Meeting the Challenge of Data Disaggregation

12.3.2 Meeting the Challenge of Data Disaggregation

Many countries will be challenged to produce official statistics that allow reporting on development indicators with such high levels of disaggregation. This is particularly so when trying to use sample survey data to disaggregate by multiple characteristics. If there is a need for information on the situation of young women living in rural areas, this requires data disaggregated by age, sex and geography. If this population is not adequately represented in the sample size, the survey data will not

be able to produce reliable estimates of this particular population. Reviewing sample design and developing new methods such as linking between data sources and small area estimation techniques is likely necessary to address data gaps.

Administrative data is going to be a major source of statistics for reporting on the SDGs. A preliminary review of data availability conducted by DOSM revealed that of the most of the proposed SDG indicators are not available from existing sources. Of those 83 indicators currently available, more than half come from agencies other than DOSM. Importantly, of those not available, almost all are likely to

be sourced from other agencies. Although the data production method for these missing indicators is not necessarily clear yet, it is likely that administrative data sources will play an essential role. This will require major investment in coordination and changes to data collection and management methods. Challenges for Malaysia that have been highlighted by DOSM include the need for more socioeconomic

13 United Nations Economic and Social Council. 2013. Forty-fourth session of the Statistical Commission. Gender Statistics: Report of the Secretary-General (E/CN.3/2013/10).

14 OECD. 2015. Boosting Malaysia’s Intellectual Property System for Innovation.

and environmental indicators, to build on lessons learned from MDG reporting, and to explore

opportunities provided by big data and open data initiatives. 15

Number of 80 indicators

Available

Not available

DOSM

Other agencies

Figure 7 - Malaysia: Preliminary Review of Data Availability for 229 SDG Indicators by Source

Agency

Source: Department of Statistics Malaysia. 2016. Country Development Results Framework:

Data and Measurement. Presented by Dr. Mohd Uzir Mahidin at the National SDG Symposium: Operationalising the 2030 agenda for sustainable development, 23 February 2016, Putrajaya International Convention Centre.

12.4 CURRENT AVAILABILITY AND OPPORTUNITIES FOR PRODUCING DISAGGREGATED STI STATISTICS The structure and governance of Malaysia’s science, technology and innovation sector presents some

challenges in producing and using official statistics. There are numerous agencies and actors involved in the implementation of the NPSTI, each with their own systems to record and manage administrative records. Achieving coherent and consistent set of statistics in this environment is a challenge. Research institutes and industry are also key players in the innovation sector and a lack of coordination between

them currently exists that may hamper efforts to improve data quality. 16

The Malaysian Science, Technology and Innovation Indicators Report is published by MASTIC and provides detailed information on educational trends and achievements in the sector. It provides some sex-disaggregated data and gender analysis on students of science and technology courses, in particular:

15 Department of Statistics Malaysia. 2016. Country Development Results Framework: Data and Measurement. Presented by Dr. Mohd Uzir Mahidin at the National SDG Symposium: Operationalising the 2030 agenda for

sustainable development, 23 February 2016, Putrajaya International Convention Centre (http://www.my.undp.org/content/malaysia/en/home/presscenter/pressreleases/2016/02/24/national- readiness-to-achieve-the-global-sustainable-development-goals.html).

16 OECD. 2015. Boosting Malay sia’s Intellectual Property System for Innovation.

 Examination grade for science and mathematics at the SPM level (% female/male share of students at each grade level; participation is balanced with girls consistently outperforming boys between 2008-2012).

 Examination grade for science and mathematics at the STPM level (% female/male share of students at each grade level; students are 55% female and boys achieve higher grades between 2008-2012).

 Degrees awarded in science and technology courses from public higher educational institutions (from 2008-2012 there were more females receiving bachelor degrees, close to

equal share receiving master’s degrees, and more significantly more males receiving PhDs).  Degrees awarded in science and technology courses from private higher educational institutions (more males than females receiving bachelor’s, master’s and PhDs between 2008- 2012).

 Researcher headcount (total number and male/female share in %; in the context of significant increases in the size of the R&D workforce, women’s participation has jumped from around

one third of these positions in 2000 to almost half in 2009-2012). An assessment of Malaysia’s intellectual property system for innovation, published by the OECD in

2015, has highlighted poor data availability as a barrier to inclusive innovation. It recommends that the Intellectual Property Corporation of Malaysia (MyIPO), the central agency for intellectual property

matters, “…improve its annual reporting system and offer more detailed statistics about the types of applicants,

technological fields, etc.” 17 Although administrative data is a key data source for STI statistics as outlined above, population and

housing censuses and other household based surveys are also a potential source of data on participation in STI related occupations and industries. For example, DOSM conducts monthly Labour Force Surveys that reveal the size and characteristics of people in employment and unemployment. The annual number of selected households is around 100,000 households and it is representative of all individuals living in private households. Excluded from the survey are persons residing in institutions

such as hotels, hostels, hospitals, prisons, boarding houses and military barracks. 18 Due to the relatively large sample size, it may be possible to produce statistics on individuals working in the STI sector that are disaggregated by the characteristics needed to analyse social inclusiveness.

MOSTI manages data on recipients of grants from the Science Fund, TechnoFund and InnoFund and a summary of the projects are published on the data.gov.my website. Although the recipients of those grants are typically firms and research institutions, data are gathered on the individuals involved in the projects that receive a grant. It may also be possible to classify projects by their social impact and location. This would provide insights as to whether disparities exist in the types of projects that are receiving support, providing a useful basis for decision-making in the future.

For example, the Australian Bureau of Statistics produces annual statistics on business expenditure on research and development based on socioeconomic objective. This classifies funding into five broad areas based on the dominant beneficiary of the research: defense, economic development, society, environment and expanding knowledge. The category of economic development are further disaggregated by sector (e.g. manufacturing, information and communication technologies). Society is comprised of four sub-categories: health, education and training, law, politics and community services, and cultural understanding. Alternative groupings are applied to understand the reach to cultural

17 OECD. 2015. Boosting Malaysia’s Intellectual Property System for Innovation. 18 Department of Statistics Malaysia. Labour Force Survey Report Metadata.

groups relevant to Australian and New Zealand: aboriginal and Torres Strait islander outcomes, Maori outcom

es, and Pacific peoples’ outcomes. 19