Total Factor Productivity Growth

By and large, the literature on the impact of foreign direct investment and technology gap on total factor productivity growth in African countries is limited and in particular, the role of natural resource endowment is often neglected. Some of the papers that attempt to address this issue are Aseidu, (2006), Davood, Siab, and Azhdar (2010), Asghari, Hilmi, and Safa, (2014) and Gylfason, and Zoega, (2001). Aseidu, (2006) examined the determinants of FDI to Africa. She employed a fixed-effects panel estimation analysis. The analysis employs an unbalanced panel data for 22 countries over the period 1984–2000. The results indicate that large local markets, natural resource endowments, good infrastructure, low inflation, an efficient legal system, and a good investment framework promote FDI. In contrast, corruption and political instability have the opposite effect.

Productivity growth in agriculture is both a necessary and sufficient condition for its development. Total factor productivity is an important measure to evaluate the performance of any production system and sustainability of a growth process (Reddy, 2009). There are several reports that total factor productivity growth is declining over the years in many parts of India even with the application of increased inorganic fertilisers (Deshpande, 2000). The cropping system is sustainable if it can maintain total factor productivity growth over time. The studies by Kumar and Mruthyunjaya (1992), Kumar et al. (1998, 2004), Srinivas et al. (2007) and Reddy (2009) highlighted that the total factor productivity growth of important crops is decelerating in India. It is argued that if appropriate measures are not undertaken to address the problem of sustainability and natural resource degradation, the future growth of agriculture in such areas would be jeopardised (Chattopadhyay and Franke, 2006).

The finding shows that R&D activity significantly affects the TFP growth of firms on the food and chemical sectors. However, R&D activity does not significantly affect the metal and textile sectors. These results might be due to the data limitation used in this study that not capture the performance of the R&D unit of firms specifically. Additionally, compared to the firms that are not located in cluster, on the food, metal and textile sectors, the firms that perform their activities in the industrial cluster tend to gain a higher TFP growth. Nevertheless, on the chemical sector, the firms that are located in a cluster is found to get lower TFP growth compared to firms outside industrial cluster, indicating the congestion effect of cluster. These findings conclude that in general, being in a cluster will benefit the firms to get higher TFP growth. However the congestion effect also should be considered to avoid the negative effect of the cluster. The results of this study also show that TFP growth on chemical, metal, food and textile sector are 5.8%, 3.3%, 7.3% and 6.4% respectively. The technical progress mainly contributes to the total factor productivity growth.

The association we find between the plant level dynamics of exporting activity and total factor productivity growth fits well into this framework. If new entrants engage in small scale exporting operations because of convex adjustment costs or imperfect information about demand in the foreign market, entries and exits will not be earth-shattering events in plants’ lives. The bulk of efficiency enhancing actions that a plant takes in connection to its exporting activity will rather be associated with changes in the intensity of the exporting activity, mainly with becoming a large exporter. This result is consistent with previous empirical findings showing that while, on average, plants that enter the export market at a given point in time do not outperform those that never export, plants that export for longer periods of time have a better performance than those than never export. Our study suggests, however, that this is not solely due to long term exporters being superior with respect to a time-invariant productivity parameter. It is rather the cumulative effect of plant heterogeneity and significant post-entry productivity gains associated with the intensification of exporting activity.

The sources of economic growth is an issue which has received much atten- tion in economic science. One of the most popular and successful ways of summarizing the contribution of factors of production and technology to out- put growth is the growth accounting framework introduced by Solow (Solow 1957). Growth accounting allows for a breakdown of output growth into its sources which are the factors of production and technological progress, and makes possible the estimation of the contribution of each source to output growth. Growth accounting leads to the well known concept of the Solow residual, which measures total factor productivity growth (TFPG). TFPG is the part of output growth not attributed to the use of factors of production such a capital or labour, but to technical change. 3 A strong positive TFPG

trade strategies, favorable external environment and sound macro-economic management, contributed to rapid and sustained economic growth and structural transformation of the economy. Structural transformation of the economy was even more rapid in the 1980s and 1990s. The early 1980s witnessed the start of a heavy industrialization drive and a second round of import substitution following a successful export-oriented industrialization strategy implemented from the beginning of the 1970s. In the 1990s there was a shift in development emphasis towards total factor productivity growth (TFP) growth (Malaysia, 2001)The Seventh Malaysia Plan). However, the economic recession of 1998 as a result of The East Asian Financial Crisis exposed some of the major weaknesses of the Malaysian economy. Among the weaknesses exposed were: over- dependence on bank loans as a source of financing, FDI and short-term capital flows to finance growing current accounts deficits, and on imports of intermediate and capital

A close examination of the empirical literature indicates two approaches commonly employed to investigate the effect of FDI on economic growth of the host country. On the one hand, there is an attempt to understand the link by looking at the effect of FDI on the growth of GDP per capita (Akinlo, 2004; Bengoa and Sanchez-Robles, 2003, and 2005; Yao and Wei, 2007; Li and Liu, 2004; Borensztein et al., 1998). On the other hand, we have those employing the approach of assessing the impact of FDI on the productivity of factors of production in the recipient country. In a well cited work on the link between growth and FDI, Borensztein et al., (1998: 134) proposed this approach to be promising; stating "The results suggest that t he beneficial effects on growth of FDI come through higher efficiency rather than simply from higher capital accumulation. This suggests the possibility of testing the effect of FDI on the rate of total factor productivity growth in recipient countries." However, so far only few tried to follow this approach (Pessoa, 2007; Liu and Wang, 2003; Liu, 2008).

The results in Figure 2 show that the main contributor to TFP growth has been technical change (or frontier shift) of 23%. In addition to this we have a 4% reduction from scale efficiency change (SEC) and a 2% decrease due to technical efficiency change (i.e., VRS TEC). At first glance the negative SEC may seem strange, given that the average farm size has increased over this period. However, we suspect that the main reason for this fall in average SEC is the fact that the larger farms have been improving productivity at a faster rate than the smaller farms, meaning that the performance gap between small and large farms has been widening, and hence giving the smaller farms increasingly lower scale efficiency scores as time passes.

Finally, looking more closely at the aforementioned figures, one sees how higher energy prices during the 1973 and 1979 oil price shocks are associated with a decline in both the technological progress and productivity growth accumulated levels. Some authors, such as Denison (1985) and Gullickson and Harper (1987), have concluded that energy prices have no impact on the growth of output at aggregate level since energy itself is only a small proportion of aggregate output. Our results point to the contrary. In line with Jorgenson (1984, 1988b) energy crises seem to be related with the decline in technological progress and productivity growth of industrialized countries and hence with their economic growth slowdown. As in Jorgenson (1988a), our results seem to support the idea that energy crises could revert production methods to periods of technological development that existed before the oil price shocks. In this post-crisis technological set, the energy price trends could result in the substitution of capital, labor, and material inputs for energy, thus reducing the energy intensity of production. Different cross-country success in handling these inputs might be responsible for the heterogeneous path followed for the technical efficiency accumulated levels plotted in Figure 4.

In the light of increasing integration and consolidation in modern global and European financial markets, evolving governance structures, alliances and changing regulatory environment, this paper provides important new evidence on the productivity, performance, and competitiveness of stock exchanges in Europe. Generally, the rapid pace of advances in innovative communication means and new technologies are deemed to be one of the major forces driving recent growth of trading in global financial markets. The potential impact of electronification is important and far-reaching for the whole trading industry. In this scenario, stock exchanges are facing a new dimension of increased competition forcing them to revise their business strategies and to undertake enormous efforts in investment and implementation programs of new technologies in order to cope with these changes and new environment. Although one might anticipate that advances in new technologies have the potential to shape the future trading landscape, however, relatively little is known empirically about the impact that technology has on the production process of the stock exchange industry. Put differently, it is unclear what actually drives productivity changes for the stock exchange industry operating in a changing environment where technological change occurs. It is at heart of this study to evaluate the nature and extent of changes in productivity in European stock exchange industry. Furthermore, this paper examines whether stock exchanges were able to raise productivity rather by a catching up process with the efficient benchmark or by intense investments in updating or upgrading their technologies.

Chapter 5 investigates the issue of plant heterogeneity and detects factors of different productivity levels across plants. The aim is to identify plant characteristics according to their productivity levels. It is of course necessary to control for different industries that might display intrinsic productivity differences, due to specific technologies of production. Additionally, it has to be demonstrated that productivity heterogeneity is not random. Then, the first tangible obvious factor of productivity differential suggested by the historical literature on Indonesian manufacturing is plant size. The second factor, suggested by the economic literature, is the vintage of the capital stock, that can have an effect on productivity via the technology embodied in the capital stock, and via the broad institutional environment in the period when operations started. The hypothesis of different capital stock vintages also calls for the examination of learning processes. The last hypothesis regards the existence of permanent or quasi-permanent differences across companies - so-called fixed effects. The scarce economic literature on the subject suggests that those differences can be of course size, but also management and labour quality, as well as belonging to a corporate group. A detailed account and analysis of the historical literature suggests that, for the case of Indonesia, further explanatory factors could be of significant importance. The first factor is ownership type, distinguishing between private domestic, private foreign and public ownership. The second factor relates to group membership and patronage (Bapak Angkat), a system of support to small plants set up by Indonesian authorities. The third additional factor is participation to the export market. Last but not least, I account for industry as well as plant cronyism. The previous chapter has underlined a strong difference of regime regarding plant size distribution, plant productivity distribution, and aggregate productivity growth before and after 1989. I study plant heterogeneity over the entire period (1975-95), but also over the two important sub-periods: 1975-89, covering to the oil boom, crisis, recovery, and deregulation periods, and 1990-95, corresponding to the post­ deregulation era and investment boom.

The second strand is based a completely different approach to modeling technological progress. In the seminal contributions of Romer (1987, 1990), Grossman and Helpman (1991), and Aghion and Howitt (1992) changes in the level of technology are due to deliberate and purposeful research and development (R&D) activities of profit seeking firms rather than a side product of investment. In these models increasing returns to scale arise either from the expansion of available product variety or improving quality of existing varieties. According to this strand economic growth is driven by product innovations done by profit seeking entrepreneurs who compare costs of innovating with the discounted stream of profits from innovation. The research and development (R&D) activity occurs in developed countries and international diffusion of knowledge is the driving force of growth in developing countries.

It is found that Quebec dairy farms could reduce their average cost by operating at a higher scale of production and by improving technical efficiency, the mean efficiency score being 0.87 for the 2001–10 period. The results also show that scale efficiency is greater than technical efficiency. These results are not surprising given that the distribution of Quebec dairy is highly skewed toward small homogenous farms and that technical and scale efficiency scores are based on efficiency benchmarks defined by farms in the sample. TFP has grown at an average rate of 9%, thanks largely to IMEs. The contributions of TC and ISE to TFP growth are very small. These results suggest that most Quebec dairy farmers are good managers operating at a suboptimal scale, facing increasing returns to scale. Clearly, the exploitation of economies of scale could bring about reductions in average costs. The rate of TC for small farms in the sample has been quite low. This was also observed in countries that have milk quotas (e.g., Sipil¨ainen 2007; Kumbhakar et al 2008). Some technological advances in milk production are tailored to large farms and small farms are likely to be increasingly disadvantaged. 9

The aim of this paper is to analyze total factor productivity (TFP) growth and its components in Asian countries applying Stochastic Frontier Analysis (SFA) to the time series data of 44 Asian countries from 2000 to 2010. Using Battese and Coelli approach, TFP is divided into technical efficiency change and technical change. TFP decomposition using SFA method for the years 1998 to 2007 indicates that in 75 % of these economies, the role of technical change in productivity growth is negative. Only in 11 countries technical change had a positive role in productivity growth. The growth of TFP shows that Japan has the highest productivity growth (2.55 %) and Saudi Arabia, Korea and Hong Kong are located in subsequent positions. Furthermore, due to the lowest technical progress, newly independent countries, such as Armenia, Azerbaijan, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan have the slowest TFP growth.

Nearly one-quarter of Pakistan’s Gross Domestic Product (GDP) is contributed by the agriculture sector and it employs nearly 44 percent of the labour force. Agricultural exports, directly and indirectly, make up a large proportion of total exports and foreign exchange earnings of the country. Agriculture in Pakistan faces considerable challenge in the 21st century. The present population of about 149 million, growing at about 1ta. 9 percent per year, is expected to double to 298 million in about 40 years. Pakistan’s agriculture has experienced rapid growth since the 1960s. The average annual growth of about 4 percent in the four decades before the onset of the new millennium has exceeded the population growth that touched about 3 percent for a substantial part of this period. 1 This rate of growth in agriculture has been sustained by the technological progress embodied in the high- yielding varieties of grains and cotton with supporting public investment in irrigation, agricultural research and extension (R&E), and physical infrastructure. Agricultural growth, in turn, has made significant contribution to the overall economic growth of about 6 percent per year during this period. Despite rising per capita income, food demand is likely to grow rapidly given the low level of current per capita income. There is a compelling need for sustained efforts to increase production of essential items (wheat, edible oils, etc.). Faced with limits to further expansion of cultivated land and slowing returns to further input intensification, productivity growth assumes a central role in meeting the challenges of the future.

Empirical studies in the US show that measured cost productivity actually decreased following deregulation (Bauer, Berger and Humphrey, 1993; Humphrey and Pulley, 1997; Berger and Mester, 2001). On the other hand, a study by Chaffai (1997) analysed the deregulation experience in Tunisia and found that total factor productivity (TFP) of banks increased following a liberalisation programme initiated in 1986. However, the rate of technical progress was higher than the rate of productivity growth, implying that the banks, on an average, became less efficient after liberalisation. 1 Thus the issue of whether financial deregulation actually helps overall development or sometimes can be so counterproductive as to hinder the process of development may be an interesting subject of debate. The issue becomes more relevant in view of the on-going global economic crisis, which originated in the US mortgage lending market and soon spread to others. As noted by analysts, uncontrolled financial innovations introduced by investment agencies and other banks, as well as by some other financial institutions, was one of the major causes of the crisis. The objective of the present paper is to study the overall performance of major Indian commercial banks in the post-financial deregulation period through a thorough analysis of their TFP growth and its major components.

Economic growth is often defined as the Quantitative change or expansion in a country's economy. Economic growth is conventionally measured as the percentage increase in real Gross Domestic Product/GDP or Gross National Product/GNP during one year. Growth can be nominal which includes inflation or real that growth adjusted for inflation. Economies can either grow "extensively" by using more resources such as physical, human, or natural-capital or "intensively" by using the same amount of resources more efficiently/productively. Total Factor Productivity Growth can be defined as the change in output holding measured inputs constant or net of the growth of measured inputs. It represents the improvements in the production technology accruing from the growth in the stock of unmeasured intangible investments such as human and R&D capital, advertising, good will, market development, information system, software, business methods, land, natural resource, water resources, the environment, and genuine technical and allocative efficiency. It also reflects the way in which technological innovation allows capital and labor to be used in more effective and valuable ways. It is also called as ‘measure of ignorance’ of the effects of all other range of variables not included to output growth. Total factor productivity growth is usually measured as a “residual” or as the effect of a time trend variable.

This study employed growth accounting equation to examine the total factor productivity of 14 commercial banks in Thailand during 2000 – 2009. The findings revealed that commercial banks in Thailand on average had low and very volatile total factor productivity with the average total factor productivity growth rate ranging from -13.35 – 10.06 percent per annum. In terms of individual bank, the findings revealed that most of commercial banks had the negative average total factor productivity growth rate during the study period, implying their lower productivity. In addition, the findings suggested that there were four factors which significantly determined the total factor productivity growth of commercial banks. They were credit risk as measured by the percentage of loan to total asset, management quality as measured by the percentage of non-interest expense to total asset, diversification as measured by the percentage of non-interest income to total asset and capital adequacy as measured by the percentage of owners’ equity to total asset. Furthermore, small commercial banks were found to have the highest total factor productivity growth whereas large banks were found to have the lowest one.

This paper will focus on the issues of total factor productivity growth (TFPG) for both 3 and 5-digit level and the performance of resource-based industries (RBIs) in Malaysia for the period 1981-1997. By using the neoclassical Cobb-Douglas production function and traditional growth accounting methodology (Solow-residual) with time discrete Tornqvist weighted value share index, the TFPG estimation for both classifications shows an interesting pattern in terms of sign and fundamental composition. The development of RBIs during the period under study is mostly input driven (moving towards a capital intensive industry), where supply effect of unskilled labour assimilates to the underlying value added growth over time.

How about capital/labour ratios? Here we …nd that the disaggregate evidence is at odds with the straightforward factor substitution story sug- gested by the aggregate data. In fact, from Table 1 we can see that in almost half of the industries examined the average annual rates of growth of Capital endowments per Labour Unit have been higher in the second part of the sample, hence accelerating exactly when labour productivity growth slowed down. It is thus not surprising to discover from Fig. 4 that the par- tial correlation between the growth in the Capital/Labour ratio and that of Labour Productivity does not appear as obvious as from the aggregate time series: wide ranges of Labour Productivity growth rates appear compatible with approximately similar rates of growth of the Capital/Labour ratio. In fact, the visual inspection of this plot may lead to two radically di¤erent conclusions by simply dropping two alternative small clusters of industries: (i ) excluding the Chemical, Non-Energy and Wood industries, which had the highest productivity growth of the entire panel in spite of very low capital intensity growth, the correlation is clearly positive;