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Aim: India has emerged as one of the most favoured destinations in the global Information Technology (IT) outsourcing market. On the other hand, the IT industry has been playing an instrumental role in transforming India’s image from a low income-backward nation to a knowledge-based economy. Furthermore, the role of IT industry has been pivotal in putting India on a higher growth path. In addition, India’s IT industry has been showing robust performance in revenue earning, particularly in export revenue. However, the performance of this industry is likely affected by some recent global phenomena, such as 2008’s subprime crisis originated in the US, uncertainties in changes in H1-B visa rules, Britain’s exit from the EU, automation etc. There are other factors, like exchange rate volatility, emerging competition from other low-cost outsourcing destination countries, are also posing threat to India’s IT-outsourcing business. Against this backdrop, it is crucial to analyse the sustainability of performance of Indian IT industry. Thus, the present study aims at assessing the performance of Indian IT industry and evaluating the determinants of performance thereafter.Design / Research methods: To realize the objectives of the study, firm level data has been collected from the Centre for Monitoring Indian Economy (CMIE) Prowess database. For empirical analysis, we have applied a two-stage method. In the first-stage, we have used Data Envelopment Analysis (DEA) based Malmquist Productivity Index (MPI) to evaluate the Total Factor Productivity Growth (TFPG) of Indian IT industry during the period from 2004-05 to 2014-15. For this purpose, a balanced panel consists of 70 IT firms has been considered. Further, the TFPG has been decomposed into three components, viz. Catch-up, frontier-shift, and scale efficiency change (SEC). Consequently, in the second-stage, three random-effects panel regression models are considered to investigate the determinants of TFPG, catch-up, and frontier-shift separately. Conclusions / findings: During the study period, the average TFP and frontier-shift has been improved. On the other hand, catch up effect is found to have declined. The variables, such as export intensity, salaries and wages intensity have positive and statistically significant impact on the catch-up and frontier-shift. Export intensity has positive impact on TFPG. Age of the firms has positive impact on catch-up and TFPG. Salaries and wages intensity has positive impact on TFPG. On an average, the firms which spent on research and Development (R&D) have experienced improvement in TFPG and frontier-shift. The public limited firms performed better than their private counterparts in terms of catch-up, frontier-shift, and TFPG. The non-group firms have performed better than the group firms in case of catch-up. On the other hand, on an average, the firms exhibiting decreasing Returns to Scale (DRS) are found to have registered deterioration in catch-up and TFPG with respect to the benchmark firms which are exhibiting Constant Returns to Scale (CRS). The firms exhibiting Increasing Returns to Scale (IRS) have shown improvement in catch-up and TFPG over the benchmark CRS firms. The impact of the US subprime crisis has been negative on catch-up, frontier-shift, and TFPG. The firms, which have spent on royalty, have experienced improvement in catch-up and TFPG. Originality / value of the article: So far in our knowledge, not so many studies of this kind have been done in the arena of empirical research pertains to the IT industry, especially in a developing country like India. Moreover, we have not found any study that covers the span of the dataset considered in the present study. In addition to this, the present study has employed a random-effects panel model to accommodate a number of time-invariant dummy variables which would not be possible in case of a fixed-effects panel model incorporated by some previous studies of this genre.Implications of the research: The identification of the determinants of TFPG and its components would help the stakeholders and policy makers of the IT industry to formulate appropriate policies which could mitigate the risks faced by the industry on one hand, and stimulate the forces that would enhance the growth of this industry on the other. For instance, to mitigate future risks, Indian IT industry should reduce its dependence on the US and UK markets. Besides, it should explore new markets in the EU, and other emerging economies where opportunities are plenty. To maintain India’s robust global position in the long run, Government of India should play the key role in providing world class infrastructure and telecommunication facilities to its IT industry. In addition to this, Government needs to rationalise and simplify the existing Indian labour law to facilitate the business of IT industry. Various stakeholders along with the Government should put necessary efforts to develop the domestic IT market as there exists ample of opportunities in future. Keywords: information technology industry, data envelopment analysis, Malmquist productivity index, random-effects model, total factor productivity, catch-up, frontier-shift, India. JEL: C23, C61, L86, O47

I. Introduction Total factor productivity (TFP) growth is an important measure of potential output growth given the nature of the diminishing returns to input use in the long run. Thus, Malaysia in her drive to enjoy sustainable growth to raise its living standards is set on focusing on TFP growth as stated in Malaysia's Second Industrial Master Plan 1996-2005. In fact, the manufacturing sector which has increased its contribution to gross domestic product (GDP) output from 19.3 per cent in 1979 to 34.2 per cent in 1996 has been identified as a key growth engine in this transformation process. Hence, it is imperative and timely for an analysis on the productivity growth performance of this sector to be undertaken. This study adds to the existing empirical literature in three ways. First, previous studies on Malaysian manufacturing have only considered the nonfrontier measure using the divisia translog index approach. To date, using the nonfrontier approach, Tham (1996, 1997) and the Productivity Report 1999 provide evidence of declining TFP growth for the Malaysian manufacturing sector in the 1990s (see Table 3). (1) How would this result compare with the use of the frontier approach? Will the frontier models also provide low TFP growth measures? This is one of the issues addressed in this article. As for the earlier studies, the nonparametric technique adopted computes TFP growth as a residual since it measures anything and everything of output growth that is not accounted by input growth. More importantly, the translog index TFP growth measure ignores the concept of technical inefficiency (by unrealistically assuming that all industries are technically efficient) and inaccurately interprets technical change as TFP growth. Thus in this study, frontier measures are used to overcome these major drawbacks. In the productivity literature, TFP growth is shown to be composed of both technical change (frontier shift) and technical efficiency (catching up effect). While the frontier effect indicates how far the efficient frontier itself has shifted over time due to the use of better technology and equipment, the catching up effect reflects how far the industry has moved towards the efficient frontier due to the better use of technology and equipment. The second difference in this study is that empirical robustness is ensured by the use of both the parametric and nonparametric frontier approaches to calculate TFP growth. Under the parametric approach, a stochastic production frontier model incorporating non-parallel shifts is estimated. With the nonparametric approach, the data envelope analysis (DEA) technique is used. Using a panel data set of twenty-eight manufacturing industries (see Appendix 1 for a list) from 1981 to 1996, a measure of TFP growth is first obtained and then decomposed to technical change and change in technical efficiency for both models. The results are then compared to previous studies with a focus on the Malaysian manufacturing sector as TFP growth studies on the aggregate economy may have broad implications that are not necessarily reflective of the TFP growth performance of specific sectors in the economy. The third contribution of this article is that the comparative performance of the results from alternative methodologies would add to similar work by Bjurek and Hjalmarsson (1990), Coelli and Perelman (1999), and Kumbhakar, Heshmati, and Hjalmarsson (1999) which provide mixed evidence of similarities in the results from the use of various models. Often, the choice of the method is said to depend on a range of factors. For instance, if the researcher simply wants to know if output growth is TFP or input-driven growth, then either approach would suffice. However, to answer questions on maximum productive or best practice output levels, the stochastic frontier can be used to understand the industries' catching up behaviour with respect to its own maximum potential, while DEA allows for the study of the performance of each industry relative to efficient industries in the sample. …

The present study aims to examine the output and total factor productivity (TFP) growth by estimating a stochastic frontier production function for a panel of Middle East and North Africa (MENA) countries, and to decompose TFP growth into technical change, technical efficiency, and scale efficiency. The empirical findings reveal that, while factor accumulation is the main driver of output growth in the MENA region, TFP growth is increasingly accounting for a respectable proportion of output growth, with improved technical efficiency playing a crucial role in productivity growth. Specifically, the average annual TFP growth is found to be 0.846%, with positive contributions from technical efficiency and scale efficiency. However, the results show that technical progress has had a negative impact on TFP growth. The positive growth in technical efficiency suggests that the countries are catching up towards the optimal production frontier. At a disaggregated level, oil-rich countries registered negative effects of technical progress, while non-oil countries registered negative effects of scale efficiency. Technical efficiency, however, accounts for the largest positive factor in TFP growth in both oil and non-oil country groups.

The overall evolution of the economy is usually appreciated by two macroeconomic indicators GDP and GVA, which by their value gives us clear information on the state of the economy. Gross domestic product (GDP), the main macroeconomic aggregate of national accounts, is the final result of the production activity of resident producer units and which corresponds to the value of goods and services produced by these units for final consumption. Gross Value Added (GVA) is the balance of the production account and is measured as the difference between the value of the goods and services produced (valued at basic prices) and the intermediate consumption (valued at the buyer's prices), thus representing the new value created in the production process. GVA is calculated before calculating the consumption of fixed capital. Since 1990, we have been confronted with a major restructuring of the way GDP and GVA are created due to the intensive process of restructuring the economy. In the paper we will analyze the basis of the processing of national statistical data, how the tourism component of the tertiary sector contributes to the formation of the aggregate indicators presented above. In 2016, Romania had a GDP of 169.6 billion euros, below the Czech level (174.4 billion euros), Greece (175.9 billion euros) and Portugal (184.9 billion euros). Data series published by the European Statistical Office show that in the first quarter of this year, Romania's GDP adjusted for seasonal influences was 44.2 billion euros, while the value of GDP- Greece was 43.96 billion euros, the Czech Republic's 44.85 billion euros, and Portugal's 47.37 billion euros. In terms of GVA training, Romania is included in the European Union's Statistical Yearbook 201 6 as the country with the largest contributions to the Gross Value Added in the economy from industry, agriculture and construction, simultaneously with the lowest Public sector contribution (administration, defense, education, health and social welfare, etc.) Although professional, scientific and technical activities have seen the largest increase in the share of Gross Value Added training, they remain below the average of 10.4% Registered on the whole EU. There is an increase in the art, entertainment, recreation and other activities related to tourism - which brought us near the European customs and contributed to the "structural convection" of the Romanian economy. Touristic activity, particularly complex, with upstream and downstream implications, generates a tourism industry, whose components contribute to the formation of GDP and national Gross Value Added We will analyze the share of tourism in Romania's Gross Domestic Product in the period 2008-2014, gross value added in the tourism industry direct gross value added from tourism and gross domestic product of tourism in 2013 and 2014.
 
 Keywords: macroeconomic indicators, tourism industry, Gross Domestic Product, Gross Value Added economy

This thesis analyzes the relationship between corruption, oil rents, and Gross Domestic Product (GDP) per capita within the Middle East and North Africa (MENA) region. The study utilizes three simple Ordinary Least Squares (OLS) linear regressions to answer the questions: Is corruption associated with poor economic performance in the MENA region? Is oil dependency in MENA associated with poor economic performance? And lastly, Is oil dependency associated with higher levels of corruption in the MENA region? The first regression evaluates the relationship between GDP per capita and corruption, the second regression assesses the relationship between oil rents and GDP per capita, and the third regression assesses the relationship between corruption and oil rents. Additionally, a robustness check using a multiple regression model is conducted allowing for a simultaneous examination of these factors' relationship with GDP per capita. This regression allows us to determine if the results from the simple regressions still apply when using a different model. This study utilizes GDP per capita data from the World Bank for the year 2021, with the exception of Syria, Yemen, and Kuwait. For these countries, the most recent available data is from 2018, 2020, and 2020 respectively. Corruption data is also from 2021 and is sourced from Transparency International's Corruption Perceptions Index which offers a reliable gauge of corruption levels. World Bank data on oil rents as a percentage of GDP provides insight into one of the region's pivotal revenue sources. Overall, this study finds a strong positive relationship between GDP per capita and CPI meaning that corruption is associated with poor economic performance in the region. Furthermore, the study finds no statistically significant correlation between either GDP per capita and oil rents or corruption and oil rents. Instead, the study finds that the relationship between oil rents, GDP per capita, and corruption can be primarily attributed to institutional quality and how oil profits are managed. To see the complete thesis, please visit https://scholar.colorado.edu/concern/undergraduate_honors_theses/td96k421x.

BackgroundDonor countries in the Middle East and North Africa (MENA) including Saudi Arabia, Kuwait and United Arab Emirates (UAE) have been among the largest donors in the world. However, little is known about their contributions for health. In this study, we addressed this gap by estimating the amount of development assistance for health (DAH) contributed by MENA country donors from 2000 to 2017.MethodsWe tracked DAH provided and received by the MENA region leveraging publicly available development assistance data in the Development Assistance Committee (DAC) database of the Organisation for Economic Co-operation and Development (OECD), government agency reports and financial statements from key international development agencies. We generated estimates of DAH provided by the three largest donor countries in the MENA region (UAE, Kuwait, Saudi Arabia) and compared contributions to their relative gross domestic product (GDP) and government spending; We captured DAH contributions by other MENA country governments (Egypt, Iran, Qatar, Turkey, etc.) disbursed through multilateral agencies. Additionally, we compared DAH contributed from and provided to the MENA region.ResultsIn 2017, DAH contributed by the MENA region reached $514.8 million. While UAE ($220.1 million, 43.2%), Saudi Arabia ($177.3 million, 34.8%) and Kuwait ($59.8 million, 11.6%) as sources contributed the majority of DAH in 2017, 58.5% of total DAH from MENA was disbursed through their bilateral agencies, 12.0% through the World Health Organization (WHO) and 3.3% through other United Nations agencies. 44.8% of DAH contributions from MENA was directed to health system strengthening/sector-wide approaches. Relative to their GDP and government spending, DAH level fluctuated across 2000 to 2017 but UAE and Saudi Arabia indicated increasing trends. While considering all MENA countries as recipients, only 10.5% of DAH received by MENA countries were from MENA donors in 2017.ConclusionMENA country donors especially UAE, Saudi Arabia and Kuwait have been providing substantial amount of DAH, channeled through their bilateral agencies, WHO and other multilateral agencies, with a prioritized focus on health system strengthening. DAH from the MENA region has been increasing for the past decade and could lend itself to important contributions for the region and the globe.

This study uses industrial panel data for Japanese manufacturing to estimate the sources of productivity growth by simultaneously considering embodied technical progress, spillover effects, and openness, after controlling for returns to scale, imperfect competition, and capacity utilization. Estimation results show the existence of considerable embodied technical progress and interindustry externalities of capital investments positively affecting productivity growth. Furthermore, embodied technical progress causes research and development (R&D) capital to affect productivity growth insignificantly, suggesting that the impact of R&D is realized only after being embodied into other capitals. From sector‐wise estimations, we notice differences in factors affecting productivity growth between the durable and nondurable manufacturing sectors. (JELD24, O30)

Purpose: This paper predicted the direct relationship between the four indicators of “Financial Inclusion” and “GDP per-capita” of the country. Previous studies presented in this scenario are qualitative in nature. Research methodology: In this paper, “step-wise multiple linear regression” is used to establish the cause-and-effect relationship between the four indicators of “financial inclusion”; “Deposit accounts per 1000 population”; “Number of credit accounts per 1,000 people”; “Bank branches per 100,000 of adult population”, and “ATMs per 100,000 of adult population” and “GDP per capita”. Results: Regression model showed only “Credit accounts per 1,000 people” have a significant relationship with the “GDP per capita”. In this article, secondary data were obtained from the RBI website and the reports of international financial institutes. Limitations: Data on “ATMs” and “Bank branches per 100,000 of the adult population” is not present before 2004, decreasing the depth of analysis. Contribution: There is a cause-and-effect relationship between the country’s “GDP per capita” and the “F.I.” “Credit accounts per 1,000 people” only have a significant relationship with GDP per capita, so the change in the number of credit account will show a change in GDP per capita for Indian economy. Keywords: Financial inclusion (F.I), GDP (Gross Domestic Product) per capita, Deposit accounts, Credit accounts, ATMs (Automated Teller Machines), Bank branches

Climate change is a major public health concern in the Middle East and North Africa (MENA) Region. MENA is recognized as an active region in oil and gas production; this directly leads to increasing levels of ground ozone and particulate matter that affect human health. Other aspects of climate change, with key consequences for public health, are water, food, and vector-borne diseases, food insecurity, mental illness, and allergen-induced diseases. Some of the MENA countries are signatories to the United Nations Framework Convention on Climate Change (UNFCCC) and actively contribute to sustaining a resilient climate that supports health and well-being. Several countries of MENA have submitted their Intended Nationally Determined Contributions (INDCs) within their resources and capacities, many conditional on their economic growth. These INDCs provide a map for mitigating and adapting to the effects of climate change. Through different domestic laws and policies, the INDCs introduce several institutional obligations to mitigate the adverse health impacts of climate change. The World Health Organization (WHO) has played an essential role in assessing the impacts of climate change and embracing several regional and domestic projects to tackle the resulting disease outcomes, most prominently witnessed in Malaria, with cholera and other gastrointestinal diseases. Other local initiatives are discussed in this chapter. Despite all of the embraced initiatives to maintain a climate resilient environment in MENA, integrating climate change considerations and policies into public health planning and vice versa, is still hindered across the region. A number of barriers and potential solutions have been discussed in this chapter.

All Parties to the United Nations Framework Convention on Climate Change (UNFCCC) were requested to communicate intended nationally determined contributions (INDCs) in a clear, transparent and understandable way before the Conference of the Parties (known as COP21) held in Paris in 2015. The Contributions were supposed to be balanced and comprehensive to ensure sustainable development and expected to include finance, technology requires, technology transfer and capacity building aiming at mitigation and adaptation. This research focuses on investigating the INDCs prepared by countries from the Middle East and North Africa (MENA), one of the most water-scarce and dry regions in the world. Following a content analysis, this research has found that INDCs from the region have not been able to reflect the desired output. Submissions were also not timely and not sufficiently ahead of time. Many countries were not able to disclose the current status. INDCs can play a significant role by providing objective, timely, and reliable information, which is missing at present in the countries from MENA.

Carragher et al.1 describe the development and application of an alcohol policy score (TEASE-16) to nine study areas in the western Pacific. In their analysis they attempt to relate the policy score to average alcohol consumption for these areas. My analysis shows that their correction for income is not required, and that their use of division to correct for consumption needs justification, because it produces a consumption variable of litres per dollar. In an attempt to analyse the cross-sectional data correctly, I obtained information on alcohol consumption for eight of the study regions from published WHO data for 2010,2 which does not separate data for Hong Kong Special Administrative Region from the rest of China. I obtained gross domestic product (GDP) and population data for 2010 from published World Bank information. I did a multiple regression analysis using methods described by Kronmal3 to avoid the well-known problem of spurious correlation arising from the use of rates.4 The results show that alcohol consumption does not appear to be related to income (measured by GDP or GDP per-capita) or to TEASE-16 for the eight regions considered. However TEASE-16 is related to GDP, but not in a linear fashion. In my regression model, I considered total alcohol consumption in each area as the response, with GDP, population and TEASE-16 score the predictors. Only population size was significantly associated with total alcohol consumption (Table 1), and examination of outliers show that this association arises because of the influence of China with its much larger population. Fitting of the submodels involving only population and TEASE-16 and only GDP and population did not alter this conclusion. Similarly, including total population aged over 15 as a predictor did not alter the conclusion. Table 1 Multiple regression results for alcohol consumption with predictors GDP, TEASE-16 and population The relationship between TEASE-16 and GDP per capita is shown in Fig. 1. It can be seen that the relationship is not linear and I confirmed this by fitting a regression model with a quadratic term in GDP. Fitting a regression relationship as in Table 1, but including the square of GDP as a predictor once again, did not alter the conclusions. The inappropriate adjustment done by Carragher et al. for income is the source of the spurious relationship between TEASE-16 and consumption, since TEASE-16 is positively related to income and the consumption scores are divided by income, resulting in a negative relationship that is an artefact of the analysis. Fig. 1 Relationship between TEASE-16 and GDP per capita in the western Pacific Neither TEASE-16 score nor income, (measured by GDP), were significantly correlated with alcohol consumption across the eight areas in the year 2010. It seems unlikely that analysis of the original data used by Carragher et al.1 would alter this conclusion (if so, it would raise concerns related to data quality), but I would be happy to undertake such analysis if the data were made available.

What social progress is and how to measure it are seemingly plain but essentially intri-cate questions that have not been clarified to date, which has led to various social problems and development failures. Designed after the Great Depression in the 1930s, Gross Domestic Product (GDP) has been, on the one hand, regarded as the greatest invention of the 20th century and is widely accepted as the primary indicator for social progress, but on the other hand, it has been criticized as knowing the price of everything but the value of nothing. The Beyond GDP Movement that has been active since the 2010s has inspired global interest in designing indicators to replace or supplement GDP, but none of them stands out as GDP’s successor. We take 12 influential indicators that consider beyond GDP and carry out a Principal Component Analysis (PCA) to investigate their correlations. The results indicate that GDP per capita (GDPP) can explain 65.61% of the information in the first principal component (PC) and account for 51.10% of the information related to the total 13 indicators, indicating its major role in social progress. Most indicators have strong correlations with GDPP, not beyond, and only the Ecological Footprint per capita (EFP) and Happy Planet Index (HPI) that have negative and weak correlations with GDPP, respectively, can provide new perspectives and values beyond GDP. Social progress is based upon various public values, and the indicators are the measurements of these values. Although GDP and economic values play major roles during social development, other indicators and their potential public values cannot be ignored. Prioritizing these public values and monitoring their indicators are essential to achieving sustainable and comprehensive social progress.

Agri-industry parks, 290-293 Agribusiness in Egypt, 292-293 trends, 287 Agribusiness MSMEs challenges facing, 295 impact of internal issues, 297-298 impact of market-related issues on, 295 Agricluster (see also Industrial clusters) business models and inclusive market development, 298-302 clustering and economics of competition, 283-284 limitation and future research, 302-304 link between agri-industrial clusters and growth of agribusiness, 284-290 MSMEs in economic progress, 282-283 research insights, 294-298 study methodology, 293-294

All Parties to the United Nations Framework Convention on Climate Change (UNFCCC) were requested to communicate intended nationally determined contributions (INDCs) in a clear, transparent and understandable way before the Conference of the Parties (known as COP21) held in Paris in 2015. The Contributions were supposed to be balanced and comprehensive to ensure sustainable development and expected to include finance, technology requires, technology transfer and capacity building aiming at mitigation and adaptation. This research focuses on investigating the INDCs prepared by countries from the Middle East and North Africa (MENA), one of the most water-scarce and dry regions in the world. Following a content analysis, this research has found that INDCs from the region have not been able to reflect the desired output. Submissions were also not timely and not sufficiently ahead of time. Many countries were not able to disclose the current status. INDCs can play a significant role by providing objective, timely, and reliable information, which is missing at present in the countries from MENA.

Assessment of the Meetings, Incentives, Conventions and Exhibitions (MICE) Industry in the Philippines