A typical empirical paper on economic growth runs a cross-sectional regression based on a chosen sample of countries. Variables expected to determine the growth rate vary among studies and interpretations of results differ but the basic setup is almost the same. Nonetheless, these cross-country growth regressions are not indisputable for determining growth due to the problems of simultaneity, multicollinearity and degrees of freedom. The problems of empirical emphasis in recent work on economic growth are also the subtlety of the theories and data limitations.
Admitting that those theories have subtle implications leads to the conclusion that cross-country regressions cannot simply distinguish among them Mankiw Nonetheless, cross-country research is a useful complement to other exploratory techniques and for some important questions there is no other possibility Temple Due to the fact that this paper is a term paper with a limited scope, only the most important theoretical basics and empirical analyses on specific determinants and their results will be presented in the following sections.
Regarding the plurality of linkages between the determinants it is not possible to make a strict division. Nonetheless, to give a better overview, the determinants are presented as individually as possible. Neoclassical and endogenous growth models do not treat the role of government explicitly and thus the impact of governmental policies was not focussed. However, a pervasive theoretical basis is still missing. A large amount of policy-oriented studies has been carried out in the past decade see e.
Temple for a survey. Nevertheless, the specific mechanisms, which link policy settings to growth, are still being disussed. In the case of diminishing returns to reproducible factors and exogenous saving rates, growth of population and technological progress, as in the neoclassical approach, government policies do not play a significant role in growth. Even in the neoclassical models this view may not be maintained, when assuming that policy may have an effect on saving behaviour through influencing the resource allocation across individuals.
On the other side, government policies may affect the rate of growth persistently if physical and human capital investment is regarded as endogenous and shows constant or even increasing returns to scale. In the second case, the cross-country convergence process does not exist any longer, even after controlling for some country-specific factors such as natural resource endowment or geographical location OECD , p.
Endogenous growth theories relax the neoclassical postulation of efficient markets and recognize market failures and the possibility of allocative and dynamic inefficiency. Consequently, the potential role for the government in the reduction of market failures is included in these theories. Nonetheless, as the governmental role is analyzed only superficially, policy implications are more general. Barro carried out a study on about countries of different levels of economic development which examines three time periods , , , in which different kinds of governmental policies are analyzed for their impact on growth rates Barro The investigated policies are government consumption, the rule of law, democracy, inflation, education and public debts.
Results concerning government consumption expenditures excluding education and defense expenditures showed a negative effect on growth rates. The results indicate statistically significant that rising the spending ratio by five percentage points lowers the economic growth rate by 0. Government consumption expenditures also affect the investment negatively, both in quantity and quality. The rule of law including solid property rights and a strong legal system, showed a significantly positive effect on economic growth and level and quality of investment.
The impact of democracy on growth showed an overall weak relation. A statistically significant inverted U-curve is found respecting growth and investment indicating that after reaching a certain level of democratization further democracy leads to a decline in economic performance.
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The negative effect of inflation on economic growth is statistically significant when it comes to inflation rates of more than twenty percent annually, probably due to the effects of inflation on the efficiency of investment. Effects of education have a strong impact on economic growth when it comes to school attainment at secondary level and upwards. The ratio of public debts — the choice of public financing between current and future taxation or between taxes and public borrowing — does not have a significant impact on growth and investment.
Levine and Zervos also could not find robust linkages between indicators of monetary or fiscal policy and long-term growth. Temple states that government policies are beneficial for building up infrastructure. Easterly , p. This opinion comes along with Burnside and Dollar , who found out that aid has a positive effect on growth in developing countries with good fiscal, monetary and trade policies.
This effect goes far beyond the direct impact of policies on growth. Aid would be more effective when thoroughly conditioned on good policies because in the case of poor policies aid resulted only in unproductive government expenditures. Poor countries can maximize their chances to grow by the right policies, although the precise content of these policies still remains under discussion.
A town in Mizoram Saiha is located the farthest from a large city at a distance of 79 kilometres from Aizawl. The growth rate of population is highest for a town in the Bangalore UA, and Mumbai is the largest city. Estimations were performed to study the impact of each of these exogenous variables on urban growth in the districts. The defense for using DDP estimates as a measure of growth is that districts or cities may grow in population, but they may or may not grow economically.
Table 3 presents estimates of the most basic regression in which the urban population of district in is regressed on human capital characteristics the literacy rate , natural amenities temperature differences , economic base ratio of employment in manufacturing to services, , distance to a large city, and public services such as road length and population coverage by schools. By choosing the district as the unit of observation, the problem of selection bias which could arise by choosing towns, has been avoided.
This study has some interesting and some expected findings. Firstly, larger manufacturing employment bases relative to that in services cause cities to be large. However, manufacturing, not service employment has a positive impact on city size. Furthermore, proximity to large cities cities with , or greater population causes cities to be larger.
This implies that a large city encourages other cities to grow within a certain vicinity. Specifically, the magnitude of the estimate on this factor implies that for every 1 kilometre that the city is closer to a city with , or greater population, its own population increases by nearly 50, Conversely, the reverse is also true in the sense that the farther a city is from a large city, it will remain small, likely due to the absence of agglomeration effects.
While public services such as road length per 1, population do not have the expected impact on city size, factors such as coverage of population by primary schools large number of persons per school cause cities to be larger. This implies that poorer level of public services such as schools implied by larger number of persons per school cause cities to be larger, not to be expected.
The correlation matrix of all independent variables was examined and none of them are big enough to suggest collinearity. Temperature differences have a positive impact on urban population growth, which comes as a surprise. The expectation was that lower temperature differences, controlling for other factors, would cause city growth to be higher hence the expected impact was negative. The actual finding here suggests that even poorer climatic conditions such as extreme weather can indeed encourage city growth, when controlled for other factors.
Table 4 also shows that the distance to a large city with a , or more population, has a positive impact on population growth. This is in contrast with the earlier result in Table 3 , where proximity to a large city increases the size of a city. The result in Table 4 implies that distance to a large city positively impacts the rate at which a city grows, if not its current size implied by Table 3. As discussed earlier, a city might grow in population, but its output might increase or decrease. Only if its output increases that city economic growth may be said to have occurred.
The findings from this regression are as expected. That is, for every 1 person extra that is covered by a primary school, there is an increase in the city output to the extent of Rs. This implies that lower school coverage increases city output, either by diverting more resources for higher education or by reducing the inefficiencies of primary education. Paul and Sridhar find that Tamil Nadu, a southern Indian state, for instance, was much more efficient in its spending on roads relative to related outcomes such as road length , in contrast with Uttar Pradesh, a northern Indian state, which spent nearly three times as much for an additional kilometer of road length.
In the most basic regression Table 6 , town population in was estimated as a function of characteristics such as temperature differences, distance to a large city, road length, population coverage with primary school, the existence of the ULCRA and the economic base of the town. Since the existence of the ULCRA or otherwise is not policy relevant in the context of the district unit of observation in the previous regressions it was not included in the previous set of regressions. This also enables econometric identification of the equations.
The correlation matrix of all independent variables was examined and none of them were found to be significant enough or suggestive of collinearity. Temperature differences have a negative impact on town population. This implies that temperature extremes cause cities to be smaller.
Proximity to larger cities causes cities to be larger as well, implying the existence of markets and scale economies, in contrast to what Mills and Becker find. The population coverage per primary school has a positive impact, suggesting that the greater the number of persons per school implying poorer level of the service , the larger the city.
Finally, the economic base of the town has a profound impact on its size. Specifically, towns with a manufacturing base are larger. However, the model is a poor explanation of variations in city populations, as may be seen in the low R 2. However, the reader should note that the R 2 is only a descriptive statistic. This suggests that the R 2 alone may not be a suitable measure of the explanatory power of a model.
Table 7 summarizes the estimates of population growth at the city level as dependent on various characteristics. The table shows some interesting findings.
DETERMINANTS OF CITY GROWTH AND OUTPUT IN INDIA
While temperature differences, distance to a large city and public services such as road length do not have significant impacts on city growth at the town level, population coverage of primary schools has an impact. This estimate shows that the greater the extent of population coverage with schools, the greater the city growth, a finding that does not concur with our expectations.
Interestingly, as with city size, a shift in the economic base towards manufacturing increases city growth, consistent with expectations and with Census defined urban areas. This means that wherever the ULCRA exists, it stifles the growth of those areas by regulating land markets unnecessarily, consistent with what Kundu argues. Specifically towns in states which have not yet repealed the ULCRA grow nearly 4 percentage points slower than cities of states which have revoked it, holding other factors constant.
There are of course a number of data caveats which should qualify these findings. Firstly, the R 2 is low in the case of most models. However, the reader should note that the R 2 is only a descriptive statistic see discussion above. Secondly, the district level NDDP are available for only half the districts in the country, with the remaining districts not yet having prepared them.
It would be better to use measures of net value added output per capita, as Au and Henderson do, but these data are not published by the states for the districts. At the district and town levels, better measures of human capital such as the proportion with high school or higher degrees are not available, with the result that potentially less accurate measures such as population coverage with schools and literacy rate are used.
This paper attempted to study city growth at different levels of aggregation—at the city level and at the district level. Summarizing the findings, at the district level, a higher proportion of manufacturing to service employment, proximity to large cities, and public services such as primary school coverage per population cause cities to be larger.
This shows that city growth as measured by output that may or may not be captured by population growth is impacted by the literacy rate or human capital. The finding at the city level is that, proximity to a large city causes a city to be larger. A city's size becomes larger and it also grows faster as it moves away from agriculture towards manufacturing. An important finding of policy interest at the city level is that strong land use controls such as the existence of the urban land ceiling act deters city growth by artificially creating a scarcity of urban land.
Given the data caveats, the findings have several policy implications. Given the selection bias with choice of towns, this paper advocates more confidence in estimates at the district level. Human capital as measured by the literacy rate has a significant impact on net output per capita.
This shows the importance of improving school infrastructure in the states to promote city output. The employment base has to move towards manufacturing and services for it to have a positive impact on net output per capita. Finally, the proximity to large cities can yield scale and agglomeration effects. Hence if incentives are provided to develop smaller towns, then their growth rate rather than their size may be expected to increase with their distance from large cities those with population greater than , These findings were used to answer the question: can individual cities grow forever?
If not, what is the optimum city size, controlling for characteristics studied here? This implies Indian cities on average can accommodate more people to the optimum. Cities that are bigger than the optimum size predicted, will experience a reduction in size due to temperature differences and their distance from a large city. Cities urban part of districts that have output higher than this, have performed so because of above average literacy rate and an above average ratio of employment in manufacturing relative to that in services. Similar to cities in China which have a strong manufacturing base, it may be worthwhile making the environment more conducive for manufacturing in Indian cities than has been the case thus far.
Future research should focus on refining the various measures used here. It should also make better use of data from a greater number of districts as it pertains to net output per capita. The author is grateful to the anonymous reviewers of this manuscript for their suggestions. Any errors that remain are the author's alone.
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Share full text access. Please review our Terms and Conditions of Use and check box below to share full-text version of article. Abstract To investigate what determines urban population and economic growth, the determinants of urban population growth and economic output in India are examined empirically. Objectives and approach Given the economic importance of cities and the lack of adequate research in an Indian context, this paper poses the following question: what determines city population growth and city economic growth output in India?
The reductions in transaction costs had an impact, not only on the volumes of trade, but also on the types of exchanges that were possible and profitable. The first wave of globalization was characterized by inter-industry trade. This means that countries exported goods that were very different to what they imported — England exchanged machines for Australian wool and Indian tea.
As transaction costs went down, this changed. In the second wave of globalization we are seeing a rise in intra -industry trade i. France, for example, now both imports and exports machines to and from Germany. The following visualization, from the UN World Development Report , plots the fraction of total world trade that is accounted for by intra-industry trade, by type of goods.
As we can see, intra-industry trade has been going up for primary, intermediate and final goods. This pattern of trade is important because the scope for specialization increases if countries are able to exchange intermediate goods e. Above we took a look at the broad global trends over the last two centuries. Let's now zoom in on country-level trends over this long and dynamic period.armaslenica.gq/sins-of-the-father-family-tree-mysteries.php
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The next chart plots estimates of the value of trade in goods, relative to total economic activity i. These historical estimates obviously come with a large margin of error in the measurement section below we discuss the data limitations ; yet they offer an interesting perspective. You can add more series by clicking on the option 'Add country'.
Each country tells a different story. If you add the Netherlands, for example, you will see how important the Dutch Golden Age was. Here is the same chart but showing imports , rather than exports. In the next chart we plot, country by country, the regional breakdown of exports. India is shown by default, but you can switch country using the option 'Change entity'.
Using the option 'relative', at the bottom of the chart, you can see the proportional contribution of purchases from each region. This gives us an interesting perspective on the changing nature of trade partnerships. In India, we see the rising importance of trade with Africa — this is a pattern that we discuss in more detail below. The so-called trade openness index is an economic metric calculated as the ratio of country's total trade the sum of exports plus imports to the country's gross domestic product. This metric gives us an idea of integration, because it captures all incoming and outgoing transactions.
The higher the index the larger the influence of trade on domestic economic activities. The visualization below presents a world map showing the trade openness index country by country. You can explore country-specific time series by clicking on a country, or by using the 'Chart' tab below the map. For any given year, we see that there is a lot of variation across countries. The weight of trade in the US economy, for example, is much lower than in other rich countries.
If you press the play button in the map, you can see changes over time. This reveals that, despite the great variation between countries, there is a common trend: Over the last couple of decades trade openness has gone up in most countries. Expressing trade values as a share of GDP tells us the importance of trade in relation to the size of economic activity. Let's now take a look at trade in monetary terms — this tells us the importance of trade in absolute, rather than relative terms.
The chart below shows the value of exports goods plus services in dollars, country by country. All estimates are expressed in constant dollars i. The main takeaway here are the country-specific trends, which are positive and more pronounced than in the charts showing shares of GDP. This is not surprising: most countries today produce more than a couple of decades ago ; and at the same time they trade more of what they produce. Here is the same chart, but showing imports rather than exports. Trade transactions include goods tangible products that are physically shipped across borders by road, rail, water, or air and services intangible commodities, such as tourism, financial services, and legal advice.
Many traded services make merchandise trade easier or cheaper—for example, shipping services, or insurance and financial services. Trade in goods has been happening for millenia ; while trade in services is a relatively recent phenomenon. Globally, trade in goods accounts for the majority of trade transactions. This interactive chart shows trade in services as share of GDP across countries and regions.
Firms around the world import goods and services, in order to use them as inputs to produce goods and services that are later exported. That is, the share of the value of exports that comes from foreign inputs. Foreign value added in trade peaked in — after two decades of continuous increase. This is consistent with the fact that, after the global financial crisis, there has been a slowdown in the rate of growth of trade in goods and services, relative to global GDP.
This is a sign that global integration stalled after the financial crisis. The integration of global value chains is a common source of measurement error in trade data, because it makes it hard to correctly attribute the origin and destination of goods and services. We discuss this in more detail below. The following interactive chart from the Observatory for Economic Complexity OEC , at the Massachusetts Institute of Technology, shows a breakdown of total world merchandise exports by product category, for You can visit the OEC website to see this composition country by country.
In this embedded interactive chart you can use the options at the bottom to change how the data is presented. If you click the option 'show all years', the frame will turn white while it loads, and then it will display a time slider, so that you can change the year for which the data is plotted. If we consider all pairs of countries that engage in trade around the world, we find that in the majority of cases, there is a bilateral relationship today: Most countries that export goods to a country, also import goods from the same country.
The following interactive visualization shows this. In this chart, all possible country pairs are partitioned into three categories: the top portion represents the fraction of country pairs that do not trade with one-another; the middle portion represents those that trade in both directions they export to one-another ; and the bottom portion represents those that trade in one direction only one country imports from, but does not export to, the other country.
As we can see, bilateral trade is becoming increasingly common the middle portion has grown substantially. The following visualization shows the share of world merchandise trade that corresponds to exchanges between today's rich countries and the rest of the world. As we can see, up until the Second World War the majority of trade transactions involved exchanges between this small group of rich countries.
But this has been changing quickly over the last couple of decades, and today trade between non-rich countries is just as important as trade between rich countries. Here is a stacked area chart showing the total composition of exports by partnership. It's the same data, but plotted with stacked series. The last few decades have not only seen an increase in the volume of international trade, but also an increase in the number of preferential trade agreements through which exchanges take place.
A preferential trade agreement is a trade pact that reduces tariffs between the participating countries for certain products. The following visualization shows the evolution of the cumulative number of preferential trade agreements that are in force across the world, according to the World Trade Organization WTO.
Health and economic growth: Evidence from dynamic panel data of years
These numbers include notified and non-notified preferential agreements the source reports that only about two-thirds of the agreements currently in force have been notified to the WTO , and are disaggregated by country groups. This figure shows the increasingly important role of trade between developing countries South-South trade , vis-a-vis trade between developed and developing countries North-South trade. In the late s, North-South agreements accounted for more than half of all agreements — in , they accounted for about one quarter.
Today, the majority of preferential trade agreements are between developing economies. The increase in trade among emerging economies over the last half century has been accompanied by an important change in the composition of exported goods in these countries. The next visualization plots the share of food exports in each country's total exported merchandise. These figures, produced by the World Bank, correspond to the Standard International Trade Classification, in which 'food' includes, among other goods, live animals, beverages, tobacco, coffee, oils, and fats. Two points stand out.
First, there has been a substantial decrease in the relative importance of food exports since s in most countries although globally in the last decade it has gone up slightly. And second, this decrease has been largest in middle income countries, particularly in Latin America. Regarding levels, as one would expect, in high income countries food still accounts for a much smaller share of merchandise exports than in most low- and middle-income-countries.
In economic theory, the 'economic cost' — or the 'opportunity cost' — of producing a good is the value of everything you need to give up in order to produce that good.
Related Determinants of Growth (General) and Regional Growth, Theory and Evidence
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