title: Empirical analysis of Pacific, Māori and ethnic pay gaps in New Zealand

authors: Cochrane, William

abstract: A descriptive profile of the four ethnic groups, by gender, is provided to illustrate the heterogeneities across populations in terms of a number of variables grouped into the following categories: individual characteristics, educational attainment, region, household and job-related characteristics. The main empirical analysis involves estimation of the statistical contribution of observed factors (within each of the aforementioned categories) towards explaining the ethnic pay gaps. As this research is primarily aimed as an input towards the Pacific Pay Gap Inquiry being conducted by the Human Rights Commission, we focus our summary of the key findings on the Pacific – European pay gap.
This includes variables on permanent contract status, an indicator if in part-time work, occupation, and industry. These results are therefore likely an indication of the strong role of occupational segregation in the labour market. An additional factor that had a relatively strong contribution to the pay gap, particularly for the Pacific – European female gap, was educational attainment. Overall, even after accounting for differences in job-related characteristics and educational attainment, amongst a number of other observed factors, it was still found that only 27 percent of the pay gap for Pacific males (relative to Europeans) could be explained; and the corresponding proportion for females was 39 percent. The remaining portion of the Pacific Pay Gap is likely to reflect a range of causes that cannot be quantified or disentangled in this analysis – such as not including other factors of importance as they weren’t observed in the data (for example, literacy proficiency or field of study for qualification attained); ethnic differences in preferences for non-wage components of the job; unconscious bias; and discrimination.

text: Empirical analysis of Pacific, Māori and ethnic pay gaps in New Zealand A descriptive profile of the four ethnic groups, by gender, is provided to illustrate the heterogeneities across populations in terms of a number of variables grouped into the following categories: individual characteristics, educational attainment, region, household and job-related characteristics. The main empirical analysis involves estimation of the statistical contribution of observed factors (within each of the aforementioned categories) towards explaining the ethnic pay gaps. As this research is primarily aimed as an input towards the Pacific Pay Gap Inquiry being conducted by the Human Rights Commission, we focus our summary of the key findings on the Pacific – European pay gap.
This includes variables on permanent contract status, an indicator if in part-time work, occupation, and industry. These results are therefore likely an indication of the strong role of occupational segregation in the labour market. An additional factor that had a relatively strong contribution to the pay gap, particularly for the Pacific – European female gap, was educational attainment. Overall, even after accounting for differences in job-related characteristics and educational attainment, amongst a number of other observed factors, it was still found that only 27 percent of the pay gap for Pacific males (relative to Europeans) could be explained; and the corresponding proportion for females was 39 percent. The remaining portion of the Pacific Pay Gap is likely to reflect a range of causes that cannot be quantified or disentangled in this analysis – such as not including other factors of importance as they weren’t observed in the data (for example, literacy proficiency or field of study for qualification attained); ethnic differences in preferences for non-wage components of the job; unconscious bias; and discrimination.

year: 2022-07

title: Te tukuihotanga o te reo Māori: He aronga motuhake – ngā mokopuna a Te Kapunga

authors: Dewes, Te Kuru o te Marama

subjects: Reo Māori

abstract: Ko te tāhū o tēnei rangahau ko te reo Māori. Ko te ariā matua ko te tukuihotanga o te reo Māori, arā, ko te tuku iho o te reo ki rō whānau mai i ngā mātua ki ngā tamariki, ā, tae rawa iho ki ngā mokopuna. Kia whakawhāiti i te kaupapa, he aronga motuhake tēnei kei te titiro hāngai atu ki ngā whānau e whā ki ngā tāone e toru o Te Whanganui-a-Tara, Tāmakimakaurau me Te Rotorua-Nui-a-Kahumatamomoe. Nā te hurahura i ngā āhuatanga reo i roto i ngā kāenga o ngā whānau e whā i kitea mehemea kei te tutuki tēnei tūāhua te tukuihotanga o te reo Māori. Tuia, he aha ngā momo āhuatanga kei te taunaki i tēnei mahi, ā, he aha hoki ngā ārai e whakahōtaetae ana i a ia.  piti atu, i noho ki te ketuketu i ngā whakaaro o ngā mātua e pā ana ki tēnei kaupapa te tuku iho i te reo ki ngā tamariki mokopuna. Kāre i aro ki te kounga o te reo, he kaute noa i ngā whakamahinga reo Māori me whakamahinga reo Pākehā. Kia whakaritea ngā āhuatanga reo o ngā whānau me ngā tamariki, i tū ngā mahi whakarongo ki ngā wāhi e toru, arā, ki rō kāenga, ki rō kura, ka mutu, i tū ki wāhi kē atu hoki. Nā runga i te whakaaro kei te mōrearea tonu te reo Māori, kua herea tēnei kaupapa ki te ahurea me te tuakiri Māori, te mana motuhake me te tino rangatiratanga.

text: Te tukuihotanga o te reo Māori: He aronga motuhake – ngā mokopuna a Te Kapunga Ko te tāhū o tēnei rangahau ko te reo Māori. Ko te ariā matua ko te tukuihotanga o te reo Māori, arā, ko te tuku iho o te reo ki rō whānau mai i ngā mātua ki ngā tamariki, ā, tae rawa iho ki ngā mokopuna. Kia whakawhāiti i te kaupapa, he aronga motuhake tēnei kei te titiro hāngai atu ki ngā whānau e whā ki ngā tāone e toru o Te Whanganui-a-Tara, Tāmakimakaurau me Te Rotorua-Nui-a-Kahumatamomoe. Nā te hurahura i ngā āhuatanga reo i roto i ngā kāenga o ngā whānau e whā i kitea mehemea kei te tutuki tēnei tūāhua te tukuihotanga o te reo Māori. Tuia, he aha ngā momo āhuatanga kei te taunaki i tēnei mahi, ā, he aha hoki ngā ārai e whakahōtaetae ana i a ia.  piti atu, i noho ki te ketuketu i ngā whakaaro o ngā mātua e pā ana ki tēnei kaupapa te tuku iho i te reo ki ngā tamariki mokopuna. Kāre i aro ki te kounga o te reo, he kaute noa i ngā whakamahinga reo Māori me whakamahinga reo Pākehā. Kia whakaritea ngā āhuatanga reo o ngā whānau me ngā tamariki, i tū ngā mahi whakarongo ki ngā wāhi e toru, arā, ki rō kāenga, ki rō kura, ka mutu, i tū ki wāhi kē atu hoki. Nā runga i te whakaaro kei te mōrearea tonu te reo Māori, kua herea tēnei kaupapa ki te ahurea me te tuakiri Māori, te mana motuhake me te tino rangatiratanga.

year: 2017

title: Landsat remote sensing of chlorophyll a concentrations in central North Island lakes of New Zealand

authors: Allan, Mathew Grant

subjects: Landsat Enhanced Thematic Mapper

abstract: We investigated the use of Landsat Enhanced Thematic Mapper (ETM) imagery to synoptically quantify chlorophyll a (chl a) concentrations. Two adjoining pairs of images of the central North Island were acquired on two different days in summer and spring 2002. 6sv atmospheric correction was compared to the cosine of the solar zenith angle correction (COST) dark object subtraction (DOS) atmospheric correction. The highest correlation between 6sv ln(Band 3) water surface reflectance and ln(chl a) was found in the 24 January 2002 image (r² = 0.954). 6sv atmospheric correction was preferable to COST-DOS as it gave more realistic reflectance values at a clear-water reference site and produced the highest correlation coefficient. The results from this investigation suggest that remote sensing provides a valuable tool to assess temporal and spatial distributions of chl a in unmonitored areas within lakes and that predictions may also be extended to unmonitored lakes within the domain of satellite image capture.

text: Landsat remote sensing of chlorophyll a concentrations in central North Island lakes of New Zealand We investigated the use of Landsat Enhanced Thematic Mapper (ETM) imagery to synoptically quantify chlorophyll a (chl a) concentrations. Two adjoining pairs of images of the central North Island were acquired on two different days in summer and spring 2002. 6sv atmospheric correction was compared to the cosine of the solar zenith angle correction (COST) dark object subtraction (DOS) atmospheric correction. The highest correlation between 6sv ln(Band 3) water surface reflectance and ln(chl a) was found in the 24 January 2002 image (r² = 0.954). 6sv atmospheric correction was preferable to COST-DOS as it gave more realistic reflectance values at a clear-water reference site and produced the highest correlation coefficient. The results from this investigation suggest that remote sensing provides a valuable tool to assess temporal and spatial distributions of chl a in unmonitored areas within lakes and that predictions may also be extended to unmonitored lakes within the domain of satellite image capture.

year: 2011