This result positions the agglomerations above the OECD average (69%) and the average for 31 countries (out of 36 countries studied). Córdoba agglomerations’ average is only below that of Canada, New Zealand, the United States, Australia and Israel (Figure 2.24). The perceived health gender gap (difference between women’s and men’s perceived health) shows that men tend to feel they are in better health than women. The gender gap in perceived health in the agglomerations of Córdoba is of 6 percentage points, just two percentage points above the OECD average (Figure 2.25). While the contrast between outcomes from objective and subjective indicators of health might seem at odds, it should be noted that the literature has been documenting these type of mismatches in recent years (e.g., Johnston et al., 2007 or Mosca et al., 2013). At the same time, when working with self-reported subjective measures of well-being, it should be always considered that these indicators might be subject to biases due to cultural aspects (Exton et al., 2015).
Figure 2.23. Life expectancy at birth gender gap, circa 2016
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
Figure 2.24. Perceived health, circa 2015
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2017), How’s Life? 2017: Measuring Well-being, https://doi.org/10.1787/how_life-2017-en.
Figure 2.25. Gender gap in perceived health, circa 2015
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2017), How’s Life? 2017: Measuring Well-being, https://doi.org/10.1787/how_life-2017-en.
Figure 2.26 shows some differences in health indicators between agglomerations. While the infant mortality rate is 7.6 in San Francisco, it rises to 9.4 in Villa María-Villa Nueva. The difference between the agglomerations with the highest and lowest perceived health scores is 9 percentage points (between Río Cuarto-Las Higueras and Villa María-Villa Nueva, respectively). Although men tend to consider themselves healthier than women do, there are some variations across agglomerations. For example, the gender gap in perceived health in San Francisco is close to the 5.5 percentage points (more similar levels of perceived health between men and women), whereas this gender gap is significantly positive and close to 9 percentage points in Villa María-Villa Nueva (men clearly self-report better health than women do) (Table 2.7).
Figure 2.26. Intra-provincial differences in health, 2018
Source: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar.
Table 2.7. Health in the Córdoba agglomerations
Source: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar.
Access to services
Access to services not only determines how available essential services such as drinking water, healthcare and education (e.g. hospitals and schools) are, but also affects the relocation of people and therefore the state of the housing market, demand for transport and work, etc. While internet access is not considered to be a basic service, it is becoming increasingly important. For example, as a tool that can reduce the lack of opportunities in education and jobs through all the information and professional networks that makes available for users.
To cover the well-being dimension of access to services in the Córdoba agglomerations, the following indicator is considered: Percentage of households with internet access in the dwelling – baseline indicator.
Households’ access to the internet in the Córdoba agglomerations is moderate considering that it is the urban area of the province. Figure 2.27 shows that 68% of households in the agglomerations of Córdoba have internet access, 6 percentage points below the OECD average of 74%. Access to internet in the agglomerations of Córdoba is higher that the country average in Mexico, Chile, Brazil, Greece and Turkey, but below three quarters (288 out of 387) of the OECD regions.
Figure 2.27. Internet access
Note: While for the majority of OECD regions this indicator refers to broadband internet access, this restriction does not apply for the Córdoba agglomerations.
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
At an intra-provincial level, there are some differences between the four Córdoba agglomerations. While 71% of households in San Francisco have internet access, only 67% of households in Río Cuarto-Las Higueras benefit from this service (Table 2.8).
Table 2.8. Access to services in the Córdoba agglomerations
Source: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar.
Security
Personal security (safety) is key to having a decent quality of life. Violence and a lack of security have a major impact not only on a victim’s physical and psychological well-being, but also on the families around the victims and, more generally, in the cohesion of society. There may also be high levels of mistrust in institutions if the authorities are unable to guarantee security. Homicides rate is a common indicator of security; however, the OECD has also recommended other indicators that could help provide further insight into this dimension of well-being (Box 2.5).
Box 2.5. Measuring personal security
Deaths due to assault (homicides): This indicator looks at cases where assault is registered as the cause of death in the administrative death certificates. It is shown as an age-standardised rate per 100 000 inhabitants. Cause-of-death statistics are obtained from national registry office systems, gathered by national authorities and collated by the World Health Organisation (WHO). Only medically certified deaths are included.
To measure personal security, the province of Córdoba uses the indicator of number of homicides per 100 000 people; however, the OECD has also recommended other indicators that could provide further insight into this dimension of well-being:
Self-reported victimisation: this indicator reflects the percentage of people having responded “yes” to the following question included in the Gallup World Poll (sometimes also available in victimisation surveys): “within the past 12 months, have you been assaulted or mugged?”
Feeling safe when walking alone at night: this indicator shows the percentage of people having replied positively to the question: “Do you feel safe walking alone at night in the city or area where you live?” The source of these data for the OECD is Gallup Word Poll (but it can also be obtained from other perception surveys).
An ideal set of indicators of personal security would inform about the various crimes and offences experienced by individuals, and would weigh these crimes according to their seriousness. However, official crime records are not highly comparable across countries due to differences in what is defined (and counted) as a crime, and in both crime reporting and recording practices. The data shown here refer to deaths due to assault as recorded in country civil registration systems, rather than homicides as recorded by the police. A recent joint report by the National Institute of Statistics and Geography of Mexico (INEGI) and the United Nations Office on Drugs and Crime provides a roadmap to improve the availability and quality of crime statistics at national and international level (UNODC, 2013, see below).
Crime victimisation surveys are a critical tool for measuring people’s experience of crime, and while these do exist in some countries, they are not based on common standards and methodologies. Survey data can bring into focus the crime problems that affect people most often and can provide measures of changes in levels of crime over time. However, the available survey data provide only a proxy for the volume of illegal acts that occur in society. First, some crimes may be underestimated or overestimated due to respondents’ subjective interpretation of what constitutes a crime. Second, some people may be reluctant to disclose information for incidents of a sensitive nature, such as sexual assaults or inter-partner violence. Third, the accuracy of victimisation surveys is influenced by people’s ability to recall past crimes (the longer the elapsed period, the less likely it is that a victimisation will be recalled accurately). Finally, unconventional types of crime such as corruption may be difficult to capture through household surveys.
Risks to people’s personal security can come from sources other than crime. Transport and road-traffic accidents, work-related hazards and the risk of natural disaster are among the factors that can affect personal security. Violent conflict and war also have a profound impact on security by putting people’s lives and livelihoods in danger (OECD, 2015a).
Sources: OECD (2015a), How’s Life? 2015: Measuring Well-being, https://doi.org/10.1787/9789264240735-es; OECD (2011), “Personal security”, http://dx.doi.org/10.1787/9789264121164-13-en; UNODC (2013), Report of the National Institute of Statistics and Geography of Mexico and the United Nations Office on Drugs and Crime, http://unstats.un.org/unsd/statcom/doc13/2013-11-CrimeStats-E.pdf.
To cover the well-being dimension of security in the Córdoba agglomerations, this section uses the indicator of number of homicides per 100 000 inhabitants – baseline indicator.
The homicide rate in the Córdoba agglomerations is 3.56 per 100 000 inhabitants; this rate is very similar to the average in Germany, and below the country value in the United States, Peru, Chile and Mexico. However, it is higher than in 75% of OECD regions (296 out of 391). In a Latin American context, the homicide rate in the Córdoba agglomerations is one of the lowest compared to the regions of Mexico, Colombia, Peru and Chile – only 3 Latin American regions (out of 105) display lower homicide rates than the Córdoba agglomerations (Figure 2.28).
Homicide rates do not vary much across the four Córdoba agglomerations. The highest difference in this indicator is observed between Villa María-Villa Nueva with a homicide rate of 2.5 murders per every 100 000 people and Río Cuarto-Las Higueras with a homicide rate of 4.2 (only 11% higher than the average homicide rate of the OECD regions, of 3.8 homicides per 100 000 people) (Table 2.9).
Table 2.9. Security in the Córdoba agglomerations
Source: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar.
Figure 2.28. Homicide rate
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
Environment
A healthy environment is key for present well-being, but also to sustain well-being over time. In terms of individual well-being, it has been shown that the quality of the environment has a crucial bearing on people’s health and may therefore constitute a public health issue. What is more, a wide range of health risks are associated with exposure to poor air quality. Evidence shows that chronic exposure to particulate matter (PM) contributes to the risk of developing cardiovascular and respiratory diseases as well as lung cancer (OECD, 2014). Exposure to fine particulate matter (PM2.5), primarily from sources such as vehicle emissions, energy production, and the burning of agricultural biomass, also poses a threat to people’s health.
To cover the well-being dimension of environment in the province of Córdoba, the following indicator is considered: Annual exposure to fine particles PM2.5 (population-weighted exposure to PM2.5 concentrations in micrograms per cubic metre) – baseline indicator. This indicator comes from the OECD Environment Statistics database (OECD, 2018b), not from the DGEyC; and it covers the whole province of Córdoba. It is worth noting that the province of Córdoba, alongside universities and technicians, has made progress in consolidating “Standards for the Air of the Province of Córdoba”. In the same line, more efforts are taking place to measure air quality from the province; however, the employed methods are still at the testing stage.
It is estimated that in 2017, 325 deaths per 1 million inhabitants in the OECD were due to poor health because of exposure to ambient particulate matter (i.e., particulate matter 2.5 or PM2.5) (OECD, 2018a). In Argentina, this estimation goes up to 337 deaths per 1 million inhabitants, representing around 15 000 deaths per year due to air pollution-related illnesses. This number of deaths in Argentina stems partly from an average annual population exposure to PM2.5 of 14.2 micrograms per cubic metre (μg/m3), a value above the levels recommended by the World Health Organisation (WHO) – of 10 micrograms per cubic metre for average annual exposure (WHO, 2006). Similarly, the province of Córdoba is facing an average exposure to PM2.5 of 15.2 μg/m3 – above of both the average levels of Argentina and the limits established by the WHO (Figure 2.29) (for further information on the definition, see Box 2.6).
Figure 2.29. Air pollution
Note: Values for Iceland and Turkey correspond to the average of 2013-2015.
Source: OECD (2018a), OECD Environment Statistics (database), https://doi.org/10.1787/96171c76-en.
Box 2.6. Measuring environmental quality
Annual exposure to air pollution: Refers to the population-wide average exposure to fine particulate matter that is less than 2.5 microns in diameter (PM2.5). The major components of particulate matter are sulphate, nitrates, ammonia, sodium chloride, black carbon, mineral dust and water. These are potentially the most harmful to health, compared to other air pollutants. The data shown here are drawn from the OECD Environment Statistics Database and are calculated from satellite-based observations reported in van Donkelaar et al. (2016). Population exposure is calculated by taking the estimates of air pollution (data obtained from satellites, ground-based stations and a chemical transport model) at a given resolution (e.g. 1 km2), multiplied (weighted) by the population living in that area (for further information on the methodology, see Mackie, Haščič and Cárdenas Rodríguez, 2016).
However, even within a single urban area, personal exposure to air pollution varies substantially, depending on where people live and work and on their occupations, lifestyles and behaviours. This means that the average population exposure can mask substantial variations and inequalities. The young, elderly and people who are already ill are particularly vulnerable to the damaging health effects of air pollution (Brezzi and Sanchez-Serra, 2014).
The concept of “environmental quality” is broad and an ideal set of indicators would inform on a number of environmental media (soil, water, air), on people’s access to environmental services and amenities, as well as on the impact of environmental hazards on human health. Unfortunately, available data are scattered and not comparable across countries. The absence of objective data on water quality is a significant gap and the perception-based measure shown may suffer from comparability problems. Data on access to green space is another important omission that could potentially be addressed in the future through satellite-based data (OECD, 2015a).
Sources: Mackie, A., I. Haščič and M. Cárdenas Rodríguez (2016), “Population Exposure to Fine Particles: Methodology and Results for OECD and G20 Countries”, https://doi.org/10.1787/5jlsqs8g1t9r-en; van Donkelaar, A. et al. (2016), “Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors”, http://pubs.acs.org/doi/abs/10.1021/acs.est.5b05833; OECD (2015a), How’s Life? 2015: Measuring Well‑being, https://doi.org/10.1787/9789264240735-es; Brezzi, M. and D. Sanchez-Serra (2014), “Breathing the Same Air? Measuring Air Pollution in Cities and Regions”, http://dx.doi.org/10.1787/5jxrb7rkxf21-en.
For more technical details:
Shaddick, G. et al. (2017), “Data integration model for air quality: A hierarchical approach to the global estimation of exposures to ambient air pollution”, http://dx.doi.org/10.1111/rssc.12227; Shaddick, G. et al. (2017), “Data integration model for air quality: A hierarchical approach to the global estimation of exposures to ambient air pollution”, http://dx.doi.org/10.1111/rssc.12227.
According to Figure 2.30, exposure to PM2.5 fine particles in Córdoba province has decreased steadily over the last 22 years. Between 1995 and 2017, exposure to PM2.5 fine particles decreased by around 20%, from 19.2 to 15.2 micrograms per cubic metre (μg/m3) – a level that is still above the World Health Organisation’s recommended limit of 10 μg/m3. This level is slightly above the OECD average (13 μg/m3) and the average for Argentina (14.2 μg/m3), and is the median value of the 23 Argentinian provinces and the city of Buenos Aires (ranked 12 out of 24) (Figure 2.31).
Figure 2.30. Evolution of the Air pollution in the Province of Córdoba, 1995-2017
Source: OECD (2018a), OECD Environment Statistics (database), https://doi.org/10.1787/96171c76-en.
Figure 2.31. Air pollution in the Argentinian provinces, 2017
Sources: OECD (2018a), OECD Environment Statistics (database), https://doi.org/10.1787/96171c76-en.
Looking within Córdoba province, 26 of Córdoba’s departments are above the limits of exposure to PM2.5 fine particles recommended by the WHO – in particular the departments of Presidente Roque S. P. and Capital with the worst PM2.5 air pollution (around the 16 μg/m3). The departments of Marcos Juarez, Sobremonte, Minas and Pocho have the lowest levels of exposure to PM2.5, with levels around the 13 μg/m3. In general, Figure 2.32 shows lower levels of air pollution in the eastern and northern departments of the province, and higher levels of PM2.5 in the southern departments and in Capital.
Figure 2.32. Air pollution in Córdoba’s departments, 2017
Source: OECD (2018a), OECD Environment Statistics (database), https://doi.org/10.1787/96171c76-en.
Civic engagement and governance
In a well-functioning democracy, citizens have a role to play by supporting and participating in civic activities. Through civic engagement and participation, people can influence and shape governments’ decision and public policies that affect well-being. One of the most common indicators of civic engagement is voter turnout (the number of individuals who cast a ballot in a national election, as a percentage of the population registered to vote). As institutional features of voting systems vary widely across countries and by types of elections, the measures shown here refer to national elections (parliamentary or presidential), which attract the largest proportions of voters in each country. Voting is one of the traditional forms of civic engagement; however, there are other ways of supporting civic activities that, while less common, are important for a democratic society to function properly. One that the OECD, in collaboration with the INEGI and now the DGEyC, has started to explore is volunteering activities.
To cover the well-being dimension of civic engagement and governance in the agglomerations of Córdoba, the following indicators were selected:
Number of people who cast a ballot as a percentage of the population registered to vote (in the last national election) – baseline indicator.
Percentage of people (aged 18 to 64) who participated in NGOs, charities, or other volunteering activities in the last 12 months.
Voter turnout in Córdoba is one of the highest in the OECD (Figure 2.33). With almost 78% of the registered population participating in the national elections in 2015, voter turnout in the Córdoba agglomerations is higher than in 75% (298 out of 391) of the OECD regions and 9.5 percentage points above the OECD average.
Figure 2.33. Voter turnout
Note: It should be considered that in Córdoba agglomerations voting is mandatory, while in most OECD regions voting is voluntary.
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
Volunteering in the agglomerations of Córdoba is low, Figure 2.34 shows that only 12% of the population aged 18 to 64 has taken part in such activities (e.g., charities, NGOs, trade unions, school councils, etc.) in the last 12 months. The Córdoba agglomerations are ranked below 19 OECD countries (out of 28 registered countries). This level of engagement is below the country average for Chile (by almost 5 percentage points) but higher than the country average for Mexico (by around 6.5 percentage points). Figure 2.35 shows that volunteering (among those aged 18 or over) in the Córdoba agglomerations is more prevalent than in 31 of Mexico’s regions (out of 32), with just the Mexican State of Colima having a higher rate.
Figure 2.34. Volunteering in OECD countries
Note: The values for the OECD refer to volunteering at least once a month over the last 12 months, while the values for the Córdoba agglomerations are not restricted to monthly frequency, and refer to the population aged 18 to 64.
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2017), How’s Life? 2017: Measuring Well-being, https://doi.org/10.1787/how_life-2017-en.
Figure 2.35. Volunteering in Mexico’s regions
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; INEGI (2014), Self-reported Well‑being Module (BIARE), https://www.inegi.org.mx/investigacion/bienestar/ampliado/default.html.
Table 2.10 shows that there are practically no differences in voter turnout between agglomerations. This is not the case for volunteering, where Gran Córdoba displays volunteering levels of only 10%, and San Francisco and Villa María-Villa Nueva present rates of around 20% – slightly above the OECD average and very similar to the country average in Belgium, Finland and Australia. Lastly, looking at the differences in volunteering rates between the genders, women are more likely to volunteer than men, especially in Río Cuarto-Las Higueras, where the volunteering rate among women is of 24.5% – around 9 percentage points higher than volunteering among men (Figure 2.36).
Figure 2.36. Intra-provincial differences in volunteering, 2018
Source: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar.
Table 2.10. Civic engagement in the Córdoba agglomerations
Source: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar.
Community and social support
Positive social relationships are a powerful source of well-being for people. Loneliness and not being part of a community tend to create low subjective well-being, civic engagement and cooperation. In extreme cases, this could even include violence and criminality. Healthy social relationships not only prevent isolation, but also offer tools and support for personal development (emotional and material support when facing difficulties). In order to measure this dimension, the OECD uses an indicator looking at the percentage of people who claim they can rely on a friend or relative in case of need. That said, further indicators are required to analyse this dimension (Box 2.7).
Box 2.7. Measuring community and social support
The perceived social network support indicator is based on the question: “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?” The data shown here reflect the percentage of the sample responding “yes”. The source for these data is the Gallup World Poll (for further information, see Brezzi and Diaz Ramirez, 2016).
Social support can come from a variety of sources (e.g. a partner, a friend, a family member, a work colleague) and can take many different forms: emotional support; practical support (e.g. caring for dependants); financial support; and career- or work-related support, to name just a few. The measure presented here focuses on help in times of need, but does not provide any information about the quality or nature of the support provided (OECD, 2015a).
Ideally, a set of indicators of social connections would describe a range of different relationships – both in terms of quality and quantity. Some of the most common approaches to measuring social connections have relied on indirect indicators, such as statistics on membership in associations (e.g. sporting clubs or religious or professional organisations) or on the density of voluntary organisations in a given area. However, such measures have been criticised because they are limited to participation in formal networks and do not describe informal connections, such as those that people maintain with friends and relatives. Moreover, formal membership in associations and its importance for people’s well-being can differ over time and across countries, thus hampering international comparability. Time-use diaries could prove to be a useful source of information about time spent with others – both in terms of quantity, but also quality (OECD, 2015a).
Various official surveys collect information on social networks and personal relationships, e.g. the General Social Surveys in Australia, Canada and New Zealand. However, most official statistics on social connections are not internationally comparable (Scrivens and Smith, 2013).
Sources: Brezzi, M. and M. Diaz Ramirez (2016), “Building subjective well-being indicators at the subnational level: A preliminary assessment in OECD regions”, https://doi.org/10.1787/5jm2hhcjftvh-en; OECD (2015a), How’s Life? 2015: Measuring Well-being, https://doi.org/10.1787/9789264240735-es; Scrivens, K. and C. Smith (2013), “Four Interpretations of Social Capital: An Agenda for Measurement”, http://dx.doi.org/10.1787/5jzbcx010wmt-en.
To cover the community and social support well-being dimension in the Córdoba agglomerations, the following indicator has been estimated: percent of adults who have at least one friend they can rely on if needed – baseline indicator.
With 97% of the surveyed population responding they can turn to a friend or relative in case of need, the agglomerations rank in the top 5% of OECD regions and first in the Latin American regions in this well-being dimension. Only 13 regions in 9 countries outperform the Córdoba agglomerations in this indicator (Figure 2.37).
Figure 2.37. Social network support, 2006-14
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
The levels of social support are very similar for men and women within the Córdoba agglomerations. On the other hand, although the rates for social support for men are very high (above 94%) in all the agglomerations of Córdoba, there are some differences across agglomerations. While almost 99% of the male population (aged 18 or more) in Río Cuarto-Las Higueras declares to have someone to rely on when facing difficulties, only 94% of men in Villa María-Villa Nueva say they have this type of support (Table 2.11).
Table 2.11. Social support in the Córdoba agglomerations
Source: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar.
Life satisfaction
The life satisfaction indicator is a measure of subjective well-being focusing on people’s overall assessment of all the aspects and circumstances that constitute their lives. It is important that people can express how they assess their own lives; therefore, the OECD has developed a raft of subjective well-being measures, which include, in addition to the life satisfaction indicator, measures looking at a person’s feelings and emotions and their sense of purpose and worthwhileness in life (see Box 2.8; OECD 2013b; OECD, 2015a).
Box 2.8. Measuring subjective well-being
Life satisfaction: refers to the mean average score on an 11-point scale. It is based on survey questions that broadly follow the format recommended by the OECD Guidelines (OECD, 2013b): “Overall, how satisfied are you with life as a whole these days?” (Or a similar translation, for the Córdoba agglomerations the question in Spanish was: “En una escala del 0 al 10… ¿Cuán satisfecho está con su vida en general?”), with responses ranging from 0 (“not at all satisfied”) to 10 (“completely satisfied”). The source for these data for the OECD is the Gallup World Poll (for further information, see Brezzi and Diaz Ramirez, 2016)
Although this chapter only uses the life satisfaction indicator to measure subjective well-being, the OECD also recommends other indicators to better analyse this dimension:
Life feeling worthwhile: refers to the mean average score on an 11-point scale, ranging from 0 (not worthwhile at all) to 10 (completely worthwhile). It is based on the question: “Overall, to what extent do you feel that the things you do in your life are worthwhile?” The data shown here come from the EU-SILC (European Union Statistics on Income and Living Conditions) ad hoc module on well-being and are available for all EU countries.
Positive affect balance: defined as the proportion of the population who reported experiencing more positive than negative emotions yesterday. It is based on responses to six different questions formulated as: “Did you experience the following feelings during a lot of the day yesterday?” Answers are provided using a simple yes/no response format. Negative affect is measured by experiences of worry, anger and sadness, while positive affect is captured by experiences of enjoyment, feeling well-rested, and smiling or laughing a lot. An individual is considered to have a positive affect balance if the number of “yes” responses to the positive questions is greater than the number of “yes” responses to the negative questions. The data for this indicator come from the Gallup World Poll.
The OECD Guidelines on Measuring Subjective Well-Being (OECD, 2013b) provide international recommendations on collecting, reporting and analysing subjective well‑being data across the three major components of subjective well-being (life evaluations, eudaimonia and affect). The Guidelines give detailed consideration to methodological issues and survey design, and include a number of prototype question modules that national and international agencies can adopt if they wish to measure subjective well-being in their surveys.
Sources: OECD (2015a), How’s Life? 2015: Measuring Well-being, https://doi.org/10.1787/9789264240735-es; Brezzi, M. and M. Diaz Ramirez (2016), “Building subjective well-being indicators at the subnational level: A preliminary assessment in OECD regions”, https://doi.org/10.1787/5jm2hhcjftvh-en; OECD (2013b), OECD Guidelines on Measuring Subjective Well-being, https://doi.org/10.1787/9789264191655-en.
To measure the well-being dimension of life satisfaction in the Córdoba agglomerations, the DGEyC has calculated the following indicator: Average reported life satisfaction (respondents aged 18 or more) on a scale from 0 to 10 (where 0 stands for worst possible life and 10 represents the best possible life) – baseline indicator.
The average life satisfaction score in the agglomerations of Córdoba is 7.5 (on a scale of 0 to 10, where 10 is the highest level of satisfaction) – above the OECD average (6.7) and higher than 84% (324 out of 385) of the observed regions. This level is very similar to the average in Canada, Switzerland and the Netherlands; compared with Latin American countries, only ten regions in Mexico and Aysén in Chile have higher life satisfaction scores than the Córdoba agglomerations (Figure 2.38).
Life satisfaction does not vary significantly across the four agglomerations of the province of Córdoba. Subjective life satisfaction is the highest in Río Cuarto-Las Higueras and Villa María-Villa Nueva (7.8), and the lowest in Gran Córdoba (7.4). Lastly, Table 2.12 illustrates there are no major differences in self-reported life satisfaction between men and women.
Table 2.12. Life satisfaction in the Córdoba agglomerations
Source: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar.
Figure 2.38. Life satisfaction, 2006-14
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en.
Composite indicators for each well-being dimension
The importance of measuring well-being from a multi-dimensional perspective is widely acknowledged. Nevertheless, using a large number of indicators for each dimension may hamper interpreting and reporting the observed results. This leads to the need for composite indicators. The OECD uses the min-max method to transform each indicator’s value into a normalised score from 0 to 10 (where 10 represents the best outcome). Subsequently, the normalised score of a well-being dimension is calculated as the simple average of the indicators’ normalised scores within the same well-being dimension (see Box 2.1).
Figure 2.39 shows the aggregated results for each well-being dimension for the Córdoba agglomerations compared with the OECD’s 391 TL2 regions. Panel A is based on the 13 classic indicators in the OECD Regional well‑being tool (www.oecdregionalwellbeing.org) – with the exception of household disposable income that was replaced with household gross income due to data availability, and the standardised mortality rate that was substituted for infant mortality rate due to the relevance of the latter in the context of Córdoba (see Table 2.13).
Figure 2.39. Ranking of the Córdoba agglomerations by well-being dimension
Note: Panel A uses the 13 baseline indicators of this report, while Panel B includes the 7 extra indicators of exclusion rate based on income, Gini index of income, long-term unemployment rate, youth unemployment rate, gender gap in employment rate, gender gap in unemployment rate, and gender gap in life expectancy at birth.
Sources: DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar; OECD (2018b), OECD Regional Statistics (database), http://dx.doi.org/10.1787/region-data-en; OECD (2018a), OECD Environment Statistics (database), https://doi.org/10.1787/96171c76-en.
Panel A (Figure 2.39) shows that using the 13 baseline indicators the agglomerations of Córdoba perform very well in the dimensions of community and social support, and life satisfaction, ranked among the top 20% of 391 OECD regions. High scores are also achieved for civic engagement, while average outcomes are registered for income, education, environment and jobs. The rankings for access to services, safety and housing are not very good for the Córdoba agglomerations, they are just slightly above the bottom 20% of OECD regions. Lastly, and primarily due to a low life expectancy at birth and above the average infant mortality rates, relative to the OECD regions, the Córdoba agglomerations rank among the bottom 20% of the OECD regions in the health dimension.
As expected, the scores and rankings for each well-being dimension are sensitive to the indicators used for the analysis. It is therefore necessary to have a set of indicators that best capture what each well-being dimension is intended to assess in the context of the region or city where the framework is applied. The weight given to one indicator relative to another and aspects such as the distribution of an indicator between different population groups are also important elements to define.
Panel B of Figure 2.39 includes income distribution measures (income poverty and Gini coefficient), as well as gender gaps in jobs, unemployment and life expectancy (long-term unemployment and youth unemployment are also included, see Table 2.13). Panel B shows a different interpretation of performance of the jobs and income dimensions, but not for health. When measures of income distribution are included, the income dimension for Córdoba rises from the fourth best to the second best of the 11 dimensions analysed. This is due to the low income inequality of the Córdoba agglomerations, relative to the OECD large regions. In contrast, when the gaps between labour market access for women and men are factored in, Córdoba’s performance in the jobs dimension drops three places relative to the 11 well-being dimensions, from the position seventh to tenth.
Table 2.13. Indicators used for rankings
*Available for OECD regions and countries.
Note: Baseline indicators are in bold.
References
Brezzi, M. and M. Diaz Ramirez (2016), “Building subjective well-being indicators at the subnational level: A preliminary assessment in OECD regions”, OECD Regional Development Working Papers, No. 2016/03, OECD Publishing, Paris, https://doi.org/10.1787/5jm2hhcjftvh-en.
Brezzi, M. and D. Sanchez-Serra (2014), “Breathing the Same Air? Measuring Air Pollution in Cities and Regions”, OECD Regional Development Working Papers, No. 2014/11, OECD Publishing, Paris, http://dx.doi.org/10.1787/5jxrb7rkxf21-en.
Brezzi, M., L. Dijkstra and V. Ruiz (2011), “OECD Extended Regional Typology: The Economic Performance of Remote Rural Regions”, OECD Regional Development Working Papers, No. 2011/06, OECD Publishing, Paris, https://doi.org/10.1787/5kg6z83tw7f4-en.
DGEyC (2018), Well-being Survey, https://estadistica.cba.gov.ar.
Exton, C., C. Smith and D. Vandendriessche (2015), “Comparing Happiness across the World: Does Culture Matter?”, OECD Statistics Working Papers, No. 2015/04, OECD Publishing, Paris, https://doi.org/10.1787/5jrqppzd9bs2-en.
INEGI (2018), National Survey of Occupation and Employment (ENOE), https://www.inegi.org.mx/programas/enoe/15ymas/.
INEGI (2014), Self-reported Well-being Module (BIARE), https://www.inegi.org.mx/investigacion/bienestar/ampliado/default.html.
Johnston, D.W., C. Propper and M.A. Shields (2007), “Comparing subjective and objective measures of health: Evidence from hypertension for the income/health gradient”, IZA DP No. 2737, http://ftp.iza.org/dp2737.pdf.
Mackie, A., I. Haščič and M. Cárdenas Rodríguez (2016), “Population Exposure to Fine Particles: Methodology and Results for OECD and G20 Countries”, OECD Green Growth Papers, No. 2016/02, OECD Publishing, Paris, https://doi.org/10.1787/5jlsqs8g1t9r-en.
Mosca, I., B.N. Bhuachalla and R.A. Kenny (2013), “Explaining significant differences in subjective and objective measures of cardiovascular health: Evidence for the socioeconomic gradient in a population-based study”, BMC Cardiovascular Disorders, https://doi.org/10.1186/1471-2261-13-64.
OECD (2018a), OECD Environment Statistics (database), OECD, Paris, https://doi.org/10.1787/96171c76-en.
OECD (2018b), OECD Regional Statistics (database), OECD, Paris, http://dx.doi.org/10.1787/region-data-en.
OECD (2018c), “Income distribution”, OECD Social and Welfare Statistics (database), OECD, Paris, https://doi.org/10.1787/data-00654-en;
OECD (2018d), Education at a Glance 2018: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/eag-2018-en.
OECD (2018e), OECD Employment and Labour Market Statistics (database), OECD, Paris, http://dx.doi.org/10.1787/lfs-lfs-data-en.
OECD (2017), How’s Life? 2017: Measuring Well-being, OECD Publishing, Paris, https://doi.org/10.1787/how_life-2017-en.
OECD (2016a), Well-being in Danish Cities, OECD Publishing, Paris, https://doi.org/10.1787/9789264265240-en.
OECD (2016b), OECD Territorial Reviews: Córdoba, Argentina, OECD Territorial Reviews, OECD Publishing, Paris, https://doi.org/10.1787/9789264262201-en.
OECD (2015a), How’s Life? 2015: Measuring Well-being, OECD Publishing, Paris, https://doi.org/10.1787/how_life-2015-en.
OECD (2015b), In It Together: Why Less Inequality Benefits All, OECD Publishing, Paris, https://doi.org/10.1787/9789264235120-en.
OECD (2015c), Measuring Well-being in Mexican States, OECD Publishing, Paris, https://doi.org/10.1787/9789264246072-en.
OECD (2014), How’s Life in Your Region?: Measuring Regional and Local Well-being for Policy Making, OECD Publishing, Paris, https://doi.org/10.1787/9789264217416-en.
OECD (2013a), How’s Life? 2013: Measuring Well-being, OECD Publishing, Paris, https://doi.org/10.1787/9789264201392-en.
OECD (2013b), OECD Guidelines on Measuring Subjective Well-being, OECD Publishing, Paris, https://doi.org/10.1787/9789264191655-en.
OECD (2011), “Personal security”, in How’s Life?: Measuring Well-being, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264121164-13-en.
Piacentini, M. (2014), “Measuring Income Inequality and Poverty at the Regional Level in OECD Countries”, OECD Statistics Working Papers, No. 2014/03, OECD Publishing, Paris, https://doi.org/10.1787/5jxzf5khtg9t-en.
Scrivens, K. and C. Smith (2013), “Four Interpretations of Social Capital: An Agenda for Measurement”, OECD Statistics Working Papers, No. 2013/06, OECD Publishing, Paris, http://dx.doi.org/10.1787/5jzbcx010wmt-en.
Shaddick, G. et al. (2018), “Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment”, Environmental Science and Technology, http://dx.doi.org/10.1021/acs.est.8b02864.
Shaddick, G. et al. (2017), “Data integration model for air quality: A hierarchical approach to the global estimation of exposures to ambient air pollution”, Journal of Royal Statistical Society, Series C (Applied Statistics), http://dx.doi.org/10.1111/rssc.12227.
UNODC (2013), Report of the National Institute of Statistics and Geography of Mexico and the United Nations Office on Drugs and Crime, United Nations Office on Drugs and Crime, http://unstats.un.org/unsd/statcom/doc13/2013-11-CrimeStats-E.pdf.
Van Donkelaar, A. et al. (2016), “Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors”, Environmental Science and Technology, Vol. 50(7), http://pubs.acs.org/doi/abs/10.1021/acs.est.5b05833.
WHO (2006), World Health Organization: Air Quality Guidelines for Particulate Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide, Global Update 2005, World Health Organisation, www.who.int/phe/health_topics/outdoorair/outdoorair_aqg/en/.
Notes
← 1. Although practically all the households in the Córdoba agglomerations have a toilet (99.9%), not all of these toilets are indoors, or of private use, or connected to sewer lines or to a septic tank.
← 2. In the OECD databases this indicator is also known as relative poverty rate, but to avoid confusion with respect to the absolute poverty indicators produced by INDEC and the DGEyC, it has been decided that in this report this indicator should be referred to as “exclusion rate based on income”.