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Background: Accurate information on age is an essential prerequisite for demographic and epidemiological studies. This study analyzed the age data quality among the literate and illiterate (combined, rural, urban) population and examined the association between data quality and literacy. Material and Method: Secondary data on age statistics and literacy were obtained from census 2011. We measured age data quality for literates and illiterates (combined, rural, urban) by transforming Whipple's index known as ABCC, abbreviated based on surnames of the developers A'Hearn, Baten, and Crayen (2009). Correlation tests between literacy and ABCC were performed. RStudio (Version 1.3.1073) was used. Result: Computed ABCC indices in majority states (union territories) for literates (data quality rough) were higher than illiterates (data quality very rough). Urban data among literates and rural data among illiterates were comparatively superior. Correlation between ABCC and literacy rates for (i) literate combined (R = 0.84, P = 3.5e-10), (ii) literate rural (R = 0.8, P = 1.1e-08), (iii) literate urban (R = 0.8, P = 1e-08), (iv) illiterate combined (R = 0.54, P = 9e-04), (v) illiterate rural (R = 0.48, P = 0.0034), and (vi) illiterate urban (R = 0.73, P = 6.4e-07) was significant. Age data quality for both literates and illiterates was poor. There was heaping at terminal digits “0” and “5” even among literates, which contradicts the theoretical expectation of quality data among literates. Conclusion: Correlations between data quality and literacy were significant, with comparatively lower magnitude among illiterates, which indicates the role of literacy in yielding quality data. Awareness, training, ADHAAR-based enumeration, and digitization may be suggested for better age data.

Background: Accurate information on age is an essential prerequisite for demographic and epidemiological studies. This study analyzed the age data quality among the literate and illiterate (combined, rural, urban) population and examined the association between data quality and literacy. Material and Method: Secondary data on age statistics and literacy were obtained from census 2011. We measured age data quality for literates and illiterates (combined, rural, urban) by transforming Whipple's index known as ABCC, abbreviated based on surnames of the developers A'Hearn, Baten, and Crayen (2009). Correlation tests between literacy and ABCC were performed. RStudio (Version 1.3.1073) was used. Result: Computed ABCC indices in majority states (union territories) for literates (data quality rough) were higher than illiterates (data quality very rough). Urban data among literates and rural data among illiterates were comparatively superior. Correlation between ABCC and literacy rates for (i) literate combined (R = 0.84, P = 3.5e-10), (ii) literate rural (R = 0.8, P = 1.1e-08), (iii) literate urban (R = 0.8, P = 1e-08), (iv) illiterate combined (R = 0.54, P = 9e-04), (v) illiterate rural (R = 0.48, P = 0.0034), and (vi) illiterate urban (R = 0.73, P = 6.4e-07) was significant. Age data quality for both literates and illiterates was poor. There was heaping at terminal digits "0" and "5" even among literates, which contradicts the theoretical expectation of quality data among literates. Conclusion: Correlations between data quality and literacy were significant, with comparatively lower magnitude among illiterates, which indicates the role of literacy in yielding quality data. Awareness, training, ADHAAR-based enumeration, and digitization may be suggested for better age data. [ABSTRACT FROM AUTHOR]

This article investigates the quality of the Romanian population data for the years 1966–2018. The following age heaping measures are being used, each with its recommended population age span: Whipple, Myers, Bachi, UNASA, Kannisto, Coale and Li. In addition, the Kannisto measure of old age overstatement is used. Our analysis corroborates the results of our measures with the demographic characteristic of the Romanian population. We aim to establish whether; (a) the data spanning the time interval 1966–1989 is more accurate than the data collected during the 1990–2018 interval, (b) the Romanian data is at least of comparable quality with the tier 2 HMD data and/or (c) the Romanian data is not grossly inaccurate and therefore suitable for research. We find support for (a) and, cautioning against the live data for the time interval 2002–2018, for (b) and (c). Limitations aside (no through overstatement or cohort analysis), this is the first study to attempt such evaluation and hopefully not the last.

This article investigates the quality of the Romanian population data for the years 1966–2018. The following age heaping measures are being used, each with its recommended population age span: Whipple, Myers, Bachi, UNASA, Kannisto, Coale and Li. In addition, the Kannisto measure of old age overstatement is used. Our analysis corroborates the results of our measures with the demographic characteristic of the Romanian population. We aim to establish whether; (a) the data spanning the time interval 1966–1989 is more accurate than the data collected during the 1990–2018 interval, (b) the Romanian data is at least of comparable quality with the tier 2 HMD data and/or (c) the Romanian data is not grossly inaccurate and therefore suitable for research. We find support for (a) and, cautioning against the live data for the time interval 2002–2018, for (b) and (c). Limitations aside (no through overstatement or cohort analysis), this is the first study to attempt such evaluation and hopefully not the last. [ABSTRACT FROM AUTHOR]

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This paper studies the evolution of numeracy in Chile for cohorts born from the 1780s to the 1970s, providing a new series of this important indicator of human capital, essential to promote economic growth. This is the longest series currently available of any human capital indicator for Chile. It shows that numeracy was very low until the early twentieth century but that, contrary to traditional interpretations, it increased gradually from the 1780s (well before the promulgation of the primary instruction law of 1860), until full basic numeracy skills were achieved by the mid-twentieth century. This transition was completed some 3–4 decades after parallel developments occurred in the leading countries of the region and some 120 years behind the most developed areas of Europe. This development was characterized by high gender numeracy inequality until the first decades of the twentieth century, as well as by a pronounced regional inequality. However, there was a quick process of convergence across provinces, completed at the same time as gender inequality was reduced. Our numeracy data is also consistent with alternative human capital indicators such as literacy and schooling, and we provide a set of explanations about why they all improved, and their timing. [ABSTRACT FROM AUTHOR]

This note is a rejoinder to Baten, Benati, and Ferber. We reiterate that, on close inspection, nineteenth‐century Italian census data contain a number of anomalies that sit uncomfortably with a straightforward age‐heaping‐as‐numeracy interpretation. In particular, we respond to Baten, Benati, and Ferber on the technical matters they have raised; then we show that our findings are robust to such criticism. Finally, we conclude with some general reflections on age heaping as a numeracy indicator. [ABSTRACT FROM AUTHOR]

Age misreporting is a common phenomenon in Demographic and Health Surveys, and there are numerous reasons for this. The trend and pattern of disparity in age heaping vary between countries. The present study assesses age heaping in the selected South Asian countries of Afghanistan, India, Nepal, Bangladesh and Pakistan using data from the most recent round of the Demographic and Health Survey. The respondent sample sizes were 203,703 for Afghanistan, 2,869,043 for India, 49,064 for Nepal, 81,618 for Bangladesh and 100,868 for Pakistan. Age heaping was assessed by respondent's age, education level, sex and level of education. Whipple's index was calculated to assess systematic heaping on certain ages as a result of digit preference. Bangladesh, Afghanistan and India showed stronger preference for ages ending with the digits '0' and '5' compared with Pakistan and Nepal among uneducated respondents. On the other hand, strong avoidance of ages ending in the digits '1', '4' and '9' was observed in Bangladesh, Afghanistan and India. However, urban–rural place of residence was not found to be associated with digit preference in the study countries. Among males, age misreporting with the final digits '0' and '5' was highest in Bangladesh, followed by Afghanistan and India, and Nepal showed the least displacement. Strong digit preference and avoidance, and upper age displacement, were witnessed in the surveys conducted in Bangladesh, Afghanistan and India on the parameters of sex and education level. Innovative methods of data collection with the measurement and minimization of errors using statistical techniques should be used to ensure accuracy of age data. [ABSTRACT FROM AUTHOR]