Spatial effects on the aggregate demand

This paper analyses if several spatial variables coming from cities and transportation system can affect the money market, specially the income velocity of circulation. The specification of the theoretical model include the income velocity of circulation into the IS-LM multipliers. Considering the Baumol-Tobin model for transaction money demand, the Central Place Theory, and some gravity models, we can conclude that the incom velocity of circulation and the supply money in monetary terms are dependent, among others, of seven spatial variables such as the country first city population, the population density, the passenger-kilometers transported by railways, and several ratios referred to some geographical variables. The model has been applied across 64 countries during the period 1978-1997. Panel data techniques has been used for estimating the model. Thresults indicate that most of the explanatory variables are significant on income velocity of circulation and the money supply. The macroeconomic equilibriunm is affected by the spatial explanatory variables because these last affect the LM curve, an hence prices and output level maybe influenced because of that.

Spatial Effects on the Aggregate Demand

The main aim of this paper is to analyse if determined variables related with transportation, demography and geography can cause fluctuations in the aggregate demand function and hence affect the prices,employment and output levels in an economy.The panel data to carry out this analysis includes 64 countries (19 european, 14 african, 17 asian, 14 american) during 21 years (1978-98).The specification of the model is based in to relate the Baumol-Tobin model for demand money transaction, with the central places theory for obtaining a relationship between money velocity and variables such as population density, passenger-kilometers and net tons-kilometers transported by railway, the first city population, and several ratios corresponding with road transportation. Panel data techniques have been aplyed and estimation results indicate that all explanatory variables are significant and all cause Granger on money velocity during this period. Unit roots test of Harris-Tzavalis and cointegration test of Chiwa Kao notify that the relationship between money velocity and this explanatory variables is not spurious and it is a long run relation. But money velocity at long run is a component of the slope of LM curve, and hence fluctuations in the explanatory variables can cause movements in the LM curve and in the aggregate demand function affecting the output level and prices.

Quality capital and economic growth

The productivity generated by capital goods is not uniform, specially over the time. The productivity obtained from phisical goods is minor than one generated by new capital goods, or quality capital goods. It seems that the difference between both kinds of capital stems from the fact that vintage capital is affected by an additional form of technical progress. When capital is affected by this kind of technical progress, it is so-called capital jelly from Solow (1960). There are hence two possible forms of understand technical progress: the classical one or, alternatively, this new class of technical progress tath affects only to capital. Both kinds of technical progress affect growth in two separate ways, and for this reason it is interesting to develop a special analysis on the investment in capital goods in order to identify what is the difference between the productivity derived from physical capital and from vintage capital. The main aim of this paper is to analyse how two types of technical progress affcet the real income growth rate in the countries belonging to three world areas: North America, the Euro zone, and some countries of the Pacific Rim, during the period 1960-2000. Precursory works of the present research have found in Hulten (1992), Greenwood, Hercowitz and Krusell (1997), Gordon (1999) and Hobijn (2000).

Spatial effects on technical progress: growth, and convergence among countries

This paper analyses how several spatial variables coming from cities and transportation system can affect money market, specially the income velocity of circulation, assuming an unit-elastic aggregate demand function and considering money velocity as a variable. Fluctuations in velocity caused by some spatial variables, under certain conditions, can affect the aggregate demand curve. The specification of the main relation-ship has found in the Baumol-Tobin model for transaction money demand, and in Christaller-Lösch central place theory. The estimation of the model has been based on panel data techniques and applied across 61 countries during 14 years in the 1978-1991 period. Theoretical and econometric results indicates that seven spatial variables like the country’s first city population, the population density, the passengers-kilometer transported by railways, and several ratios referred to some geographical variables, can provokes fluctuations on aggregate demand curve in the short run. In the long run, the aggregate supply can be also affected by means of these variables. In order to checking this question, considering that these spatial variables are not product factor, we propose to observe if these variables can affect the technological progress coefficient, A, concerning to an aggregate production function, according to a neo-classical growth model. Results by means of the Mankiw, Romer and Weil method, and also by means of an endogenous growth model of technology diffusion, indicates that some spatial variables affect the speed of convergence relative to the real per head income, across these 61 countries. However, a certain amount in some of these variables generates a congestion process in some countries. For checking it, we utilize a Barro and Sala i Martin endogenous growth model which reflects government activities. The concluding remarks indicates that some of these spatial variables above mentioned increases the speed of convergence but generates congestion in some countries. These spatial variables also affect the aggregate supply, and hence the price and output levels. Key words: transportation, regional growth, convergence, congestion. JEL Class.: R41

Megacities and Countries: Urbanization and Real Convergence

A new urban revolution begun in the second half of the XX century and it is going to challenge the relation between the size and economic role of cities: on one side, the last decades have witnessed the emergence and the never seen growth of a number of Mega-cities, with more than 9 million inhabitants, most of them being located in less developed countries. On the other side, the globalization of the post-industrial economy generates a new urban spatial organization where a few number of cities concentrate a disproportionate part of economic power, creation, decision and control. Most of the largest cities are in the less developed countries, while the most powerful world cities are mainly located in the developed countries. It results that size seems to be neither a necessary nor a sufficient condition for obtaining the status of world city. A condition to be a world city is the access to the economic power. A conventional city has a region behind, and there is no region without city, neither city without region. But a Mega city has, at least, a country behind. When in a country there are several Mega cities, competition for the specialization emerges among them. However, within a country, regions are more closely related. But due to the globalization, the rise of high technology applied to the telecom networks, the expansion of the financial markets, generally located around the big cities, together the great multinational companies, and the strong development in multimodal transportation networks, cause a great growth in some places of the World, attracting immigration and amenities. Nevertheless, although it seems to have convergence among the big cities considered in this work, not all these cities are similar. The main aim of this paper is to analyze the comparison between the real convergence-divergence among 50 Mega-cities of the World, and the convergence-divergence among its corresponding countries, using several growth models, studying the possible existence of Clubs convergence among these cities and countries

Spatial Effects on the Aggregate Demand

The main aim of this paper is to analyse if determined variables related with transportation, demography and geography can cause fluctuations in the aggregate demand function and hence affect the prices,employment and output levels in an economy.The panel data to carry out this analysis includes 64 countries (19 european, 14 african, 17 asian, 14 american) during 21 years (1978-98).The specification of the model is based in to relate the Baumol-Tobin model for demand money transaction, with the central places theory for obtaining a relationship between money velocity and variables such as population density, passenger-kilometers and net tons-kilometers transported by railway, the first city population, and several ratios corresponding with road transportation. Panel data techniques have been aplyed and estimation results indicate that all explanatory variables are significant and all cause Granger on money velocity during this period. Unit roots test of Harris-Tzavalis and cointegration test of Chiwa Kao notify that the relationship between money velocity and this explanatory variables is not spurious and it is a long run relation. But money velocity at long run is a component of the slope of LM curve, and hence fluctuations in the explanatory variables can cause movements in the LM curve and in the aggregate demand function affecting the output level and prices

Quality capital and economic growth

The productivity generated by capital goods is not uniform, specially over the time. The productivity obtained from phisical goods is minor than one generated by new capital goods, or quality capital goods. It seems that the difference between both kinds of capital stems from the fact that vintage capital is affected by an additional form of technical progress. When capital is affected by this kind of technical progress, it is so-called capital jelly from Solow (1960). There are hence two possible forms of understand technical progress: the classical one or, alternatively, this new class of technical progress tath affects only to capital. Both kinds of technical progress affect growth in two separate ways, and for this reason it is interesting to develop a special analysis on the investment in capital goods in order to identify what is the difference between the productivity derived from physical capital and from vintage capital. The main aim of this paper is to analyse how two types of technical progress affcet the real income growth rate in the countries belonging to three world areas: North America, the Euro zone, and some countries of the Pacific Rim, during the period 1960-2000. Precursory works of the present research have found in Hulten (1992), Greenwood, Hercowitz and Krusell (1997), Gordon (1999) and Hobijn (2000)

Spatial effects on the aggregate demand

This paper analyses if several spatial variables coming from cities and transportation system can affect the money market, specially the income velocity of circulation. The specification of the theoretical model include the income velocity of circulation into the IS-LM multipliers. Considering the Baumol-Tobin model for transaction money demand, the Central Place Theory, and some gravity models, we can conclude that the incom velocity of circulation and the supply money in monetary terms are dependent, among others, of seven spatial variables such as the country first city population, the population density, the passenger-kilometers transported by railways, and several ratios referred to some geographical variables. The model has been applied across 64 countries during the period 1978-1997. Panel data techniques has been used for estimating the model. Thresults indicate that most of the explanatory variables are significant on income velocity of circulation and the money supply. The macroeconomic equilibriunm is affected by the spatial explanatory variables because these last affect the LM curve, an hence prices and output level maybe influenced because of that

Technical progress effects on productivity and growth in the Commonwealth of Nations (1993-2009)

The productivity generated by capital goods is not uniform along the time. When there exist conventional physical capital goods the productivity obtained is minor that the one generated by quality capital goods. To obtain a correct measure of growth in presence of this embodied technical progress there exist three schools: first, the traditional growth accounting school appears due to limitations existing in the measures in efficiency units of the quality of the real investment, because of the investment is not really comparable along the time. The analysis is based in to adjust the quality or productivity of the investment goods constructing hedonic prices indices. This school is represented among others by Hulten (1992), Jovanovic and Nyarko (1996), Bartelsman and Dhrymes (1998), and Gordon (1999). The second school analyzes the productivity using longitudinal micro-level data sets. The most important contributions of this school are Griliches and Ringstad (1971), Olley and Pakes (1996), Caves (1998), McGuckin and Stiroh (1999), and Tybout (2000). The third school is the equilibrium growth accounting school, which measures the balance growth by means of vintage capital models, being represented by Greenwood, Hercowitz and Krusell (1997), Campbell (1998), Hobijn (2000), and Comin (2002). The main aim of this paper is to analyze which are the effects of the two form of technical progress, neutral and directly embodied while capital is accumulated, on the economic growth and the labour productivity. The application has been made to compare the responsibility of the embodied technical progress on the economic growth and productivity during the period (1993-2009) in the most representative economies of the Commonwealth of Nations. The vintage capital model has been made taking quarterly and annual data to each country, coming from the OECD Statistics. We use multivariate time series and cointegration techniques, in special autoregressive integrated moving average and vector autoregressive models (VAR), and autoregressive distributed lags models (ARDL)