The quality of state governance as a source of international differences in total factor productivity

Total factor productivity and the Environmental Kuznets Curve: A comment and some intuition

Analysis of at the Firm Level: An Application to Life Insurers: Comment The non-parametric measurement of total factor productivity (TFP) has become widely accepted and applied to many industries [2], and more recently to life insurers. A limitation of this approach is that differences in TFP may arise due to factors other than production efficiency. Denny, Fuss and Waverman [5], for example, identify two additional attributes to differences in TFP across time: exploitation of economies of scale, and changes in deviation from marginal cost pricing. To help overcome these limitations, various researchers employed the parametric technique of neoclassical cost estimation [10]. The parametric approach is a more general and detailed specification of the production structure of an industry or a company than the TFP approach. Further, in most recent studies of this type, researchers have utilized flexible functional forms in order to avoid imposing unnecessary restrictions on the production technology (see for example Berndt and Khaled [1] among many others). This approach is appealing but is often expensive to undertake and requires substantial time series observations. For practitioners, however, TFP analysis has various advantages. TFP is a relative measure showing how the ratio of total output to total input changes from one period to the other. It is relatively inexpensive to perform and since the data are displayed in an index number form, it is easy to identify anomalies in the data. In addition, TFP and the variables used in its construction may reveal valuable information on trends and changes in an industry. Moreover, Diewert and Morrison [7] have added another factor, the terms of trade effect, in explaining productivity. This Comment extends the study of Weiss [13] in which new techniques for measuring output of life insurers are developed and used in computing divisia and exact indexes of TFP for one stock and one mutual insurer over a five-year interval. Weiss [13] maintains that because the rate of technological change calculated using the Tornqvist approximation (TFP) equals the exact measure of the shift of the variable cost function due to technological change ( t) (see Diewert [6]), the nature of returns to scale is constant. In her words, Productivity theory suggests that superlative indexes such as the exact index and the Tornqvist-Theil approximation to the divisia index yield similar results if the production function reflects constant returns to scale, regardless of whether the insurer is acting competitively... [13, p. 74]. Given the result obtained by Weiss, which indicates that the divisia and exact indexes vary directly with each other, it was concluded that ...the sample insurers' production functions exhibited constant returns to scale over the sample periods [13, p. 74]. While this is true if all inputs are variable, if some of the inputs are fixed (e.g. number of square feet of home office building and constant dollar capital input in Weiss), the statement is in error, primarily because the shift in the variable cost function due to technological change ( t) is greater than the negative value of the overall production relationship's rate of technological change ( t). that is, t at t where at is the mean share of variable relative to total costs (at The latter can be proven with the aid of two equations: t = 1/2 (at + at - 1) t + R ( ) where t is the shift in total cost function due to technological change and R ( ) is a remainder term of first differences which are at least of second order. t = - t + R* ( ) where t is the rate of technological change corresponding to the joint production function (of all inputs), and R* ( ) is a remainder term of finite differences which are at least of second order.(1) Now suppose there is a constant mark-up, over marginal cost pricing structure. …

PurposeThe purpose of this paper is to study the impact of infrastructure and governance quality on the state-level productivity of Indian manufacturing for the period 2008–2011.Design/methodology/approachThe authors first rank Indian states on their quality of governance using benefit-of-the-doubt approach. Next, to explain state-level differences in total factor productivity (TFP), the authors assess the impact of a composite index of governance on industrial TFP of Indian states using alternate techniques and controlling for endogeneity. The authors also decompose the composite effect of governance in terms of economic, social and financial infrastructure and other key governance dimensions, which serves as another robustness check for the findings.FindingsThe authors find that TFP varies significantly across states, so does governance quality. Further, results suggest that TFP of Indian industries is sensitive toward public service deliveries of economic, social and financial infrastructure. However, the authors fail to find any impact of law and order indicators, for instance, rate of violent crimes, police strength and judicial service quality on the manufacturing productivity. The estimated coefficient of governance index is robust across alternate methodologies.Originality/valueTo the authors’ knowledge, this is the first study to assess the impact of regional governance factors on the manufacturing sector of India. The study has identified governance factors that impact manufacturing productivity in the Indian states. Findings suggest that an effective way to eliminate regional growth inequality in India is to ensure that the lagging states initiate reforms to improve the quality of institutions, regulation and governance. Findings of the study contribute to the limited literature on governance at the regional/sub-national level.

The coexistence of barriers to labor mobility with large output-per-worker disparities driven by Total Factor Productivity (TFP) differences suggests that the world's labor force is misallocated across countries. We investigate the extent and consequences of this potential misallocation in the context of a simple two-location growth model, in which production requires capital, labor and an essential immobile factor (land). We characterize the magnitude of labor movements implied by an efficient long-run allocation, and derive their implications for capital accumulation. Quantitatively, even for moderate TFP differences, we find substantial increases in world output associated with efficient allocations. These output increases are driven by large movements of labor from low to high TFP countries, as well as by a sizeable increase in the capital stock and changes in its endogenous division across countries. Our results are robust to a large set of parameter values, including unrealistically conservative ones.

Enhancing the total factor productivity in forestry is an important part of deepening the reform of the collective forest rights system. Based on the survey data of 295 forest plots in 12 towns of Liuyang City, Hunan Province, China, the study utilized a three-stage DEA model to assess the total factor productivity of forestry at the plot level. The empirical study employs Tobit and fractional regression models to investigate the effects and differences of forestry subsidies and forestry regulatory policies on the heterogeneous total factor productivity of different types of forests. The study found that: (1) the mean value of plot-scale forestry total factor productivity is 0.127, and there are obvious differences in total factor productivity among timber forests, economic forests, and mixed forests; and (2) afforestation subsidies and nurturing subsidies significantly positively influence high-level TFP. Ecological benefit compensation positively affects high-level TFP, but is not significant at any level of TFP. Forestry regulatory policies negatively impact high-level TFP, but are not significant at any level of TFP. This paper puts forward countermeasure suggestions to improve forestry subsidy policies, optimize forestry regulatory policies, and improve forestry total factor productivity from the perspective of heterogeneous forest types.

In this work, we develop a trade model that explains the pattern of trade between countries based on differences in total factor productivity (TFP) while also accounting for differences in relative factor endowments. The novelty stems from the introduction of production functions derived by combining the Ricardian and Heckscher–Ohlin–Samuelson (H-O-S) theories, with TFP differences serving as the basis of comparative advantage. To this end, a testable hypothesis is derived. For the empirical measurement of the TFP in each industry and country, a constant elasticity of substitution (CES)-type production function was employed, and the TFP was calculated as the Solow residual from the production function’s fixed term. To offer a better understanding, the model was tested for the bilateral trade between Germany and Russia, and Germany and the Czech Republic. It was found that TFP differences can be used as a basis for explaining comparative advantages and, consequently, the bilateral pattern of trade between two countries.

(ProQuest: ... denotes formulae omitted.)1. INTRODUCTIONThe work of Solow (1957) and Abramovitz (1956) and more recent analyses (Casseli, 2005; Hall and Jones, 1999) demonstrate that total factor productivity (TFP) is the key driver of long-run income growth. Klenow and Rodriguez-Clare (1997) estimate that roughly 90 percent of the differences in of income per capita can be explained by differences in total factor productivity. It is also well documented that advanced economies (OECD) lead technological change and innovation while developing economies lag behind in the technological frontier and tend to adopt (with a lag) technologies developed in technology-leading countries (Besley and Case, 1993; Archibugiand and Pietrobelli, 2003). In addition, technologies created in leading countries may not be appropriate to be used in technology-backward economies (Basu and Weil, 1998; Acemoglu and Zilibotti, 2001). Thus, there are significant differences in levels and of productivity between advanced and developing economies.Studies examining cross-country TFP differences find strong evidence against global TFP convergence (Klenow and Rodriguez-Clare, 1997; Hall and Jones, 1999; Di Liberto et al. 2011). Di Liberto, Pigliaru and Chelucci (2011) show that most countries underperform respect to the U.S. in terms of TFP growth (p.168) as well as that the TFP gap across countries is persistent.1 While there is strong empirical evidence against global TFP convergence, there is evidence in favor of club convergence. Miller and Upadhyay (2002) group countries by income quartiles and find that there is absolute TFP convergence for countries in the lowest and highest income quartiles, but no convergence for countries in intermediate income quartiles. Kumar and Chen (2012) find that health and education have a significant positive effect on TFP and conditional TFP convergence. Papalia and Silvia's (2013) results also support club convergence. Madsen (2007, 2008) show that knowledge transmitted internationally through trade and patents has contributed significantly for TFP convergence among OECD countries. Di Liberto and Usai (2013) show that a polarization is taking place across European regions, with only a few regions emerging as TFP leaders while most regions are lagging behind, causing the TFP gap between these two clusters to widen.Loko and Diouf (2009) provide a comprehensive discussion of the factors that determine TFP and might explain the patterns (convergence, or lack thereof) discussed above. For the sake of simplicity, this study groups the factors affecting TFP into three categories. The first group consists of macroeconomic factors that either hinder or boost productivity growth. Economic instability (e.g inflation), a large government, and taxation distortions supposedly create market inefficiencies and, thus, negatively affect productivity (Barro, 1991; Loko and Diouf, 2009). On the other hand, overall openness to international trade and capital mobility are expected to boost productivity growth. International trade spurs competition - which leads to innovation - as well as serves as a channel for technology diffusion among nations. Thus, economies that are more open to trade are expected to have higher productivity (Dollar and Kraay, 2004; Wacziarg and Welch, 2008; Barro and Sala-i-Martin, 1995). The same rationale applies to capital flows. Openness to capital flows (Foreign Direct Investment) is associated with technology diffusion and knowledge transfers, which in turn boosts productivity (Borensztein et al. 1998). The composition of output (i.e, if intensive in services, agriculture, or manufacturing) has also been identified as a driver of productivity growth. In particular, nonagricultural economies have experienced faster productivity (Poirson, 2000; Jaumotte and Spatafora, 2007).The second group of factors includes variables that measure the quality of labor (human capital). …

Why some countries are so much richer than others is a question of central interest in economics. Low aggregate income per worker in poor countries is mostly accounted for by low labor productivity and high employment in agriculture. This thesis attempts to understand cross-country income difference through examining productivity differences at the sector level in agriculture and in non-agriculture. Between rich and poor countries, there is a 45-fold difference in agricultural output per worker and a 34-fold difference in mean farm size. In the first chapter, I argue farmer’s skill as a plausible explanation for these differences. The model features heterogeneity in innate agricultural skill, on-the-job skill accumulation, and span-ofcontrol in agricultural production. I show that low total factor productivity (TFP) in poor countries not only induces more individuals with low innate skill to choose farming, but also reduces the incentive to accumulate skill. Between rich and poor countries, the model generates substantial difference in farmer’s skill, which translates into differences in agricultural productivity and farm size distribution. Quantitatively, the calibrated model explains half of the cross-country differences in agricultural output per worker, and successfully replicates the size distribution of farms in both rich and poor countries. Cross-country productivity differences are asymmetric across sectors. The labor productivity gap between rich and poor countries in agriculture is twice as large as that in the aggregate, and ten times larger than that in non-agriculture. The 2 second chapter shows that these sectoral productivity differences can arise solely from difference in aggregate TFP. I extend the framework in the first chapter to allow for different skill in non-agricultural production as well. Low TFP distorts the allocation of skills across sectors and discourages skill accumulation on the job. To discipline the initial skill distribution and skill accumulation, the model is calibrated to match earnings distribution and age-earnings profiles in both agriculture and non-agriculture in the U.S. The model’s implications are then examined using a sample of 70 countries that covers a wide range of development. Between rich and poor countries, the model accounts for most of the productivity differences at the sector level productivity difference in agriculture in the model is 1.8 times larger than those in the aggregate and 6 times larger than those in non-agriculture. As in the data, the share of farmer in the labor force in the model declines from 85 percent in the poorest countries to less than 2 percent in the richest countries. These results suggest that policy aiming at improving overall efficiency should be prioritized. Abstract Approved: Thesis SupervisorApproved: Thesis Supervisor Title and Department

With the supply of water, energy and food facing severe challenges, there has been an increased recognition of the importance of studying the regional water–energy–food nexus. In this paper, Inner Mongolia, including 12 cities in China, was selected as a research case. A super-efficiency slack based measure (SBM) model that considered the undesirable outputs was adopted to calculate the regional total factor productivity (TFP) and the Malmquist–Luenberger index was used to investigate the change trend of the TFP from 2007 to 2016 based on understanding the water–energy–food nexus. Finally, influential factors of the TFP were explored by Tobit regression. The results show that the 12 Inner Mongolia cities are divided into higher, moderate and lower efficiency zones. The higher efficiency zone includes Ordos, Hohhot, Xing’an, and Tongliao, and the lower efficiency zone includes Chifeng, Xilin Gol, Baynnur, Wuhai and Alxa. There is a serious difference in TFP between Inner Mongolia cities. During the study period, the TFP of the water–energy–food nexus in Inner Mongolia cities shows a rising trend, which is mainly driven by the growth of technical progress change. However, the average ML values of the lower and moderate efficiency zones were inferior to the higher efficiency zone in six of the ten years, so the difference between Inner Mongolia cities is growing. According to the Tobit regression, the mechanization level and degree of opening up have positive effects on the TFP, while enterprise scale and the output of the third industry have negative effects on the TFP. Government support does not have any significant impact on the TFP. Finally, suggestions were put forward to improve the TFP of the water–energy–food nexus in Inner Mongolia cities.

There are large differences in income per capita across countries. Growth accounting finds that a large part of the differences comes from the differences in total factor productivity (TFP). This paper explores whether barrier to entry is an important factor for the cross-country differences in TFP. The paper develops a new model to link entry barriers and technology adoption. In the model, higher barriers to entry effectively reduce entry threat, and lower entry threat leads to adoption of less productive technologies. The paper demonstrates that technology adopted in the economy with entry threats is at least as good as the technology adopted in the economy without entry threats. Moreover, the paper presents numerical simulations that suggest entry barriers could be a quantitatively important reason for cross-country differences in TFP and are more harmful to productivity in the countries with monopolists facing inelastic demand.

This paper brings together development accounting techniques and the dual economy model to address the role that factor markets have in creating variation in aggregate total factor productivity (TFP). Development accounting research has shown that much of the variation in income across countries can be attributed to differences in TFP. An outstanding question is what creates these differences in TFP in the first place. The dual economy model suggests that aggregate productivity is depressed by inefficient factor markets that allocate too many factors to low productivity work in agriculture. Data show wide variation in marginal products of similar factors within many developing countries over the time period 1970-1990, offering prima facie evidence of this misallocation. Variation across countries in factor market efficiency is shown to account for 30 - 40% of the variation in income per capita. These dual economy effects can also explain 90 - 100% of variation in aggregate TFP across countries. Finally, this evidence shows that the inverse relationship of income per capita and the share of labor in agriculture across countries is driven by factor market inefficiencies and not by differences in relative productivities.

Regional difference in total factor productivity and spatial features: Empirical Analysis on the Basis of a Sectoral Translog Production Function

PurposeThe paper aims to investigate the difference in total factor productivity (TFP) among those firms with and without outsourcing in a developing country like Vietnam. Also, it explores the effect of outsourcing activities on total factor productivity with a specified concentration on the Vietnamese small and medium-sized enterprises (SMEs).Design/methodology/approachThe panel data set of SMEs used in this study was originated from biannual surveys conducted under the collaboration between educational organizations and government agencies: Stockholm School of Economics (SSE), Department of Economics – the University of Copenhagen, the Institution of Labor Studies and Social Affairs (ILSSA) in the Ministry of Labor, Invalids and Social Affairs (MOLISA). In this study, the model is developed based on the production function in accordance with the model of Girma and Görg (2004). The firms’ TFP is the difference between the actual and the predicted output as with the approach by Levinsohn and Petrin (2003).FindingsThis study finds out that firms with outsourcing have higher total factor productivity than those without outsourcing activities. In addition, the more firms spend on outsourcing, the higher total factor productivity they can gain. Outsourcing to SMEs in a developing country can significantly increase its TFP by means of either maintaining core competencies or searching external resources in conducting some internal activities.Originality/valueAlthough outsourcing has been widely applied by large firms, the research studying its impact on productivity at firm level is limited. Especially, this study can shed light on the impact for the case of SMEs in a developing economy.

One would expect to find differences in total factor productivity (TFP) associated with factor allocation, given the technological change m the dairy sector over time and the regional disparity of regulations affecting production The authors use a National Income and Product Accounting procedure to calculate total income and product, TFP, and sources of growth for seven dairy States m different regions The average TFP growth for the seven States was 2 5 percent per year Florida and California had higher TFP growth rates, but interspatial TFP estimates indicated Wisconsin and New York had greater relative TFP levels in both 1978 and 1982

The production and diffusion of knowledge have increasingly been seen as potential causes of the observed international differences in total factor productivity and, in turn, as possible sources of economic growth. This paper presents the results of a causality study between business visits and multifactor productivity using a unique database that covers 30 sectors for 17 countries over the period 1998-2007. The results suggest that there is a causal link in some of the most innovative sectors from business visits to productivity. Business visits emerge as a fundamental channel for the spread of knowledge.