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Determinants of the Performance of Insurance Companies in Tunisia

This article aims to study the determinants of performance of Tunisian insurance companies for the period from 2002 to 2018 using the panel data method. The variables used are microeconomic and macroeconomic. Our results conclude that the determinants of the performance of Tunisian insurance companies are the capital structure, solvency, risk capital management, premium growth, volume of capital, age of the firm and financial investments.

Introduction
In recent decades, a large literature has attempted to measure efficiency in the financial services sector. Many of the studies in this field have been concerned with the banking sector, for example Berger et al (1987), Ferrier and Lovell (1990) and Berger and Humphrey (1991). Studies in the life insurance sector are much less developed and the pioneers in this area are Cho (1986), Boose (1990) and Grace and Timme (1992).
In Tunisia, there are few studies on insurance. Chaffai and Ouertani (2002) and Zammit et al (2010) were the first to focus on the insurance sector. And in a context where the global economy is in turbulent times, measuring and studying the determinants of performance in the financial services sector is becoming a major issue.
Indeed, this sector deserves special attention because the subprime crisis originated in the banking sector and spread to the entire financial system. Starting from this framework of analysis, we have tried to study the performance factors of Tunisian insurance companies. To do this, we first studied the explanatory factors of efficiency. These factors may be microeconomic or macroeconomic variables.
The rest of the article will be organized as follows: In the second section, we will review the main studies on the determinants of the performance of insurance companies. In the third section, we describe the sample and the model used. In the fourth section, we present the results and their interpretations.

Literature Review
The literature examining the performance of insurance companies has attracted the attention of several researchers; especially the interest in understanding the factors responsible for financial performance. The performance of insurance companies is generally influenced by internal factors specific to insurance and external factors relating to the macroeconomic environment. Internal determinants can also be called microeconomic determinants.

Microeconomic Determinants
The academic literature provides evidence that suggests a strong relationship between the performance of insurance companies and several microeconomic factors. The latter which the literature proposes as important determinants, are: the size of the firm, the capital structure, the solvency, the leverage, the age of the firm, and so on.
The relationship between the size of the firm and the performance of insurance companies has been much discussed between researchers. Some show that size has a positive effect on performance such as Cummins and Rubio-Misas (2006), Eling and Luhnen (2010), Malik (2011) and Biener et al (2016) who consider that when the firm expands its performance increases; this is explained by achieving economies of scale.
In the insurance industry, having to buy a lot of insurance premiums reduces income volatility because risk sharing is effective. Others suggest that very large companies suffer from economies of scale because it is difficult to manage a large insurance company, which leads to additional costs. Fenn et al (2008) and Mwangi and Murigi (2015) support this idea. In the same vein, the holding of capital by public authorities hampers performance. Indeed, Neelaveni (2012) showed that private insurance companies perform better than public ones. In the same vein, Kumari (2013) indicates that there is a significant increase in the commercial performance of Indian life insurance companies after privatization. Solvency is the ability of a company to meet its long-term fixed expenses and thereby achieve growth.
Many studies have been conducted to determine the effect of solvency on the performance of insurance companies; Bawa and Chattha (2013) find a non-significant relationship between solvency and performance.
Several empirical studies have found a negative relationship between leverage and the performance of insurance companies like Malik (2011) who conducted a study on the Pakistani insurance sector and Wabita (2013) who conducted a study of insurance companies insurance in Kenya.
However Boadi et al (2013), conducting a study on insurance companies in Ghana found a positive but not significant relationship between leverage and insurance performance.
As for the age of the firm, it has been considered important in several studies; two streams of research exist. The first line of research argues that age could help companies become more efficient through learning.
Thus, older companies are more experienced and can benefit from learning, so they do not need innovations and can therefore take advantage of their superior performance as shown by Vigaykumar and Kadirvelu (2004) and Bates et al. (2008).
However, the second line of research shows that older societies are more bureaucratic and are not flexible enough to be able to adjust quickly to change, and can be overtaken by younger and more flexible firms.

Macroeconomic Determinants
In addition to the microeconomic variables, there are several empirical works that suggest that external factors like; GDP, interest rate, inflation, unemployment and M2 money supply; are important determinants of the performance of insurance companies.
Gross Domestic Product or GDP represents the market value of all finished goods and services produced in a country in a given year, and the increase in GDP generally reflects the household's disposition of larger income streams and the increased profitability of companies. The studies carried out on this subject confirm the existence of a positive relationship between GDP growth and the profit increase of the firm.
Oshinloye et al. (2009) point out that a country cannot achieve meaningful development without the existence of good insurance and that insurance is indispensable in any nation. Ezirim and Muoghahu (2002) add that insurance is seen as an indispensable tool for economic progress and growth and development.
Thus, insurance is closely linked with the country's economic growth. Crowley (2007) defines the interest rate as the price a borrower pays for the use of money borrowed from a lender. Economists support the idea that the interest rate is the price of the capital's speech over time; the monetarist uses the interest rate to attract more funds and as interest rate increases attract more savings and lower interest rates will encourage investors to look for other investments. Insurance companies themselves realize more income on their investments when interest rates rise but if these rates are too high this can affect the level of premiums.
Jhingan (2002) defines inflation as a persistent and substantial increase in the general level of prices and the price increase tends to deduce total household expenditures which in turn lead to lower GDP and reduce the wealth available to households. Thus, inflation can reduce insurance income by reducing the number of insurance policies written.
However, Molyneux and Thornthon (1992), Guru et al. (2002) and Abreu and Mendes (2002) have shown that there is a positive relationship between asset returns and inflation suggesting that the growth of inflation will be favorable to the increase in bank profits. The increase in the unemployment rate decreases the present and future purchasing power of households, which leads to a fall in the production of goods and services, because generally the fall in demand leads to a fall in supply. Unemployment has a negative impact on household cash flows and thus increases the burden of debt.
For companies, the increase in the unemployment rate may reduce production as a result of the fall in effective demand and, with regard to insurance companies, the increase in unemployment may reduce the number of rfm.ideasspread.org Risk and Financial Management Vol. 1, No. 1;2019 insurance contracts and affect thus their income.
As regards the money supply, Haruna et al (2013) define it as the entire stock of money and other liquid payment instruments in the economy of a particular country at a specific time. The increase in the supply of money lowers interest rates, which in turn produce more investment.

Sample
The sample of our study is made up of thirteen resident insurance companies listed on the Tunisian Stock Exchange for the period from 2002 to 2018. We only selected anonymous insurance companies for the sake of availability of data and for the homogeneity of the results, in addition the theoretical foundations are more applicable on these firms.
These insurances account for nearly 90% of the market share of the sector. The other six companies are excluded for lack of information on the period studied.

Model
In order to test the relationship between the independent variables and the financial performance of Tunisian insurance companies, we perform a linear regression analysis on panel data. The panel data has the property of combining two dimensions, the transversal dimension that is to say individual and the longitudinal dimension that is to say temporal. According to William et al (2010), the construction of a model implies the specification of relations between two or more variables; thus our model is generally defined by the following linear relation: With: -y: represents the dependent variable.
-(i,t) indicate the insurance and the year respectively.
-α: it is a constant.
-x: represents the vector of the independent variables.

Presentation and Analysis of the Results
To determine the proper estimation method of the equation, we performed some tests.
The modified Wald test makes it possible to test the specific hypothesis of inter-individual homoscedasticity. The null hypothesis assumes that the variance of the errors is the same for all individuals.
The result of this test is equal to 0.0000 so we deduce the presence of heteroscedasticity between individuals. The Breush-Pagan test makes it possible to detect intra-individual heteroskedasticity which assumes different variances between the error terms of the same individual.
The result of this test leads us to accept the null hypothesis and indicates the presence of homoscedasticity. The Hausman test makes it possible to determine whether the coefficients of the two estimates (fixed and random) are statistically different. This test compares the variance-covariance matrix of the two estimators.
The result of this test is equal to 0.0000 so we reject the null hypothesis and we must favor the adoption of a fixedeffect model. The estimation of the parameters of our model by the Ordinary Lesser Square method gave the following results: 0.0000 ***, ** and * correspond to significance levels of 1%, 5% and 10%. Values in parentheses are p-value.
The value of Adjusted R square is 44.66% and the R square of the model is 51.91%. This means that 51.91% of the variations in the dependent variable (PF) are explained by the regression model. The Fisher statistic has a pvalue equal to 0.0000; the model is therefore statistically significant.
The variable "TAIL" has a negative but statistically insignificant effect on the performance of Tunisian insurance companies. Indeed, an increase in Ln TAIL leads to a decrease in the financial performance of insurance. This means that the biggest insurers are less efficient and so smaller companies are more efficient. This result is consistent with those of Yuengert (1993), Mwangi and Iraya (2014) and Zanghieri (2008).
The significance of the capital structure variable "STRC" implies that the latter is a determining factor in the financial performance of Tunisian insurance companies. The negative sign of the coefficient attached to this variable is consistent with that anticipated. So capital ownership by the state negatively affects the performance of Tunisian insurance and private companies are subsequently performing better. This result is consistent with that of Neelaveni (2012).
The variable "LEV" or financial leverage has a negative but insignificant sign on the performance of Tunisian insurance companies. In fact, the increase in the level of debt relative to equity raises insurance costs and therefore leads to a decline in its financial performance. This result is consistent with those of Malik (2011) and Wabita (2013).
On the other hand, the variable "SOL" has a positive and statistically significant sign. So the solvency of the insurance has a positive effect on its financial performance. This result is consistent with that anticipated.
The variable "CMR" or risk capital management has a negative sign statistically very significant. So a large share of capital and reserves relative to total assets negatively affects the performance of insurance and insurance companies that hold a smaller share of capital and reserves are subsequently more successful. This result is consistent with that anticipated.
The "CRP" variable, which represents premium growth, also has a statistically significant negative sign. This implies that rapid and excessive growth in premium volume is hampering the performance of insurance companies as such growth creates more risk and a requirement to hold more technical reserves. This result confirms those of Charumathi (2012) and Kim et al (1995).
The capital volume variable "VOC" has a positive and statistically significant sign. Indeed, an increase in LnVOC leads to an increase in the financial performance of insurance. This implies that insurance that holds a larger volume of capital is more efficient than insurance that holds less. This result is consistent with that anticipated.
The variable "AGE" has a negative and statistically significant sign. So in the Tunisian insurance sector, the older rfm.ideasspread.org Risk and Financial Management Vol. 1, No. 1;2019 the firm, the less efficient it is and the newly created insurance are more efficient. This result invalidates the work of Vigaykumar and Kadirvelu (2004).
The new variable "PLF" or financial investments has a positive and statistically significant sign. The significance of this variable implies that the latter is a determining factor in the performance of Tunisian insurance companies. The positive sign of the coefficient attached to financial investments shows that insurance companies that have a larger share of financial investments are performing better. This can be explained by the fact that income from financial investments, in addition to other income, increases the result of the insurance company and subsequently its performance.
The "CR" variable that represents the market concentration index has a negative and statistically insignificant sign. The negative sign of this variable shows that a high concentration of the market hinders the performance of insurance companies. This result is not consistent with our hypothesis.
The variable "TAILSEC" which represents the size of the insurance sector has a positive but statistically insignificant sign. The positive sign of this variable implies that the size of the sector positively affects the performance of insurance companies. The non-significance of this variable is the result of the difficulty experienced by Tunisian insurance companies in taking advantage of the economies of scale they can achieve through this sector.
The "GDP" variable, which represents annual GDP growth, has a positive but statistically insignificant sign. So GDP growth positively affects the performance of insurance companies. This is because the country's economic growth has positive effects on business sectors, including the insurance sector. The non-significance of this variable shows that Tunisian insurance companies do not benefit from the country's economic growth.
The variable "TI" or interest rate has a positive and statistically insignificant sign. Thus, the increase in the interest rate positively affects the performance of insurance companies. In fact, bonds represent a large part of the inverted assets of insurance companies and the increase in interest rates brings higher bond income. The non-significance of this variable shows that Tunisian insurance companies do not use these revenues effectively.
The "INF" variable for inflation has a negative and statistically insignificant sign. The increase in inflation has a negative effect on insurance companies. This is explained by the fact that the increase in the consumer price index affects insurance performance by increasing the number of claims, expenses and technical provisions.
The variable "CH" or unemployment has a positive and statistically insignificant sign. Thus the increase in the unemployment rate has a positive effect on the performance of insurance companies. This result is contrary to our expectations.
The money supply variable "M2" has a negative and statistically insignificant sign. So the increase in money supply negatively affects the performance of insurance companies. This is explained by the fact that the increase in the money supply increases inflation if it is not correlated with the economic growth of the country.
The variable "POP" which represents the annual growth rate of the population has a statistically insignificant positive sign. Indeed, the increase in the population leads to an increase in the performance of insurance companies. This can be explained by the increase in the number of potential customers. The non-significance of this variable shows that Tunisian insurance companies do not take advantage of this opportunity.
The variable "IC" which represents the perception index of corruption has a positive but statistically insignificant sign. The increase in corruption has a positive effect on the performance of insurance companies. This result is surprising and contrary to our expectations.
The variable "VEH" which represents the number of vehicles has a positive and statistically significant sign. Thus, the increase in the number of motor vehicles has a positive effect on the performance of insurance companies. This is explained by the fact that the automobile branch dominates the Tunisian insurance sector. This result is consistent with that anticipated.

Conclusion
At the level of this work, we have tried to determine the factors that can influence and affect the performance of Tunisian insurance companies during the period 2002-2018. The interpretation of the results has allowed us to understand that only the microeconomic factors are determinant of the performance of insurance companies. Macroeconomic factors do not have significant effects on performance. Capital structure, solvency, risk capital management, premium growth, volume of capital, age and financial investments are the determinants of the performance of Tunisian insurance companies.
Thus, the performance of an insurance company is a set of determinants that are related to the activities of that rfm.ideasspread.org Risk and Financial Management Vol. 1, No. 1;2019 company. Each determinant must be carefully managed so that the results of the activities are positive and satisfactory and meet the expectations of all stakeholders.
As a research perspective, the analysis can be continued by applying to other MENA countries that are subject to the same regulatory implications as Tunisia to see if there is a country where the insurance sector is performing well. It may also be interesting to know if the insurance sectors of other countries are affected by the crisis.

Declaration of interest
The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.


extfieldsofstudy:
Medicine
Environmental Science

text:
Experimental evidence on promotion of electric and improved biomass cookstoves

Significance Three billion people rely on traditional stoves and solid fuels. These energy use patterns exacerbate the global climate crisis (via increased carbon emissions) and forest degradation/deforestation (via daily fuelwood collection), and expose billions to toxic air pollution generated by dirty fuels. Widespread adoption of improved cookstoves (which use cleaner fuels or burn solid fuels more efficiently) may ease this “triple burden,” but recent research casts doubt on their potential, given low and slow diffusion. We challenge this pessimism based on a multiyear, three-phase field study comprising diagnosis, design, and experimental testing involving 1,000 rural Indian households. We show that demand for these improved energy technologies is high when supply chains are robust, technologies match local needs, and income and liquidity constraints are relaxed.

Improved cookstoves (ICS) can deliver "triple wins" by improving household health, local environments, and global climate. Yet their potential is in doubt because of low and slow diffusion, likely because of constraints imposed by differences in culture, geography, institutions, and missing markets. We offer insights about this challenge based on a multiyear, multiphase study with nearly 1,000 households in the Indian Himalayas. In phase I, we combined desk reviews, simulations, and focus groups to diagnose barriers to ICS adoption. In phase II, we implemented a set of pilots to simulate a mature market and designed an intervention that upgraded the supply chain (combining marketing and home delivery), provided rebates and financing to lower income and liquidity constraints, and allowed households a choice among ICS. In phase III, we used findings from these pilots to implement a field experiment to rigorously test whether this combination of upgraded supply and demand promotion stimulates adoption. The experiment showed that, compared with zero purchase in control villages, over half of intervention households bought an ICS, although demand was highly price-sensitive. Demand was at least twice as high for electric stoves relative to biomass ICS. Even among households that received a negligible price discount, the upgraded supply chain alone induced a 28 percentage-point increase in ICS ownership. Although the bundled intervention is resource-intensive, the full costs are lower than the social benefits of ICS promotion. Our findings suggest that market analysis, robust supply chains, and price discounts are critical for ICS diffusion.
improved cookstoves | technology adoption | Indian Himalayas | supply chain | price subsidies I mproved cookstoves (ICS) can make households healthier, improve local environments, and reduce pollutants that cause climate change (1)(2)(3)(4)(5). * Yet, despite evidence on their efficacy, widespread diffusion has proven challenging (7,8). In recent years, field experiments more rigorously attribute low demand to income and liquidity constraints, social networks and peer effects, and households' undervaluation of health risks (8)(9)(10). Collectively, the limited research so far suggests that a complex combination of factors interact with local contexts (such as tastes and preferences) to limit adoption (11). It also partially explains why demand for-and impacts of-ICS technologies in certain settings can be high (12)(13)(14)(15), while efforts documented elsewhere are disappointing (8,16). Unfortunately, much of this evidence is idiosyncratic and patchy, and rarely derived from projects and policies implemented by firms and governments (17). Critically, it also ignores supply-side aspects such as product characteristics, supply chains, and the enabling environment (18).
We respond to this knowledge gap (of lack of adoption studies that jointly consider supply and demand promotion) by con-ducting a multiyear, multiphase study in the Indian Himalayas. Phase I started with a series of diagnostic steps (spanning 18 mo) to uncover the nature of low ICS adoption. In phase II, we implemented a set of pilots to simulate a mature market and designed an intervention that would reduce both supply and demand constraints. Finally, in phase III, we experimentally tested a package of interventions, spanning an additional 18 mo, in a sample of ∼1,000 households living in nearly 100 rural Himalayan communities. Our principal hypothesis, derived from insights gleaned from the diagnosis and design phases, was that ICS demand would be highly sensitive to a multipronged intervention combining (i) a well-developed technology supply ecosystem (characterized by delivery, demonstration, promotion, and financing) with (ii) demand-stimulating subsidies. Additionally, our second hypothesis was that the welldeveloped supply chain alone would lead to considerable ICS adoption; that is, one of the treatment arms of our randomized Significance Three billion people rely on traditional stoves and solid fuels. These energy use patterns exacerbate the global climate crisis (via increased carbon emissions) and forest degradation/deforestation (via daily fuelwood collection), and expose billions to toxic air pollution generated by dirty fuels. Widespread adoption of improved cookstoves (which use cleaner fuels or burn solid fuels more efficiently) may ease this "triple burden," but recent research casts doubt on their potential, given low and slow diffusion. We challenge this pessimism based on a multiyear, three-phase field study comprising diagnosis, design, and experimental testing involving 1,000 rural Indian households. We show that demand for these improved energy technologies is high when supply chains are robust, technologies match local needs, and income and liquidity constraints are relaxed.
controlled trial (RCT) mimics the typical practices of a local supplier-combining delivery, retail, marketing, sales, and finance. This multiphase applied research plan-first diagnosing why adoption is low, then designing treatments to overcome practical hurdles, and only then field testing hypotheses experimentally-exemplifies "use-inspired basic research" necessary to address sustainability challenges (19).
Our approach is grounded in existing theories of household choice, which postulate that households will adopt environmental technologies (i) if they know and value expected benefits (20) and (ii) if suppliers have incentives to minimize costs of marketing, retailing, financing, and delivering (21). Various aspects of these theories have been tested over the last decade in rural India by our team with regard to clean water (22,23), safe sanitation (24), and ICS (4,25). Finally, our approach also drew on emerging evidence from the ICS literature (26,27), especially recent calls for research that embraces multiple implementation challenges (28).

Study Design
Given this background, we designed a multiphase study involving diagnosis, design, and experimental tests in the Indian Himalayas. These are briefly summarized next.
Phase I: Diagnosis. A series of preparatory tasks preceded the actual experiment, starting with background research in three main areas: (i) systematic analysis of ICS adoption (17), (ii) cross-country analysis of ICS sales, and (iii) cost-benefit simulations for assessing the net benefits of changes in cooking technologies (2). The first two activities provided insight on demand-and supply-side barriers, respectively, and highlighted the dearth of experiments on common strategies for promoting behavior change in cooking. The third revealed how the economic case for ICS was contextual, thereby pointing to the need for careful field preparation before testing for potential impacts.
The desk research was followed first by a set of focus groups with over 100 households in 11 rural Indian communities (both near and far from our final sites in Uttarakhand) to better understand knowledge and perceptions of different stoves and fuels, traditional cooking practices, and preferences for improved stove features (7,29).
Next, we designed and tested a household survey instrument, which was based on findings both from the focus groups and from many years of work in India on other household environmental health issues. These instruments included discrete choice experiments, which allowed us to better understand preferences for alternative ICS features (7). The baseline surveys in 2012 also allowed us to more generally understand preintervention characteristics of the target population.
Phase II: Design. Based on lessons from these diagnostic preparations, we conducted a series of eight small-scale pilots in three different rural settings (30). The pilots addressed various intervention components related to information/demonstrations, stove technology and choice, installment finance, rebates or subsidies, and institutional partnerships. The wide range in sales achieved across pilots demonstrated the challenges facing promoters of these technologies, but also helped crystallize three main lessons that informed the design of our main experiment. First, ICS (of any kind) were largely unavailable in our setting, except for liquefied petroleum gas (LPG) stoves, which were nonetheless often in short supply. Electric coil stoves could be found in markets in larger cities but were absent in local markets in our study communities. Electric induction stoves were similarly absent; where available, households were not interested because of the prohibitive costs of replacing all cookware for induction-appropriate options. Second, unsurprisingly, liq-uidity was a major barrier to adoption, and financing payment in installments or price discounts seemed to increase purchase. Third, households expressed widely varying demands for different ICS features, and giving them choice among distinct stove types improved adoption.
These activities highlighted the need to (i) establish a functional ICS supply chain with products that matched household preferences; (ii) ease income, liquidity, and information constraints; and (iii) respond to heterogeneous household tastes for ICS attributes such as price, smoke emissions, and fuel needs through multiple choices (e.g., biomass and electric ICS). Therefore, we designed an experimental supply-and-promotion intervention that combined (i) in-house delivery of suitable ICS; (ii) demonstration, financing, and rebates; and (iii) the ability to choose an electric and/or biomass ICS (Table 1).
Phase III: Test (with Randomized Intervention). We tested a twolevel experimental intervention to target the primary barriers we identified: limited supply of ICS, lack of choice, lack of information, and income/liquidity constraints. The first level entailed acquiring and transporting ICS (electric and improved natural draft biomass) from urban wholesalers, developing storage and maintenance systems, training sales personnel for home delivery and for marketing (including information, communication, and cooking demonstrations), and providing installment finance spread over three payments provided every 2 wk. The second level provided one of three price discounts (rebates), the magnitude of which was revealed to the household before the purchase decision. This rebate was different from a traditional subsidy in that it was delivered only at the time of the third installment visit if a household had visibly used the new ICS (as confirmed by sales team members). Households were allowed to purchase up to one improved biomass and one electric stove at their randomly assigned rebate level. Further, our intervention distributed an informational pamphlet to households, conducted household demonstrations on proper cookstove use, and provided the option to pay on an installment-based payment plan (with 2% interest charged on the second and third payments).
The supply intervention (level 1) was randomized at the hamlet level among 97 hamlets located in two districts of Uttarakhand in the Indian Himalayan Region. Within the 70 treated hamlets, rebate offers (level 2) were further randomized across sample households (SI Appendix, Fig. S1).

Results
Our results reveal high demand for ICS among study households. Over 50% of the households targeted by the intervention purchased at least one of the two intervention stoves (SI Appendix, Table S1). Larger rebates, meanwhile, increased purchase rates (Fig. 1), rising from 28% for the negligible rebate ("High price"), to 55% at the medium rebate (about 20% of retail price) level, and 74% at the highest (33% of retail price) level. This observed rebate sensitivity is consistent with other studies that point to the positive effects of financial rewards in the form of climate-or usecredits on demand for ICS (4). Further, while roughly two-thirds of targeted households availed of the installment finance option to pay (over three payments provided every 2 wk), all were able to pay in full. Thus, compared with some of the early work on willingness to pay for ICS, we document varied but high demand for ICS.
We note two key results beyond the average treatment effect. First, households overwhelmingly preferred the electric stove over the biomass ICS: Approximately 40% bought an electric stove, while 15% bought a biomass stove (SI Appendix, Tables S1 and S2), suggesting high desirability for nonbiomass improved alternatives in this setting. Relatedly, every rupee of the rebate was approximately twice as effective in increasing ownership of electric stoves compared with the improved biomass stove (SI Appendix, Table S3). As revealed by postintervention surveys, the overwhelming majority of respondents indicated that they liked electric stoves because of the following attributes: (i) lack of smoke, (ii) speed of cooking, (iii) portability of the stove, and (iv) attractiveness of the stove (SI Appendix, Table S5), similar to our phases I and II findings on preferences for ICS. Second, we observe strong supply chain and promotion effects. That is, if we compare outcomes for the "High price" treatment arm and the control arm, we see that the combination of supply and promotion without a meaningful price subsidy induced 28% of households to purchase intervention stoves. Further, there was no increase in LPG ownership during our study period in the control arm relative to the treatment arm, likely because they were not actively promoted. In contrast, 15% of all treatment households purchased the biomass ICS, despite it being less popular than the electric ICS and LPG (SI Appendix, Tables S1 and S2).
In addition, we document four sets of supplementary outcomes. Postintervention surveys showed that treatment households (i) were more aware of clean stoves (by over 6 percentage points or 9%, although preintervention awareness in this population was already high); (ii) used approximately two fewer kilograms of solid fuel per day, a 15% reduction; (iii) spent 10 fewer minutes per day collecting fuel (or more than one fewer hour per week, a 9% reduction); and (iv) were more likely to report using clean fuels in the past 24 h (by 26 percentage points, double the reported rate in the control group) (Fig. 2 A-D; addi-tional tests in SI Appendix, Table S1). Further, while traditionalstove ownership remained largely unchanged (exhibiting only a 2 percentage-point decrease), treatment households reduced traditional stove use by over 40 min/d (nearly 15%) (Fig. 2E). Postintervention surveys suggest that respondents are sensitive to the cost, convenience, and the taste of food prepared on traditional stoves (SI Appendix, Fig. S4), consistent with other studies that have found stove stacking (26). Moreover, through their effect on acquisition of new technologies, larger rebates successfully induced greater use of ICS and clean fuels, and decreased weighed solid-fuel use (SI Appendix, Table S4). † We further investigated whether this ICS ownership persisted over time. In the postintervention first-round follow-up (∼3 mo after the intervention), most households that had purchased intervention stoves still owned them (Fig. 3), and nearly 70% of owners reported using them in the week before the survey (SI Appendix, Fig. S2). Because past work has typically only examined impacts on immediate use, we implemented a second shorter survey to examine ownership and use ∼15 mo after the intervention. Even at this stage, ownership rates of intervention stoves remained largely unchanged (Fig. 3). However, use rates had declined by about 10 percentage points (SI Appendix, Fig. S2). This could be partly due to maintenance and service infrastructure; at the time of this second survey, about 15% of the adopters reported that their stoves needed repair, suggesting that stove malfunction and lack of maintenance and servicing partly explain the decline in use, as observed previously in the Indian context (16).

Discussion
Despite their promise for delivering health and environmental benefits, adoption and use of ICS remain disappointingly low. Unfortunately, rigorous field-based evaluations of both demandand supply-side constraints for ICS adoption are rare, which, in turn, hampers the design and implementation of effective policies. Rarer still are multiyear, multiphase studies that combine extensive preparatory work to diagnose barriers to adoption and generate hypotheses with careful design and experimental testing. Our multiphase study offers the following perspectives on the challenge of crafting policies that would be effective for reducing cooking-related harms. First, households face multiple constraints and have varying preferences for improved cooking technology, reflecting differences in culture, geography, institutions, and markets. Phases I and II of our multiphase research approach provide an example of how careful fieldwork and product testing can help uncover potential levers for promoting ICS (7,29,30). Our findings also suggest that implementers cannot focus on "silver bullet" stoves and fuels; at a minimum, they should study their local context and offer options so that households can choose those most suited to meeting their needs.
Second, while phase III (the experimental intervention) of our study showed that the intervention bundle (subsidy plus finance, retail, and marketing) was very effective in promoting adoption and use, the impact of the nonsubsidy supply chain aspects alone was also strong (as demonstrated by the comparison of the control and "High price" arms in Fig. 1). That there was no increase in LPG or electric stove ownership in the control arm relative to the treatment arm, whereas 15% of all treatment households purchased the less popular biomass ICS, demonstrates both (i) that demand for ICS can be met by the right combination of provision, information, and incentives and (ii) that widespread ICS ownership is constrained by supply challenges. Despite global attention to the problem of traditional cooking, ICS are † Additional details on robustness of self-reported use data are presented in SI Appendix, Appendix S1.  Table S1 for additional details.
simply not available where they are most needed, and demand in our setting could only be stimulated once a supply chain was established. That said, the supply chain improvement only targeted initial purchase; sustained use requires greater attention to the enabling environment (finance, marketing, regulation, maintenance, and customer service)-for electricity and LPG alike (31).
Third, households clearly preferred the electric stove over the biomass ICS, reflecting our early findings from the field suggesting that households strongly prefer stove attributes that LPG and electric stoves possess relative to biomass ICS (32,33), and this tracks well with stated preferences for stove attributes revealed by our phase I (diagnosis) work. While electric stoves are unlikely to be viable in settings where electricity is unavailable or unreliable, rural electrification rates have increased rapidly from 68 to 78% globally between 2005 and 2015 (34). In India, this increase has been even more striking, growing from 57 to 83% over the same period. This growth has been accompanied by calls for research on electric and LPG stoves (6). Our study responds to implementation aspects of such calls by examining household demand for these new technologies. In so doing, we show how social science research on household preferences and field implementation can complement the research by epidemiologists and engineers on energy access (35). Ultimately, we demonstrate that it is possible to overcome barriers to ICS adoption and that households are willing to pay substantial prices for ICS. Although the multifaceted intervention bundle looks resource-intensive on paper, in practice, private firms often engage in such concerted marketing and sales. Our calculations show that the learning and delivery costs of $17 per household are generally lower than the full social net benefits of switching households out of dirty fuels (SI Appendix, Appendix S2). Because our experiment shows that poor rural households are highly sensitive to price, we cannot fully pass these supply costs on to consumers and must seek creative solutions, such as carbon financing (36). To scale up and sustain success, energy access programs and projects could start by better understanding local demand and developing robust regional supply chains.

Materials and Methods
Below, we summarize our sample design, survey implementation, and the statistical approaches for analyzing the experimental data.
Sample Design. The sampling frame for the experimental study consisted of 97 geographically distinct hamlets located in 38 gram panchayats (GPs) in the Bageshwar and Nainital districts of Uttarakhand (SI Appendix, Fig. S3). ‡ These 38 GPs were drawn from the prior census of 2,105 GPs using a statistical matching methodology. Specifically, half of the sample (19 villages) consisted of GPs where the nongovernmental organization (NGO) partner for the study had done prior work (not related to ICS promotion), while the other half of villages were observationally similar villages selected using propensity score matching to achieve institutional balance (37). As such, the sample may not be representative of all rural Uttarakhand. Nonetheless, it does allow us to argue that the results obtained are not wholly dependent on prior institutions.
Within each of the 38 selected GPs, we randomly selected households according to the size of the GP. In small GPs, a minimum of 20 surveys were collected; in medium GPs, 30 surveys were collected; and in large GPs, 40 surveys were collected. If a GP was divided by distinct landmarks (e.g., half the village was to the north of the main road, half the village was to the south), the target number of surveys was split equally among these groups. Upon arrival in the village, the population of the GP was divided by the target number of surveys, and every nth household (no more than every eighth house) was surveyed until the target number of surveys was reached. This strategy ensured that surveys were collected throughout the entire extent of the GP and created variation in the number of hamlets sampled in each GP. The "official" number of distinct hamlets sampled in this way was 106; the smallest of these were later combined with nearby hamlets for the purpose of the stove promotion intervention to yield the final set of 97 hamlets.
Efforts were made to interview each sampled household. If a randomly selected household was unavailable during the entire day of fieldwork in a particular hamlet, or if it did not have an eligible respondent (i.e., the primary cook and/or head of the household were unavailable) or refused to participate, neighboring houses were randomly selected as replacements. Field supervisors performed household introductions, recorded GPS coordinates and elevation data, and oversaw quality control checks in each village. The final sample at baseline consisted of 1,063 households. ‡ The specific location in Uttarakhand was selected for two principal reasons. First, we wanted to work in northern India, which is a hot spot for cooking with traditional stoves and fuels and all of its associated harms. Second, we sought to leverage a new partnership-focused on clean energy-with a local NGO.
Survey Design and Implementation. Surveys were repeated at four points in the study: (i) at baseline, (ii) during the intervention, and (iii) at two followups. Respondents in the baseline survey (both male and female heads of household) answered questions on environmental and stove-related perceptions, household sociodemographics, stove and fuel use, and socioeconomic characteristics, and participated in a stove decision exercise designed to elicit preferences for an ICS. In addition, we conducted a 24-h fuel weighing activity and recall of detailed cooking behavior. Respondents were asked at the beginning of the period to bring more than enough biomass fuel to meet their needs over the next 24 h, this amount was weighed, and the amount remaining 24 h later was also weighed to measure fuel consumption. In the survey, women answered questions related to sociodemographics, and stove and fuel use, whereas men typically completed the socioeconomic sections, unless they were unavailable.
The survey conducted during the intervention was short and only administered to treated households; it mainly aimed to document which households were purchasing which ICS option and why. The main (first-round) follow-up instrument was similar to the baseline survey except that questions about stove purchase were replaced with questions about exposure to the sales campaign. The second-round follow-up was conducted to evaluate continued ownership and use of improved devices ∼15 mo after the intervention.
Randomization. The experimental intervention was composed of community-and household-level randomized components. Communities (hamlets) were randomized at the first level, which entailed acquiring and transporting ICS (electric and improved natural draft biomass) from urban wholesalers, developing storage and maintenance systems, training sales personnel to provide home delivery and marketing (including information, communication, and cooking demonstrations), and providing installment finance spread over three payments provided every 2 wk. Within treatment communities, households were further randomized into one of three rebates, which provided one of three price discounts, the magnitude of which was revealed to the household before the purchase decision. Households in different rebate groups and the control arm were balanced on baseline factors, and household attrition in the experiment was similar across groups (SI Appendix, Tables S1 and S2). The final analytical sample is 987 households for the measurement of impacts after accounting for 7% attrition between the baseline and follow-up.
Statistical Analysis of RCT Data. Our main analyses (presented in Fig. 2) are based on simple means comparisons. These comparisons are generally supported by regression comparisons of treatment and control households at follow-up (presented in SI Appendix, Table S1). For the latter, we estimated the following model: where y ij is the outcome of interest (stove purchase, ownership or use, fuel consumption, and time spent collecting fuel) for household i in hamlet j. The variable t ij is an indicator for treatment status (0 if control, 1 if treated), β 0 and β 1 are coefficients obtained using ordinary least squares regression, and ij is the household-specific error term. Given that exposure to the intervention was assigned at the hamlet level, all standard errors are clustered by hamlet. Fig. 2 highlights the 90% confidence interval for the mean of each outcome. In some of the results we present (SI Appendix, Table S3), we also test for heterogeneity using the randomly assigned rebate level, where R ij is the rebate level expressed in INR. In addition to these analyses, we also derived difference-in-differences estimates (SI Appendix, Table S2 without the rebate and SI Appendix, Table S4 with the rebate) that control for differences across households via the inclusion of household fixed effects, even though treatment and control households were balanced at baseline, as shown in SI Appendix, Tables S6 and S7. The full model (including tests for heterogeneity using the rebate level) is the following: y ijt = β 0 + β 1 · post it + β 2 · t ij · post it [3] + β 3 · t ij · R ij · post it + γ i + ijt , where post it is equal to 1 if measured at follow-up and is equal to 0 at baseline, and γ i represents a household fixed effect. SI Appendix, Fig. S1 depicts our final study design and shows the timing of surveys and intervention activities: from selection of villages and households


extfieldsofstudy: Economics Physics

text:
Forecast and Analysis of Volatility Trend of Chinese Stock Market Based on R Language

In the stock market crash in China’s capital market in 2015, the bubble quickly spread from the Internet industry to the entire stock market. It can be seen that the financial system has inherent instability and endogenous vulnerabilities. A small shock may be amplified by the financial system and have a contagious effect, which ultimately has a huge impact on the entire financial system and the entire economy. Therefore, it is possible to accurately predict the trend of China’s stock market volatility, and it is of great significance and reference value for China’s economic development to prevent risks and stabilize the economy in a timely manner. Based on the characteristics of industry index volatility, this paper studies the risk transmission mechanism between industries by examining the main risk source industry, risk transmission intermediary industry, risk conduction characteristics between industries, and the dynamic changes of risk conduction, and finally through R language processing. The data is used to predict the volatility of China’s stock market in the first half of 2018.

Introduction
Some foreign scholars have emphasized the importance of network connectedness analysis in financial research, such as Jackson (2008) and Babus (2016). Relevant empirical research is mainly the risk transmission between banks and financial institutions. Diebold and Yilmaz (2014) study the risk transmission between some US financial institutions through network analysis; Demirer and Diebold (2016) extend the network risk conduction analysis to Between global banks. Diebold and Yilmaz (2009, 2012, 2014) describe risk-conducting frameworks and metrics in a series of papers, emphasizing the measurement of risk-conducting effects based on variance decomposition, measuring the risk-conducting effects with connectedness and conducting with total risk. Effects to describe systemic risks. According to their risk conduction framework, the risk conduction effect can be divided into three levels. The first level is the total risk conduction effect of all elements in the system on the element, and the second level is the total of all elements in the system to all other elements. The third level of risk transmission is the systemic risk, which is the total risk conduction effect.
Demirer and Diebold (2016) used the above risk conduction measurement framework to analyze the global bank risk conduction network, used the LASSO regression method to compress variables to estimate the high-dimensional VAR model, and used network analysis to process the results of high-dimensional variance decomposition. Their research shows that the global bank risk transmission is regional clustering, the financial crisis and the European debt crisis, the systemic risk will rise. This study provides guidance for the empirical approach of this paper.
In terms of existing research results in China, the current research on the characteristics of industry risk transmission is mainly carried out from three perspectives. More common is the impact of a single risk on a certain industry, such as housing price fluctuations, implied guarantees and banking systemic risks (Song Lingfeng, Niu Hongyan, Liu Zhilong, 2018); secondly, the study of cross-regional risk transmission from the spatial dimension, such as the mainland, Dynamic analysis of linkages between Hong Kong and Taiwan stock markets (Wu Wei, Wang Zhi, 2014), regional financial risk accumulation and prevention control (Li Yanbai, Wang Xuelun, 2017); finally, research on risk linkage characteristics between industries . Chen Jianqing et al. (Study on Systematic Financial Risk Spillover among Financial Sectors, 2015) empirically analyzed the systemic risk marginal spillover effects and the total risk spillover effects between financial sectors, and found that systemic financial risk spillovers between financial industries are Positive and asymmetric, focusing on the study of financial risk within the financial industry, domestic scholar Li Xiaobo selected the composite index, business index, public index, industrial index and real estate index, the research results show that between five industries There is a volatility correlation, but the study ignores the impact of other market volatility spillovers, which have more or less impact on the accuracy of the parameters.
Although foreign related papers emphasize the adaptability of network analysis methods to risk transmission mechanism research, the existing literature only studies the risk transmission network between financial institutions, and does not involve the research of high-dimensional risk conduction networks between stock market industries.And there are fewer studies on forecasting the volatility trend of the stock market. However, the relevant research on the risk transmission problem of China's stock market industry has fewer industry variables, and the research focuses on a single industry or several sub-markets and sub-sectors, while ignoring the influence of other industries, resulting in lower accuracy of estimation results; It mainly uses the spillover effect to analyze, and the analysis process is single. It only emphasizes the dynamic change process of the interaction between industries under static analysis and ignores the risk conduction effect. The existing literature has almost no empirical research on the risk transmission of the whole industry in the stock market. One possible reason is the complexity of high-dimensional data processing.
In view of the above situation, this paper mainly uses R language to study the Chinese stock market, and finally predicts the volatility trend of China's stock market in the first half of 2018. The main innovations of this paper are: On the one hand, this paper studies the risk transmission mechanism of all industries in China's stock market through full sample estimation, which makes the accuracy of estimation results greatly improved. On the other hand, this study is not limited to predicting the volatility trend of the final stock market, but also focuses on the dynamic process of risk transmission mechanism.This paper uses the R language combined with static analysis and dynamic analysis to get the predicted results.

Systematic risk trend analysis
The research data in this paper selects all the A shares of the Shanghai Stock Exchange and the Shenzhen Stock Exchange from July 1, 2007 to December 30, 2017 as a research sample, with a total of 8,831,150 data of 3,470 stocks,that is, there are a total of 2,545 data. This paper estimates the total risk-conducting effect, the magnitude of systemic risk. Using 60 days to roll backwards for the rolling sample period, the continuous change of the overall risk conduction effect with time is obtained,as shown in Figure 1. Then we make the timing diagram and decompose the trend of the timing diagram, as shown in Figure 2.
There are two high peak points in the figure, which are March 2008 and August 2015, corresponding to the occurrence of financial crisis and stock market crash. This means that the occurrence of the crisis makes the industry's risk transmission stronger and systemic risk greater. Volatility reflects the degree of stock price change. In a rational market, stock price volatility depends on two factors: future expected return and discount rate. Under the premise that the discount rate remains relatively stable, the future expected return is rooted in new information. Arrived. In an irrational market, speculation can cause irrational fluctuations in stock prices [1] .

Risk conduction effect prediction
In view of the overall risk transmission effect of the stock market mentioned above, this paper selects the ARMA model to predict future trends. The ARMA model is called the autoregressive moving average model. It is a time series analysis model proposed by American statistician GEBox and British statistician GM jenkins in the 1970s [2] .The ARMA model can be divided into AR model, MA model and ARMA model. The ARMA model is a combination of the AR model and the MA model.
The mathematical formula of the AR model is: The mathematical formula of the MA model is: The mathematical formula for the ARMA model is: The ARMA model is often referred to as ARMA(p,q). When p=0, ARMA(0,q)=MA(q); when q=0, ARMA(p,0)=AR(p) [3] .
Judging from the previous timing diagram, this paper considers that the overall risk conduction effect is stable.
Using the autocorrelation plot and the partial autocorrelation plot plotted in R language, we can find that the overall risk conduction effect is tailed by the autocorrelation coefficient, while the partial autocorrelation coefficient is truncated at the first order.

(4)
The final prediction results are as follows:

Conclusion
As can be seen from Figure 5, in the first half of 2018, the volatility of the stock market will show a steady decline, and the actual situation of the Chinese stock market as of now is basically consistent with the forecast results of this paper. Therefore, this paper believes that the above prediction process has certain reference role and utilization value.
Stock market volatility is an important indicator of the stock market's degree of floating. Its existence can promote the development of the stock market to a certain extent. Investors participate in the stock market and certainly hope to make a profit. Only when the stock price fluctuates, will investors attract investors. In order to find opportunities to obtain income, if the stock market does not have volatility, it will not get investors' attention; in addition, the volatility of the stock market also reflects the degree of risk in the stock market, investors always hope to minimize The risk of obtaining the greatest return, then studying the volatility of the stock market can provide a basis for investors to make investment decisions.
From a macro perspective, when policy makers issue new policies, they must estimate in advance how this policy will affect the market. Only by predicting the fluctuations in the stock market in advance can we ensure that the correct and effective development is made. Policy measures to promote the stable development of the stock market.


The heterogeneity of village cadres and poverty reduction-Based on the survey data of 757 administrative villages in western minority areas

By using the data of 7 provinces, 757 administrative villages and 31615 famers in the minority regions of the Midwest, this paper investigates the relationships between heterogeneity of village cadres and poverty reduction. And we find that the age of the village cadres, ethnic identity, investment promotion, income and the education background have a significant positive impact on farmer poverty reduction. Using village collective financial income as the instrumental variable, we find that village cadres of every 1% increase in wage income, the poverty rate will be reduced by 0.007%. Therefore, we table four proposals: the choice of the village and village officer should follow the principle of “young and old”; encouraging to choose village cadres of ethnic minorities; encouraging investment promotion and investment stay in the village; setting income of village cadres within a reasonable range. The conclusions above is of great political significance to solve the problem of poverty reduction of the heterogeneity of village cadres.

Introduction and principle
The Chinese party and government have been paying close attention to poverty alleviation work, and poverty alleviation has been at the core. To ensure that farmers are lifted out of poverty, the government should raise the spirits of the masses of cadres in the poverty-stricken areas and resolutely change the confidence and determination of poverty and backwardness. Do precisely lies in grassroots governance for poverty alleviation, and public officials such as village cadres and party members and cadre's responsibility are clear and know whether or not in place, whether solid style, methods properly, to a certain extent determines the effect of poverty reduction, affects the image of the government. Therefore, the impact of the heterogeneity of village cadres on poverty reduction is of great practical significance to the current 70 million poor people. The existing literature rarely deals with the influence of the heterogeneity of village cadres on poverty reduction. Therefore, from the theoretical perspective, studying the heterogeneity of village cadres also has theoretical significance for the impact of poverty reduction. The main contribution of this paper is to use the central university for nationalities in 2012 school of economics and the Chinese academy of social sciences institute of ethnology and anthropology research data, in view of the poor areas the Midwest 7 provinces, empirical research on the effects of heterogeneity research on poverty reduction effect of village cadres, and from the two aspects of the farmers' income and the income gap analysis on various aspects of village cadres quality influence on rural economic development.Arnim Scheidel (2014) believes that the poverty problem mainly comes from the shortage of land resources. Farmers need to make a diversified livelihood and make new economic activities to achieve poverty reduction. Swati Dutta (2015) tested that malnutrition in most places would affect the accumulation of farmers' assets, while the impact of illiteracy was uneven. In the study of India, Deaton&Dreze (2002) believed that factors such as health and education could have an impact on poverty. Cheng, Jin Yanhong and others (2014) also believe that the human capital represented by health and education will affect the income level of farmers and have an impact on rural poverty alleviation. Chen qi (2012) believes that human capital investment will have a greater impact on poverty reduction. While investigating the causes of poverty, Armando Barrientos (2010) found that the majority of respondents  MOOD (2005) believed that the economic goals and election of village cadres influenced the development of villages, and the ability of village cadres to be leaders led economic growth.

Materials and experiments
On the basis of the above literature, we carried out the following research with questions. Based on the definition of village cadres' heterogeneity and poverty reduction, we make several assumptions: Hypothesis 1: the time of the village cadres and the age of the village cadres are positively correlated with the farmers' poverty reduction.Hypothesis 2: the education of village cadres and the presence of village cadres are positively correlated with the reduction of poverty reduction.Hypothesis 3: village cadres are positively correlated with poverty reduction among ethnic minorities and farmers.Hypothesis 4: the income standard of village cadres is positively correlated with the poverty reduction. The datawas being investigated by central university for nationalities school of economics and the Chinese academy of social sciences institute of ethnology and anthropology. This study based on primary data collected from 757 village leader spreading over 757 villages belonging to 7 provinces. Such as the Inner Mongolia autonomous region, Ning Xia hui autonomous region, Qing Hai province, Xin Jiang uygur autonomous region, Guang Xi Zhuang autonomous region, Gui Zhou province, Hu Nan province.Though the study area is located in the poor rural areas, the results can be generalized across to the whole country. Rural survey gathers administrative villages population employment and economic, agriculture and village collective land, finance and management, public service aspects of the micro information, fully reflects the basic situation of the Midwest rural economy.
Model setting: probit regression model: The model is establishing in this section to prove the relationship between the heterogeneity of village leaders and the investment of farmland irrigation facilities. Specific as follows: (1), the explanatory variable whether povertyi represents the first person to be out of poverty, 1 represents poverty reduction, and 0 representatives have not yet reduced poverty. The 15 control variables involved in the four aspects of farmers' individual characteristics, social environment, natural environment and public service are included. This paper makes a certain arrangement of data variables, and the descriptive statistics of all variables form table 1.

Discussion and conclusion
The measurement software used in this paper is stata13, and before verifying the results, the correlation between all variables is tested to prove that there is no correlation between all variables. The correlation coefficient between all variables is under 0.5, which satisfies the criteria of the regression.After a collinear test, it is proved that there are no multiple collinear problems between the models, so this paper argues that the regression results of the model are more accurate and reliable. Table 2 main report the heterogeneity of the village cadres regression results on poverty reduction, of which the first (1), (3) column is in poverty reduction/various poverty), village cadres as time (year), age (age1), education (education) and income (earning), investment promotion and capital introduction (invite) and national (nationality), not the introduction of control variable Probit and OLS regression results. The results show that the age and investment attraction of village cadres have a significant positive impact on farmers' poverty reduction and are significant at the 1% level. The income and ethnic groups of village cadres have a positive impact on the poverty reduction of farmers and are significant at the 1% level of the Probit model, which is significant at the 5% level of OLS model. The education of village cadres has a positive impact on poverty reduction, which is significant at the 10% level.The (2) and (4) columns are the analytical results after the regression of Probit and OLS with all four class control variables.The results showed that the age of the village cadres to villager's poverty reduction coefficient is 0.02, the influence of the symbol is positive, and the 1% significance level, prove that the age of village cadres has a positive promoting effect on the farmers' poverty reduction, prove the hypothesis of a set. The influence of the village cadres education on poverty reduction coefficient is 0.05, the symbol is positive, and the 10% significance level, and whether the village cadres to carry on the investment promotion and capital introduction on poverty reduction coefficient is 0.39, the influence of the symbol is positive, at 5% significant level, education and investment promotion and capital introduction of village cadres in the farmers' poverty reduction has a positive role in promoting, prove the hypothesis 2 set is correct. The influence of village cadres on poverty reduction is 0.3, the symbol is positive, and in the Porbit model, it is significant at the 1% level. In the OLS model, it is significant at 5% level. It shows that the minority attributes of village cadres can have a positive impact on poverty reduction and proves the inference of hypothesis 3. The influence coefficient of village cadres' income on poverty reduction is 0.0003 and the symbol is positive. It's significant at the 10% level. It shows that the income of village cadres has a positive impact on poverty reduction, which is consistent with hypothesis 4.  Note: ***, **, * respectively indicates the significance of parameters at the level of 1%, 5%, and 10%. The values in parentheses are the robust standard deviations. It remains the same in the following table.
According to the conclusions of this paper, the following policy recommendations for poverty reduction are as follows: (a) Village cadres should attach great importance to the culture of their own quality, and strive to improve the level of record of formal schooling education, become the high quality human capital, should also actively learn the experience of senior, knowledge and experience will have the biggest effect.
(b)The government should encourage to break on the village of village cadres election ethnic discrimination, properly enlarge the proportion of ethnic minority cadres, not only to a certain extent, to get more policy support,combined with the feelings of the relationship among nationalities, ethnic minorities and honest will eliminate some of rent-seeking behavior of village cadres, to reduce incidents of infringement to the interests of farmers.(c) The village cadres shouldaccording to the strategic development plan of the village, actively draw up the plan and encourage the enterprises to invest in the factory. On the premise of not affecting the ecological environment of the village, the economic development of the villages and the employment proportion of the villages will be improved, so as to realize the harmonious coexistence between man and nature and economy. (d) At the economic level of its village, the income level of village cadres is reasonably set. It is necessary to avoid the problem that the low income of village cadres can cause the lack of enthusiasm of the village cadres, and also avoid the harm of the rent-seeking behavior of the village cadres to the economic income of the peasants.


s2fieldsofstudy: Economics Business

text:
IMPACT OF HIGH TECHNOLOGY EXPORT ON ECONOMIC GROWTH: AN ANALYSIS ON TURKEY

PurposeHigh-technology exports, with their high added value, are considered as one of the determinants of growth in recent years. The increasing competitiveness of developing countries in international markets depends on the country's capacity to produce and export high-tech products. Therefore, the export of high technology products is especially important for the economic growth and development of the countries. In this study, the effects of the causality analysis on the economic growth of high-technology exports in Turkey has been investigated. MethodologyGranger Causality Analysis was used to determine the direction of the relationship and the results of the variance decomposit ion were evaluated. VAR model is primarily applied in the model in order to determine the short-term causality relationship between the variables while the analysis is performed with the Granger causality, impulse-response and variance decomposition methods. FindingsAfter 1980, Turkey has transitioned from import substitution to export-oriented industrialization strategies and largely completed the liberalization process during 1990s. Important policies for industrialization have been implemented by improving the export structure within the scope of competitive advantages. When the impulse-response and variance decomposition results are evaluated together, the outcome is consistent with the Granger Causality Test results where high-technology export is effective on GDP. ConclusionIn conclusion, it was found out that high technology exports affect economic growth. Therefore, Turkey has to put more emphasis to increase high technology shares in its exports and incentivize high technology production.

INTRODUCTION
The ability to transform the knowledge into innovation and to use it in today's world determines the international competitive power of countries. Therefore, many countries are in an effort to open their national economies to foreign countries and to integrate it with the world economy. Within such scope, economic growth and development of countries and creating employment opportunities are not only associated with their abilities to innovate, but also it is closely associated with their potential to export these innovations. The countries need to develop, make production for increasing their competitive power, and market the said production abroad (Avcı et. Al., 2016: 50-51).
Since high-technology production means the production of high value-added products at the same time, particularly the developed countries have the leading positions worldwide in terms of exports including high-technology products. Therefore, production and export of the products including high-technology products are important factors in terms of financing the growth and development of countries, which have adopted export-oriented growth, through increasing their export revenues (Yıldız, 2017: 27).
Journal of Business, Economics and Finance -JBEF (2019), Vol.8(3 There are many factors that are effective on ensuring economic growth and its sustainability. Particularly, having high-technology sectors, manufacturing goods with high added value, and increasing exports have an important share amongst these factors. Specifically, high-technology efficiency is seen as the driving force behind economic development and growth for the countries implementing an export-oriented growth policy (Hobday et. al., 2001: 209). Therefore, increasing the share of high-technology products within the total exports takes part among the main objectives of countries in terms of international competition and economic growth.
When differences between the countries' income per capita and economic growth levels are examined, it is understood that the technological infrastructure, natural resources, manpower, economic and political stability are the important determinants. Developing countries transfer technology into their own countries in order to reach to the level of developed countries; allocate a higher share of resources to education; and try to foster research and development activities (Göçer, 2013: 216).
Turkey has gone into the effort of opening its economy to foreign countries, and to integrate with the world countries especially after 1980. In this regard, the policies based on import substitution were replaced by export-oriented policies, and important steps were taken towards financial liberalization in 1989. The relationship between high-technology export and economic growth in Turkey is analyzed for the period of 1989-2017 in this study. Granger causality test is utilized to analyze this relationship, and results of the impulse and response and variance decomposition are evaluated.
According to the decisions taken on January 24 th , 1980 in Turkey, export-oriented development policy was commenced to be implemented, and the export activities started to increase with membership to the Customs Union in 1996. A significant change occurred in the structure of export, which consisted of labor-intensive products such as agriculture and textile products, in 1980s when durable household goods such as major home appliances and automotive parts replaced labor-intensive products in export. However, export of Turkey featuring high-technology products hasn't reached to the desired level despite such change. Since imported input is predominantly utilized in the production process for Turkey, an increase in the industries' production, which is performed through employment of high-technology, also increases the demand for imported input. At the same time, a significant portion of the added value to be obtained thereof goes abroad because the local industries, which may provide input to hightechnology industries in Turkey, are not sufficiently developed (Konak, 2018: 71-72).
Export shares of industries according to their technology levels is an important indicator demonstrating the technological production levels. Accordingly, industries are grouped as low-technology, medium low-technology, medium high-technology and high-technology. The shares of Turkey's export between the years 2003 and 2019, following Turkey's membership to the Customs Union, are given below according to levels of technology.

Graph 1: Distribution of Imports of Turkey According to Technological Status (%)
Source: TÜİK Turkey's exports technological structure is undergoing a transformation according to the share of exports by technological level.
In the beginning of 2000s, while almost half of its exports are produced with low technology, later this share has decreased over time, whereas the exports of products with low-medium and medium-high technologies have increased consistently. However, the exports of high-tech products remains at similar low levels.
According to first quarter data of 2019, Turkey's low technology share in total exports is 33%, low-medium technology is 27.8%, medium-high technology is 35.7% and high technology is 3.6%. The main reasons for the low level of exports of Turkey can be associated with import high value-added products rather than produce locally, low level of shares allocated to research and development activities, lack of skilled labor, lack of direct foreign capital inflows and innovation policies. Developed countries have transformed their low and medium technology production capacities like textile, clothing, machinery, plastics, into advanced technology and high-value-added production like electronics, computers, software, pharmaceuticals, aircraft. Turkey has remained behind this technological development and competition (İlhan and Gelgeç, 2018: 35-54).

LITERATURE REVIEW
Capital movements between the countries have accelerated with the increase in international trade, and accordingly the pace of advancement in technology increased depending on this development. Within that context, the will to possession of hightechnology has become one of the primary goals of both developed and developing countries because countries may manufacture products with high added value by this means, and export these products alongside gaining competitive power in international markets. One of the most important targets of countries in today's world is exporting products with high added values and hightechnology (Saray and Hark, 2015: 348).
Furthermore, the studies which discuss the effects of export on economic growth, address various benefits such as increasing efficiency arising from exports; facilitation of capital goods imports through the revenues to be obtained from exports; utilization from scale economies depending on the increase in scales of relevant companies; and transfer of new information and methods into the country via the relations to be established with foreign markets through export operations (Gerni et. al., 2008: 5). The recently conducted studies particularly emphasize the importance of high-technology exports. Accordingly, empirical studies conducted with the objective of explaining the relationship between high-technology exports and economic growth are given place in this part. Cuaresma and Wörz (2005) tested the hypothesis that exports in technology-intensive industries have a higher potential for positive externalities coupled with higher productivity levels using a data set, covering 45 industrialized and developing countries for time period 1981-1997. The estimation results, using a random effects model and employing an instrumental variables estimator, support the hypothesis of qualitative differences between high-and low-technology exports with respect to output growth.
In the study of Değer (2007), aimed to reveal the importance of the product composition of exports in the economic growth processes of the developing countries and concluded that technology-intensive product exports have positive effects on economic growth. The biggest and significant effect on economic growth is labor intensive manufacturing industry exports.
In the study of Falk (2009), new evidence on the impact of the change in the high-tech export share on economic growth in OECD countries was presented with a dynamic growth model on panel data for 22 OECD countries for 1980-2004, in which the data is measured as 5-year averages. It found out that both business R&D intensity and the share of high-tech exports are significantly positively related to the GDP per working age population.
Lee in the paper dated 2011, empirically investigated the extent to which technological characteristics in exports affect the patterns of trade-led economic growth across countries. The findings are robust to the presence of various control variables as well as the consideration of parameter heterogeneity and in the endogeneity of export structures.
In the study of Kılavuz and Topcu Altay (2012), the effect of different classifications of export and import on economic growth in 22 developing countries in the 1998-2006 period was tested. The findings revealed that only high-technology manufacturing industry export, investment and low-tech manufacturing industry import have a positive and significant effect on growth.
Göçer (2013) examined effects of R&D expenditures on high technology exports, information-communication technology exports, total exports and economic growth as well as the effects of high technology exports on the balance of foreign trade for 11 developing Asian countries by using data of 1996-2012 period. As a result of the analysis, it has been determined that an increase by 1% in R&D expenditures raised the high technology export by 6.5%, the information-communication technology exports by 0.6% and the economic growth by 0.43%.
In their 2013 study, Aditya and Acharya investigated the export-growth relationship at disaggregate levels -disaggregation both at the country level and at the level of exports -focusing on the diversification and the composition of exports of countries.  (2016) investigated the effect of technology intensity product export on economic growth with time series analysis for Turkey. According to the findings obtained from co-integration testing results, low and medium technology intensive products have significant and positive effects on Turkey's economic growth. On the other hand, according to Granger causality results, there are unidirectional causal relationships from exports of medium and high technology intensive products to economic growth. Yıldız (2017) investigated the effect of the high-technology export on the economic growth for BRICS countries and Turkey for the period 2005 -2014 is analyzed by using panel data techniques. The results of show that high-technology export is significantly positive effect on the economic growth for BRICS countries and Turkey. Özkan and Yılmaz (2017) examined the share of R&D expenditures in GDP, the share of high-technology exports in total exports and its relationship with GDP via data of 1996-2015 period for Turkey, and 12 member countries of the European Union. The results for the entire panel reveal that R&D expenditures positively affect high-technology exports and GDP.
In the study of Kızılkaya, Sofuoğlu and Ay in 2017, they examined the effect of foreign direct investment and openness on high technology product export with panel data analysis method in 12 developing countries over the period of 2000-2012. According to analysis results, foreign direct investment and openness have a positive impact on high technology product export. Within the framework of empirical findings, we presented some policy recommendations for developing countries.
In the study of Konak (2018) analyzed World Bank data of 1992-2016 period for selected OECD member countries and Turkey's high technology export and its impact on economic development. According to the analysis result, Turkey's exports mainly are based on "low", "medium-low" and "medium-advanced" technology.

DATA AND METHODOLOGY
The relationship between high-technology exports and economic growth in Turkey for the period 1989-2017 is tested by using the Granger causality test in this study. The Augmented Dickey-Fuller (ADF) tests are preferred for examining the stationarity of series in the analysis. VAR model is primarily applied in the model in order to determine the short-term causality relationship between the variables while the analysis is performed with the Granger causality, impulse-response and variance decomposition methods. Gross Domestic Product (GDP) and high-technology export amount (HTE) variables are used as economic growth indicators in the analysis where annual series from the 1980-2016 period are employed. The data set employed in the analysis has been obtained from the World Bank.

Stationarity Analysis
Fake regression problem may be encountered when worked with the non-stationary time series in the econometric analysis studies. Therefore, the stationarity of variables should be checked before analyzing the relationship between variables. Otherwise, using the non-stationary series in analysis may lead to obtaining unreliable results including spurious relationships (Sevüktekin and Nargeleçekenler, 2007: 312). Therefore, Augmented Dickey-Fuller (ADF) unit root test, in other words stationarity analysis, was performed in this study. If the variables are not stationary at the level value in the stationarity analysis, differences of the series are taken and they are made stationary. If the test statistics are smaller than the determined critical value in the analysis, the series is accepted as stationary (Kaya and Öz, 2016: 644). As may be seen in Table 1, since the absolute values of ADF-t statistics, which are obtained for the HTE variable at the level values, are smaller than the MacKinnon absolute values at significance levels of 1%, 5% and 10%, they are non-stationary. When the first differences of this variable are taken, it is determined to be stationary. GDP variable is determined to be stationary at the level.
The assumption that errors are independent from each other and have constant variance in the ADF test causes problems when both autoregressive and moving-average elements are involved. Philips (1987) and Philips and Perron (1988) developed a unit root test with the assumption that there may be autocorrelation and heteroscedasticity between the error terms. Due to both the autocorrelation and heteroscedasticity problem as well as the objective to obtain healthier information on stationarity of these variables, Philips-Perron (PP) test is also utilized in this study in addition to the ADF test. The results of the PP tests are listed in the below table. When results of the stationarity test are considered in the Philips-Perron test method, it is seen that the GDP variable is stationary at the level while the HTE variable becomes stationary when its first difference is taken just as in the ADF test.

Granger Causality Test
Granger causality test is applied in order to determine direction of the relationship between the high-technology export and economic growth variables. Granger causality test is conducted with the help of below-given equations (Granger, 1969: 424-438).
Here m expresses the lag length and the error terms of u1t and u2t are assumed to be independent from each other (Granger, 1969: 431). In Granger causality can be both from X to Y and from Y to X. The dependent variable is first added to the model with an appropriate lag count, and then the other variable is added to the model. The error sum of squares pertaining to these models are figured, and then the F statistics, which is developed by Wald, is calculated (Yılmaz, 2005: 70).  (Granger, 1969: 431).
The second step required for determination of lag count of the model to be established is creating the VAR model, and setting the appropriate lag count. The appropriate VAR model lag length is determined as 2 according to the LR, FPE, AIC and HQ criteria. According to the short-term Granger causality test results, the null hypothesis "HTE is not the cause of GDP" is rejected. Similarly, the null hypothesis "GDP is not the cause of HTE" is accepted. In line with these findings, it is accepted that there is a one-way causality relationship between high-technology export and economic growth in the short term.

Variance Decomposition and Impulse-Response Analysis
The dynamic relations between the variables are examined with the variance decomposition and impulse-response analysis in the VAR model. It is examined in variance decomposition what percent of the change in variance of a variable results from itself, and what percent thereof results from other variables. Variance decomposition may be also used as an auxiliary assessment about whether the variables are internal or external. Accordingly, if almost one hundred percent of a change in a variable's variance is explained by its own value, this variance is characterized as an external variable. Impulse-Response Analysis is a method employed for determining the effect of one-unit standard deviation shock, which is delivered from one variable to another, on the dependent variable, and the length of this effect (Tarı, 2006: 452-453).
This part features variance decomposition and impulse-response analysis for the purpose of determining the dynamic relations between variables within the framework of causality test results. When we examine the exposition of high-technology export on GDP, it is seen that the changes occurring in GDP mostly results from itself, but this weight starts to decrease after the second period. While the exposition of share of high-technology export is approximately 22 in the third period, it rises to 25.72 in the tenth period. When the variance decomposition results of hightechnology export are evaluated, it is seen that the rate of GDP explaining the change in high-technology export is around 8.7 through several periods, and that it doesn't demonstrate any major changes.

Graph 2: Impulse -Response Results
It is seen in Graph 2 that the effect of a shock occurring in high-technology export on GDP starts to response positively after the second month, then the degree of response increases following which the effect diminishes until it gets closer to zero. However, the effect of a one-unit shock occurring in GDP first reacts negatively, but a positive increase is experienced after the fifth month, and eventually the effect is observed to diminish.
When the impulse-response and variance decomposition results are evaluated together, the outcome is consistent with the Granger Causality Test results where high-technology export is effective on GDP.

CONCLUSION
Competitiveness in international markets is increasingly dependent on the capacity to produce and export technology. The development of trade relations between countries increases the economic growth by providing consumers with higher quality and cheaper consumption, while increasing the country's competitiveness. Countries that want to get more share of this competitive environment need to allocate more shares to high technology product exports.
Turkey, after the 1980, has started to implement export oriented and externally open industrialization policies in order to increase the share in the global competition. However, although there has been a transformation and change in the industry, it is observed that in terms of production and added value, low and medium-low technology products still have the highest shares. The share of high-technology products in exports is very low. The fact that domestic demand for high and medium high technology products is imported from abroad, this leads to the country imports being higher than exports.
In this study, the impact of economic growth of high-technology exports in Turkey was examined by Granger causality analysis, variance decomposition and impulse-response analysis. The results reveal the importance of high technology exports in terms of ensuring high and sustainable economic growth. As a result of the causality analysis, it has been determined that there is a causal relationship between high technology product exports and economic growth. In addition, the results of variance decomposition and impulse-response analyzes also support this relationship. Therefore, Turkey has to put more emphasis to increase high technology shares in its exports and incentivize high technology production.