Chapter 4. A scorecard to track sustainable development in the North
This chapter proposes a series of indicators to assess the achievement of regional goals, as discussed in the previous chapters. The scorecard tracks the past performance of these indicators and sets targets for 2037. Where data are not available, it suggests information that national and local governments could collect to make the scorecard effective. The scorecard also maps each regional objective in the Action Plan against the Sustainable Development Goals and targets, ensuring coherence between policies adopted by the North with international standards of development.
The scorecard proposes indicators for measuring implementation and performance of the action plan until 2037
Scorecards with measurable indicators are used to monitor the implementation of policies and the achievement of desired results. The following scorecard proposes indicators to accompany the action plans presented in previous chapters. It can be used for monitoring, decision making and ensuring accountability towards citizens, and should be regularly updated and made publicly accessible.
Each indicator is designed to provide a snapshot of the status quo and to establish a target for 2037. The scorecard presents the following values for all indicators:
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The level attained by Thailand at the launch of the National Strategy 2037 and the 12th National Economic and Social Development Plan (NESDP) (2017 or latest available year).
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The level reached during the five years prior to the launch of the NESDP.
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The objectives to be attained by 2037. Box 4.1 provides details about the computation of regional objectives.
Regional objectives for 2037 are computed with respect to the past performance of regional champions. For example, the target for productivity of the agricultural sector is computed according to the following three steps:
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1. The province with the highest value added in agriculture in 2016 is identified (Nakhon Sawan)
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2. The average annual growth rate of agricultural value added in Nakhon Sawan between 2012 and 2016 is computed.
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3. The regional target is computed by assuming that between 2016 and 2037 the average regional agricultural value added is growing at the same rate of the best performing province as computed in the second step.
Note that this computation is possible only when data about provincial performance with respect to the indicators of the scorecard is available.
To help with the preparation of a multi-dimensional reporting system, the scorecard also maps each item in the Action Plan to the relevant Sustainable Development Goal.
Northern provinces develop capabilities to exploit their full potential
An effective strategy and strong LAOs drive the development of the North of Thailand
Create a strategy for developing the Northern Region, building on local discovery (Expected result 1)
The development of the North relies on the transformation of the structure of the local economy. Northern provinces need to reallocate resources to diversify their economies while specialising into sectors and products for which they could develop (or already have) comparative advantages. On the one hand, diversification is necessary to maximise productivity gains and minimise the risk of potentially adverse external shocks (such as trade wars or natural disasters). On the other hand, “smart” specialisation allows local economic actors to express their full potential, given the existing natural, economic and institutional endowments. The fine equilibrium between diversification and specialisation policies can lead to significant productivity gains.
The scorecard contains measures of sectoral productivity, diversification and specialisation to monitor structural reforms (Table 4.1). Higher productive gains in the agriculture and manufacturing sector (as measured by the value added per worker in each sector) would be a sign of successful structural transformation. The Herfindahl-Hirschmann index is a good measure of the average diversification of the local economy (Chapter 2). The number of sectors in which provinces could develop a comparative advantage can be an indicator of “smart specialisation”.
The North could improve sectoral productivity by following in the footsteps of regional best performers. For example, the productivity of the agricultural sector in the best performing province – Nakhon Sawan – grew by 4.9% each year between 2012 and 2016. If this rate is maintained, average regional productivity could triple by 2037. Similarly, manufacturing productivity in the most productive manufacturing province – Kam Phaeng Phet – has increased by 7% each year between 2012 and 2016. If the North converged to this rate, regional manufacturing could become four times more productive. The average Northern province could increase its tourism revenue per number of visitors by benchmarking policies to the experience of the regional champion in the field – Chiang Mai.
Diversification of economic activity is an important correlate of development at middle-income levels of GDP. Northern provincial economies should thus keep on diversifying. The Herfindahl-Hirschmann index measures economic concentration (higher values) and diversification (lower values). Between 2012 and 2016, this index has been decreasing because economic activity is spread more equally across sectors. If the Northern provinces were diversifying at the same speed as the regional best performer, the index would decrease further by 0.01 points every year until 2037.
The number of manufacturing sectors with a revealed comparative advantage is increasing. The analysis of the industrial census suggests that in 2017 Northern provinces on average had revealed comparative advantages in 73 sectors, up from 67 in 2012 (Table 4.1). This figure increased from 2012. The regional best performer, Phichit, “created” the highest number of sub-sectors between 2012 and 2017 (around 3 per year). With a similar performance, the North could almost double the number of sectors contributing significantly to the provincial value added. Note that these figures refer only to the manufacturing sector, obtained through the Industrial Census. More detailed information about the value added generated by other sub-sectors (for example, in agriculture and tourism) would allow for a more complete assessment of the strengths of the Northern provinces.
Moving forward, the analysis of the production space (Chapter 2) could shed further light on the sectors or products in which provinces could specialise further. Provinces could pinpoint more precisely the products in which they have developed a comparative advantage with respect of the rest of the country by exploiting granular data from enterprise surveys, industrial censuses and regional input-output table.
Enhancing structural transformation and realising productivity gains in the North requires a new framework for social and economic development. For instance, Chapter 2 proposes a new way of conducting regional policies that differs from the traditional top-down industrial policies and relies instead on grassroots participation of local economic actors in planning, implementation and monitoring. This new framework would moreover bring the need for continuous innovation to the fore.
The Action Plan in Chapter 2 presents a bottom-up and data-driven approach designed to uncover the local potential for innovation. At the centre of this new regional policy framework are the “Smart Labs”, small and dynamic working groups gathering local authorities, entrepreneurs from rising and innovative sectors and academic experts. Depending on the number of sectors and economic activities emerging as comparatively advantageous from the data analysis, each province may have several Smart Labs. Provincial, regional and central authorities need to consolidate the development strategies produced by each Smart Lab (Chapter 2).
The purpose of the Smart Labs and the data-driven discovery process is to expand the productive capabilities of the North while achieving the Sustainable Development Goals. By opting for bottom-up specialisation process, Northern provinces could experiment a new model of growth that is inclusive of all local economic actors and respects local ambitions, potential and constraints (SDG 8). The participatory and data-driven nature of the “smart specialisation” process could help unleashing all local forces of innovation (SDG 9).
Strengthen LAOs in the Northern Region – experiment with innovation in tax collection and transfers (Expected result 2)
The “smart specialisation” process is effective if local authorities have enough fiscal leverage to exploit local potential and tackle obstacles to local growth. The action plan proposes three sets of policies aiming at building local fiscal capacity (Chapter 2). Two of them entails the experimentation of methods for property tax collection and of a simplified but more effective system of transfer, respectively.
The scorecard proposes indicators to track the fiscal empowerment of LAOs. This is crucial to develop local productive capacities. Local authorities need to strengthen their fiscal position and autonomy in order to better exploit local potential and tackle obstacles to local growth. The proposed indicators measure the share of local revenues collected by provinces and the income from transfers. LAOs should be able to increase more taxes as a share of total revenue and depend less on total grants. Moreover, Thailand should prefer general grants to conditional ones and thus invert the current trend.
Northern cities drive regional development
Re-organise urban policies for performance and accountability (Expected result 3)
Cities can become drivers of regional development once they are properly defined. Urban areas in Thailand are currently defined along administrative borders and therefore underestimate the actual number of “urban users”: most of them commute every day from neighbouring administrations. The North could pioneer such a redefinition to better tailor and co-ordinate the provision of integrated urban services.
To redefine its cities, the North could apply the OECD Functional Urban Area (FUA) methodology. A preliminary analysis shows that there are four main metropolitan areas with more than 100 000 inhabitants in the North: Chiang Mai, Phitsanulok, Chiang Rai and Mae Sot (Table 2.3, Chapter 2). To refine this result and define FUAs, relevant authorities should survey the commuting time of households living in the existing urban centres – as defined by NSO – and within a distance of 300 km.
Once FUAs are defined, they should be equipped with Metropolitan Authorities and Transport Authorities (Table 4.3). Each Metropolitan Authority is a government layer, established through legislative decree and similar to the existing metropolitan area of Bangkok and Pattaya. The Metropolitan Authority co-ordinates the provision of public services and investments in infrastructure throughout the whole FUA. Those living within the Authority’s boundaries elect a mayor, who is then directly accountable for the execution of the development plan for the FUA. A Transport Authority could work independently from the Metropolitan Authority to co-ordinate the transport system of all existing administrative units encompassed by the FUA.
Inclusive and sustainable urban infrastructure (Expected result 4)
Metropolitan areas and transport authorities should periodically monitor well-being and access to services through households’ and users’ surveys (or alternatively, remote sensing and satellite data). Generally speaking, effective metropolitan areas are characterised by infrastructure allowing for faster commuting and access to basic services, such as water and sanitation. To this end, Table 4.4 proposes a series of ideal indicators that metropolitan authorities could monitor.
To ensure accurate policy targets the indicators require frequent updates. The necessary data collection could be carried out on a yearly basis through a combination of door-to-door and online surveys. In addition, satellite imagery and remote-sensing data could provide real-time information relevant for certain indicators, such as commuting patterns.
Universities and colleges become key drivers of regional development
The tertiary education system in Thailand could work to develop entrepreneurship among its students and in local communities (Table 4.5). To achieve this objective, universities and local colleges need to adapt their coursework to the needs of the local labour market and incorporate entrepreneurship values. Identifying such courses requires a census of the current offer of tertiary institutes (Byun et al., 2018[2]). Entrepreneurship graduate classes could be both theoretical and empirical. Theoretical classes introduce students to methodologies for the identification of entrepreneurial opportunities, the development of a business model and plan, patent law, accounting and finance, entrepreneurial marketing, HR strategy and venture growth strategy. Hands-on sessions complement theoretical classes with internships and analysis of relevant business case studies.
Universities and entrepreneurs can also form partnerships to develop specific research questions for business development. Chapter 2 presented the Christian Doppler Research Association in Austria as a possible example for Northern provinces to follow.
Selected indicators could then measure the impact of entrepreneurship graduate programmes and support (Table 4.6). One possible indicator is the share of youth and adults with advanced ICT skills. The Ministry of Education could, moreover, survey adults of working age to measure their skills and to establish how they are used at home, at work and in the wider community. The OECD Programme for the International Assessment of Adult Competencies (PIAAC) provides excellent guidelines in this regard. The Ministry of Education could also organise a census of courses offered by tertiary institutes and assess their “level of entrepreneurship”. Central and local authorities, as well as potential international donors, could assess the performance of university-enterprise labs by measuring the number of patents or peer-reviewed articles published by affiliated researchers.
Northern provinces adopt a risk management approach to water security
The scorecard proposes a series of indicators to monitor the adoption of a risk management approach to water and disaster management in the North. The first group of indicators are adapted from the OECD Water Governance Indicators. The NESDC and the OECD independently assessed the performance of Thailand with respect to the OECD Water Governance indicators. The OECD based the assessment on the material and information collected during the workshops and the meetings with local authorities in Chiang Mai and Chiang Rai, held in November 2018 (Table 4.7). It is preferable to assess current performance against these indicators and set future targets as part of a multi-stakeholder participatory process. Thailand could revisit these indicators possibly via the NWRC and collate the views from each line ministry and stakeholder involved in water management. This could be implemented via a facilitated workshop. In the long term, the North should aim to reduce the impact of floods and droughts and improve the wastewater sector to curb pollution.
As part of the multi-stakeholder review of performance, Thailand should also consider collecting the data and tracking indicators concerned with the implementation of water-related SDGs and the water security indicators developed by the Asian Development Bank as part of its Asian Water Development Outlook (AWDO) exercise. The AWDO indicators are a composite indicator of five water-related dimensions – household, economic, urban, environment and resilience (ADB, 2016[4]).
Northern provinces need to centralise data to improve the management of disaster risk and water resources. One of the main results of the MDCR is the lack of harmonised and centralised data on the impact of floods and water usage. In order to enhance the management of natural resources and prevent future losses from floods and droughts, the North could pioneer a new harmonised and accessible dataset of water security. Table 4.8 proposes a series of indicators that this dataset could include.
Going forward: Measuring the SDGs at regional level for an integrated performance measurement framework
The SDGs are the global tool for measuring multi-dimensional development. The above proposed scorecard links each action to an SDG. However, going forward it would be desirable to collect data on SDG indicators at provincial and regional level in Thailand to allow for an integrated performance measurement framework. As an example of such a framework, Table 4.9 links the expected results of the action plan to the relevant SDG targets. Once data for these SDG targets is available at regional and provincial level this could be used for performance tracking with the same methods and principles as the scorecard presented in this chapter.
References
[4] ADB (2016), Asian Water Development Outlook 2016, https://www.adb.org/sites/default/files/publication/189411/awdo-2016.pdf.
[2] Byun, C. et al. (2018), “A study on the effectiveness of entrepreneurship education programs in higher education institutions: A case study of Korean graduate programs”, Journal of Open Innovation: Technology, Market, and Complexity, Vol. 4/3.
[6] FAO (2016), AQUASTAT, http://www.fao.org/nr/water/aquastat/data/query/index.html?lang=en (accessed on 21 January 2019).
[1] Jittrapirom, P. and G. Emberger (2013), Chiang Mai City Mobility and Transport Survey (CM-MTS) Report 2012, Technische Universität Wien, Vienna, https://www.ivv.tuwien.ac.at/fileadmin/mediapool-verkehrsplanung/Diverse/Forschung/International/CM-MTS/CM-MTS.pdf (accessed on 24 January 2019).
[3] OECD (2018), Implementing the OECD Principles on Water Governance: Indicator Framework and Evolving Practices, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264292659-en.
[5] Singkran, N. (2017), “Flood risk management in Thailand: Shifting from a passive to a progressive paradigm”, International Journal of Disaster Risk Reduction, Vol. 25, pp. 92-100.