Firm dynamic productivity

308 views

Published on

Slide presentation for DDSS Conference 2010

Published in: Education, Technology, Real Estate
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
308
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
4
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • Economic growth can be described as changes or increments in the level of production of goods and services in the economy of a country over a period of time. To the extent that economic growth is reflected in urban growth, it is often manifested in changes in land use patterns. In general, some amount of growth can be captured in the existing building stock and associated land use patterns, but increasing growth tends to induce land use change.   The problem and difficulty is to how to incorporate economic growth in agent-based models. Although there are differences between models, integrated land use transportation and cellular automata models typically first estimate a model of economic growth such as a spatial input-output model, sometimes linked to assumptions or a model of external urban growth. These models then predict the impact of change in the economy in one sector or region on other sectors in other regions. This change is assumed to reflect a changing demand for land use. Finally, this additional demand is allocated across locations as a function of accessibility and suitability. This mechanism of including economic growth into these models is hierarchical and aggregate and far removed from the premise of agent-based modelling of behavioural realism. The question then is what alternative approach may be employed?   Based on the notion that location decisions are part of their business model and its inherent dynamics, it has been argued that perhaps the demography of firms or Firm demography may be valuable approach. This concept focuses on the demographic transformation of firms in the region and its correlation with land use changes. Firm demography can be defined in terms of the lifecycles of a firm which are similar to human lifecycles that consist of birth, death, and changes in the number of employees of the firm. Seminal work on this concept goes back to David Birch’s study on ‘job generating processes’ in the USA (Birch, 1979). He advocates that the economic development of a region consists of birth, death, and migration of firms which can be considered as the basic components of development (Van Dijk and Pellenbarg, 2000b). In recent years, firm demography has caught some attention in organisational sociology (Carroll and Hannan, 2000), economics and economic geography (Van Wissen, 2002), industrial organization (Geroski, 1995; Audretsch et al., 1997); (Caves, 1998), regional science (Van Dijk and Pellenbarg, 2000a) and transportation (Moeckel, 2005) . We decided to use the concept of firm demography in this study, but the concept probably needs some elaboration. Existing models have basically used the concept only to have an accounting system that predicts the future number of firms in particular segments and their size as a function of births, deaths and relocations. To the extent these drivers of change are predicted, often again the well-known aggregate models are used. This remains far from a richer theoretical framework. Hence, the challenge is how to model decisions about growth/decline, start and closure and relocations as a function of external factors. The special links between firm dynamics and urban dynamics will give firm demography the connotation of an intermediate concept linked with economic growth.
  • Demography of firms or firm demography can be defined as the study of demographic events in the firms population in a particular area (Geenhuizen and Nijkamp, 1994). Interest in the firm demography approach can be traced back to the 1970s and this concept been attributed to seminal work by David Birch in his study on ‘job generating processes’ in the USA (Birch, 1979). He advocates that the economic development of a region can be considered as the basic components of development (Van Dijk and Pellenbarg, 2000b). Research on firm demography has caught some interest in industrial organisation (Geroski, 1995; Audretsch et al., 1997; Caves, 1998), organisational sociology (Carroll and Hannan, 2000), regional science (Van Dijk and Pellenbarg, 2000a), economics and economic geography (Van Wissen, 2002) and transportation (Moeckel, 2005).
  • Klang Valley is a region in Malaysia which is comprised of Kuala Lumpur and its fringes and neighbouring cities and towns in the state of Selangor. Klang Valley has no official boundary, yet it is currently comprised of six districts which are Kuala Lumpur, Putrajaya, Gombak, Hulu Langat, Klang and Petaling (Figure 2). The physical site of the area was encouraged by the development of commercial, industrial and residential activities which made Klang Valley grow rapidly. With a population of 5.78 million people (Department of Statistics Malaysia, 2008) and areas covering up to 2,826 km 2 , Klang Valley has the most urbane transportation hierarchy in Malaysia. Rather than a road system, integrated rail transport which consists of monorail, electrified commuter, light rail transit (LRT) and Express Rail Link (ERL) are build for Klang Valley’s residents and workers to commute within the region which make it the most important region in Malaysia. This region is also served by two airports: the Kuala Lumpur International Airport (KLIA) in Sepang, which is the main international airport for Malaysia, and Sultan Abdul Aziz Shah Airport in Subang, which handles general aviation and turbo-prop domestic flights (2010).
  • This study relies on three sets of data which have been obtained from various sources. These are (1) firm data; (2) Klang Valley’s GIS data; (3) statistics and economic data for Klang Valley and Malaysia. Firm data have been obtained from Company Commission of Malaysia (CCM) which consists of registered companies in Malaysia. Klang Valley’s GIS data was acquired from Klang Valley Federal Territory (KVFT) and consists of data about administrative boundaries, land use, transportation and physical characteristics. Statistics and economic data were obtained from the Department of Statistics Malaysia, Kuala Lumpur City Hall (KLCH) and the Economic Planning Unit (EPU).
  • The firm data which were obtained from CCM were in spreadsheet format. In order to display firm locations in spatial environments, geocoding was used. Geocoding or address matching is the process of assigning a location, usually in the form of coordinate values, to an address by comparing the descriptive location elements in the address to those present in the reference material (ESRI, 2003). Based on the data, addresses come from common address formats of a company premise number followed by the street name, postal zone and area.   Figure shows the geocoding process for this study. Firstly, road data was created as an address locator. An address locator is a compulsory indicator for geocoding which allows conversion of textual descriptions of locations into geographic features. Road data is selected because it is the most suitable matching entity and can represent the firm location. To geocode addresses, company data will be the input and road information will be the key field for matching the address
  • This analysis covers firms in the Kuala Lumpur area between 1990 and 2007. Figure 2 shows the dynamic performance of firms operating between 1990 and 2007. In total, there are 57 737 firms including dormant firms in the Kuala Lumpur area. A dormant firm in this study is described as a firm which is not active or out of business. As can be seen in Figure 2, there were three peak years of firm growth: 1995, 2000 and 2004. From year 1995 onwards, the growth of new firms started to decline especially from 1997 to 1999 due to the global financial crisis. Dormant firms have shown a rise and decline similarly to the trends observed for new firm’s birth.
  • The evolution of new firms by sector is described in Figure 3. The Company Commission Malaysia divided the company data into 11 sectors includ ing Dormant firms (sector 1) and firms which are not adequately defined or missing (sector 2). From the figure, it can be seen that sector 10 (Financing, Insurance, Real Estate, Investment and Business Services) encompasses the largest share of new firms (35.6 per cent) which provided the impetus for economic growth during 1990 to 2007, followed by sector 8 (Wholesale and Retail trade, Restaurant and Hotel) with 31.1 per cent, sector 7 (Construction) with 7.3 per cent, sector 11 (Community, Social and Personal Services) with 6.5 per cent, sector 5 (Manufacturing) with 4.7 per cent, sector 9 (Transportation and Communication) with 4.4 per cent, and sector 3 (Agriculture, Hunting, Forestry and Fishing) with 0.6 per cent. Sectors 10 and 8 which make up the service sector cover almost 67 per cent of firms, which is more than two-thirds of overall firm births in Kuala Lumpur. The rapid expansion of these sectors was due to the growth of four service industry groups in Malaysia—finance, insurance, real estate and business services; estate and business services; wholesale and retail trade, hotels and restaurant; and transport, storage and communications—which accounted for more than 60 per cent of all new jobs in Kuala Lumpur (Morshidi, 2000). Firm births in Sector 4 (Mining and Quarrying) and sector 6 (Electricity, Gas and Water) shows a very modest growth in this period of only 0.3 per cent. Although firm births in sector 5 are small, it is the country’s engine of growth. Other sectors, especially sector 3, remain significant and an important component of the Malaysian economy.
  • The current available infrastructure in Kuala Lumpur ensures that employees can commute to work and companies can distribute their products. The comprehensive road and rail network makes Kuala Lumpur more accessible by reducing commuting times. This should lead to higher economic growth in the country as a whole. Proximity to highways and major roads appears to be an important determinant of firm location choice in urban economic research (Maoh et al., 2005). Simple proximity analysis was performed in this study. Multi-ring buffer tools were used to classify firm locations around five transportation features which are highway network, major road network, rail network, rail station and transportation terminal. The results of the analysis, choosing 100 meter and 500 meter as the buffer, can be seen in Tables 2 and 3. Table 2 shows that most firms in Kuala Lumpur are located within a range of 500 meter from the major road (87.7 per cent). This is probably because the major road network in Kuala Lumpur is easy to access and was gazetted as a commercial area zone in the KLSP. The proximity of firms to the Terminals is slightly less because only four major bus terminal are located in Kuala Lumpur area.
  • As shown in Table 3, differences between sectors are small.
  • The Central Business District (CDB) is the strategic zone in Kuala Lumpur (Figure 8). It covers of 18.13 sq. km. bounded by major highways namely Jalan Tun Razak from the east to the north, Mahameru Highway to the west and the Middle Ring Road One to the south (Kuala Lumpur City Hall, 2004). Based on the analysis, 38.2 per cent of the firms in Kuala Lumpur are located in the CBD area. As shown in Figure 9, most entering firms in the CBD area are in sector 10 (share of 37 per cent), followed by sector 8 with 29.9 per cent. Figure 10 shows the spatial distribution of firms in the CBD area.
  • This paper has examined some firm dynamics in the Kuala Lumpur area between 1990 and 2007. The results of a firm demographic analysis, using GIs tools, show that Financing, Insurance, Real Estate, Investment and Business Services sector play an essential role in the economic growth for Kuala Lumpur area. The importance of transportation links in the way of road networks and rail connections influence the decision of locating new firms. The analysis also shows that most of firms are located close to transportation features. In general this paper presented progress made in the development of multi-agent model of urban dynamics in the context of economic growth. In the early stage of development, we consider of the firm demography approach as part of the model to be developed. A basic problem in firm demographic research is the lack of data (Van Dijk and Pellenbarg, 2000c). For this study, partial data is available such as date of start-up, firm address, firm status and type of firm but no information is available about migration, closure date or employment status. Thus, model opportunities are limited to data availability. This paper does not yet present details of a more complex analysis and specific model. The next step will focus on the design of the model and on the preparation of the data for more detailed analysis and simulation.
  • Firm dynamic productivity

    1. 1. FIRM DYNAMIC PRODUCTIVITY AND ECONOMIC GROWTH Noordini binti Che’Man Harry Timmermans
    2. 2. <ul><li>INTRODUCTION </li></ul>/ Urban Planning Group PAGE DDSS 2010
    3. 3. INTRODUCTION / Urban Planning Group PAGE URBAN PLANNING TRANSPORTATION MODELLING Modelling Urban Dynamic Cellular Automata Integrated Land Use Transportation Model Weakness Weak Behavioral Basis DDSS 2010
    4. 4. / Urban Planning Group PAGE Alternative !! MULTI – AGENTS SYSTEM MODELS Agent represent desicion maker Allow investigating urban land use as complex system DDSS 2010
    5. 5. How to Incoporate Economic Growth? / Urban Planning Group PAGE ECONOMIC GROWTH FIRM DEMOGRAPHY Urban growth Land Use pattern DDSS 2010
    6. 6. DEMOGRAPHY OF FIRM <ul><li>Study of demographic events in the firm population in particular area (Geenhuizen and Nijkamp, 1994). </li></ul><ul><li>This concept been attributed to seminal work by David Birch (1979) in his study on ‘job generating processes’ in the USA . </li></ul><ul><li>Firm location choice- many factor influence- vary by business sector and city. </li></ul><ul><ul><li>transportation features </li></ul></ul><ul><ul><li>the CBD area. </li></ul></ul>/ Urban Planning Group PAGE DDSS 2010
    7. 7. <ul><li>STUDY AREA </li></ul>/ Urban Planning Group PAGE DDSS 2010
    8. 8. STUDY AREA / Urban Planning Group PAGE DDSS 2010
    9. 9. Kuala Lumpur Planning System Growth / Urban Planning Group PAGE IATBR 2009
    10. 10. DATA SETS <ul><li>Firm data </li></ul><ul><li>Klang Valley’s GIS Data </li></ul><ul><li>Statistic and economic data for Klang Valley and Malaysia </li></ul>/ Urban Planning Group PAGE DDSS 2010
    11. 11. <ul><li>ANALYSIS & </li></ul><ul><li>RESULTS </li></ul>/ Urban Planning Group PAGE DDSS 2010
    12. 12. FIRM DATA ANALYSIS / Urban Planning Group PAGE Firm Data Locator DDSS 2010
    13. 13. FIRM DYNAMIC ANALYSIS <ul><li>Firm Dynamic Performance (1990 – 2007) </li></ul>/ Urban Planning Group PAGE Total = 57 737 firms DDSS 2010
    14. 14. FIRM DYNAMIC ANALYSIS <ul><li>Firm Dynamic Performance by sector </li></ul>/ Urban Planning Group PAGE 35.6% 31.1% 7.3% 8.1% 0.6% 0.3% 4.7% 0.3% 4.4% 6.5% 1.2% DDSS 2010
    15. 15. FIRM LOCATION ANALYSIS <ul><li>Proximity Analysis </li></ul>/ Urban Planning Group PAGE Distribution of firms 100meter and 500meter from transportation feature DDSS 2010
    16. 16. FIRM LOCATION ANALYSIS <ul><li>Proximity Analysis </li></ul>/ Urban Planning Group PAGE Percentage of Firm by Sector - 100meter and 500meter from transportation feature
    17. 17. Proximity Analysis / Urban Planning Group PAGE DDSS 2010
    18. 18. Proximity Analysis / Urban Planning Group PAGE DDSS 2010
    19. 19. Area Analysis / Urban Planning Group PAGE Distribution of Firms by sector in Kuala Lumpur CBD area. DDSS 2010
    20. 20. / Urban Planning Group PAGE DDSS 2010
    21. 21. CONCLUSION <ul><li>The importance of location and transportation links—influence the decision of new firms. </li></ul><ul><li>Firm demography concept as solution to modelling of economic development as bottom-up development process. </li></ul><ul><li>partial data is available—date of start-up, firm address, firm status, type of firm but no information is available about migration, closure date or employment status—model opportunities are limited to data availability. </li></ul>/ Urban Planning Group PAGE DDSS 2010

    ×