An Analysis of Exports and Growth in Pakistan

The paper examines the export-led growth (ELG) paradigm for Pakistan, using data of the period from 1970-71 to 2003-04. The paper uses a number of analytical tools, including Unit Root Test, Phillips- Perron Tests, Co-integration Johansen Test, and the Granger Tests. The paper sets three hypotheses for testing the ELG paradigm for Pakistan; (a) whether GDP and exports are cointegrated, (b) whether exports Granger cause growth, and (c) whether exports Granger cause investment. The time series data on GDP growth, export growth and investment GDP ratio (proxy for capital formation), and the labour employed were used. The data were tested for stationarity using the Augmented Dickey-Fuller (ADF) test and Phillips-Perron test (1988), and then the relationship between GDP growth rate and the growth rate of other variables was determined using OLS with AR (1). The major finding of the present study is that growth rate of export, total investment, and labour employed have positively affected the GDP growth rate

Congestion and Safety: A Spatial Analysis of London

Spatially disaggregate Enumeration District (ED) level data for London is used in an analysis of various area-wide factors on road casualties. Data on 15335 EDs was input into a geographic information system (GIS) that contained data on road characteristics, public transport accessibility, information of nearest hospital location, car ownership and road casualties. Demographic data for each ED was also included. Various count data models e.g., negative binomial or zero-inflated Poisson and negative binomial models were used to analyze the associations between these factors with traffic fatalities, serious injuries and slight injuries. Different levels of spatial aggregation were also examined to determine if this affected interpretation of the results. Different pedestrian casualties were also examined. Results suggest that dissimilar count models may have to be adopted for modeling different types of accidents based on the dependent variable. Results also suggest that EDs with more roundabouts are safer than EDs with more junctions. More motorways are found to be related to fewer pedestrian casualties but higher traffic casualties. Number of households with no car seems to have more traffic casualties. Distance of the nearest hospital from EDs tends to have no significant effect on casualties. In all cases, it is found that EDs with more employees are associated with fewer casualties.

The Livestock Economy of Pakistan: An Agricultural Sector Model Approach

The Pakistan Agricultural Sector Model (PASM) developed by Davies et al. (1991) was modified to enhance the livestock sub-sector. Nutrient-based rations replaced feedstuff-based rations and dry matter minimum and maximum constraints (stomach capacity) were added. Several initial simulations were undertaken to examine the structure of the modified model and its impact across the crop and livestock sub-sectors. These simulations included relaxing exogenous livestock numbers and selected crop hectarage constraints, and requiring that green forage be fed in the season grown. Most importantly, the results demonstrated that fodder hectarage will grow with livestock numbers to insure that sufficient green forage is available seasonally. Two other analyses were performed to demonstrate the need to specify linkages between the crop and livestock sub-sectors. An analysis of transforming the livestock sub-sector from traditional to feedlot-based technology demonstrated that the reduced numbers of non-milking cattle needed for a given output of meat would provide the potential for increased production of various crops and other livestock products. Also, expanded cotton and Irri rice exports, hypothesised to occur through trade liberalisation from the Uruguay Round of the GATT, highlighted other inter-relationships between the crop and livestock sub-sectors. Greater production of both livestock and other crops might accompany the expansion of cotton production but less livestock feed would be available with expanded exports of Irri rice.

Time series count data models: an empirical application to traffic accidents

Count data are primarily categorised as cross-sectional, time series, and panel. Over the past decade, Poisson and Negative Binomial (NB) models have been used widely to analyse cross-sectional and time series count data, and random effect and fixed effect Poisson and NB models have been used to analyse panel count data. However, recent literature suggests that although the underlying distributional assumptions of these models are appropriate for cross-sectional count data, they are not capable of taking into account the effect of serial correlation often found in pure time series count data. Real-valued time series models, such as the autoregressive integrated moving average (ARIMA) model, introduced by Box and Jenkins have been used in many applications over the last few decades. However, when modelling non-negative integer-valued data such as traffic accidents at a junction over time, Box and Jenkins models may be inappropriate. This is mainly due to the normality assumption of errors in the ARIMA model. Over the last few years, a new class of time series models known as integer-valued autoregressive (INAR) Poisson models, has been studied by many authors. This class of models is particularly applicable to the analysis of time series count data as these models hold the properties of Poisson regression and able to deal with serial correlation, and therefore offers an alternative to the real-valued time series models. The primary objective of this paper is to introduce the class of INAR models for the time series analysis of traffic accidents in Great Britain. Different types of time series count data are considered: aggregated time series data where both the spatial and temporal units of observation are relatively large (e.g., Great Britain and years) and disaggregated time series data where both the spatial and temporal units are relatively small (e.g., congestion charging zone and months). The performance of the INAR models is compared with the class of Box and Jenkins real-valued models. The results suggest that the performance of these two classes of models is quite similar in terms of coefficient estimates and goodness of fit for the case of aggregated time series traffic accident data. This is because the mean of the counts is high in which case the normal approximations and the ARIMA model may be satisfactory. However, the performance of INAR Poisson models is found to be much better than that of the ARIMA model for the case of the disaggregated time series traffic accident data where the counts is relatively low. The paper ends with a discussion on the limitations of INAR models to deal with the seasonality and unobserved heterogeneity

Modelling area-wide count outcomes with spatial correlation and heterogeneity: an analysis of London crash data

Count models such as negative binomial (NB) regression models are normally employed to establish a relationship between area-wide traffic crashes and the contributing factors. Since crash data are collected with reference to location measured as points in space, spatial dependence exists among the area-level crash observations. Although NB models can take account of the effect of unobserved heterogeneity (due to omitted variables in the model) among neighbourhoods, such models may not account for spatial correlation areas. It is then essential to adopt an econometric model that takes account of both spatial dependence and uncorrelated heterogeneity simultaneously among neighbouring units. In studying the spatial pattern of traffic crashes, two types of spatial models may be employed: (i) classical spatial models for higher levels of spatial aggregation such as states, counties, etc. and (ii) Bayesian hierarchical models for all spatial units, especially for smaller scale area-aggregations. Therefore, the primary objectives of this paper is to develop a series of relationships between area-wide different traffic casualties and the contributing factors associated with ward characteristics using both non-spatial models (such as NB models) and spatial models and to identify the similarities and differences among these relationships. The spatial units of the analysis are the 633 census wards from the Greater London metropolitan area. Ward-level casualty data are disaggregated by severity of the casualty (such as fatalities, serious injuries, and slight injuries) and by severity of the casualty related to various road users. The analysis implies that differentward-level factors affect traffic casualties differently. The results also suggest that Bayesian hierarchical models aremore appropriate indeveloping a relationship between areawide traffic crashes and the contributing factors associated with the road infrastructure, socioeconomic and traffic conditions of the area. This is because Bayesian models accurately take account of both spatial dependence and uncorrelated heterogeneity

How to Solve the Fronthaul Traffic Congestion Problem in H-CRAN?

The design of efficient wireless fronthaul connections for future heterogeneous networks incorporating emerging paradigms such as heterogeneous cloud radio access network (H-CRAN) has become a challenging task that requires the most effective utilization of fronthaul network resources. In this paper, we propose and analyze possible solutions to facilitate the fronthaul traffic congestion in the scenario of Coordinated Multi-Point (CoMP) for 5G cellular traffic which is expected to reach ZetaByte by 2017. In particular, we propose to use distributed compression to reduce the fronthaul traffic for H-CRAN. Unlike the conventional approach where each coordinating point quantizes and forwards its own observation to the processing centre, these observations are compressed before forwarding. At the processing centre, the decompression of the observations and the decoding of the user messages are conducted in a joint manner. Our results reveal that, in both dense and ultra-dense urban small cell deployment scenarios, the usage of distributed compression can efficiently reduce the required fronthaul rate by more than 50% via joint operation

An Analysis of Exports and Growth in Pakistan

Trade is presumed to act as a catalyst of economic growth and the growth in exports leads to increase in the incomes of factors of production, which in turn increases the demand for input for further expansion in production. The resultant pressure on domestic capacity may stimulate technological change and investment opportunities. Also increase in demand due to raising incomes of the factors of production on account of exports may spill over into other sectors of the economy. A part of such growths could also be diffused abroad through technical assistance and aid. According to Emery (1967) empirically proved that higher rates of exports growth leads to higher economic growth. Traditionally, a developing country had the choice of two alternative trade strategies for supporting industrial development, export promotion or import substitution. A consensus has emerged among many development economists that an export expansion policy by permitting resource exploitation according to comparative advantage and by allowing for utilisation and exploitation of economies of scale leads to higher growth rates of output and employment, greater technological progress and availability of foreign exchange. These in turn enable the countries with export oriented policies to attain higher rates of growth of GNP vis-à-vis countries following import substituting industrialisation [Donges and Muller-Ohlsen (1978)]

The Pakistan Agricultural Research System: Present Status and Future Agenda

Alarming food supply and demand deficits are projected to the year 2020 and beyond for Pakistan, based on its current low investment/low growth agricultural sector. Evidence suggests that agricultural productivity growth and increases in production may not keep pace with past growth rates. Part of the problem is an underfunded and poorly managed agricultural research system that can not hope to contribute significantly to increasing agricultural productivity now or in the future. The World Bank-assisted Agricultural Research II Project (ARP-II) was initiated to partially overcome some of the funding problems and provide institutional development in the areas of organisation, planning, and management of the research system at both the federal and provincial levels. A National Master Agricultural Research Plan (NMARP) was one of the principal goals of the ARP-II as part of improving research planning and management. The objective of this paper is to review the reasons why the Pakistan agricultural research system needs to be revitalised, review the status and problems of the present agricultural research system, and outline a future agenda for Pakistan’s agricultural research system based on the plan developed for the NMARP

Renewable energy RD&D expenditure and CO2 emissions in 15 European countries

Purpose: Renewable energy is an important component to the complex portfolio of technologies that have the potential to reduce CO2 emissions and to enhance the security of energy supplies. Despite RE’s potential to reduce CO2 emissions, the expenditure on renewable energy research, development & demonstration (RERD&D) as a percentage of total government energy research, development & demonstration (ERD&D) investment remains low in developed countries. The declining ERD&D expenditure prompted this research to explore the relationship between CO2 emissions per capita and RERD&D as opposed to ERD&D. Methodology: An econometric analysis of annual CO2 emissions per capita during the period 1990 – 2004 for the 15 pre-2004 European Union (EU15) countries was carried out. It was hypothesized that the impact of RERD&D expenditure on the reduction of CO2 emissions would be higher than that of ERD&D expenditure, primarily due to several RE technologies being close to carbon neutral. Country-level GDP per capita and an index of the ratio between industry consumption and industrial production (IICIP) were introduced in the analysis as proxies to control for activities that generate CO2 emissions. A number of panel data econometric models that are able to take into account both country- and time-specific unobserved effects were explored. Findings: It was found that random effect models were more appropriate to examine the study hypothesis. The results suggest that expenditure on RERD&D is statistically significant and negatively associated with CO2 emissions per capita in all models, whereas expenditure on ERD&D is statistically insignificant (ceteris paribus). Originality: The findings of this paper provided useful insight into the effectiveness of renewable energy RD&D investment in reducing CO2 emissions and are of value in the development of policies for targeted RD&D investment to mitigate the impacts of climate change