How Technology Investments Destroy and Create Manufacturing Jobs: Firm Evidence From Sweden

Tax policy and business fixed investment in the United States

This paper analyses the impact of taxation on corporate financing and corporate investment in machinery and equipment in Canada. A coherent macroeconometric model of the firm's real and financial decision process is theoretically developed and empirically tested on Canadian data. Estimates of the impact of taxation in general and of the 1987 Canadian government's White Paper in particular, are analysed. The estimates suggest that income taxation has a negative but relatively small impact on equipment investment in Canada, and that models that ignore the link between the real and financial decisions overestimate the impact of taxation on real investment. With respect to tax reform, the White Paper reduces the incentive to save and invest in equity capital, and is expected to decrease real capital investment in the long run.

This paper derives and estimates models of nonresidential investment behavior in which current and future tax conditions directly affect the incentive to invest. The estimates suggest that taxes have played an independent role in affecting postwar U.S. investment behavior, particularly for investment in machinery and equipment. In addition, the paper develops a method for assessing the impact of tax policy on the volatility of investment when such policy is endogenous. Illustrative calculations using this technique, based on the paper's empirical estimates, suggest that tax policy has not served to stabilize investment in equipment or nonresidential structures during the sample period.

The study uses the flexible accelerator model to examine determinants of the level and growth of investment in machinery and equipment for a sample of tea-processing firms in Uganda. Using a dynamic panel data model, we find that, in the long run, the level of investment in machinery and equipment is positively influenced by the accelerator, firm-level liquidity, and a favourable investment climate in the country. Depreciation of the exchange rate negatively affects investment. We conclude that firm-level strategies that increase output and profitability, and a favourable investment policy climate, are imperative to the growth of the tea industry

The German economy is divided on both the supply and demand sides. Consumption and parts of the service economy are again experiencing sharp falls while exports and some industrial sectors are be-nefiting from a worldwide economic recovery. Global business will grow by 5 per cent in 2021 but not uniformly: China and the USA are currently the pillars of the global economy, while the euro area is weakening due to a third wave of infections. The first quarter decline in Germany's real GDP and the second quarter's rising infection figures are again dampening expectations for 2021 as a whole. The German Economic Institute (IW) expects growth of only 3 per cent this year and a return to pre-crisis activity levels only early in 2022. Assuming that the pandemic is successfully suppressed and both competitiveness and Germany's attractiveness as a location for investment hold up, the country's economy should grow by a good 4 per cent in 2022. Having slumped by 6 per cent in 2020, private consumption will stagnate in 2021 as a result of the constraints on business. Fiscal changes will acce-lerate inflation, which will nonetheless remain below 2 per cent. Investment in machinery and equipment is picking up only slowly and is unlikely to make good the sharp declines of 2020 until next year, but investment in construction is making modest but steady progress. As the economy recovers, the num-ber of those in gainful employment will enjoy an upturn but is not expected to regain the pre-crisis level of 2019 within the timescale of this forecast. A rising share of long-term unemployed will make it difficult to cut unemployment in Germany rapidly. Germany's budget deficit will continue to rise, rea-ching some 4 ¾ per cent of GDP in 2021, with around 3 per cent expected in 2022.

This paper completes the comparative analysis of the investment demand behaviour, of a sample of specialised arable crop farms, for farm buildings and machinery and equipment, as a function of the different types and levels of Common Agricultural Policy support, in selected European Union Member States. This contribution focuses on their quantitative interdependence calculating the relevant elasticity measures. In turn, they constitute the methodological tool to simulate the percentage expected change in average net investment levels associated to the implementation of the, recently proposed and currently under discussion, reductions in the Pillar I Direct Payments disbursed under the Common Agricultural Policy. Evidence suggests a statistically significant elastic and inelastic relationship between both types of subsidies and the investment levels for both asset classes in Germany and Italy, respectively. An elastic dependence of investment in farm buildings on decoupled subsidies exists in Hungary while changes in the level of coupled payments appear to translate into less than proportional changes in the demand for both farm buildings and machinery and equipment in France. Coupled payments appear to influence the UK demand for both asset classes in an elastic manner while decoupled support seems to induce a similar effect on investment in machinery and equipment. Since the currently discussed Common Agricultural Policy reform options imply, almost exclusively, a reduction in the level of support granted through Direct Payments, simulated effects were expected to reveal a worsening of the farm investment prospects for both asset types (i.e., a larger negative investment or a smaller positive one). The actual evidence largely respects this expectation with the sole exception of investment in machinery and equipment in France and Italy reaching smaller negative or larger positive levels irrespectively of the magnitude of the implemented cuts in Direct Payments.

The paper aims to evaluate the impact of support on Lithuanian farms investments. Logistic regression, also called a logit model, was used to determine the probability of investing. The regression was estimated on cross-sectional data set of Lithuanian family farms participating in the Farm Accountancy Data Network. It was specified for investments in machinery and equipment. The research showed that support encourages investments in machinery and equipment. However, scenario analysis revealed that the impact of different support scenarios on the probability of investing is relatively small. It was also confirmed the importance of farm characteristics in making investments.

The aim of this paper is to investigate the relationship between R&D expenditure and investment in machinery and equipment in order to test for causality. New growth theory emphasises the role of R&D in creating blueprints needed to produce new capital goods implicitly assuming causality running from R&D to investment. Other recent studies using firm level data have investigated the relationship between innovative activity and investment in fixed capital. In this paper we use aggregate data from the US economy on R&D expenditure in the industrial sector and aggregate investment in machinery and equipment. Standard Granger causality tests, together with the Hsiao version, are then performed, showing that causality runs from R&D to investment. In addition we perform a cointegration analysis allowing a test of possible long-run feedbacks. This dynamic representation shows that any feedback between investment and R&D is only significant in the long run.

One of the main trends observed in the manufacturing sector at the current stage of economic development is the introduction of smart technologies, i.e. innovative solutions that integrate sensors, automation, artificial intelligence and the Internet of Things, to increase the efficiency, accuracy and flexibility of production processes. Leading countries in this area, in particular the USA, China and EU countries, are implementing various measures to stimulate the development of smart industry, including budgetary and fiscal one, with a total cost of tens of billions of dollars per year, however, less economically developed countries need to choose the most effective directions of resource allocation, which requires assessing the impact of investments on the performance of the manufacturing sector. The article is devoted to the analysis of the impact of investments in smart technologies on the development of the manufacturing industry using the example of Germany, which is the largest economy in the Eurozone and one of the leaders in the development of smart industry. For this purpose, a production function model was built that takes into account not only traditional production factors, such as labor and capital, but also the factor of digitalization. The model is based on a multiplicative production function (which includes standard factors such as capital in the form of the cost of machinery and equipment, as well labor in the form of total wages in the industry), modified by adding the cost of intellectual property as a separate factor (which in the processing industry consists of the cost of software, databases and scientific and technical research). Modeling on the example of Germany showed that the growth of this smart factor gives a greater increase in added value than the growth of the capital factor, which indicates that stimulating the development of smart industry should be carried out in the form of directing investments into projects of a specific profile: which increase the cost of machinery and equipment, as well as the digitalization factor. Moreover, with the account technical feasibility and economic feasibility, the share of investments in the digitalization factor should be higher than the share of capital investments. A comparison of the structure of investments in production factors in Ukraine and Germany was carried out, which showed that the current structure of investments in the manufacturing industry in Ukraine does not meet the criteria for the development of smart industry due to Ukraine's more than 10-fold lag in terms of the ratio of investments in the digitalization factor to investments in machinery and equipment. Recommendations were also provided to improve the structure of statistical information by including additional indicators that take into account the features of smart industry.

PurposeThe purpose of this paper is to examine the crowding-in or crowding-out relationship between public and private investment in India.Design/methodology/approachThe autoregressive distributed lag (ARDL) bounds testing approach is used to estimate the long run relationship between public and private investment using annual data from 1971-1972 to 2009-2010.FindingsBased on the empirical findings, it is observed that aggregate public investment has a positive effect on private investment both in the long run and the short run. In contrast to the findings of previous studies, no significant impact of public infrastructure investment on private investments is found in the long run, while non-infrastructure investment has a positive impact on private investment in the short run. Among the various categories of infrastructure sector, a positive and significant impact in the case of electricity, gas and water supply is observed. Similarly, the result indicates that public investment in machinery and equipment and construction have substantially influenced the private sector machinery and equipment in the long run and the short run. In the case of the role of macroeconomic uncertainty, the results find a negative and significant impact on private investment and the impact is higher in the short run than in the long run.Originality/valueThe present study extends the literature in three important ways: First, the study attempts to capture heterogeneity of public investment as well as disaggregate effects of two different categories of public infrastructure on private investment. The extent to which two different types of public assets impact the private investment in machinery and equipment investment is also examined. Second, ARDL model is used to examine the long-run relationship between public and private investment. Third, the study incorporates macroeconomic uncertainty into the empirical analysis to examine the role of macroeconomic volatility in determining private investment decision.

Testing Unified Growth Theory: Technological Progress and the Child Quantity–Quality Trade-off

Textile and apparel industries of Pakistan are the largest industrial sector contributing about 60% to total exports and 25% of value-added of the manufacturing sector. The degree of competitiveness in this sector is closely associated with their Total Factor Productivity (TFP), which can be enhanced through trade liberalization, efficient and effective use of Information and Communication Technology (ICT), protection of intellectual property rights (IPRs) along with the investment in human capital, and advertisement expenditures. Additionally, investment in machinery and equipment and the role of subsidies cannot be ignored in enhancing TFP growth. This study utilizes data from the 2005-06 Census of Manufacturing Industries. Total factor productivity has been calculated employing the methodology of Tomiura (2007). The results indicate TFP is positively and significantly affected by ICT, IPRs, investment in machinery & equipment, human capital, import duties, subsidies for exports on TFP growth. The safeguarding textile and apparel industries from foreign competition relocates resources towards improving TFP whereas incorporation of ICT and IPRs enables firms to realize benefits of foreign developed technology which further improves TFP growth. Furthermore, the effect of advertising expenditures on TFP is positive but it lacks statistical significance.

Investment is usually treated as an homogeneous variable in the literature. The wide differences in the nature of inputs used in investment projects may lead to interesting insights that are not captured in the conventional approach. In this paper I decompose investment in machinery and industrial real estate and examine the impact of economic uncertainty on each of these components. Using panel data estimation methods for the Brazilian industry, I found that uncertainty exerts a harmful effect on both types of investment. However, the effect is much more intense with machines, which possibly is a consequence of their high reversibility costs.

This research produced evidence about the contribution of investment and other sources to the growth process of Latin America during 1960-2002, and provided answers to the questions listed above unless from an historical perspective. The combined growth accounting and regression analysis, and used data for the six largest Latin American countries: Argentina, Brazil, Chile, Colombia, Mexico, and Venezuela. These countries produce nearly 90 per cent of Latin America's GDP. Alternative growth accounting methodologies were used to measure the contributions of the sources of growth to GDP growth during 1960-2002. The study also provides evidence of the effects of investments in machinery and equipment and construction structures, and the effects of private and public investment on per capita GDP growth. The research found evidence of the primary role played by total factor productivity in explaining the difference between fast and slow growth experiences. Extending the traditional growth accounting approach did not change this conclusion. It also found that investment in machinery and equipment, and private investment were most effective in raising per capita GDP growth, but that key policy related variables, including education, were essential ingredients contributing to per capita GDP growth. Evidence of mutual causality between private investment and growth, and inconclusive evidence regarding the incidence of FDI and infrastructure on private investment were also found in this research.

New Growth Theory points to three potential influences on output and productivity growth–investment in human capital, R&D and investment in machinery and equipment (M&E). However, much of the literature focuses on human capital and R&D as sources of growth. Few efforts have been made to estimate the impact of M&E investment. This article presents empirical models that endeavour to fill this gap. Using panel data on 20 Canadian manufacturing industries (1961–1997) and time-series data (1961–2000) for the entire Canadian manufacturing sector, this article finds that the elasticities of output with respect to M&E capital stock and M&E investment are well above capital's share of national income suggested by a constant-returns-to-scale Cobb–Douglas production function. However, the coefficient on labour is near its income share. The results also suggest that M&E investment is not the only source of growth because the elasticity of output with respect to structures investment is also well above its income share, indicating the possible existence of complementarities between the two types of capital.