Capital Cost and Capital struCture of Businesses in the Meat industry listed on the WarsaW stoCk exChange – a disCussion aBout the proBleM of Valuation

the cost of capital is an important parameter in the assessment of efficient use of sources for business financing. this is also a measure enabling the indirect verification of the basic aim of the company’s long-term business operations – an increase in value. so far, not fully transparent model of evaluating the cost of equity, both in theory and practice, has been developed. as a result, estimations concerning the cost of capital and value of companies are to a large extent subjective. the research – using the caPM model together with its accepted parameters – proves the stability and precision of the applied methodology. estimating the cost of capital is of special significance in companies from the Polish meat industry. these entities (in the years under investigation) were obliged to increase investment expenditures as a result of Poland’s accession to the european union and the necessity to adjust to western-european standards and further consolidation – both in the vertical and horizontal dimensions. Pro-developmental activities required changes in sources of finance and as a result – changes in capital structure. the dilemma concerning “safer” yet more expensive equity, or “cheaper” yet less stable foreign (interest) capital remains unsolved to this day. keywords: finance, cost of capital, agricultural companies. Jel codes: G3, G21, Q1. Problems of Agricultural Economics Zagadnienia Ekonomiki Rolnej 4(357) 2018, 80-102 p-ISSN 0044-1600

Zagadnienia ekonomiki rolnej / Problems of agricultural economics introduction
The years between 2001 and 2017 were especially significant for the meat industry. This was a very dynamic decade for the sector, characterized by great change. Between 2001 and 2006 the fundamental aim for most meat enterprises was to meet the restrictive EU sanitary and quality requirements and adjust production to Western European standards. At the beginning of the 21 st century, the meat sector in Poland was given little chance of achieving this task (in 2001 only 3.05% of meat industry businesses operating in Poland fulfilled these EU requirements). However, these standards were achieved due to the efficient use of aid resources and expenditures for investment and modernization (by 2006 approx. 40% of meat industry enterprises had obtained authorization for the EU market trading, 98% of which were large and medium-sized entities). Huge investment outlays also led to many significant changes in the structure of liabilities for this group of enterprises. Entities from the meat sector were faced with the dilemma of choosing between cheaper external sources of finance, or increasing their equity, which would be a more stable albeit more expensive source (Obidzińska, 2008). The consequences of the decisions made within this scope can still be felt today.
Despite many positive forecasts for the meat industry at the beginning of the last decade, many (of the then diagnosed) problems remain unsolved. In hindsight, it occurs that low profitability and efficiency require further lowering of costs and intensifying consolidation processes. As a result, extra investments in the meat business are required with the purpose of lowering operational costs and further increasing the quality of products (Rawa, 2009). Moreover, finding savings in the financial activity of enterprises is not without significance. The (as near as possible) optimal capital structure seems to be an appropriate aim. This involves identifying dispensable external financing and indicating unused developmental potential associated with an inappropriate use of financial leverage, whilst limiting the cost of equity.
The article proceeds to discuss and review capital cost and capital structure of businesses in the meat industry listed on the Warsaw Stock Exchange. Section 1 deals with aim of the research, methodology. Section 2 is based on a theoretical background: capital cost -valuation dilemma. Section 3 illustrates the meat industry in Poland -characteristics and identification of the research problem. Section 4 presents our research findings. Section 5 presents the general conclusions and limitations of the study.

aim of the research, methodology
The main aim of the research was to identify the weighted average cost of capital of meat sector enterprises listed on the Warsaw Stock Exchange between 2001 and 2017. The following specific objectives were achieved (within the scope of the main aim): • A diagnosis of the usefulness of long-term returns on investment from the base index and using annual rates of return from the index -a subjective assessment; • Establishing the cost of (owner) equity in investigated enterprises so that they result from the β parameter;

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• Estimation of the β for investigated enterprises so that it corresponds to the calculation methodology and accepted risk-free returns and long-term rates of return from the base index; • Identifying the dependency between capital structure and the cost of foreign capital.
To achieve the main aim and specific objectives, the following research hypothesis was formulated: h 1. Cost of equity in meat sector enterprises is dependent on the β coefficient; h 2. Growth of invested foreign capital cost corresponds to the growth of interest (owned) in the structure of liabilities. Amongst commonly used equity valuation models the Capital Asset Pricing Model (CAPM) was selected. The choice of this model of capital asset valuation was determined by its practical appreciation as well as it being the most popular model of (owner) equity valuation. The choice of the CAPM model enables relative data transparency. It relys on generally accessible data which facilitates and, in many cases, makes it possible to evaluate the cost of equity (Bartholdy and Peare, 2003). Research conducted by PricewaterhouseCoopers reveals that nearly 70% of practices use the CAPM model to evaluate the cost of equity, which is evidence of its acknowledgment (Byrka-Kita, 2008). The CAPM model used in the study is the first simple version. The authors are aware of the direction of the inclusion of additional risk factors, which are offered by improved versions of the capital asset pricing model (e.g. the modification of Fama and French). Identification of the cost of equity in the conditions of the Polish economy, in which the young capital market is not the main part of the financial market, is difficult due to structural limitations. The main limitations include the short age of the Polish Stock Exchange. According to Pereiro's research, because of volatility of stock prices in emerging markets the estimation of the beta parameter and its application to the valuation of the cost of equity capital using the CAPM model raises many doubts (Pereiro, 2010). The identification of the relationship between the beta parameter and the cost of equity, allows for assessment of the scale of impact of individual CAPM parameters on the cost of equity. This research is also a tool for identifying the suitability of selecting model parameters, so that, for example, the market risk premium and the risk-free rate do not limit the impact of the company's systemic risk on the final calculation of the cost of equity volatility of individual securities and a limited number of companies operating on the stock exchange. As a result, the use of multi-indicator models, based on divisions of the research sample and the identification of risk factors such as SMB and HML (the three-factor Fama and French model), seems impossible to implement in the perspective of the limited sample size (9 companies). Nevertheless, the authors agree on the necessity to enrich the CAPM model with additional risk factors, mainly resulting from the industry characteristics. Other, less popular models used to evaluate the cost of capital include: • The Gordon Model -limited use of this model is associated with a lack of continuity in paying out company dividends (Ross, Westerfield and Jordan, 2008), especially visible in companies belonging to the meat sector in Poland; • APT model -limiting the arbitrary price theory model is based on difficulties in identifying groups of universal factors for all assets alongside additional considerations of variability at times of risk attributed to particular groups of assets (Kolb, 1995). Despite a number of listed advantages of the CAPM model, several limitations can be found. One of the most basic reservations concerns a lack of unequivocal procedures for the selection of model components such as risk-free returns, underlying rate of return time horizons, discussion about the methodology for determining the β parameter, or the very debate about the credibility of the β coefficient as a measure of risk associated with investing in assets of a given enterprise (Fierla, 2008).
Comparing the advantages and disadvantages of the CAPM model shows that despite an outline of the rules for selecting particular parameters, its construction is to a large extent of an expert character and the credibility of the findings depends on the expertise and experience of researchers and their tendency to "market-orient" the valuation (Laderman, 1988). One must also realize that the CAPM model, in the same way as other known, available and applied equity valuation models gives approximate results (K. Jajuga and T. Jajuga, 1994).
The research covered all meat sector enterprises listed on the Warsaw Stock  (2013)(2014)(2015)(2016)(2017). Stock market quotation of companies came from the stooq.pl website, which offers free access to stock market data and their export to files enabling further editing.
The CAPM model used in the research takes on the form of Security Market Line (SML) presented in formula 1 (Duliniec, 2001): where: R -expected rate of return (cost of equity), R f -risk-free rate, R m -(underlying) rate of return from market, β -measurement of risk associated with investing in a given security.
The first stage of constructing the CAPM model was the choice of an instrument with a rate of return identical to risk-free ones. Fifty-two-week treasury bonds are commonly applied in scientific literature, as well as long-term government bonds (Pluta, 2009). The research used 10-year treasury bonds with a risk-free rate of 4(357) 2018 return (see Franc, 2003). This approach is more widely acknowledged in subject literature, though certain flaws in the concept of risk-free rates of return are known, including long-term ones (Patena, 2011). The choice is justified for a number of reasons (Grzywacz, 2012): • Corporate equity is characterized by a long-time horizon (which is a recommended period of analysis) and is the most stable component of liabilities. Investment in the capital of a given company (especially in the case of retained profit) is usually oriented towards its growth in value, development and cashing in on a satisfactory rate of return for its owners. This indicates a necessity for analyzing the cost of the company over a long period of time. The risk-free return should also be of a long-term perspective. • The yield from 10-year treasury bonds includes many corrections which are not included in 52-week treasury bonds. The most important is inflation. • The yield from 10-year bonds is better adjusted to discounted cash flow, and a 10-year period seems appropriate for share capital. • The application of yield from 10-year bonds, as a measure of risk-free rates, limits the influence of short-term fluctuations of risk-free interest rates, which is a rational, market-oriented and logical approach. A rapid change of risk-free rates is unlikely. High standard aberrations would indicate additional risk associated with the variability of "safe" rates of return, which would then require additional, subjective corrections. The next step in the construction of the CAPM model was to establish market rates of return. The WIG-SPOZYWCZY index was chosen in the light of research into the meat sector. This index has been listed on the Warsaw Stock Exchange since January 5, 1998, which hinders precise calculations. An effect of focusing the research towards a long-term horizon and consistent use of long-term measures should be a 10-year period of analysis of underlying return. For example, in 2010 the years encompassing "WIG-SPOZYWCZY index -rate of return" were 2001-2010, a 10-year time horizon. However, in 2001-2006 this condition was not achieved. In this case, the researchers chose the maximum possible time period (to evaluate the rate of return from the index). The research used daily fluctuations of underlying index prices from 1998-2017. Another limitation in the research was also high standard aberrations in rates of return, which is characteristic of fledgling capital markets (such as Poland) (Duliniec, 2001). Due to negative rates of return from the index in given years, it is impossible to apply the harmonic mean as a method of averaging the results. According to Damodaran (1994), the research used arithmetical average as a method of establishing annual underlying rates of return. In this case, it was justified not by time-horizon but by instability, changeability, and "young age" of the Polish stock exchange.
The measure of systemic risk in the CAPM model is the β coefficient. The studies of Sharpe and Cooper (1972); Douglas (1968); Black, Jensen and Scholes (1972), Fama and MacBeth (1973) (see Da, Guo and Jagannathan, 2012) confirm the validity of the CAPM model and its compatibility with reality, and indicate the β coefficient as an appropriate measure of undiversified risk. Although, these studies were questioned and modified many times, no better measure or model has been found to explain the dependency between expected rate of return from invested capital and associated risk (Byrka-Kita, 2008). One of the biggest difficulties in constructing the CAPM model is establishing the time horizon when estimating the β parameter (Duliniec, 2001). The use of the β parameter in Poland's economic environment is sometimes questioned. This is because of its high changeability over time (lack of stability) which is confirmed by some studies based on Chow's test (Kuziak, 1999). According to studies conducted by Osińska and Stempińska (2003), lengthening the time horizon to measure the β indicator, enables its stabilization. But, applying too long a horizon, tarnishes the current condition of the company with data from the distant past. Though no scheme regarding the depth of retrospection has been worked out for the β parameter, academic literature most frequently uses 12-to 60-month periods for analysis. But 12-month time horizons are often criticized, especially due to their lack of stability (Byrka-Kita, 2008). The estimation of the β parameter is conducted on the basis of two methods (Osińska and Stempińska, 2003): • The recursive method, where the first attempt was composed of 48-month elements (depending on data availability) with each next attempt increased by another 12 months, not exceeding 120 months in total.
(I n48 = ln-3, ln-2, ... , ln ), (I n36 = ln-2, ln-1, ... , ln ) On account of the consistent analysis of capital cost in the investigated companies from a long-term perspective, a 48-month period was applied for the estimation of the β parameter, based on daily price fluctuations of the analysed papers and WIG-SPOZYWCZY index. This approach constitutes a rational compromise between standards accepted by institutes involved in providing commercial data about capital markets. Most of these companies, for example Morningstar Ibbotson, Value Line or Standard & Poor, basing on the studies of Black et al. (1972), use a 60-month time perspective for the β parameter estimation. Bloomberg, which uses a 24-month time horizon, is an exception (Patena, 2011). A 48-month period of retrospection used to estimate the β parameter ensures relative stability of the indicator over time and does not warp the assessment of current company conditions due to the past data.
Financial statements published in the EMIS Intelligence from the years between 2001 and 2017 were used to establish the cost of foreign capital for the companies under research. The cost of foreign capital was established as the relation of financial costs (profit and loss statement) to the average rate of interest liabilities (average from year n and year n-1). Establishing foreign capital cost as a relation of interest expenses (from the cash flow statement) to interest liabilities is also accepted in scientific literature (Łukasik, 1998).

theoretical background: capital cost -valuation dilemma
The issue concerning capital structure and its cost is just as fascinating as it is difficult to unequivocally evaluate. Academic discussions concerning the optimization (quasi-optimization) of capital structure and cost have been ongoing for decades. They first appeared in the 1960s in the works of Modigliani and Miller (1958;1963) and were continued by many renowned economists (such as Stiglitz 1974;Myers and Majluf, 1984;Masulis, 1988;Brown, Lajbcygier and Wong, 2012). Heated discussions also took place during the 40 th Anuual Meeting of the European Finance Association, which featured presentation of the opinions of Chen, Harford and Kamara (2013) and Fairhurst (2013). The issue of capital structure optimization remains "enigmatic" in the world of finances. So far, no solution has been found which would allow entrepreneurs to practically optimize their capital structure. Varying opinions can already be found at a theoretical level (see Lagerkvist, 1999;Acharya, Shin and Yorulmazer, 2013). Many other current factors influencing the financial condition of enterprises additionally complicate decisions related to capital and their resulting consequences (see Hüttel, Mußhoff and Odening, 2010;Boubakri, Guedhami, Mishra and Saffar, 2012;Arouri, Rault, A. Sova, R. Sova and Teulon, 2013).
For many years, discussions concerning the necessity to correct the method of calculating the cost of equity have been pending (see Franc-Dąbrowska, 2009). This is a basic problem in the procedure of estimating "real effects" for the business. Financial results (in their classic form) do not encompass the cost of (owner) equity, thus failing to give information about the real benefits resulting from the use of this capital. This is a particularly important problem for agricultural enterprises. In such entities, attempts have also been made to estimate and assess the cost of (owner) equity and its relation to capital structure. Moreover, the results of these attempted estimations, made for agricultural enterprises (see Franc, 2003;Kulawik, 1998), have been unsatisfactory. In many cases businesses were unable to pay for this factor of production.
Agricultural enterprises are particularly sensitive to meteorological changes (especially plant producers) and natural growth processes in both plants and animals. Therefore, the associated risks undertaken by such businesses are relatively high and require consideration in (the cost of equity) financial statements. Agricultural enterprises are also highly equipped with fixed assets, which to a large (financial) extent are part of (owner) equity. Thus, part of the (owner) equity becomes "tied up" and cannot be "released" quickly or (in some cases) at all (if, for example, the entrepreneur wishes to resign from running the business).
Another interesting dependency characterizing agricultural enterprises is that entrepreneurs running such businesses on their own account (and risk) experience better effects (of management) -as stated in the findings of Franc-Dąbrowska (2010). Similar observations are recorded in data analysis of "Ranking 300". Their results show that the highest rate of return from sales is found amongst entrepreneurs who purchase the majority of their estate, next come enterprises whose estate is mainly leased, followed by single-companies of the Agricultural Property Agency of the State Treasury. These groups of enterprises can be categorized in the same way, using criteria from the value creation index (ranking..., 2012). The results presented by the "Ranking 300" team confirm those in previously mentioned studies. Here, emphasis should also be placed on the problem of evaluating the cost of (owner) equity, as a rare resource, especially in times of economic crisis (see Boratyńska, 2009).
When considering the problematic issues of valuation of (owner) equity costparticularly with reference to agricultural enterprises - Kulawik's (ed., 2008) proposal should be highlighted. The author introduced a modification of the capital asset valuation model and corrected the β coefficient (a measure of systematic risk resulting from the volatility of the company's share price). Kulawik acknowledged individual risk for agricultural enterprises, (differentiating it) on the basis of operational activity. This proposal should be considered important, both from a theoretical and practical perspective. Another approach, based on research of the opinions of those managing agricultural enterprises were presented by Franc-Dąbrowska and Kobus (2012), who stated that only some entrepreneurs see the need to evaluate the cost of (owner) equity, and if necessary, it is calculated on the basis of deposit account interest rates (as an alternative source of obtaining income).
An interesting perspective was presented by Boratyńska (2009), who focused on the important issue of the cost of company bankruptcy. The author's findings show that the cost of bankruptcy proceedings is indirectly incurred by both the creditor and debtor. As a result, the creditor's claim cannot be fully met, whereas the debtors liabilities can rise. This is another issue worth taking into consideration with regard to the cost of invested capital and the targeted capital structure. In turn, Zawadzka's (2009) findings proved that the significance of short-term liabilities is variable and, in Poland, is dependent on the sector. Similarly, Boratyńska (2012), identified bankruptcy models for enterprises of the (food) meat sector.
Such perspectives in research show the need for further analysis of the capital structure and cost, with particular focus on the specifics of the meat sector.
Why is establishing the cost of (owner) equity so important? Due to the fact that it constitutes a necessity and basis for making decisions concerning the sources of business financing, thus shaping the capital structure (Miller, 2009) 1 . It further carries consequences when making decisions about where to designate profit -be that to inject (owner) equity or, for example, pay out dividends. Capital structure optimization models are insufficient for agricultural enterprises. This is because these companies function in many various legal forms, at different levels of proprietorship, and with varied types of production (of different dependencies on agroclimatic conditions and biological features of plants and animals). This is a particular group of enterprises, enabling food security for its people.
Moreover, because of historical determinants -farmers constitute a large employment and residential group in Poland. Due to this group's specificity, it is not always possible to transfer generally available solutions. It is, however, possible (by using the findings of researchers) to acknowledge the specific conditions of this group of entities and thus for authors to attempt to evaluate the cost of (owner) capital and its relation to capital structure. Moreover, in our opinion a modification of the proposed methods, rather than a proposal of new ones, should be made. This stems from the fact that a balance for optimal capital structure has been proposed and in its general form should not be questioned. But then, it is not always suitable for the researched entities and thus requires improvement. We also call for agricultural sector entities not to be excluded from the domain of scientific considerations. The very scale of their activity as well as the owners' need for recommendations are reasons enough to include agricultural enterprises into the scope of scientific interest and debate. This is despite a current deficit (within its ranks) of teams specializing in capital structure management. Nonetheless, agriculture's role in economic sciences has always been a starting point for all other sectors.
Tables 1 and 2 present selected data concerning the beta level. These data show the results for six of the largest US food processing entities whose total market share was 94% (International Markets Bureau, 2012). A relatively large variance in the beta parameters over time was observed for respective companies and between them. If, however, the average size is taken into consideration, the beta values move towards levels oscillating at 1, which is accepted as characteristic for the agricultural sector (every person needs food to survive). Similar observations are found in the data presented in Table 2. By analysing recursive beta for six of the largest US market enterprises, quite a significant variation of the beta levels is observed. However, the average value is slightly Zagadnienia ekonomiki rolnej / Problems of agricultural economics below level 1. Moreover, the largest entities of the US market are characterized by a higher recursive beta value (on average) in relation to the average value of the entire food processing sector.

Meat industry in Poland -characteristics and identification of the research problem
The successful adjustment of the Polish meat industry enterprises to Western European standards failed to solve many or even most of the problems of the sector (Wieczorkiewicz, 2008). These businesses still remain rather unprofitable in comparison to the average for the food industry in general. Low profitability and thus a limited capacity for profit margins, gives the meat sector little significance as a source of increasing equity (Kalwasińska, 2011). A significant barrier for the development of the meat sector is its high fragmentation (in 2009, seven of the largest meat factories in Poland had barely a 20% share in the market) (Kowalski, 2011). Another problem is high dependency on the supply and prices of raw meat. A solution seems to be vertical and horizontal integration. This currently involves the costly takeover of bankrupt or failing meat plants. A role model worth emulating may be, for example, the Danish meat market.
As a consequence of the lack of vertical integration in the Polish enterprises the supply of raw meat is fragmented, which leads to differences in meat sensory features 2 . This causes difficulties in the production of homogenous, ready-made products. Thus, Polish meat products are not competitive in comparison to Danish products on restrictive markets such as Japan or Russia (Polish meat on this market is also less competitive than meat imported from the US) (Obidzińska, 2008). Moreover, though the number of companies (in the meat sector) dropped from 6 thousand in 2000 to 1.2 thousand in 2010, significant fragmentation is still a barrier to development (Knapp-Stefaniuk, 2010). According to data from 2017, there were about 1250 enterprises from the meat industry in Poland, of which 170 were poultry meat processors. At the same time, 40% of all enterprises in the meat industry were small enterprises (mainly family enterprises) in which employment did not exceed 9 employees (Rawa, 2017).
The meat sector in Poland is characterized by an obvious mismatch of assets and liabilities. As a consequence, due to a long deadline for the collection of payments and short deadline for paying off current liabilities, meat sector companies are faced with a cash flow gap and thus a requirement for additional sources of financing (Sroka, 2010). This pattern was especially visible during the financial crisis which, although failed to affect Polish entrepreneurs to such an extent as their Western European competitors, caused serious difficulties in settling payments on time for foreign investors (especially Ukrainian). As a result, an additional demand for capital was observed amongst Polish businesses in the Polish meat industry (Drewnowska, 2010).
Polish consumer choices, when selecting meat products, are governed by quality and price. As a result of a slowdown in economic growth, an effect of the financial crisis, a lower demand for meat was observed (Górnicka, 2005). A change of culinary preference also played its part. A growing demand for vegetarian and vegan food products, associated with the so-called healthy eating, was noticed. Despite this, in 2002-2017, as a result of economic growth, decreasing unemployment and an increase in the wealth of the Polish society, these was over 9% increase in meat consumption. However, the demand for meat remained sensitive to changes in its prices and depends on the amount of income of consumers (Milan, 2017).
Another significant difficulty for Polish businesses of the meat industry was the strengthening of the Polish Zloty (PLN) in relation to foreign currencies. In 2008--2010, this made Polish meat products significantly less competitive on foreign markets. Moreover, during this time, Polish enterprises also became less competitive, in comparison to their foreign competitors on the domestic market (Cukiernik, 2008). In addition, the difficulties of the meat industry are aggravated by the ASF virus, which in 2014-2017 led to the liquidation of 3.7 thousand farms involved in the production of raw material. The challenge that the meat industry will face in Poland from the beginning of 2019 will be the ban on the use of GMO soy protein in feed, which accounted for around 20% of feed demand in Poland (Milan, 2017).

Research findings
The moving window method (MW48) and the recursive method (REK) were used to estimate the β parameters in companies from the meat sector. The results are presented in Table 3.

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The application of the recursive method gives higher results of stability to the β parameters in all of the investigated companies. This result is in line with expectations, however, the final result may not be accepted as credible on account of the method used. The moving windows method with a 48-month time period made the research more realistic and separated the assessment of current company conditions from the past data. The estimation of the β parameters with this method showed greater variation over time. There was (on average) an 11.16% difference to the β parameter values estimated by way of the recursive method. The application of the moving windows method (with a 36-month period) is shown for the Indykpol S.A. Company in Figure 1. The moving window method of a 36-month time span and less, failed to show stability for the β parameters over time. In 2003, an 8-fold decrease of the β indicator as observed in Indykpol S.A, after which a 15-fold increase was observed in 2004. Such fluctuations do not allow for adaptation in further research and analysis. The β indicators, estimated using this method, differed on average by 138% to those calculated analogically using the recursive method.
For further research, related to the valuation of the cost of equity, the beta coefficients were calculated using the 48-month moving window method. The basic descriptive statistics, including the division into poultry enterprises and red meat companies, pertaining to the estimated parameters are presented in Table 4.  The average value of the beta parameters for poultry enterprises (0.41) is significantly lower than the average calculated for red meat companies (1.13). In the case of poultry enterprises, the minimum and maximum values of the beta parameters are also lower. In the case of poultry enterprises, there is also a lower standard deviation of the estimated beta parameters, however, the coefficient of variation is higher (47%) than the analogue, calculated for red meat companies (23%). This indicates a lower stability of the beta parameters over time and higher variation at the level of individual enterprises.
According to the accepted method, the authors of the paper established the cost of equity in companies (under research). The results are presented in Table 5.
The β parameters for the companies under investigation in 2001-2017, show a certain regularity. The cost of equity was evidently higher for Gobarto, Tarczyński, Kania, PKM Duda and Sokołów in relation to Indykpol, Pamapol, Drop and Drosed. There is 12.6% difference in the average cost of equity separating these companies during the entire research time-period. In the case of red meat companies only six times did the cost of equity achieve a value below that of 12.6%, which constitutes approx. 12% of the analysed cases for these companies. But, the cost of equity for: Indykpol, Drosed, Drop and Pamapol exceeded 12.6% only once from amongst 44 observations. A similar regularity is noticeable in Table 3, presenting the β indicators estimated by way of the moving windows method with a 48-month time period. Red meat companies (PKM Duda, Gobarto, Tarczyński, Kania and Sokołów) distinguished themselves once again with high values for the β coefficient. In the case of these companies, its value frequently exceeded 1.0 (71% observations). For the poultry meat companies, the value of the β coefficient failed to exceed 0.8 during the entire time-period.
This evident regularity has its basis. The meat industry as a whole can be generally divided into the processing of poultry and the processing of other meats. The identified regularity results from a number of factors having a fundamental impact on the overall systematic risk of enterprises. This stems from, inter alia, peculiarities of business cycles for various types of livestock. In the case of red meat industry, a longer production cycle of the raw material is observed and a higher depend-ence of its price on a wide range of factors: feed prices, current economic situation, weather factors, diseases, variability (sensitivity) of demand. In the case of poultry enterprises, the production cycle is much shorter, enterprises are more vertically integrated, which limits the impact of variability of other factors. In addition, poultry meat enjoys growing recognition from customers, mainly due to its taste and price. On the basis of the investigated companies' financial statements published by EMIS Intelligence, the following Polish business classifications (using the codes from 2007) were observed PKM Duda,Gobarto,Tarczyński,Duda;Pamapol,Drosed,Drop.  In conclusion it can be stated that companies involved in the processing of poultry incur a lower cost of equity and are characterized by lower β coefficient values to companies involved in the processing of other meats (excluding poultry). According to investors, owners and other company capital providers, companies involved in the processing of poultry incur lower business risk than companies involved in the processing of other meats (excluding poultry). This conclusion is an interesting basis for further, deeper operational activity risk analysis into particular meat sub-sectors, using the Hamady and pure play method (Brigham, 1996).
Upon analyzing all the estimated parameters of the CAPM model, it is worth considering which of them should determine the final size of cost of equity and which should remain neutral. Higher cost of equity should be incurred by companies involved in higher risk, thus a higher β coefficient. These values should strongly correlate. The dependency between the β coefficient and cost of equity for all companies under investigation is presented in Figure 2.  Figure 2 presents a significant dependency between the β coefficient and cost of equity. The correlation coefficient amounted to 0.89, which means that the cost of equity is strongly determined by the size of the β coefficient. This conclusion means that the use of the CAPM model was correct for one of the basic assumptions. The greater the risk, the higher the returns expected by the capital provider, which for the receiver of capital -the enterprise -means a higher cost of equity. This conclusion verifies hypothesis H1, which has proved correct. An analysis of the remaining CAPM model parameters indicates a lower value for correlation coefficients in relation to cost of equity, whose module did not exceed 0.32. This means that the subjectively chosen parameters of the CAPM model, i.e. the risk-free rate and the market rate of return, along with the adopted methodology for their estimation, have no fundamental impact on the final results in terms of the cost of equity.
The cost of foreign capital calculated on the basis of financial statements ranged between 4.12% and 14.03%. In the case of cost of foreign capital, the previous difference between poultry and other meat processing entities is imperceptible. This time, no characteristic features for these two groups could be found. For cost of foreign capital, no evident directions of change could be found either. An analysis of the reasons for the change in the cost of foreign capital gave rise to the formula- tion of hypothesis H2, indicating a dependency between the cost of foreign capital and the level of interest debt. This regularity failed to be proven for all companies under research (correlation coefficient amounting to 0.28) and respective companies (the correlation coefficient ranged between 0.11 and 0.67). Thus, no basis can be found to accept hypothesis H2. Deeper analysis showed a strong correlation between the cost of foreign capital and total share of debt in liabilities. This regularity was not seen in the entire group under investigation (correlation coefficient equaled 0.57), but in particular companies this coefficient ranged between 0.67 and 0.89.
The lack of dependency between cost of foreign capital and total share of liabilities in the entire research sample suggests that an individual assessment is made by providers of (interest) capital in relation to particular companies. Therefore, no defined level of company debt at which capital providers increase its cost, can be distinguished. The assessment is made individually in relation to each of the entities under investigation. However, an evident regularity indicates that the share of debt liability in liability structure (for example bank loans) does not determine the final cost of foreign capital. The providers of interest capital place greater emphasis on total liabilities than interest liabilities. This situation shows that money owned to suppliers, which in the meat industry constitutes a significant share in the structure of liabilities, are not a cost-free source of short-term capital. An indirect cost of a high level of short-term interest-free liabilities (money owned to suppliers, wages and others) is an increase in the cost of interest liabilities.
Knowledge of the cost of equity, cost of foreign capital and share of particular sources of capital in the structure of liabilities enabled the calculation of the weighted average cost of capital (WACC), which is presented in Table 6.
The results presented in Table 6 (analogically to Table 5), show a clearly higher weighted average cost of capital for red meat companies. These companies incurred a weighted average cost of capital of over 13.2%. Companies involved in processing of poultry saw a weighted average cost of capital amounting to 7-8%, i.e. 5-6 pp lower than in the case of PKM Duda, Gobarto, Tarczyński, Kania or Sokołów. These findings indicate a lack of homogeneity in the specifics of the meat industry, as well as various sources of financing the businesses and their cost.
An analysis of values presented in Tables 5 and 6 reveals a lower level of weight average cost of capital in relation to cost of equity. Total cost of capital decreased in relation to equity due to its dilution with cheaper foreign capital. Additionally, the use of foreign (interest) capital enabled entrepreneurs to adopt the so-called tax shield method, which lowers the final cost of foreign capital. This explanation corresponds with the theory of finances in the scope of cost of capital and is proven in a number of empirical studies.

Conclusions
The research reveals that (owner) equity is a more expensive source of financing in the companies under research, than foreign capital. Moreover, the final cost of foreign capital is subject to a decrease due to the possibility of including costs of interest in tax deductible expenses, enabling the use of the tax shield mechanism.
The research carried out in accordance with the adopted methodology has proved that the proposed mechanism for estimating the cost of capital is appropriate. It was confirmed that the final result of the level of the cost of equity depends on the level of the β index, and thus depends on the level of risk characterizing the given enterprise. The remaining parameters of the CAPM model did not distort the final results. The hypothesis H1 was confirmed. The obtained results do not differ significantly from global results (Zarzecki and Byrka-Kita, 2005).
The hypothesis H2 was verified as a result of the research. It was not possible to prove that the cost of foreign capital increases with the share of interest liabilities in the structure of liabilities. In addition, it was found that the cost of foreign capital depends on the total share of liabilities in the structure of liabilities (including 4(357) 2018 non-interest liabilities). Therefore, not only the share of interest liabilities determines the cost of foreign capital, but above all the sum of liabilities. In this type of research, it is necessary to take into account the individual approach of capital providers to specific entities. The dependence proposed by the authors has been confirmed separately for each unit from the research sample. The research allowed identification of characteristics of two subgroups of the meat industry: • Poultry processing -characterized by lower β coefficients in relation to medium size, low cost of equity, lower average weighted cost of capital; • Meat processing (without poultry) -was characterized by a higher level of the β in relation to the sector average, higher cost of equity, higher average weighted cost of capital. The presented observations indicate the lack of uniformity in the specificity of the meat industry. Therefore, there is a need for further analysis in both proposed sub-segments of the meat sector.
Certain problems pointing to the CAPM limitations and imperfections remain unresolved and require further multidimensional research.

Limitations:
• In the case of negative cost of equity, there is a dilemma concerning its logical interpretation. Assuming that the calculated value is the rate of return from invested capital, this explanation is logical. However, the assumption that this value is the cost of capital brings with it certain difficulties. • The dilemma concerning the valuation of equity in cases when company equity is negative due to unsettled losses remains unsolved.

Findings of the research
In our assessment one should: • Recommend calculating the cost of equity and include it in the balance of business efficiency -to enable a real assessment of invested capital efficiency. • Recommend an individual approach to the cost of equity. One should not automatically use proposed solutions found in scientific literature. The specifics of each sector, branch and even respective entities (in our view) determine the level of the cost of capital. • In the case of a model approach to estimating the cost of equity, we recommend including additional risk factors -both those resulting from improved versions of the simple CAPM, as well as other factors specific to the different branches of the industry. • Recommend paying attention to the fact that despite a seeming dependency between interest liabilities and the whole cost of liabilities, this dependency is not directly proportional. • Recommend further research into the assessment of the cost of equity, its influence on the decisions concerning sources of financing business operations, acknowledging the specifics of entities.


Does Environmental Governance Matter for Foreign Direct Investment? Testing the Pollution Haven Hypothesis for Indian States

This paper attempts to examine the role of environmental governance on foreign direct investment by testing the pollution haven hypothesis for 21 Indian states for the period 2002–2010. To test for the hypothesis, this study computes an abatement expenditure index adjusted for industrial composition at the state level using Annual Survey of Industries plant-level data. The methodology used is based on that proposed by Levinson (2001). The index compares actual pollution abatement expenditures in a particular state, unadjusted for industrial composition, to predicted abatement expenditures in the same state. (The predictions are based on nationwide abatement expenditures by industry and each state's industrial composition.) If the adjusted index is low for a state, it implies that the state has poor environmental governance, which would be expected to induce foreign firms to invest. However, the results do not find any evidence of the pollution haven hypothesis in the Indian context. Other infrastructure and market-access-related variables are more important in influencing a foreign firm's investment decisions than environmental stringency.

the high growth rates that resulted from such reforms. Many host economies also devised suitable incentives to attract FDI. Another reason often cited in the literature is that relatively lenient environmental regulations in an economy can attract FDI. This is a process that has been described as a "race to the bottom" (Grossman andKrueger 1991, Xing andKolstad 1998). Keller and Levinson (2002) posited that a key factor influencing a foreign firm's choice of location could be the compliance costs of local environmental regulations.
One of the ways in which compliance costs can be measured is to look at how much firms are spending on pollution abatement. If these costs are aggregated across firms in a particular location, they reflect the environmental governance aspects in that location. All other things being equal, a firm in one Indian state having higher pollution abatement expenditures in comparison with another firm in the same sector in a different state indicates more stringent environmental governance in the first state. This paper seeks to identify the impact of actual abatement expenditures on the location choices of foreign firms in India by computing an index of abatement expenditure for firms in each state using plant-level data from the Annual Survey of Industries for the period FY2002-2003 to FY2009-2010 Earlier studies attempting to measure environmental regulations have used either pollution intensity (see, for example, Mani, Pargal, and Huq 1997;Jha and Gamper-Rabindran 2004;Dietzenbacher and Mukhopadhyay 2007) or pollution abatement costs divided by one of the following: total employment, gross state domestic product (GSDP), or a state's manufacturing output without controlling for industry characteristics (see, for example, Friedman, Gerlowski, and Silberman 1992;Duffy-Deno 1992;Crandall 1993). A key problem with such measures is that they fail to adjust for industrial composition. States that are home to pollution-intensive industries such as steel, fertilizers, and chemicals will incur relatively high pollution abatement costs whether or not they have stringent regulations. Thus, pollution abatement costs that account for industrial composition are needed to assess a state's regulatory stringency.
In this paper, I compute industrial-composition-adjusted abatement costs using unit-level data from the Annual Survey of Industries for the period FY2001-2002to FY2009-2010. The data are aggregated at the National Industrial Classification (NIC) 3-digit and 2-digit levels, and then computed as an index. Subsequently, I use panel data techniques to test for the pollution haven hypothesis for 21 major states in India. The results do not validate the pollution haven hypothesis in the Indian context.
The remaining paper is organized as follows. Section II explores how FDI and the environment are linked. Section III discusses measurements of environmental governance in the literature. This is followed by an explanation of the methodology used to assess the role of environmental governance on FDI in different Indian states in section IV, which also explains the methodology used to construct the industrial-composition-adjusted environmental governance index. Descriptive statistics and other control variables are given in section V. Section VI reports the estimation results. The paper concludes with a discussion of the policy implications in section VII.

II. The Relationship between Foreign Direct Investment and the Environment
The relationship between FDI and the environment in the literature can be grouped into three main strands: (i) environmental effects of FDI flows, (ii) competition for FDI and its effects on environmental standards, and (iii) cross-border environmental performance (Pazienza 2015). Despite extensive empirical work and case study evidence, there is still not a clear understanding of the associated phenomena (Erdogan 2014, Pazienza 2015.
With respect to the environmental effects of FDI flows, Pazienza (2015) argues that greater integration of the world economy through increased investment flows (and trade) and mobility of factors will impact the environment through the (i) scale effect (moving from a small to global scale), (ii) technique effect (adoption of cleaner technology), and (iii) composition effect (a shift in preferences to cleaner products and greater environmental protections with increases in income) (Kathuria 2008, Pazienza 2015. The net of these three effects is reflected in the ultimate impact on the environment. The literature exploring the relationship between FDI and environmental regulations discusses two distinct phenomena: (i) the pollution haven hypothesis, and (ii) the "race to the bottom" or "regulatory chill hypothesis." In the context of FDI, the pollution haven hypothesis emphasizes the possibility that investors seek economies in which to locate with fewer regulatory requirements and therefore cheaper costs of operation for industries. Interestingly, most authors who focus on the pollution haven hypothesis have adopted an empirical approach (see Dean 1992 for a survey taken before 1990 and Erdogan 2014 for a recent survey).
This strand in the literature has often been used to oppose globalization given the impacts of foreign investment on local environmental standards. Generally known as the "race to the bottom" or "regulatory chill effect," the argument states that foreign firms may induce governments to reduce local environmental standards or freeze them at suboptimal levels (Erdogan 2014). Evidence shows that in the People's Republic of China, provinces compete intensely for foreign capital by offering promises of preferential treatment to potential foreign investors, which can include a tacit (or explicit) commitment to lax enforcement of environmental standards (Esty and Gentry 1997). In resource-seeking industries, where products are homogeneous, minor cost differences can translate into large gains in market share. Consequently, foreign investors occasionally exert considerable pressure on recipient economies (Erdogan 2014). These competitive pressures can also operate in the opposite direction as investors insist on higher environmental standards. For example, foreign investors in Costa Rican banana production have insisted upon the application of high environmental standards as their European customers demand an environmentally sound product (Gentry 1999, Erdogan 2014.
The focus of the present study is on testing for the pollution haven hypothesis in Indian states rather than on the responsiveness of environmental standards to FDI.

III. Pollution Abatement Costs as a Measure of Environmental Governance
Three broad methods have been used in the literature to characterize environmental stringency (Keller and Levinson 2002): (i) qualitative indexes of regulatory stringency, (ii) quantitative measures of enforcement on the part of states and economies, and (iii) compliance costs incurred by plants. Crandall (1993) and Friedman, Gerlowski, and Silberman (1992) were among the first to use industrial-composition-unadjusted pollution abatement costs (as a share of either GSDP or employment) as a measure of environmental regulation. Later studies by Levinson (2001) and Keller and Levinson (2002) used industrial-compositionadjusted pollution abatement costs to measure the level of environmental regulation. Though variation in state-level environmental stringency is less than variation across economies, state-level variation provides three benefits. First, there are much better data on a state's environmental costs than on costs at the international level. Second, states are more comparable with one another than different economies on nonenvironmental parameters (Keller and Levinson 2002). In cross-economy studies, costs are different due to prevailing market conditions in different economies rather than purely the result of abatement-related costs. This bias is less if an analysis is conducted across states within the same economy. Third, most studies on decision-making processes with regard to location show that environmental regulations have a very small role in these decisions (OECD 1997). Factors like political stability, size and growth potential of markets, access to other markets, labor costs, ease of repatriation of profits, transparency and predictability of administrative and legal frameworks, cultural affinity, infrastructure, and quality of life are more important (Erdogan 2014). Many of these factors are the same across states within an economy; thus, the major key variable influencing the locational choices of foreign firms would be environmental costs.
The use of pollution abatement (operating) expenses as a measure of abatement costs is preferred for two reasons (Keller and Levinson 2002). First, operating expenses for pollution abatement equipment are easier to identify separately. Abatement capital expenses may be difficult to disentangle from other investments in the production process that have little to do with pollution abatement. Second, abatement capital expenditures are highest when new investment takes place. This implies that Indian states with thriving economies such as Gujarat and Tamil Nadu that have sufficient manufacturing investment also tend to have high abatement capital expenses regardless of the stringency of their environmental laws. Moreover, operating costs show a more consistent year-to-year pattern (Levinson 2001), while capital expenses can vary in line with industry business cycles. This implies that pollution abatement expenditure can be used as a proxy variable for environmental regulation. Incidentally, the Annual Survey of Industries includes the following three variables: (i) expenses incurred in the repair and maintenance of pollution equipment (which was discontinued in 2008), (ii) gross addition of pollution control equipment expenses during the year, and (iii) gross closing expenses of pollution control equipment at the end of the year. 2 In this paper, I use the latter two measures (gross addition expenses and gross closing expenses on pollution control equipment) to compute an index of environmental governance.

A.
Measuring Environmental Governance Friedman, Gerlowski, and Silberman (1992); Crandall (1993); and List and Co (2000) used measures like pollution abatement costs divided by either total employment or GSDP. A key problem with such measures is that they fail to adjust for industrial composition. Based on Levinson (2001), I compute an industry-adjusted abatement expenditure index for 25 Indian states for different time periods to see if FDI inflows are affected by any variation in abatement expenditure (reflecting the degree of environmental governance). The index compares actual pollution abatement expenditure in a particular state, unadjusted for industrial composition, to the predicted abatement expenditure in the same state. These predictions are based solely on economywide abatement expenditures by industry and each state's industrial composition. This paper improves on Levinson (2001) and Keller and Levinson (2002) by computing industry-adjusted abatement expenditure at the NIC 3-digit level instead of the NIC 2-digit level.
Let the actual abatement expenditure per unit of output be denoted as follows: 3 where P st is pollution abatement expenditure in state s in year t, and Y st is the manufacturing sector's contribution to the GSDP of state s in year t. S st is the unadjusted measure of compliance costs. By failing to adjust for the industrial composition of each state, it probably overstates the compliance costs of states with more pollution-intensive industries and understates the costs in states with relatively clean industries. To adjust for industrial composition, compare equation (1) to the predicted pollution abatement expenditure per unit of GSDP in state s: where N is the total number of industries. In India's case, industries are indexed from 15 through 36 (covering 22 industries) following the 2-digit manufacturing NIC codes. Y ist is the contribution of industry i to the GSDP of state s at time t, Y it is the economywide contribution of industry i to national GDP, and P it is the economywide pollution abatement expenditure of industry i. In other words, S st is the weighted average pollution abatement expenditure (per unit of GSDP), where the weights are the relative shares of each industry in state s at time t. To construct the industry-adjusted index of a state's stringency, S * st , I compute the ratio of actual expenditures in equation (1) to the predicted expenditures in equation (2): when S * st exceeds 1, industries in state s at time t spend more on pollution abatement than similar industries in other states. When S * st is less than 1, industries in state s at time t spend less on pollution abatement. By implication, states with large values of S * st have relatively more stringent regulations than states with small values of S * st (Levinson 2001).

B. Hypothesis
A low adjusted index score for a state implies that the state has poor environmental governance, which would induce foreign firms to invest. In other words, this study tests for the pollution haven hypothesis; that is, a negative relationship between FDI and environmental governance. To test for this hypothesis, I have used the following equation that relates FDI to environmental governance after controlling for several state-specific effects such as net state domestic product (NSDP) per capita, share of manufacturing in NSDP, quality of infrastructure, and geographic dummy (proximity to coast): β is the estimated parameter of a state's abatement expenditure index and is predicted to have a negative influence on FDI inflows; that is, the more stringent a state's environmental governance, the smaller its FDI inflows. The index also uses a lag given that a firm's decision to invest, especially with regard to FDI, is not instantaneous. Rather, an established pattern of governance may induce a firm to invest in the subsequent period. γ 's are the coefficients of control variables. The control variables included are per capita net income of the state (NSDPc); share of manufacturing in NSDP; quality of infrastructure, especially the availability of electricity as measured by installed capacity (Instlcap) and transmission and distribution (T&D) losses; investment received by the state that has been implemented through an industrial entrepreneurs memorandum (IEM); availability of human capital (Literacy); and proximity to the coast. The likely effects of these control variables are summarized below.

Market size and demand
A bigger market attracts FDI (Kathuria, Ray, and Bhangaonkar 2015), due to significant potential demand and economies of scale (Walsh and Yu 2010). Market size is measured by NSDPc. A larger market size is hypothesized to have a positive sign (List and Co 2000;Keller and Levinson 2002;Fredriksson, List, and Millimet 2003;Drukker and Millimet 2007). The variable is used in log form.

Manufacturing share
NSDP accrues from the primary (agriculture), secondary (manufacturing), and tertiary (services) sectors. The manufacturing sector is relatively more capital and energy intensive in comparison with the agriculture and service sectors. A large manufacturing share in a state's NSDP reflects its status as an industrial state, which is likely to attract more FDI. Therefore, the current study uses the share of manufacturing in NSDP (Manushr) as a control variable.

Availability of power
Due to the significant capital investment required, a potential foreign investor often assesses whether a state has sufficiently available power before making an initial investment. Relatively high installed capacity implies the likelihood of available power, which is also likely to attract more FDI (Mukherjee 2011). Although installed capacity is often a good measure of power availability, this may not be the case in the Indian context where many states have T&D losses as high as 50% (Srivastava and Kathuria 2014). Actual power availability is more important for an investor than installed capacity. The level of T&D losses also indicates the effectiveness of industrial regulations in the state. Thus, I take both installed capacity (Instlcap) and T&D losses as control variables impacting the likelihood of foreign firms investing in a state. A state with low installed capacity and high T&D losses is expected to have low levels of FDI.

Proximity to a coast
Many foreign firms invest in developing economies due to cheap labor and to establish a manufacturing hub for exporting and participation in worldwide supply chains (Zhang and Song 2000). From a foreign investor's point of view, a manufacturing hub requires international connectivity in the form of a seaport so that components and final goods can be imported and exported easily. Proximity to a port reduces the transaction costs of the producer. Therefore, a state that is home to a seaport will attract more FDI (Neelakanta, Gundimeda, and Kathuria 2013). A dummy variable that is equal to 1 if a state has a seaport and 0 otherwise is used.

Clustering effect
An existing stock of investment in a state can generate positive spillovers through linkages (Kathuria 2016). It is also indicative of conducive conditions for investment. The IEM implemented in each state may capture this clustering effect as it reflects the readiness of a state to attract investment. 4 The IEM is also a reflection of better institutional characteristics like good governance, political stability, low levels of corruption, and ease of doing business. We hypothesize that the more the IEM is implemented in a state, the more FDI it will attract unless the congestion costs exceed the cost of relocating (Adsera and Ray 1998). Dunning (1998) has argued that though FDI in developing economies is often prodded by traditional factors-such as market size, lower input (labor) costs, and the cheap availability of natural resources-physical and human infrastructure, along with the host economy's macroeconomic environment and institutional framework, play a crucial role. At the state level, physical infrastructure is reflected by the availability of power and pucca (permanent) roads, while the literacy rate indicates the availability of human capital. The present study controls for the human capital effect through the state-specific literacy rate (Literacy).

Time dummy
As my data are for 9 years, a time dummy (TIME) is employed that accounts for any macroeconomic changes occurring during the period that would affect all Indian states.

D. Data
One key problem to undertake the empirical analysis is the nonavailability of appropriate FDI data. I need state-wise FDI in the manufacturing sector. However, data on FDI inflows available from the Reserve Bank of India (RBI) and the Department of Industrial Policy & Promotion under the Ministry of Commerce and Industry are either by sector or by RBI region. RBI regions correspond to regional offices, and cover several states. 5 To solve this problem, I use responses to questions raised by members of Parliament on state-wise FDI. Data are summarized in Table  A1. The data for all other variables were collected from different government agencies. The data for NSDP per capita and manufacturing share were obtained from the Central Statistical Organisation, power availability and T&D losses from the Ministry of Power and various reports of the Planning Commission, and IEM data from the Ministry of Industry and the Handbook of Statistics on Indian Economy. State-wise literacy rates were taken from 2001 and 2011 census data.

E. Econometric Specification
For the given objective, several estimation models exist. However, a simple pooled ordinary least squares (OLS) model would yield biased and inconsistent parameters if time-invariant covariates are omitted. If omitted time-invariant variables are correlated with the environmental governance variable, a fixed effects (FE) model will provide a consistent and unbiased estimate of the parameters while simultaneously controlling for unobserved unit heterogeneity. On the other hand, if these omitted time-invariant variables are uncorrelated with the environmental governance variable, a random effects (RE) model would provide a more efficient estimate than an FE model. The validity of these assumptions is examined by a Hausman test. In case of the presence of autocorrelation and heteroskedasticity, I will be using the generalized least squares method that corrects for these two problems. For the estimation purpose, I limit the sample to only 21 states and union territories for which data are available for all the variables for the period FY2002-2003 to FY2009-2010 This is because many northeastern states and union territories have neither received FDI nor are any consistent data available for their T&D losses or power consumption, thereby restricting the number of states and union territories for analysis to 21. 7 The final econometric model estimated is The estimations were carried out in STATA 12. Table 1 presents state-wise summary statistics for abatement costs after controlling for industrial composition (S * ) at the 3-digit and 2-digit NIC levels (equation 3) and without controlling for industrial composition (S) (equation 1). The correlation between adjusted (3-digit NIC data) and unadjusted abatement expenditure index is 0.9.

V. Descriptive Statistics
From Table 1, it can be inferred that several states which appear to have higher abatement expenditures as per the unadjusted index have a much lower ranking once industrial composition is accounted for. States like West Bengal and Meghalaya, which are among the top five in terms of unadjusted pollution abatement expenditure, get a much lower ranking once industrial composition is accounted for. Similarly, states like Uttarakhand and Jharkhand have a higher ranking after controlling for industrial composition. This implies that using the unadjusted measure of compliance would give a misleading picture of some states' relative stringency. Column 2 of the table gives adjusted abatement expenditure using a 2-digit NIC code. The rankings and values hardly change. The correlation between the two is 0.99. Table 1 also indicates that there are nine states for which industry-adjusted abatement expenditure is greater than 1, implying that they are spending much more than their industrial composition suggests. Uttarakhand, and Dadra and Nagar Haveli-which started with a high level of environmental stringency but became more lenient during the review period. Of the remaining states, eight experienced a decline in the value of the index with an increase in the middle period (2005)(2006)(2007), while five showed increased stringency over the entire 9-year period with a decline in the value of the index in the middle period. The last row of Table 2 gives the average value of the abatement index for all three periods, which indicates that there is hardly any change in environmental stringency across all states over the entire review period.  Figure 1 gives the plot for environmental stringency measure between period 1 and period 3. States lying above the 45-degree line showed increased stringency between the two periods, while states falling below the line experienced a decline in environmental stringency. With the exception of Andhra Pradesh and Odisha, the stringency of environmental governance declined in all states between period 1 and period 3. Figure 2, which gives a scatter plot between ln(FDI) and the lagged value of the industry-composition-adjusted abatement cost index, does not indicate any perceptible relation between the two. Table 3 reports the mean values of different variables used in the analysis. It shows huge variation in the values for all variables. There are states like Assam, Bihar, and Jharkhand, which hardly received any FDI. On the other hand,   Maharashtra tops the list with an average of INR44.62 billion over this 9-year period. 8 Similarly, the share of manufacturing in NSDP is less than 5% in Bihar, compared with more than 25% in Gujarat, Goa, and Puducherry. Regarding installed capacity, Maharashtra, Andhra Pradesh, Karnataka, Gujarat, and Tamil Nadu each have more than 5,000 megawatts of power generation capacity, while states like Goa do not produce any electricity. The northern states, which do not receive much FDI and have fewer electricity installations, are also plagued with high T&D losses. Four states-Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh-account for 40% of all T&D losses during the review period, which may discourage FDI from coming to these states. Of the five states with the highest values for human capital, as measured by the literacy rate, only Delhi has received substantial FDI, while the other four states are not even among the top 10 recipients of FDI.

VI. Results and Discussion
Before estimating the model, correlations are noted between the different control variables. Table 4 gives the Spearman correlation matrix and reports the significance of the correlation coefficient at the 5% level. A state with higher NSDP per capita is able to attract more FDI (correlation = 0.33) and have a high manufacturing share (positive correlation) with very high literacy (correlation = 0.86) and low T&D losses (negatively correlated). A state with high installed capacity is not only able to attract more FDI (correlation = 0.57), but also more domestic investment (IEM) (correlation = 0.69), and does not have any correlation with T&D losses. Similarly, a coastal state has high FDI (correlation = 0.35) and a high manufacturing share (correlation = 0.3). As expected, a literate state has a high manufacturing share and low T&D losses. Consequently, with partial correlation being statistically significant for several of the variables, I could not use all the controlled variables together. Table 5 reports the results for the econometric estimations. Equation (5) was estimated first by pooling the data for all states (column 1). As discussed, due to omitted variables, the OLS results were expected to be biased. Therefore, panel data techniques were also required and both FE and RE models were subsequently run. An F-test was carried out to see whether individual FEs exist or not. Since the F-value (6.2) is greater than the tabulated value, it implies that the null hypothesis (pooled OLS) is rejected and that FE and RE models need to be estimated separately. Columns 2 and 3 give the results of the FE and RE   estimations. Whether these omitted variables (state-specific differences) are fixed or random is tested using a Hausman test (last row). This is a test for the correlation between the error and the regressors. Under the null hypothesis of no correlation between both, the RE model is applicable and its estimated generalized least squares estimator is consistent and efficient. Under the alternative, it is inconsistent. Since the test's statistic (chi-square value = 4.96) is significant only at the 29% confidence level, one cannot reject the null hypothesis. To see whether RE are needed, a Breusch-Pagan Lagrange-Multiplier (LM) test is carried out. Results lead to the rejection of the null hypothesis, in favor of the alternative, i.e., the RE model. Row 1 shows that the industry-composition-adjusted pollution abatement expenditure index (S * ) is negative, though statistically insignificant, and thus has no impact on FDI investment. This implies that states' environmental norms do not figure in the investment decision of foreign firms. With respect to control variables, a state with high per capita income (ln[NSDPc]), which reflects a bigger internal market, can attract more FDI. A state with more domestic investment (ln[IEM]) is not able to attract more foreign investment in statistical terms. On the other hand, proximity to the coast and the availability of infrastructure, as proxied by installed capacity, has a direct bearing on foreign firms' location decisions. High installed capacity implies that power is more readily available in a state. Thus, foreign firms are expected to prefer these states. Similarly, coastal states attract more FDI due to their increased opportunities to export.

A. Econometric Analysis
Given that panel data is used where values of different variables change over time, the possibility of autocorrelation exists. A Wooldridge test for autocorrelation (where the null is no first-order correlation) (F-value = 0.98, p = 0.33) negates this possibility. A Pesaran cross-sectional dependence test is then employed to check whether the residuals are correlated across panels, as cross-sectional dependence (contemporaneous correlation) can lead to biased results. The test value of 2.7 is significant at less than 1%, suggesting that there is cross-sectional dependence. A modified Wald test is also carried out to test for group-wise heteroskedasticity. A very high value of chi-square (175) indicates that the null of homoscedasticity (constant variance) is rejected. Given the problem of heteroskedasticity, a panel corrected standard errors model was subsequently employed and the results are reported in column 4. S * retains the same sign and significance level even after the correction. All other control variables also retain the same sign and significance level except for domestic investment (ln[IEM]), which becomes highly significant. The results suggest that FDI flows to states that are coastal and have high installed capacity, high per capita income, and more domestic investment. Environmental stringency does not influence a foreign firm's location decision when other infrastructure and market-access-related factors are considered. In other words, the results do not validate the pollution haven hypothesis in the Indian context.

B. Robustness Test
To see whether results are robust or not, I estimated several variants of the model. Table 6 reports the results where some of the control variables are either dropped or alternate control variables are used. Column 2 (model 2) uses T&D loss instead of installed capacity. The impact of the environmental governance index (S * ) variable on FDI remains the same. The coefficient of the T&D loss variable has the expected sign, though it is not statistically significant. In model 3, the coastal variable used in model 2 is dropped. In model 4, literacy is substituted for per capita income (ln[NSDPc]), which was used in the base model. In model 5, the manufacturing share is used instead of investment in the state (ln[IEM]). In model 6, only the environmental governance index variable (S * ) and year dummies with no control variables are used. Lastly, model 7 uses state dummies and time dummies while all of the control variables continue to be excluded.
As can be seen from Table 6, the environmental management index (S * ) variable remains statistically insignificant in all variants of the model. The results are thus robust to alternate control variables and to the noninclusion of control variables. Most of the control variables retain the same sign and significance as Notes: *** , ** , and * denote significance at the 1%, 5%, and 10% level, respectively. # denotes significance at the 15% level. Source: Author's calculations.
predicted. When state dummies are included in all the variants (models 2 to 5), the main variable remains statistically insignificant. When NSDP is used instead of NSDP per capita, the main variable retains its sign and significance. Lastly, the results did not change when all models were reestimated by computing S * at the NIC 2-digit level. The results also remain the same irrespective of how I compute S * . The use of both gross closing expenditure and gross addition expenses on pollution abatement yield the same outcome. Based on the results, this study does not validate the pollution haven hypothesis in the Indian context.

VII. Conclusion
This paper examines the impact of environmental governance on FDI by testing the pollution haven hypothesis for 21 Indian states for the period 2002-2010. An abatement expenditure index was computed and adjusted for industrial composition at the state level using the methodology provided by Levinson (2001). The industry-adjusted abatement expenditure index was greater than 1 for nine states, which implies that these states spend more on abatement measures than their industrial composition suggests. The index also shows that over the 9-year review period, six states showed an increasing trend of abatement expenditure, while eight states showed a decreasing trend.
The paper then uses this industry-composition-adjusted pollution abatement expenditure index to test the pollution haven hypothesis in a panel data framework. The study finds that environmental stringency does not influence FDI decisions once panel-specific heteroskedasticity is accounted for. The paper concludes that a coastal state with high levels of per capita income and available power will attract more FDI. Environmental stringency does not influence foreign firms' decisions when other infrastructure and market-access-related factors are considered. The results were subsequently tested for robustness by using alternate control variables in a panel corrected heteroskedastic model. The results were found to be robust for the inclusion of control variables. To conclude, the study does not validate the pollution haven hypothesis in the case of Indian states.
There are several possible reasons why the study was not able to either validate or refute the pollution haven hypothesis in the Indian context. First, though foreign firms establish operations abroad due to low operational costs, the relevance of pollution abatement costs in comparison to total operating costs may be limited (Erdogan 2014). Second, even if these costs are high, they may still be lower than in other economies from where FDI is originating or in alternate destinations. Therefore, it may not matter where to invest within a particular economy. Finally, studies have suggested that foreign firms generally seek consistent environmental enforcement over lax enforcement (see, for example, OECD 1997), which may also hold true in the case of Indian states.
While the paper's important findings have some limitations, it can be extended to address these limitations. As mentioned, parliamentary questions were relied on to get state-wise FDI data, which showed an extremely high value of FDI for one state in a particular year. Moreover, the paper considers all FDI inflows in the 21 states under review. Instead of total FDI, only manufacturing FDI could be considered to assess the effects of environmental governance. Another extension of the present study would be testing the pollution haven hypothesis only for FDI that is associated with pollution such as investment in the chemicals and fertilizer industries. Lastly, if a race-to-the-bottom dynamic, rather than the pollution haven hypothesis, is applied in the Indian context, then FDI and environmental governance would be endogenous; the testing of which requires the use of instrumental variable estimations, which would be a further extension of the present work. (a) to (d): Government has put in place a liberal and investor-friendly policy on foreign direct investment (FDI) under which FDI up to 100% is permitted on the automatic route in most sectors/ activities where no prior approval of the Government is required. For FDI proposals in sectors/activities requiring prior Government approval, the Foreign Investment Promotion Board (FIPB) acts as a single-window clearance authority. Under the liberalized economic environment, investment decisions of investors, including location, are based on techno-economic and commercial considerations.
A statement on state-wise foreign direct investment (FDI) proposals approved during the last 3 years is at Annex-I.
Statement on FDI inflows during the last 3 years as reported by the regional offices of the Reserve Bank of India is at Annex-II.
Currently, a tabular information regarding the status of establishment of industry pursuant to the approvals is not maintained. (b) The steps taken/being taken by the Government for improving the industrial climate are the creation of world class infrastructure; promotion and facilitation of industrial investment including the foreign direct investment; improvement in business environment; and development of industry relevant skills. Government has also announced a national manufacturing policy with the objectives of enhancing the share of manufacturing in GDP to 25% within a decade and creating 100 million jobs. The policy seeks to empower rural youth by imparting necessary skill sets to make them employable. The policy is based on the principle of industrial growth in partnership with the States. The central government will create the enabling policy frame work, provide incentives for infrastructure development on a public-private partnership (PPP) basis through appropriate financing instruments and the State Governments will be encouraged to adopt the instumentalities provided in the policy. The proposals in the policy are generally sector neutral, location neutral and technology neutral except incentivisation of green technology. While the national investment and manufacturing zones (NIMZs) are an important instrumentality, the proposals contained in the policy apply to manufacturing industry throughout the country including wherever industry is able to organize itself into clusters and adopt a model of self regulation as enunciated.
(c) A statement showing the statewise details of foreign direct investment proposals approved during the last 3 years and current year is at Annexure 2.


Growth Convergence and Divergence in SAARC

We test for output convergence during 1960-2017 amongst the leading member countries of the South Asian Association for Regional Cooperation (SAARC): Bangladesh, India, Nepal, Pakistan and Sri Lanka. The context is SAARC’s commitment to eventual monetary union. Existing research, using established unit root tests, finds little support for the convergence hypothesis, even when structural breaks are permitted. We use two more recently developed approaches, both of which allow greater flexibility in the depiction of convergence. The method developed by Enders and Lee (2011) allows for a smoothly evolving trend in relative per capita incomes, rather than a (possibly breaking) linear trend. The technique introduced by Phillips and Sul (2007, 2009) allows for the possibility of convergent sub-groups. Even with these more accommodating test procedures, we find minimal evidence of growth convergence within the full group of countries. We find that these countries can be allocated to two non-overlapping convergence clubs, with India and Sri Lanka enjoying the more favourable growth path. These findings raise questions regarding the current feasibility of monetary union for SAARC.

convergence in per capita GDP that will facilitate eventual monetary union.

Previous Studies
We begin with a brief introduction to the methods that have been employed. Let y t be a country's per capita output series in logarithms. A negative relationship between y t -y s and y s , s<t, is called "β -convergence" and implies convergence over time upon a country-specific steady state. A group of countries that are β-convergent with a common steady state can be expected to show a diminishing cross-sectional range of variation: "σ-convergence". A rigorous approach to testing for such a long-run diminishing trend in cross-sectional variance has been developed by Sul (2007, 2009).
Alternatively, consider y t -r t where r t is log per capita output for some reference country or group.
Rejection of a unit root in y t -r t constitutes "stochastic convergence" (Li & Papell, 1999). Full convergence additionally requires that deterministic terms are insignificant in the test regression or are convergent on zero.
Turning to existing studies of the SAARC countries, Chowdhury (2004Chowdhury ( ), using data for 1960Chowdhury ( -2000 found no evidence of β-convergence or σ-convergence. Evans and Kim (2005), however, concluded in favour of β-convergence for Bangladesh, Pakistan and Sri Lanka, but not India. Evans and Kim (2011) found stochastic convergence in a panel of 13 Asian countries, and individually for Bangladesh, Pakistan and Sri Lanka. Jayanthakumaran and Lee (2013), employing the Lumsdaine and Papell (1997) "two breaks" unit root test for data covering 1973-2005 rejected stochastic convergence. Solarin et al. (2014), employing the Lee and Strazicich (2003) unit root test, with two endogenously dated breaks under both null and alternative hypotheses, found that only Bhutan shows evidence of stochastic convergence to the SAARC group mean.
These existing investigations of growth convergence within SAARC offer only slight support to a convergence hypothesis. Convergence is not always confirmed at the individual country level, and, when it exists, may be limited to "stochastic convergence". This prompts two questions: (i) whether evidence for convergence might be discovered by testing procedures that permit more flexibility in the specification of structural breaks, and (ii) whether evidence of convergence might exist for some subset(s) of the SAARC countries. These two questions are addressed below using data covering 1960-2017.
We continue with an introduction to the data in section 3, testing for stochastic convergence, using a flexible non-linear trend, in section 4, a search for convergent sub-groups in section 5, and conclusions in section 6.

Data
The key variable for this study is the logarithm of real per capita GDP, in 2010 $US, observed annually from 1960 until 2017, and graphed in Figure 1. are excluded from the study because of the shortness of their GDP data series. These excluded countries together contribute no more than 1.0% of total SAARC GDP in the years where their data are available.

Assessing Stochastic Convergence
In an earlier working paper (Daly & Khan, 2016) we have applied a battery of unit root tests to assess stationarity of each SAARC5 member country's log per capita income relative to that of the group overall. Without structural breaks in the test equation, no test rejected a unit root at the 5% significance level for any country. When testing with one structural break (Zivot & Andrews, 1992), or two structural breaks (Lumsdaine & Papell, 1997) the unit root was rejected at significance levels around 10% for some countries but with no consistency in the selection of countries or the dating of structural breaks. These tests, however, characterise structural change with a small number of segmented linear trends, which may not best accommodate the progress of developing economies.
For a more flexible characterisation of structural changes, Enders and Lee (2011) propose that, if breaks are not too many in number or too extreme in magnitude, a "flexible Fourier trend" Lagrange Multiplier (LM) unit root test, using a single trigonometric frequency, is appropriate. We apply that test here (Table 1), pre-testing for linearity and using the Schmidt and Phillips (1992). LM test with a linear trend when linearity is not rejected at a 10% significance level. The test equation is augmented with lagged terms as suggested by Enders and Lee (2011) with the lag length here determined by the rule: "long enough so that residual autocorrelation is absent and an additional lag would not improve the AIC".  , Table 1a).
It is apparent in Table 1 that, even with this very flexible trend specification, the unit root null hypothesis is only rejected at a 5% significance level for Nepal. It is rejected marginally for India at a 10% significance level but we discount this result as a consequence of India's dominant size within SAARC-India has contributed over 70% of SAARC5 GDP throughout the sample period and over 80% in recent years. This lack of growth convergence across the board questions the sustainability of SAARC-wide monetary union in the near future. Nevertheless, it is possible that some sub-group(s) of countries might show evidence of mutual convergence-a question to which we now turn.

Searching for Convergence Clubs
We begin by looking for instances of stationarity in bilateral relative (log) per capita GDP, using the Enders and Lee (2011) procedure. Table 2 shows that several countries are involved in at least one country pair where the unit-root null is rejected at a significance level of 10%, suggesting that the possibility of some conver gentsub-group(s) should not be ruled out.   , Table 1a).
For a more formal, search for convergence clubs within SAARC5 we use the method of Sul (2007, 2009). Their "log-t test" tests for long-run decline in the cross-sectional variance of log per capita GDP. The log-t test is obtained from a regression in which the regressand is based on the negative log of the cross-sectional variance series and the regressor is the logarithm of a linear time trend. A significantly negative coefficient is taken as evidence of divergence. We find that applying the log-t test to the SAARC5 group yields a negative t-statistic with a p-value below 0.0001, rejecting convergence for the full group.
The question remains whether there might be some convergent sub-groups. Sul (2007, 2009) propose an algorithmic search strategy for exploring the 2 n -1 subgroups but here, with only n=5 countries, it is computationally feasible to simply test all possible sub-groups. We find that, in most sub-groups, the t-tatistic indicates divergence, beingsignificantly negative, with a p-value that is zero to four decimal places. Table 3 shows those few cases that have a larger p-value. The log-t test identifies (Bangladesh, Nepal, Pakistan) and (India, Sri Lanka) as two mutually exclusive and independently convergent sub-groups. Given that Sri Lanka and India are the two highest ranked SAARC members in terms of per capita GDP during recent years, our results point to the possibility that the SAARC group is exhibiting "twin track" development, with India and Sri Lanka as a fast-track core group and the other nations being peripheral to this.

Conclusion
Employing a flexible, data-determined, representation of structural change, we find that Nepal is the only member of SAARC5 which has per capita income stochastically converging on the group mean.
Nevertheless, when this same approach is used to assess stationarity of bilateral income relativities, we find some suggestion of stochastic convergence for sub-groups within SAARC5. Using the Sul (2007, 2009) log-t test to identify such convergent sub-groups, we conclude thatthere is evidence suggesting twin-track development paths within SAARC5. India and Sri Lanka appear to be a convergent group enjoying a relatively favourable growth path, with Bangladesh, Nepal and Pakistan converging on a less prosperous growth path. This finding questions the sustainability of full monetary union for the SAARC countries.