The Spatial Patterns and Local Economic Determinant of Industrial Agglomeration in Semarang District, Indonesia

Urbanization creates opportunities for Indonesia, potentially to boost economic growth and create vibrant cities (metropolitan). Urbanization and agglomeration economies should be an important element in Indonesia‘s development as a mid-income country. Manufacturing industry becomes a dominant economic sector in metropolitan area such as Semarang that shows urbanization-industrialization relationship. Industrial agglomeration potentially induces socio-economic changes in the region. To prepare these changes, it is important to understand the spatial dynamics of agglomeration and predict its determinants locally. This paper aims to answer questions related to the spatial patterns and determinants of industrial agglomeration in Semarang Regency, a periphery of Semarang metropolitan. Nearest Neighbor Analysis is used to identify spatial patterns, followed by Ellison and Glaeser Index to measure agglomeration strength, and Specialization Index to measure industrial specialization. Geographically Weighted Regression is used to identify determinants of agglomeration. Analysis uses geographical database of Large and Medium Industries in 2016 and related sub-district based data. Result shows 11 of 21 sub-sectors of industries geographically form clustered (agglomerated) pattern. Six of them are strongly agglomerated (most localized). High specializations in these six sub-sectors occur in 14 sub-districts. Result obtains a significant spatial regression model explains the effect of independent variables simultaneously occurring in three sub-sectors: beverages; wearing apparel; wood and products of wood and cork, except furniture, articles of straw and plaiting materials. Partially, industrial agglomeration by three sub-sector’s specializations in sub-district level is determined by variables: industrial employment; vocational school; Gross Regional Domestic Product; population; arterial road; agricultural land availability; and agricultural households.

INTRODUCTION
Indonesia's economic growth has been accompanied by rapid urbanization that has transformed Indonesian cities. The urbanization has progressed rapidly since the 1990s, and in 2015 about 67.5% of Indonesia's population lives in urban areas. Urbanization opens up opportunities to generate regional economic growth and encourage the formation of the metropolitan regions. Urbanization can drive productivity, economic opportunities, and increase income. Urban areas are generally economically more productive and competitive than rural due to positive externalities in the form of agglomeration. Urban areas create opportunities for the establishment of localization economies through the clustering of related activities. In contrast, urbanization economies may emerge in dense urban areas where the transaction cost of doing business is lower, and knowledge spillover opportunities are high. With the benefits of agglomeration, businesses within such economies tend to be more economically productive, as demonstrated by a faster rate of growth in GRDP than rural areas. The issue of urbanization and industrialization cannot be separated in the growth of cities in Indonesia (Bappenas, 2012). Industrialization is dominated by Large and Medium Industries (LMIs), which are significant in production and employment. Kuncoro (2002) identified LMI's agglomeration was concentrated in the metropolitan areas. Specialized LMIs generally have vertical relationships with global suppliers and access to international markets, and has better access to infrastructure (ports, arterial roads). The proximity of the geographical concentration of LMIs to urban areas aims to obtain urbanization economies, as demonstrated by the market size in urban areas.
Industrialization in Indonesia has been biased in LMIs and urban areas. On the other side, Small and Micro Industries (SMIs) is still become a 'marginal' player in the scheme of industrial development or only gets a small profit. In some industrial regions, poverty incidence has even emerged that showed poverty emerged along with the growth of modern industry (medium-large) because its local economic base did not grow and local communities were marginalized (Kuncoro, 2002;Muta'ali, 2011). The industrial agglomeration (dominated by LMIs) closely related to urbanization tends to grow rapidly by utilizing the benefits of agglomeration economies, but on the other hand, SMIs generally still faces low productivity issues (Tambunan, 2011). Inward investments included in LMIs should impact job creation and encourage local businesses that are dominated by micro and small-scale businesses. Investment in a particular industrial sector should encourage the growth of related industries (Rostow & Rostow, 1990;Ward et al., 2002).
Local economic development concerns in linkages, cooperation, or synergy among local economic actors. The linkage among the industrial sector with the local economy in order to increase the competitiveness of the local economy, as Porter (1990) argues that the local economy as the product of the competitiveness of the local economic actors such as local companies and local industries.
The research on the linkage of industrial agglomeration and the local economy in Semarang District focuses on economic actors (the business sector) related to the local economy. Semarang District was chosen as a research area because the region is growing significantly become the domination of urban areas toward a metropolitan region, namely Metropolitan Semarang, one of the economic development regions in Indonesia with a significant peri-urbanization (Firman, 1998;Bappenas, 2012). The manufacturing industry is the largest sector contributing to the district's GRDP reaching above 40%. The region is also growing rapidly in urban areas, about 40% of its population lives in urban areas, thus showing the phenomena of industrialization and urbanization in the region. On the other side, the problem of unemployment and poverty still occurs despite the relatively large contribution, value-added, and industrial sector growth in the region. The unemployment rate in this region is still around 2.3% or 15,864 people and the poor population is still quite large, reaching 81,310 people or 8.5% (BPS Kabupaten Semarang, 2016). These phenomena show that people's income distribution in the region has not been equally realized.
Previous studies show that Semarang District, in general, is not supported by strong industrialization because of the domination of footloose industries in the region, which do not have strong forward and backward linkages with the region. The linkages of industrial and agricultural sectors are weak, indicating industrialization, which is expected to occur linkages, but leakages are happening in the region. The region only becomes the location of industrial activities and the weak linkages among the sectors cause significant benefits in the local economy (Wilonoyudho & Keban, 2011;Hardati, 2014).
Some of the findings in this research show that industrial agglomeration in Semarang District is still dominated by the wearing apparel industry sub-sector, which tends to be located around urban and industrial areas, has a strong vertical relationship with foreign suppliers and international market access. Industrial agglomeration with the labor-intensive and footloose industry characteristics. But, the research also found the food industry sub-sector in the region has a strong relationship with the local economy that differs from previous studies by Wilonoyudho &Keban (2011) andHardati (2014). The food industry sub-sector establishes sectoral specialization at the local (sub-district) level and potentially forms industrial clusters. This sub-sector geographically tends to be located around the raw material area and has a strong linkage with the horticulture and livestock sub-sector or the agriculture sector, which is generally located in rural areas.

DATA AND METHODS
The spatial pattern analysis of industrial agglomeration on 307 units of Large and Medium Industries (LMIs) consists of 66 large companies and 241 medium companies. The main factors analysis of the local economy and its effects on industrial agglomeration is carried out in the sub-district unit, in 19 sub-districts. The research flow chart can be seen in Figure 1. This research uses primary and secondary data for analysis. Primary data is the geographical coordinate data of the LMIs companies (307 units) were obtained by field mapping using GPS. Secondary data include: (1) LMIs data according to the industrial classification or industrial sub-sectors based on the Indonesian Standard of Industrial Classification (ISIC), the number of workers in each LMIs and data on types of investments, value of investments, and markets; (2) local economic conditions per sub-district include 102 variables; and (3) data on the characteristics of the study area. Secondary data was obtained through institutional surveys at the Cooperative, SMEs, Trade and Industry Agency; Statistic Agency (BPS); Planning Agency (BAPPEDA); and Investment and One-Stop Service Agency (DPMPTSP) of Semarang District.

Figure 1. Research Flow Chart
Spatial analysis of industrial agglomeration measures three variables: (1) industrial spatial distribution patterns (Yunus, 2010); (2) the scale of industrial agglomeration is measured by indicators of labor (Ellison & Glaeser, 1997;Kuncoro, 2002;Ellison, Glaeser, & Kerr, 2007); and specialization to measure the industrial concentration in the region (Porter, 1990;Kuncoro, 2002). Nearest Neighbor Analysis is used to analyze the spatial pattern of industrial agglomeration is the district level unit. The calculation results Nearest Neighbor Index (NNI) values that interpret to form a pattern: (1) clustered; (2) random; or (3) dispersed (O'Sullivan & Unwin, 2010;Muta'Ali, 2015). Industrial agglomeration is shown by the NNI results that illustrate the clustered pattern of LMIs. The strength (size) of industrial agglomeration is measured by the Ellison and Glaeser Index or EG Index (Ellison & Glaeser, 1997;Ellison et al., 2007). The EG Index calculation follows up on the NNI results, which shows certain industrial sub-sectors that are agglomerated by adding a number of workers variable. The unit of analysis of the Specialization Index is in the sub-district unit. High specialization in a certain industry will accelerate industrial growth (Kuncoro, 2002).
Factor analysis is used to determine the main factors in the local economy. Factor analysis in the research uses 102 local economic variables in units of sub-districts that describe local economic resources, types of business at the local level, and production of the main commodities of the local economy (Rustiadi, 2018;Ward et al., 2002). Spatial regression using Geographically Weighted Regression (GWR) is used to identify industrial agglomeration's determinant factors. GWR is a geographical or local regression that can explain the relationship between data variables spatially. The GWR method results in local models that vary for each location, thus differentiating it from global regression models (Fotheringham, Brunsdon, & Charlton, 2000;Scott & Janikas, 2010;Arsyadana, 2015).
The dependent variable (Y) is the Specialization Index per industrial sub-sector, while the independent variable (X) is the main factor of the local economy as a result of Factor Analysis. The unit of analysis in the Spatial Regression Analysis is at the sub-district level. There are five calculation of variable Y: Y1 = food industry specialization index; Y2 = beverage industry specialization index; Y3 = wearing apparel industry specialization index; Y4 = non-metallic mineral industry specialization index; and Y5 = furniture industry specialization index. The X variable used in each calculation are: X1 = factor score of livestock-associated with horticulture region; X2 = factor score of horticultural region; X3 = factor score of horticultural associated with plantations region; X4 = factor score of plantation region; X5 = factor score of plantation associated with livestock region; X6 = factor score of urban region; and X7 = factor score of industrial region.

Spatial Pattern of Industrial Agglomeration
The spatial distribution of the LMIs companies locations in Semarang District is illustrated in Figure 2. The spatial pattern analysis results with NNI show there are 10 of 21 sub-sectors of LMIs geographically form clustered patterns (see Table 1). The clustered patterns of LMIs means industrial agglomeration happened in the region. The EG Index calculation with the labor variable complements the result of NNI, which only uses geographical location variables. The results of the EG Index (ϒ) calculation shows there are 5 of 10 LMIs subsectors in Semarang District most localized or agglomerated (see Table 1).

Figure 2. Spatial distribution of LMIs by sub-sector (ISIC) in Semarang District
The most localized industries in Semarang District are: (1) wearing the apparel industry; (2) food industry; (3) beverage industry; (4) furniture industry; and (5) non-metallic mineral product industry. The most localized agglomerated LMIs sub-sectors can be identified as having advantages and spillover, and potentially provide positive agglomeration externalities. The five most localized sub-sectors generate large workers: wearing the apparel industry (79,805 workers), food industry (3,133 workers), beverage industry (2,445 workers), furniture industry (2,693 workers), and non-metallic mineral products industry (1,637 workers). The five sub-sectors' total employment generation is 89,713 workers (72% of the total LMIs workers). Industrial Specialization Index Analysis is carried out in more detail at the sub-district level, so it shows the character of the economic locality as a special sub-sector region. The LMIs Specialization Index calculation results show 14 of all 19 sub-districts in Semarang District are specialized regions in certain sub-sectors. The specialization region shows a comparative advantage over other districts. High specialization in a certain industry will accelerate the growth of the industry in the region. The spatial description of industrial agglomeration in the specialization regions can be seen in Table 2. Proximity specialization regions to the urban areas and transportation infra-structure due to import content, export orientation and labor factors. Agglomeration creates positive externalities in the interrelationship among companies that potentially establish industrial clusters. 4. Specialization region of non-metallic mineral products industry (ISIC-23) Bergas, Ambarawa, Sumowono, Tuntang and Tengaran • Proximity some regions to urban areas and infrastructure due to market factors (construction). • Proximity some regions to mining areas as sources of raw materials but unsustainable in the long term due to limited time to extract mining. 5. Specialization region of furniture industry (ISIC-31) Pringapus, Tengaran, Jambu, and Susukan • Proximity some regions to urban areas and infrastructure due to export orientation and outward materials. • Proximity some regions to forestry and plantations areas as sources of raw materials (wood).

Local Economic Factors Determinant of Industrial Agglomeration
The main factors of the local economy in Semarang District are the results of factor analysis using 102 variables. Factor analysis results in 7 of 15 factors or components (with cumulative eigenvalues of 77.13%). Then identification of the characteristic of the factor based on commodity categories or economic activities of the initial variables of analysis. The characteristic of a factor in line with the characteristics of the local economy at the sub-district level. The main factors of the local economy in Semarang District are: Factor 1 livestock associated with horticulture region; Factor 2 horticulture region; Factor 3 horticulture associated | 105 with plantations region; Factor 4 plantation region; Factor 5 plantations associated with livestock region; Factor 6 urban region; and Factor 7 industrial region. The factors in each sub-district have different strength levels indicated by the value of factor score.
The spatial regression analysis using GWR identifies how the local economic factors determine industrial agglomeration. Spatial regression analysis is carried out on five industrial sub-sectors that have sectoral specialization. The dependent variable (Y) in the spatial regression analysis is the value of each LMIs subsector's specialization index in each sub-district. The explanatory variable (X) in the analysis is the local economic factors in each sub-district. The results of the GWR analysis are described in Table 3.

5.
Furniture industry (ISIC-31) 403.47 150.00 -0.52 Significant (52%)*) *) The coefficient of determination or absolute value of adjusted R 2 is significant if it is close to 1 or 100% (we consider significant if the absolute adjusted R 2 is above 50%) The result of GWR shows a significant spatial regression model explains how much the independent variables together determine the sub-sectors: (1) food industry (adjusted R2 54%); (2) wearing apparel industry (adjusted R2 57%); and furniture industry (adjusted R2 52%). The spatial regression model for other sub-sectors is considered not significant because the determination coefficient value is ≤50%. The spatial regression method using GWR produces a different local model for each location that it differentiates with global regression. To find out the predictor variables (local economic factors), which significantly determine the response variable for each sub-district conducted t-test by calculating the value of t (t count), namely the comparison between Estimated value with Standard Error in each variable for each sub-region, then the value of t count compared to t table. The t count value greater than t table shows significant variables in each subdistrict (see Table 4). (+) X 6 (Urban region) All sub-district (+) X 7 (Industrial region) All sub-district 3. Furniture industry (ISIC-31)

No variable significant
All sub-district The results of the spatial regression analysis showed that local economic factors significantly determine the LMIs agglomeration in two sub-sectors: (1) food industry; and (2) wearing the apparel industry, as the following explanation.

Local Economic Factors Determinant of Industrial Agglomeration
The livestock associated with the horticulture region (X1) and the horticultural region (X2) has a positive effect on the agglomeration of the food industry (see Figure 3 and Figure 4). The increase in local economic factors in the livestock and horticulture region is influenced by the increase in these commodities. The bigger production of livestock and horticultural commodities can increase the agglomeration of the food industry. The influence of local economic factors on food industry agglomeration can be seen in Getasan Subdistrict as a center of dairy milk production (production up to 20.7 million liters or 80% of total district production). 5 of 8 cooperatives/groups of dairy farmers/collectors in Semarang District are located in Getasan Sub-district. Companies in dairy milk processing are CV. Cita Nasional in Getasan Sub-district and PT. Cimory in Bergas Sub-district. | 107 The food industry that produces meat/eggs product is related to livestock businesses in Semarang District. Producer of chicken products (fresh meat, sausage, and nugget) PT. Ciomas Adisatwa in Pabelan Subdistrict and CV. Indocipta Mitra Sejahtera in Getasan Sub-district) get the raw material from the surrounding area. Semarang District is a potential region of the livestock sub-sector, such as chickens, with total production up to 3,888 tons (BPS Kabupaten Semarang, 2016).
PT. Java Egg Specialties in Bergas Sub-district produces eggs and mayonnaise products get the supply of eggs from a group of farmers in Tuntang Sub-district, despite competing with egg suppliers from East Java Province. Eggs are a potential product in Semarang District, with production up to 228 million (BPS Kabupaten Semarang, 2016). The growth of the livestock sub-sector in the region encourages the breeding industry, PT. Japfa Comfeed Tbk. in Tengaran Sub-district, which produces DOC to supply farmers in the region and to other regions in Central Java Province. Jambu, Banyubiru, Bandungan, Ambarawa, and Sumowono sub-districts are horticulture regions that produce commodities such as herbal plants, mushrooms, cassava, and yam to supply the raw materials for producers of herbal and food products such as UD Wijaya and UD Pertiwi in Jambu Sub-district, UD. Bumi Lestari in Sumowono Sub-district, and KUB Makmur Sentosa in Ambarawa Sub-district. The supply of raw materials of horticultural products also occurs across sub-districts, even across districts, as happened at PT. Sumber Boga Abadi and PT. Mangkok Mas in Bergas Sub-district.

Local Economic Factors Determinant of Wearing Apparel Industrial Agglomeration
The urban region (X6) and industrial region (X7) determine the wearing apparel industry agglomeration (see Figure 5 and Figure 6). This influence shows that the stronger the region's character as an urban and industrial area will increase the concentration of the wearing apparel industry. The proximity of industrial agglomeration to urban and industrial regions shows this sub-sector spatially depends on infrastructure, particularly transportation accessibility in an urban and industrial region. The infrastructure supports the accessibility of labor, raw materials, and product distribution.
The wearing apparel LMIs is the largest industrial sub-sector in Semarang District, consists of 87 companies and generates 79,805 workers (917 workers per company on average). It is categorized as a labor-intensive industry. The study results by Wijaya, Kurniawati, & Hutama (2018) show that the labor-intensive industry in the region has attracted many migrant workers from other regions in Central Java Province. Daily workers' mobility or commuters and the growth of settlement areas around the industrial region become issues in Semarang District. Data from DPMPTSP of Semarang District in 2016 shows 57% of wearing apparel LMIs companies are Foreign Direct Investment (FDI) companies. 60% of wearing apparel companies are located in industrial bonded zones. The high dependence on import-content materials in the wearing apparel industry agglomeration confirms the findings by Kuncoro (2002) that the LMIs agglomeration in the metropolitan area is associated with import content and export orientation. The concentration of FDI companies in Semarang Regency also tends to proximity to the same country of origin (for example, FDI companies from South Korea) that confirms the findings by Rofi (2015).

CONCLUSION
The spatial patterns of industries in Semarang District are geographically concentrated or clustered, which shows the phenomena of industrial agglomeration. The particular sub-sectors of the industry are strongly agglomerated. It reflects the interrelationship among companies (including business cooperation), the concentration of employment, and knowledge transfer that increase the positive externalities of industrial agglomeration. Industrial agglomeration also shows high sectoral specialization in certain sub-sectors that potentially establish industrial clusters, as occurred in the food industry and wearing the apparel industry. High specialization in a certain industry will accelerate the growth of the industry, that means potential industrial agglomerations will lead to positive externalities in the job opportunities, capital attractiveness, increased skills or knowledge, growth of related industries and services, and other functional relationships among industries, and improve regional competitiveness (Porter, 1990;Kuncoro, 2002;Fan & Scott, 2003).
Certain sub-sectors of industry tend to be located around urban areas and infrastructure, which shows the proximity of industrial agglomeration to the population concentration to utilize urbanization externalities as reflected in market size in urban areas. Industrial agglomeration in metropolitan areas is influenced by import content, export orientation, labor, and better access to infrastructure (Kuncoro, 2002;Fujita, 2002;Qi, Fang, & Song, 2008). Other sub-sectors such as the food industry that primary process products such as livestock products and plantations, the beverage industry, the wood industry, and the furniture industry geographically tend to be located around the raw material concentration. The proximity of industries to the raw material sources is in line with one of Marshall's industrial agglomeration theories that economically industrial locations consider the proximity to input suppliers to save transportation costs (Marshall Jr, Lynch, & Smith, 1919). The spatial regression model shows some local economic factors that significantly determine industrial agglomeration in Semarang District. It occurs in two sub-sectors: the food industry and the wearing apparel industry. The local economic factors of livestock and horticulture regions determine the industrial food agglomeration. Meanwhile, the local economic factors of urban and industrial regions determine the wearing apparel industrial agglomeration. The strong relationship of food industry agglomeration and horticultural and livestock region shows the proximity of industry to the source of raw materials. It means the proximity of food industry agglomeration to the rural areas. So the development of the food industry needs to be integrated with the rural economic development based on the agricultural sector (including the livestock and horticulture sub-sectors). In this context, industrial and agricultural development can be carried out within the rural diversification concept framework. This concept emphasizes the importance of non-agricultural employment opportunities and the increased income of rural households due to the relative increase in industrial and commercial activities related to agricultural activities (Rijanta, 2012). Rural industrialization is in line with the concept of spatial integration in rural-urban linkages as mutually relationships in the term of economic linkages (Rondinelli, 1985;Douglass, 1998).
The finding that the food industry sub-sector in Semarang District has a strong relationship with the local economy differs from previous studies (Wilonoyudho & Keban, 2011;Hardati, 2014) because of the different methods and variables used. The study by Wilonoyudho & Keban (2011) used descriptive analysis, focus group discussion, interviews, and observations, not specifically examining spatial determinants of industry. A study by Hardati (2014) used the variables of agriculture and non-agriculture sectors in spatial analysis. In contrast, our study based on the geographical distribution of industries and employment is classified in subsectors and uses spatial statistical analysis techniques.
The relationship of urban region and the wearing apparel industrial agglomeration is in line with literature on the positive correlation between industrial agglomeration and urban agglomeration (Kuncoro, 2002;Fujita, 2002;Qi, Fang, & Song, 2008). Our findings that show the industrial region determine the wearing apparel industrial agglomeration, particularly in the peri-urban, confirms the study by Kuncoro (2002), Fan & Scott (2003), and Rofi (2015). This phenomenon shows industrialization and urbanization issues cannot be separated in urban development, including in Semarang District.
The inter-sectoral linkages in the wearing apparel industry are not significant compared to the food industry because most wearing apparel companies in Semarang District are categorized as the footloose industry. The footloose industry relies heavily on imported raw materials, export markets, and works within bonded zones. However, there are other business activities related to suppliers or service providers for LMIs wearing apparel (embroidery, fabric printing, packaging, and other subcontracts) and utilization of wearing apparel production waste by SIs to produce various goods (mattresses, doormats, and household appliances). Those businesses economically are not significant compared to the value of the LMIs wearing apparel.
The spillover impact of the wearing apparel industry sub-sector mainly occurs in the concentration of employment and knowledge or skills transfer. The wearing apparel sub-sector in Semarang District mostly labor-intensive industry which attract large migrant workers (around 50%) from other regions in Central Java Province even outside the province, resulting in highly inter-regional labor mobility. This finding is in line with the study by Bartik (1991) that high inter-regional labor mobility causes an increase in labor demand, which will be followed by an increase in labor supply from other regions, so that the industrial sub-sector is not optimal in reducing unemployment at the local level in the long run. Spillover in knowledge and skills transfer of in the field of wearing apparel encourages the growth of small scale similar businesses, although not economically significant. High inter-regional labor mobility causes transportation problems and some migrant workers who settle around industrial areas triggering land use changes in the region. Labor is a major issue in the wearing apparel industry sub-sector.
This study concludes that industrial agglomeration in Semarang District, as part of the metropolitan area, is still dominated by the wearing apparel industry sub-sector, which tends to be located around urban and industrial areas, has a strong vertical relationship with foreign suppliers and international market access. Industrial agglomeration with the labor-intensive and footloose industry characteristics explains the dependency theory that metropolitan cities in developing countries have a high dependence on the economic system of developed countries. High dependence in terms of supply and international markets in the long term can lead to inequality, including inequality in urban and rural areas (Myrdal, 1968;Rustiadi, 2018).
Industrial agglomeration in certain sub-sectors in Semarang District, such as the food industry sub-sector, establishes sectoral specialization at the local (sub-district) level and potentially to form industrial clusters. This sub-sector geographically tends to be located around the raw material area and has a strong linkage with the horticulture and livestock sub-sector or the agriculture sector, which is generally located in rural areas. Food industry agglomeration encourages spatial integration or linkages between rural and urban areas (rural-urban linkage) can explain the interdependency theory, which bridges the modernization theory and dependency theory by reducing the gap between regions. An approach to the interdependency theory focuses on developing regional networks based on the clustering model by providing opportunities for the development of local economic factors (endowment) in a region. The development of small towns in periphery regions or rural areas can counterbalance the tendency of over-urbanization in the metropolitan area (Rondinelli, 1985;Douglass, 1998;Rustiadi, 2018).
The theoretical implications in the scientific field of geography and regional development and policy implications based on the study results can be input for regional development, particularly in Semarang District as follows.
1. The linkage of industrial agglomeration and the local economy in the metropolitan region can complement the concept of spatial integration or rural-urban linkages (Rondinelli, 1985;Douglass, 1998). The spatial linkage that works in peri-urban and rural areas can be a counterbalance to the tendency of over-urbanization in metropolitan areas (Friedmann, 1992;McGee, 1994;Firman, 1998;Rustiadi, 2018;Wilonoyudho & Keban, 2011).


Accountability in agricultural governance and food security in Nigeria

e2019089. https://doi.org/10.1590/1981-6723.08919 Abstract This study explores the relationship between agricultural governance and food security in Nigeria and employs two main approaches: descriptive and econometric. The econometric approach engages Autoregressive Distribution Lag (ARDL) in examining the long-run relationship between the indicators of food security and agricultural governance for the period 1985 to 2016. The results show that in the long-run, agricultural performance contribute to food security in Nigeria. The findings from the descriptive analysis unveil that Nigeria has the highest number of undernourished people, which increased by 22% between 2000 and 2001. Between 2000 and 2015, the population grew by 30.60% with the rate of violence increasing by 55%. The study recommends, among others, that accountability in addressing the challenges in the implementation of food security programs and ensuring timely distribution of food resources econometric method, using the ARDL cointegration on the long-run and short-run relationship The ARDL results show that there exists a long run relationship food security indicator (number of people undernourished), agriculture indicators (agricultural production and agricultural guarantee scheme fund), indicators of governance and accountability (voice and accountability, political stability and absence of violence, control of corruption and rule of law) and population. The result shows that in the long-run increase in agricultural production reduces the number of undernourished people by 2.89%, agricultural credit guarantee scheme fund to farmers enhance food production base thereby reducing undernourishment by 18.6%. Akin to Malthusian population theory, increase in population increases undernourishment by 74.8%, the for this high increase is because of low food production and many people chase little available food produced. Based

Introduction
The challenge of effective food security has remained a critical issue for consideration by various government admirations in Nigeria (Osabohien et al., 2020;Ejikeme et al., 2017); because, food security stands as an important factor for human survival (Babatunde et al., 2007;Titus & Adetokunbo, 2007;Dias et al., 2017).Extant studies have presented the subject of food security from several perspectives: government's involvement, climate change and the need for availability of food and related resources for human consumption Ike et al., 2017).
Despite the effort of successive government administrations in Nigeria, non-governmental organizations (NGOs), and the international agencies, the challenge of achieving food security has remained a herculean task (Osabohien et al., 2018b). However, while the government has made frantic efforts through various budgetary allocations, supports from international agencies, and so on . Androsova et al. (2016) the instrumentality of accountability, equitable distribution and preservation of food resources, which could lend relevant support in ensuring food security tend to have been inadvertently neglected in the literature. These factors motivate our study, which draws the attention of academia and practitioners to the need for accountability in the area of food distribution, in line with appropriate government policies.
While there are several studies on food waste (e.g. Ayotamuno & Gobo, 2004;Thi et al., 2015), the study addresses the subject of waste from the perspective of the involved and affected stakeholders. This study recognizes the contextual meaning of waste rather than the universal meaning (Womack & Jones, 2003). The study, therefore, underlines the subject of waste as an ongoing debate, which only the affected stakeholders should be involved in defining Evidence from the Living Standards Measurement Study-Integrated Survey on Agriculture (Osabohien et al., 2020) showed that in 2015, about 26.4% of households reported food reduction in Nigeria. This reduction in food depicts 2.3%-point increase from 2015. This number is significantly higher in some of the geo-political zones in Nigeria. In the South-South and North-East of Nigeria, the share of households with a reduced number of meals increased by 14.1% and 6.3% points, respectively (Osabohien et al., 2020). Moreover, in the urban area, households (29.8%) reported the incidence of food reduction than rural households, which was 24.1%. Overall, about 19.6% of households reported food inadequacy in in 2016. The percentage is considerably higher in the South East with the value of 34.3%.
However, from LSMS-ISA (Osabohien et al., 2020) the share of households' food inadequacy is considerably high across the Nigerian regions. In the South-West and North-East, 22% and 20.3% of Nigeria's households reported to be food insufficient, respectively. More urban households (23.5%) reported food inadequacy than rural households (16.9%). The highest proportion of households reported shortages in January (50.3%) and February (32.8%). This pattern is the same for the three southern zones. However, there are important differences in the northern zones. The month where food shortages were most common is July in North Central (49.4%), August for North East (58.2%) and February for North West (30.7%). Increase of food prices due to ineffective price control through governance is the major cause of greater concern among rural (15.3%) than urban households (8.3%).
The above is essential as the challenge of distribution along the relevant value chain has resulted in the scarcity of certain food resources. Hence, the poor and lower class of the society are usually excluded through hiked prices occasioned by increased cost along the value chain. This points out the need for strong value chain and distribution of food resources in terms of food management in the interest of citizenry (Ufua et al., 2018). Accountability in the context of this study, promotes the use of records and data for planning food security issues, with due attention given to all stakeholders who are either involved or affected in the planning and implementation of food security programs (Fritschel et al., 2015). This could be achieved through the practice of meaningful engagement with the stakeholders at each stage of the implementation of food security programs (Ufua et al., 2018). This would result in mutual understanding between the stakeholders and the Braz interveners that may undertake the task of designing the right food distribution strategy and facilitate a conflict-free platform to execute the task of accountable food distribution (Womack & Jones, 2003;Ufua et al. 2015;Osabohien et al., 2019b).
The study is structured as follows: the next session presents the literature review, followed by the adopted methodology, next is discussion and the last session is conclusion, which includes managerial implications, and suggestions for further research.

Key Issues from the Literature
In rethinking the strategies for sustainable development in ensuring food security in Nigeria, the potentials of agriculture can be enhanced through institutional frameworks, governance and accountability. From the empirical study of Osabohien et al. (2018b) employing the Auto-regressive Distributed Lag (ARDL) technique in examining the role of institutional framework on food security, pointed out that institutional framework in Nigeria exerts a negative effect on food security, due to weak institutional quality in Nigeria. According to Osabohien et al. (2018a) the Nigerian agricultural sector remains an important sector of the economy, owning to the fact that the sector employs approximately 75% of the total work force, especially in the rural communities where most of the farmers earn their livelihood.
A study by Munene et al. (2018) employed the adaptive governance approach and noted the need to redirect strategies to achieve sustainable development. This would be more effective through the implementation of the framework requiring non-traditional management and governance approaches for substantial reduction of food waste. It was noted that Adaptive governance (AG) has been known to be the medium to drastically change the link between development and disaster risk, with potentially far-reaching implications for policy and practice to ensure food security. Osabuohien et al. (2018c) used qualitative method with focus group discussion to examine how local institutions contribute to food (rice) production in Ogun State, Nigeria where they pointed out that local institutions play a key role in food production. In the study by Herbel et al. (2012), it was shown that achieving food security and the enhancement of dietary level is at the heart of the Sustainable Development Goals (SDGs). In line with that, Sidibé et al. (2018) noted that achieving food security can be done through the enforcement of rules and laws designed at the national level which remains one of the central institutional mechanisms for efficient multi-scale governance in most countries.
According to Osabohien et al. (2019c) policymakers are increasingly enlightened on the food security perspective, which has over the years reflected poorly in institutional terms. This study fills this gap by addressing the question as to what forms of accountability and governance is more appropriate to govern food systems in a more holistic way to achieve sustainable development goals (SDGs) by year 2030. In Africa, food security in relatively is high on the policy agenda of governmental authorities all over the globe (Candel, 2014). 'Food security governance' relates to the 'formal and informal' rules and processes through which interests are expressed, and decisions which are germane to food security in a country are prepared, implemented and enforced on behalf of members of society.
Studies by Rodrik (2010), Osabuohien et al. (2018c) to achieve food security reveal the need for equal opportunity in resource allocation and the delivery of services; coherent and coordinated policies, institutions, and actions. This means that the challenge for policymakers interested in addressing the key policy issues are to redesign strategies that allow countries to have a stable and affordable food supply that is equitably distributed as household food insecurity continues to be widespread with strong inequities across and within countries governance and strategies. Given the pathetic economic situation in some critical parts of the country, for example; the north east (Scribner, 2017;Ajayi & Adenegan, 2018), where starvation has been prevalent due to insurgency, the use of the right approach to addressing the national challenge of food insecurity, based on a platform of accountability, have remained a maximum requirement for achieving the right results of this subject area. Thus, from the fallouts in the literature, this study addresses the gaps in knowledge and takes up the debate to a new level with respect to the issues of food security and accountability in Nigeria.

Empirical model
The food system concept is poorly reflected in institutional terms at local, national, and international levels (Osabuohien et al., 2018c). Approaching food insecurity issues from systems perspective reveal and in turn enhance important governance challenges and opportunities, because, it requires more holistic approach to fully address. By its nature, food governance institutions are fragmented and cut across the usual boundaries between sectors, administrative jurisdictions, public and private domains, temporal and spatial scales and diverse normative frameworks.
The study applied econometric and descriptive approaches to achieve its objective. The study engaged time series data from 1985 to 2016 sourced from the Central Bank of Nigeria (CBN) statistical bulletin, World Governance Indicators (WGI), World Development Indicators (WDI) and Food and agricultural Organization (FAO). The descriptive approach engaged tables in examining the state of food security in Nigeria in relation to Economic Community of West African States (ECOWAS) sub-region threshold, while the econometric method employed the Autoregressive Distribution Lag (ARDL) to examine the long-run relation between governance and food security. The study adopted the Malthusian theory of population growth model and states the implicit function of the model as shown in Equation 1: Where Y represents food security proxied by number of people undernourished, A represents agricultural production and agricultural credit guarantee scheme fund; X represents population growth; Z represents the four governance components used in this study which are: control of corruption, voice and accountability, rule of law, and political stability and absence of violence. Insight of the ARDL model is drawn from the study of Osabohien et al. (2018a). The reason for the use of ARDL approach to cointegration is built on the premise that time series variables trend in difference order of stationarity, hence the traditional approach to cointegration becomes inefficient.
Where: Δ is the change in operator and the t 1 ECM − denotes error correction term. γ represents the speed of adjustment from the short-run to the long-run equilibrium Given the above, the ARDL model is represented in Equation 4 n n n n The dependent variable, food security is proxied by the number of people who are undernourished (npu). Agriculture is proxied by agricultural production and Agricultural Credit Guarantee Scheme Fund, population as the number of people. The study builds on Malthusian theory of population. This is because according to Malthus theory, population grows exponentially while food production grows arithmetically doubling with each cycle; in this wise, while food production is likely to increase in a series of twenty-five-year intervals in the arithmetic Braz progression; population is capable of increasing in the geometric progression. This situation of arithmetic food growth with simultaneous geometric human population growth predicted a future when people would have no resources to survive with. This means many people tends to chase few available food in turn leads to food insecurity. The data, sources and measurement of the variables for the study are presented in Table 1:

Results and discussion
This section presents the results obtained from the two methods of analysis: the descriptive and econometric methods engaged in the study presented in sub-sections (4.1) and (4.2)

Descriptive results
Despite of the overall progress in reducing food insecurity across the world, West Africa remains the region with the highest number of people who are under or malnourished (Food and Agricultural Organization of the United Nations, 2017). Some countries have shown progress in terms of food security in recent years, this progress occurred in most countries in Europe, Eastern and South Eastern Asia, as well as in Latin America, while Nigeria showed no progress as the country lags among West African countries. Food security can be referred to the state where all people always have physical, social, and economic access to adequate, safe and nourishing food which meets their dietary needs and food preferences for an active and healthy life (Food and Agricultural Organization of the United Nations, 2017).
United Nations Sustainable Development Goals (SDGs) aims to transform the world economies by year 2030 with 17 specific goals of which the second goal that is, achieving food security and improving nutrition and promoting sustainable agriculture is specifically focused on food and nutrition. It aims at achieving sustainable food security, end of all forms of malnutrition, double the agricultural production and income of small-scale food producers, and ensure sustainable food security.
In this study, we have considered some necessary indicators of food security which are: Number of people who are undernourished, Political stability and Absence of Violence/Terrorism; per capita food supply variability, Per capita food production variability and Total population. As presented in Figure 1 Braz. J. Food Technol., Campinas, v. 23, e2019089, 2020 | https://doi.org/10.1590/1981-6723.08919 6/12 Addressing the food security issue in Nigeria demands prompt accountability which could help demarcate the current situation in its entirety, highlighting the key areas affected, and encourage advancement of relevant methods that can resolve the issue. This would create a platform of food supply resilience aimed at keeping the developed approach on a rapid response to emerging food security challenges. Accountability, among other factors points to the fact that food security would pose the challenge of low per capita productivity, especially in food production, which is relevant to food security. Figure 1 shows the food production level measured as food production variability in West African countries. Nigeria ranked third among the countries that with the lowest figure (ranging between 2.4 to 14.5). It shows the inadequacy of the current food security systems, insufficient resource availability and accountability on the use of resources during the period under review. These challenges therefore call for improvement on the current investment profiles on food production and security. It is widely believed in literature that increase in production generates more food capable of reducing food shortage and the exclusion of the poor as a result of hunger as experienced in France and England (Fogel, 2004). The improving supply of food in both countries showed efficient production of food systems. In terms of food production, Nigeria as the most populated country in Africa with over 180 million people lags behind other West African countries as its food production observed to be lower. In this regard, more attention is needed to boost food production, food preservation and distribution, which could form a notable base for projecting the entire economy to better performance in the future. Collaboration with supportive agencies could be helpful.
In line with the above, it was observed that in Mali, the food production (especially food crops) has conventionally formed the bedrock for the pursuit of food security agenda (Sidibé et al., 2018). This idea has been a long position of giving main concern of successive governments since Mali's gained independence, in 1960. Structural responses to food insecurity in Mali have mainly consisted of strategic reforms to enable the nation to enhance agricultural production for the attainment of food security (Bélières et al., 2008).
Per capital food supply variability is another main indicator for measuring food security. Food supply variability results from a combination of instability and responses in production, trade, consumption and storage, in addition to changes in government policies such as trade restrictions, taxes and subsidies, stockholding and public distribution (Osabohien et al., 2018b). In Nigeria, unevenly distribution of food probably reflects in price instability, which effects vulnerable households' ability to make long-term adjustments to their resource constraints (Lele et al., 2016). It is necessary to understand the nature of fluctuations in a food system which can aid scholars and policy makers on the strategies to be employed in enhancing the food systems in Nigeria.
It has been predicted that food demand will increase in the coming years in all countries, especially Nigeria with high population and to off-set this food demand, strategies for efficient and effective supply of food to Braz all households in Nigeria needs to be put in place to avert food shortage, and this can be done is through innovation (like warehouse and other storage facilities, Osabohien et al., 2019c). Populations spread of countries in West African sub-region during the period under review. Nigeria, which is the focus of this study has high population growth rate. This has not been reflected on food production and security practice in Nigeria. Instead, the growth in national population has resulted in a further complexity in terms of availability of food that meets the demands of the population density, especially in urban areas where food production is minimal and the demand is high (Ojo, 2004;Echebiri & Edaba, 2008;Jhingan, 2003) Because food security involves different aspects which are made up of: availability, access to food and food utilization, its strategies has to be essentially engaged by government, non-government organisations. Food security governance can be said to have two main interactions: the formal and informal interactions' which cut across balance among public and private individuals with the goal of achieving food security (Candel, 2014). experience of agriculture and food governance in Brazil and South Africa have confirmed that food security situation can be considerably enhanced by adopting three essential procedures such as: creating a new social policy programmes, formulation of an agency exclusively to manage the effort of other ministries in quest for food security objectives and together with stakeholders in the decision-making procedure Effective food security governance requires a broad, systems think, problem-solving strategy which tackle the difficulty of food security, cut across national, regional, and local levels, and is adjustable across time. When governments fail to tackle the crisis situations in nation and structural factors, they invariably fail to address food security and its affect (Shamah-Levy et al., 2017;Candel, 2014). Ironically, results obtained showed that Nigeria food security governance has taken on post-political uniqueness which hampers rather than enhancing growth of ending food shortage and achieving the right to food for all. Quantitative details about the absence of terrorism in West African sub-region. The data describes the level of security of life and property among West African countries.
Compared to other West African countries, Nigeria ranks as a high-risk country on this record as its political stability and absence of violence continues to be negative and higher than other regional countries [-5.2 in 2001; -2.7 in 2015] (Food and Agricultural Organization of the United Nations, 2017). This could be traceable to the inherent crises' herdsmen in both the northern and southern parts of the country as the violence between the Fulani herdsmen and farmers have become one of Nigeria's most constant security challenges and have left thousands of people displaced and dead in recent years (Vanguard, January 11, 2018). Crisis in these locations have adversely affected food production and supply, because when there is crisis in these locations, there would be further challenge on food security which would in turn result to the challenge of food shortage in supply to the various parts of the country where demands are high, leading to higher prices and scarcity. There could also be wastage of scarce food resources due to emergence of crisis that could prevent distribution.

Results from econometric analysis
This sub-section of the study presents the econometric method. The starting point of presenting the econometric result is by presenting the summary statistics of the variables as shown in Table 2. The results show the summary statistics of the selected variables that were engaged in this study, the variables are as aforementioned. The mean, standard deviation, minimum and maximum are as presented, to bring to bear the real information required for the study

Unit root test for stationarity
To conduct the ARDL effectively, the unit root test for stationarity was conducted to determine the integrating order of the selected variables. This is considered as a necessary step in order to validate the assumption that none of the variables should be stationary at second differenced (that is, I [2]). This assumption is aimed at preventing the issue of 'spurious result. Insight of the ARDL methodology was drawn from the empirical work of Ouattara et al. (2006). Ouattara et al. (2006) has it that F-statistic that Pesaran (2007) presented seems ineffective when differentiated at order two [I (2)], since the method is based on the premise that variables either co-integrated at order zero [I (0)] or co-integrated at order one [I (1)]. Therefore, engaging a unit root tests in the ARDL approach to cointegration is to ensure that none of the variables is integrated of order 2 as presented in Table 3    The study's econometric method, using the ARDL cointegration approach, focuses on the long-run and short-run relationship as presented in Tables 4, 5. The ARDL results show that there exists a long run relationship food security indicator (number of people undernourished), agriculture indicators (agricultural production and agricultural guarantee scheme fund), indicators of governance and accountability (voice and accountability, political stability and absence of violence, control of corruption and rule of law) and population. The result shows that in the long-run increase in agricultural production reduces the number of undernourished people by 2.89%, agricultural credit guarantee scheme fund to farmers enhance food production base thereby reducing undernourishment by 18.6%. Akin to Malthusian population theory, increase in population increases undernourishment by 74.8%, the reason for this high increase is because of low food production and many people chase little available food produced. Based on governance and accountability, the increase in corruption rate affects undernourishment by 3.94%, political stability and absence of violence contribute to the attainment of food security. It is confirmed in this study that corruption weakens the system and as result increases undernourishment by 0.74% and 21.06% respectively. The result from the error correction mechanism is presented in Table 6 is Table 6. Estimates from error correction mechanism (measurement identifiers see Table 1). Note: *, **, *** means that variables are statically significant at 1%, 5% and 10% respectively, while LD shows that variables were lagged and differenced based on Akaike information criterion (AIC), Hannan-Quinn information criterion (HQIC) and Schwarz's Bayesian information criterion (SBIC). LD means lagged and differenced Source: The Authors'.

Conclusion
Accountability in ensuring food security is a multifaceted assignment, as it is subjective by collective factors operating at diverse tiers of the social-ecological model. These factors comprise the accessibility to a sufficient food supply and access to food from the federal to the state and local government levels. Access to the food supply is in turn mainly influenced by agricultural production; this means that; the higher the production, the more people gain access to food. At Macro-level, food access is driven by factors such as food prices, job opportunities, minimum wages, and social protection policies or programs. The relationship among central, state and local systems of food are characterized by boundaries drifting and high level of insecurity, which makes attempts at steering even more complicated. This is the reason why food security governance, which considers all the drivers, governing activities, and policy outputs and outcomes involved in feeding the population, has gained so much interest as a field of study in recent years This study explores the importance of food security in Nigeria, considering accountability and governance as key variables. The study found that it is a worthwhile practice for Nigeria to pursue stability as this can form a background to channel the national economy to address food shortage challenges. This could be done through an aggressive support initiative and other pragmatic actions to engage stakeholders to embark on effective food production and distribution that meet household demands. In order to meet households food demand, agricultural incentives should be granted to farmers to increase food production, this is evident from the result obtained in the study which shows that in the long-run increase in agricultural production reduces the number of undernourishment by 2.89%, agricultural credit guarantee scheme fund to farmers enhance food production base thereby reducing undernourishment by 18.6%. Also, the focus on preservation logistics would also be needed to augment current effort to solve identified problems.


extfieldsofstudy:
Medicine
Computer Science

text:
Extending the hierarchical decision matrix to incorporate a dominance ranking score for economic systematic reviews

As the base of clinical evidence grows, it is increasingly common to conduct economic evaluations in addition to clinical evaluations of effectiveness in order to inform health policies. For economic systematic reviews there is currently no agreed-upon quantitative method to obtain a pooled economic effect size. With no suitable quantitative method available, the hierarchical decision matrix stands out as a tool that enables a visual summary of different types of economic studies, but there are limitations with the hierarchical decision matrix. We extended the hierarchical decision matrix with a weighted scoring system (termed dominance ranking score) to allow for useful information of a study design to be incorporated. • The scoring system of the dominance ranking score incorporates weighting factors that are based on sample size and effect size of a study.• The dominance ranking score enables a more differentiating analysis of dominance levels.• For systematic reviews that include partial economic studies, both the hierarchical decision matrix and the dominance ranking score assist to indicate the level of economic potential for a particular intervention, which facilitates the conduct of subsequent full economic studies.

• The scoring system of the dominance ranking score incorporates weighting factors that are based on sample size and effect size of a study. • The dominance ranking score enables a more differentiating analysis of dominance levels.
• For systematic reviews that include partial economic studies, both the hierarchical decision matrix and the dominance ranking score assist to indicate the level of economic potential for a particular intervention, which facilitates the conduct of subsequent full economic studies. Specifications

Background
Systematic review of clinical effectiveness has become the gold standard for evidence-based medical research. As the base of clinical evidence grows, it is increasingly common to conduct economic evaluations in addition to clinical evaluations of effectiveness in order to inform health policies [1 , 2] . Economic evaluations can be classified into two main types: empirical economic evaluations which collect patient-level data on costs and outcomes, and decision model (simulation) based economic analyses [2] . Empirical economic evaluations involve data collection and synthesis of existing evidence using the framework of a systematic review, while economic decision modeling involves the data collection and synthesis of existing evidence in order to generate a new economic evaluation using the framework of a decision model [3] . For either type of economic evaluation, the economic outcomes are expressed using a variety of measures, such as cost effectiveness, cost utility, cost benefit and incremental cost effectiveness ratios (which measure relative efficiency and are expressed as incremental gains in clinical effectiveness, health utility values or monetary valuations) [3][4][5] .
Systematic reviews of empirical economic studies involve several stages of research which are similar to systematic reviews of clinical effectiveness: defining the review question and inclusion criteria for study eligibility; identifying and collecting data; appraising methodological quality; analyzing collected data and undertaking meta-analysis (where appropriate); and summarizing and presenting results [4 , 5] . On the other hand, economic decision modelling involves computing the expected costs and outcomes of alternative interventions, based on a synthesis of evidence for the probabilities, costs and outcomes associated with each event in the pathway flow of the intervention [3] .
However, in the systematic review of empirical economic studies, there are currently no agreedupon methods to obtain a pooled economic effect size from included empirical trials using metaanalysis or other quantitative synthesis methods [4 , 6] . The main reasons are the generalizability of the empirical economic studies. Economic trials are context and time sensitive [3] , as resource use and unit costs are dependent on local settings, clinical practices and currency values at a particular time. This generates high heterogeneity if the economic studies in a systematic review come from different clinical contexts in different countries. Only if the studies have similar resource use and unit costs, with economic outcomes expressed in a common metric, then meta-analysis could be appropriate [4] .
Given the current lack of quantitative synthesis methods, systematic reviews of economic studies have been restricted to methods of using descriptive narratives (via summaries or tables) [4] or plotting the magnitude and direction of cost and effectiveness using the CE-plane [7] , or applying visual summaries in a hierarchical decision matrix [1 , 5] . Here, we propose methods to extend the hierarchical decision matrix in order to enhance its use and address some of its limitations.

Advantages of the hierarchical decision matrix
The hierarchical decision matrix (also known as dominance ranking framework) [1 , 5] enables a visual summary of various economic studies that have different economic outcome measures (e.g. cost-effectiveness, cost-utility, cost-benefit), which would otherwise not be possible in a quantitative meta-analysis approach [8] . Although the hierarchical decision matrix is not a quantitative synthesis method, its hierarchical structure enables an interpretation of the dominance levels of an intervention  Table 2 Dominance ranking score scale.
based on assessment of benefits for both cost and health outcomes within a trial. If there are more studies in a certain color band, it would indicate a dominance level which is associated with that band.
Another advantage of the hierarchical decision matrix is that it allows for economic synthesis even when only partial economic data (e.g. cost minimization data) is available. For emerging research areas, basic economic cost data is usually collected in parallel to a trial on clinical effectiveness. These trials do not usually incorporate extensive economic analysis, such as calculations of incremental cost effectiveness ratio or acceptability curves. In such a scenario, the hierarchical decision matrix allows for rudimentary economic studies with only cost minimization data to be synthesized.

Limitations of hierarchical decision matrix/dominance ranking framework
The hierarchical decision matrix shows the distribution of studies in the three different bands, where a predominance of the number of studies in a certain band will indicate the likely implication of the intervention. However, if there are equal numbers of studies across two or three bands, no clear conclusion can be drawn.
Furthermore, this approach also does not take into account sample sizes and effect sizes of the clinical trials, which affect the statistical power of a trial to detect an effect. To illustrate, in an example where there are three studies and one of them is within the band that favors the intervention, while the other two studies are within the band that rejects the intervention. Under the current hierarchical decision matrix, it will imply that the intervention is to be rejected. However, the two studies that reject the intervention could be smaller trials while the study that favors the intervention may have a larger sample size and greater precision. Rejecting the intervention in such a scenario may lead to an inaccurate conclusion. If we can extend the hierarchical decision matrix to incorporate additional study aspects, it may provide more informative analysis of dominance levels.

Methods
We extend the hierarchical decision matrix using a weighted scoring system which is described here. We term this method the dominance ranking score.
The data transformation of the benefit value to make it suitable for use in the dominance ranking score is shown in Table 1 . To calculate the traditional dominance ranking we transform the qualitative expression of the benefits in terms of ' + ' and '-' into quantitative data that can be calculated. This process of converting qualitative data into quantitative data is called quantitizing [9] and this approach of data transformation is commonly used in mixed method analysis and synthesis [10][11][12] . For the dominance ranking score, in order to align the direction of the scoring for health outcome to be the same as the direction for cost outcome, a better health outcome for the intervention of interest is Table 3 Hierarchical decision matrix/Dominance ranking framework (all studies) [16][17][18][19][20] .

Table 4
Summary of dominance levels (based on hierarchical decision matrix).
given a negative sign. Note that this is in opposite contrast to the hierarchical decision matrix, where a better health outcome is given a positive sign [1] .
We know the sample size of a trial improves the precision of the trial and the larger the sample size, the smaller the confidence interval [13] . We also know that for meta-analysis, studies are weighted by their variance, with larger studies having more weight [14] . To give a similar weighting to larger studies, which should have more precision in their effects, we can modify the existing hierarchical decision matrix by adding a weighting to the cost and health benefits that is based on their sample sizes.
The weighting calculation schema are as follows: Where: n = sample size of a study; N = total sample size of studies in a sub-group analysis; Benefit Value = −1, 0 or + 1 Where: k = number of studies included in a sub-group analysis By adding a weighting based on sample size to the cost benefit, the cumulative dominance ranking score will range from −1 to + 1. Similarly, by weighting the health benefit, the cumulative ranking score will range from −1 to + 1. Assuming that both cost and health benefits play an equal role in determining the overall ranking result, the weighed cost and health benefits can be averaged to obtain the overall dominance ranking score. Based on this approach, we can color-code the dominance ranking scores per Table 2 to visually represent the level of dominance of the intervention.
If the overall score is in the green zone ( −1 ≤ score < 0), then the result favors the intervention, with a score in the lighter green zone ( −0.5 ≤ score < 0) showing a moderate dominance of the intervention and a score in the deeper green zone ( −1 ≤ score < −0.5) showing a strong dominance of the intervention. If the overall score is in the red zone (0 < score ≤1), then the result favors the control, with a score in the lighter red zone (0 < score ≤ 0.5) showing a moderate dominance of the control and a score in the deeper red zone (0.5 < score ≤1) showing a strong dominance of the control. In the case that the overall score is equal to 0, then it would mean that neither the intervention nor control dominates, i.e. a neutral state (indicated by the grey zone).

Results
Using a case study example from a published economic review [15] , we illustrate calculations of the dominance ranking score and presentation of its color-coded scale. In the case study example, five studies were included for economic review and Tables 3 and 4 show the results of the original review. From Table 3 , there are more studies under the green band, which indicates that the intervention is favored. For the sub-groups analysis ( Table 4 ), all sub-groups except for the subgroup "mild/moderate", indicate that the intervention is favored. Under the "mild/moderate" subgroup, there is one study favoring the intervention and one study rejecting the intervention, which leads to an inconclusive result.
Using the data transformation key in Table 1 , these studies were assigned values of either −1, 0, + 1 for their cost and health benefits. Thereafter the cost and health benefits were weighted based on sample sizes of the studies ( Table 5 ). Table 5 Dominance ranking score (all studies) [16][17][18][19][20] .

Table 6
Summary heatmap of dominance levels (based on dominance ranking score).
The calculations are then repeated for every subgroup and a summary can be tabulated as shown in Table 6 . As a comparison, the results using the hierarchical decision matrix and its summary tabulation are shown in Tables 3 and 4 respectively.
In Table 4 , for "mild/moderate" subgroup, as there was one study in each band, no clear interpretation can be drawn; whereas under the dominance ranking score ( Table 6 ), we can glean further analysis. In this case, that overall, for the "mild/moderate" subgroup, there is a moderate dominance for control. For the cost benefit, moderate dominance for control is shown and for the health benefit, a neutral state is shown. Under a dominance ranking score which incorporates sample sizes, we can have not only a more differentiated analysis for overall dominance level, but also analysis which can be specific to dominance levels of cost and health benefits.

Sensitivity analysis
To illustrate changes to the summary heat map, sensitivity analysis of varying the weights of the cost and health outcomes was conducted based on two weight fractions: 25% Cost/75% Health and 75% Cost/25% Health. The resulting heat maps are as shown ( Tables 7 and 8 ). Table 7 Summary heatmap of dominance levels (25% Cost/75% Health).

Discussion
In the following sections, improvements of the dominance ranking score are described.

Weighting the effects of cost and health outcomes
In the case study example illustrated above, we had assumed that cost and health outcomes had an equal role in determining the overall dominance ranking score. For certain trials this may not be so, for example in certain infectious diseases with high mortality rates, the health outcome might have a larger emphasis. With sufficient evidence and justification provided, reviewers can alter the weights of the cost and health outcomes for the dominance ranking score, thus enabling researchers the flexibility to determine the ranking score that is applicable to their research context. The revised calculation schema incorporating weights to cost and health outcomes is: Where: W cost = weight applied to cost outcome; W health = weight applied to health outcome; k = number of studies included in a sub-group analysis

Sensitivity analysis
Weighting fractions were applied for cost and health benefits, and this resulted in slight changes to the summary heatmap. For 25% Cost/75% Health weighting fraction, only the sub-group of 'severe patients' was changed. Instead of a strong overall dominance by the robotic intervention, a moderate dominance resulted. This was due to a shift in dominance level towards Health Benefit, which has a moderate robotic dominance. For 75% Cost/25% Health weighting fraction, only the sub-group of 'all studies' was changed. Instead of a moderate overall dominance by the robotic intervention, a strong dominance resulted. This was due to a shift in dominance level towards Cost Benefit, which has a strong robotic dominance. The sensitivity analysis demonstrated that varying the weighting fractions would generate slightly different heatmaps, and researchers would need to consider their research context when determining the weighting fractions.

Incorporating multiple weighting factors
With a weighting scheme, various aspects of a study design can be incorporated as multiple weighting factors. In our method, besides the sample sizes, we could incorporate one other weighting factor that mirror key considerations when conducting a systematic review: effect sizes of trials.

Effect size weighting
Effect size data could be an additional weighting factor. Studies that show greater effect sizes will have more weight and the effect size weighting will shift the dominance ranking score towards such studies. The calculation schema to incorporate effect size weighting is: Where: | e | = modulus of effect size of a study; E = sum of effect sizes of studies in a sub-group analysis; [ | e | E ] cost is the effect size weight for cost data; [ | e | E ] health is the effect size weight for health data; F = number of weighting factors applied (here F = 2); n = sample size of a study; N = total sample size of studies in a sub-group analysis; W cost = weight applied to cost outcome; W health = weight applied to health outcome; k = number of studies included in a sub-group analysis; Benefit Value = −1, 0 or + 1 Note: Effect sizes can have positive or negative values, depending on whether intervention is favored, or control is favored. As the direction of effect size is already indicated by the sign of the benefit value, only the modulus (i.e. absolute value) of the effect size is needed for calculation of effect size weighting. As there are both cost and health (i.e. clinical effectiveness) outcomes, there will also be two effect size data: one for cost outcome and one for health outcome. Note that the presented weighting scheme applies to effect sizes of continuous data (i.e. weighted mean difference, standardized mean difference), as the meta-analysis of such data is centered on a neutral value of "0", with either side representing effects favoring intervention or control on symmetrical scales.

Increasing the precision of the dominance ranking score
The inclusion of confidence intervals would provide more precision to the dominance ranking score. However, at this early stage of the proposed methodology, it is our intent that varying levels of dominance be easily interpreted via a simple bar scale ranging from −1 to + 1. As we gain more experience with the use of the currently proposed dominance ranking score, confidence intervals could be incorporated in the future.

Weighting factors
We have shown how weights based on factors relevant and important to most reviews, namely: sample size and effect size of studies can be considered in the analysis. The weighting factors serve to incorporate useful aspects of a study design into the hierarchical decision matrix/dominance ranking framework and helps to provide more information for analysis and understanding of dominance levels. In our example, we have demonstrated a technique to increase discriminatory power in the analysis, through incorporating a weighting scheme that is based on sample sizes of the included studies.

Effect size weighting factor
With an additional weighting factor that incorporates effect size, the dominance ranking score can be further enhanced for a better analysis of dominance levels. However, effect size data for economic cost measures is often not available, as meta-analysis for economic cost data is not encouraged due to resource use and cost data that are sensitive to variability across settings and between countries [8] . Even if economic effect size data is available, the different economic outcome measures used in economic trials (e.g. cost-effectiveness, cost-utility, cost-benefit, cost minimization) also make the comparison of effect sizes currently not possible, so we urge caution when incorporating effect size weighting. For economic studies where the cost setting is similar, with low variability across resource use or clinical practice, and the economic outcome measure is the same, effect size weighting may be appropriate. If only effect size data for clinical effectiveness is available, it is not suggested to conduct effect size weighting for the health benefit without a similar effect size weighting for the cost benefit. Such an approach, while incorporating more information for health benefit analysis, will render an unbalanced weighting approach towards the overall interpretation of dominance levels.
Another consideration to incorporating effect size weighting factor is that outlying studies in a meta-analysis would skew the dominance ranking score towards such studies. Unless the causes for the outlying effect can be explained, such outliers should be viewed with caution and be justified if included.

Sample size weighting factor
The dominance ranking score is flexible to allow multiple weighting factors to be incorporated. However, reviewers might wish to limit their weighting factors and only incorporate those factors that are most relevant to their review. Given that effect size weighting has its limitations and are to be used with caution, we would recommend that sample size weighting be the preferred weighting factor. Sample sizes are usually reported (unlike economic effect sizes) and can be easily extracted from studies. It is also a form of objective data, in that it is not subject to interpretation by reviewers.

Benefit values
It is important to note that the weightings are associated with a benefit value ( −1, 0, + 1) which is, itself, a categorical expression of the direction of dominance. As such, it may not be appropriate to have too many weighting factors, as the starting point (i.e. the benefit value) is not a precise measure of dominance level. For example, in the scenario that all studies in a review have lower intervention cost outcome than control, this will mean that the cost benefit values assigned would all be −1. In such a case, regardless of the weightings, the calculated cost dominance level would always be −1.
Hence the dominance ranking score is useful when studies have heterogeneous benefit values and can help to better differentiate the dominance level. The dominance ranking score is also useful when the hierarchical decision matrix shows an unclear interpretation (such as equal number of studies in each band).

Extending the hierarchical decision matrix
In no sense do we claim that the dominance ranking score is able to perform a quantitative metaanalysis of economic data or that the hierarchical decision matrix is irrelevant. We merely extend the hierarchical decision matrix tool to have more differentiating information incorporated into it and thereby enable a more informed analysis of dominance levels to be performed. In fact, we suggest reviewers conduct a review tabulation using the hierarchical decision matrix and then supplement their analysis with the dominance ranking score.
We also recognize that for decision making, good quality economic trials or models that represent the specific context of the healthcare system is necessary. However, the proposed dominance ranking score and the associated hierarchical decision matrix are still useful when evaluating partial economic studies, such as studies with only cost minimization data. Such cost minimization studies are usually conducted for emergent research areas where basic economic data is collected, in parallel to a trial on clinical effectiveness. On this spectrum of rudimentary economic studies, we see a role for the hierarchical decision matrix and the dominance ranking score to provide some degree of dominance analysis. Through such a 'pilot' analysis, it could indicate the level of economic potential and pave the way for a full economic study.

Sub-weighting of weighting factors
If the use of multiple weighting factors is appropriate, it is possible to further weight each individual weighting factor, such that one can assign more priority to a certain factor. For example, if effect size is more relevant to a review, the effect size weighting can be given a higher weight. This will shift the direction of the dominance ranking score towards studies that have larger effect sizes. If such further sub-weights are adopted, it is suggested that justifications for the levels of subweightings be reported and that these sub-weights be consistently applied for both cost and health benefit calculations. The sum of the sub-weights should always add up to 1.

Conclusion
For economic systematic reviews there is currently no agreed-upon quantitative method to obtain a pooled economic effect size. With no suitable quantitative method available, the hierarchical decision matrix stands out as a tool that enables a visual summary of different types of economic studies. Extending the hierarchical decision matrix with a weighted scoring system (termed dominance ranking score) allows for useful information of a study design to be incorporated and enables a more differentiating analysis of dominance levels.
Various study characteristics can be incorporated via weighting factors in the dominance ranking score, although the appropriate use of each weighting factor needs to be considered. In this paper, we have incorporated weights based on factors that we deem are relevant to most reviews, namely: sample size and effect size of studies. Given that effect size weighting has its limitations, we would recommend that sample size weighting be the preferred weighting factor, as sample sizes are usually reported and are a more objective form of data.
For systematic reviews that include partial economic studies, we see a role for the hierarchical decision matrix and the dominance ranking score to provide some degree of dominance analysis, which would otherwise not be possible. This analysis could improve the utility of such reviews by indicating the level of economic potential for a particular intervention, which may facilitate the conduct of subsequent full economic studies.