31 January 2016
Family Businesses; The Cornerstone
for a Shockproof Economy?
A Research about the Effect of Family Businesses on the
United States GDP Growth Volatility
by
F. M. B. Pladdet
10206310
Supervised by
R. Zhuo MSc
Thesis Submitted in Partial Fulfilment of the
Requirements for the Degree of
Bachelor of Science
in the
Department of Economical Sciences
Faculty of Economics and Business
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Statement of Originality
This document is written by Student F.M.B. Pladdet, who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Preface
Before you lies the Thesis ‘Family Businesses; The Cornerstone for a Shockproof Economy?’. This thesis is written in order to fulfil the requirements for the degree of Bachelor of Science. I worked on this thesis from October 2015 until January 2016, eventually writing the last piece of my thesis in Toronto, Canada.
Through this preface I would like to thank my supervisor Rui Zhuo for the supervision and help during the process of writing this thesis. You were of great help. I also would like to thank Joseph Astrachan, Carl Magnus Bjuggren and Dan Johansson for thinking with me how to structure the research and help me how to quantify family businesses. Last I would like to thank everyone who helped me find the right data by either replying my emails or guiding me in the right direction and the people who supported me during the process.
This thesis took more time than I had expected. This was mostly to the difficulty of finding the right data and because it was very hard to interpret the results.
I hope you enjoy reading this thesis. Mathijs Pladdet
Abstract
This thesis is about the relation between the GDP contributed by Family businesses and the volatility of the GDP growth. This is researched by performing a time series analysis on data of the United States in the period 1959 till 2011. The conclusion of this thesis is that there is no significant effect of Family Businesses on the GDP growth volatility.
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Table of Contents
Preface... 2
Abstract... 3
Table of Contents... 4
List of Figures... 5
List of Tables... 5
1. Introduction...
2. Literature Review...
2.1 Family Business Definition...
2.2 Family Business Strategies...
2.3 The Costs of Being a Family Business...
2.3 GDP Growth Volatility Affecters...
3. Method...
3.1 Quantifying Family Businesses...
3.2 Regression Equation...
3.3 Data Description...
4. Results ...
5. Conclusion...
References...
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List of Figures
Figure I – Growth Volatility line graph
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Figure II – Percentage Family Owned scatterplot...
Figure III – Turnover of Sole Proprietorships and Partnerships/Companies...
Figure IV – Percentage Family Owned scatterplot (HP filter applied)...
List of Tables
Table I - Correlation between variables
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Table II -Estimates on Volatility......21
1. Introduction
The financial and economic crisis of 2008, ranked as one of the most traumatic global developments since World War II (Sinclair, 2010), nearly led to the collapse of the Monetary Union in Europe (Grauwe 2010). These crises, driven by financial innovations (Pilbeam, 2013), caused European banks to need bailouts, investors to stop investing, and countries to slip into a recession (Sarkar, 2011). During this recession the GDP growth of the European Union dropped to -4.4% (Worldbank, 2015) and the GDP growth of the United States to -2.8% (Worldbank, 2016). During recessions the GDP growth tends to be more volatile. Growth volatility data of the United States show peaks during the first and second oil shock and the recessions of 1958 and 2007. This can have negative effects on the GDP growth and the stability of a country.
Research indicated that family businesses tend to be less volatile during these crises and are less affected by these recessions than non-family businesses (Berent-Braun et al., 2011). One of the reasons is that managers of family firms tend towards having little external debt or equity financing to be able to keep the shares of the company within the family (Dunn and Hughes, 1995). Furthermore, previous research shows that these managers overestimate the risk of debt and prefer a company to remain at the status quo with the corresponding financial rewards above possible future growth (LeCornu et al, 1996). Another reason for family businesses to be less affected by recessions is that the strategy and financial structure of family firms are focused on long-term preservation to be able to pass the company through to the next generation (Astrachan & Shanker, 2003; Heck & Stafford, 2001; Sharma, 2004; Abdellatif, Amann, Jaussaud, 2010). These unique factors of family businesses being less volatile could affect a country’s GDP growth volatility when a big share of the GDP comes from family businesses.
It is also possible that family businesses have a bad influence on the stability of a countries GDP growth. Family businesses tend to be risk avers, which following the research of Morck and Yeun leads to less innovation (2003). This could have an effect on the countries innovative climate and lead to a decreased international competitive position,
It is possible that with an increase of the number of family businesses in a country, the family business mentality will lead to a more long-term focused, risk-averse and less volatile economy. This could have practical implications for governments trying to stabilize the economy. It could also be that the effect on innovation and investments of family businesses destabilize countries making its GDP growth more volatile. This discussion is why this paper will present an explorative research on the influence of the percentage of the GDP contributed by family firms on the volatility of the GDP growth by analysing the data of the United States in the period 1959-2011.
The method used for examining the effect of family businesses on the GDP growth volatility will be an OLS regression. This regression follows the model of Denizer et al. (2002). The model used, contains GDP volatility influencers and a parameter reflecting the percentage of GDP contributed by family businesses. The different factors affecting the GDP growth and the ability to absorb shocks included vary from exchange rate regimes and political regimes (Edwards & Yeyati, 2005; Hausmann & Gavin, 1996) to openness of a country (Head, 1995).
This paper will consist of four sections. In section 2, the literature review, I will define family businesses, elaborate on the differences between family business strategies and non-family business strategies, the cost of being a family business and introduce GDP affecters and fluctuation. In section 3 I talk about the methodology of this research, including a data description and a model specification. In section 4 I show the result and this paper will end with section 5 where I conclude and give answer to the question if family businesses affect the GDP growth volatility of a country.
2. Literature Review
2.1 Family Business definition
There is no universal definition for a family business. Handler (1989) stated defining the family business as one of the first and most challenging task for family business researchers. This is due to the lack of consensus on the definition. Some researchers use a specific definition for distinct research purposes (Dean, 1992). Dyer (1986) defines a family business as an organization in which decisions regarding its ownership or management are influenced by a relationship to a family or families. Rue and Ibrahim (1996) add that at least one family member is employed or expects to be employed by the business. Fiegener, Brown, Prince and File (1994) vouch for the relevance of the next leader to be a relative of the current leader and employed by the firm. These different definitions make it hard for family business researchers to build their research on old research.
Besides lack of consensus between researchers, there is also lack of consensus between countries (Family business Issues, 2008). In Austria family businesses are, defined by regional legislation as an autonomous economic entity regularly and sustainably providing for the family’s income. The Italian civil code describes family businesses as enterprises in which members of the family unit work and have ownership. In Bulgaria and Slovakia a business is a family business in the case of a self-employment or Sole Proprietorship. These divers definitions make it hard for researchers to conduct comparative research between countries.
Astrachan, Klein and Smyrnios (2002) state the importance of unambiguousness and transparency for the definition to be functional and operationalizable. They say that the most important factor of the definition is that it is modular and leads to reliable and valid results. They state that this is not possible with a bi-polar definition of nonfamily versus family business. Astrachan et al. (2002) suggest a continuum scale, which ranges from no family involvement in an enterprise to full family involvement. Although this definition could lead to more reliable data, it is very data intensive.
There are also researchers that work with narrow and broad definitions for family businesses (Shanker & Astrachan, 1996; Bjuggren, Johanson & Sjörgen, 2011; Astrachan & Shanker, 2003; Kirchhoff & Kirchhoff, 1987). Bjuggren, Johanson and Sjögren’s (2011) definition ranges from requirement of some family participation and family control over the strategic direction of the business, to requirement of more than one member of the owner’s family to have management responsibility and that multiple generations of the family are involved in the business.
To conclude, family businesses can either be labeled by the ownership of one family (Berry, 1975; Lansberg, Perrow & Rogolsky, 1988), by the managerial influence of one family (Burch, 1972; Barnes & Hershon, 1976) or by family business culture (Litz, 1995; Dreux IV & Brown, 1999). Since this paper will make a differentiation between family and nonfamily businesses and needs a definition, which leads to a quantifiable variable, this paper will follow the definitions of Astraschan and Shanker (2003) and Bjuggren et al. (2011). This paper will use three different definitions ranging from broad to narrow and show the effects of family businesses on the volatility of growth of the GDP in a country.
The broad definition is:
‘A firm where a family controls the strategic direction and there is family participation’ The middle definitions is:
‘A firm where founder/descendant runs the company and is intended to remain in the family by being primary source of income’
The narrow definition is:
‘A firm with more than one member of the owner’s family to have management responsibility and that multiple generations of the family are involved in the business.’
2.2 Family Business Strategies
The strategy of family firms has changed over time and differs from non-family businesses (Jones, 1982; Ward, 1988; Zahra et al. 2004). Kreiser, Ojala, Lamberg and Melander (2006) show that family firms tend to adopt conservative strategies in the early stages of their strategic growth, such as conservative financial structures and tight decision making within the family, to avoid debt and maintain control over the company. Their study shows that in later stages family firms tend to become more entrepreneurial and take more risk. An important factor remaining is that family businesses are more long-term orientated, because it in general wishes to maintain control over the firm for an extended period of time (Kreiser et al., 2006). This long-term orientation does not always include rapid growth. Casson (1999) shows that family firms are typically growing through reinvesting profits, rather than takeovers or mergers.
Family businesses are often seen as an entrepreneurial firm. In essence this is true, however risk taking is a central engagement for entrepreneurs (Brockhaus, 1982) and this is not the central engagement for family businesses (Kreiser et al., 2006).
Kreiser et al. (2006) sum the advantages of family businesses as personal style of management, independence from the resources outside of the family, family identity and culture and lower level of bureaucracy.
To conclude, family firms differ from non-family firms in strategic decisions, financial structure and business culture. Some researchers claim these differences to be the reason family businesses are perceived more stable other see these differences as possible risk (Casson, 1982; Colli, 2003; Schulze et al., 2003).
2.3 The Costs of Being a Family Business
Family businesses incur costs for being a family business. Poza, Hanlon and Kishida (2004) described these costs. They state that family businesses are prone to nepotism and inflexible management. They also show that unity within a family is important for a family business to succeed and can otherwise lead to friction within the company. This could lead to a situation where the goals of the managers and owners do not align leading to agency cost unique to family businesses.
Morck and Yeung (2003) state that it is likely that when corporate control is passed on to the next generation, this heir is likely to be less able than the founder and the heir’s heir even more. This altruism and adverse selection could lead to the downfall of the business. Ward (1987) supports this finding in his research. He shows the low
percentage of family businesses survive more than one generation. Also for the other staff, the pool for recruitment is limited (Dyer, 2009). In companies that have a strict family-only policy could possibly lead to a situation where a family may not have enough qualified family members to run a successful company. When in this situation the family-only policy is maintained, incompetent family members could get a key position within the company, leading to a less competent company.
There is also the concern that in public family companies, managers tend to act for the controlling families and not for the shareholders in general. This could lead to risk averseness within the company and negative effects on competitiveness due to low investment in innovation (Morck & Yeun, 2003). Morck and Yeun (2003) state that the tendency to act for the controlling family can drive investors out of the industry, making it harder to lend money.
These costs can lead to a family business being less efficient than non-family businesses (Dyer, 2009).
2.4 GDP Growth Volatility Affecters
The volatility of the GDP growth is affected by multiple variables. Based on literature these affecters can be categorized in four categories: trade and financial openness, financial system development, price variability and flexibility, policy volatility and others.
The trade and financial openness are associated with increased volatility of GDP growth (Easterly, Islam & Stiglitz, 2000). The research of Head (1995) examined 56 countries and showed that the openness of the trade economy has an effect on the volatility of the GDP growth. He found that the more open a country, the bigger is the effect of a foreign shock. This makes the GDP growth more volatile. The same holds for the financial openness. Although financial openness makes it easier to absorb shocks it also makes a country more exposed to volatility of other countries and dependence of foreign credit makes a country in this case more vulnerable (Easterly, Islam & Stiglitz, 2000). Loayza, Rancière, Servén and Ventura (2007) state that there are strategies to coop with the volatility that comes with openness. They state that the most important point is to improve the economies shock absorbers. This mainly focuses on countercyclical fiscal policies, self-protection, self-insurance, full hedging and insurance. The relevance of shock absorbers is visible when looking at historical GDP growth volatility.
The financial system development associated with reduced volatility. Greater credit or a deeper financial system leads to less GDP growth volatility (Easterly, Islam & Stiglitz, 2000). This is mainly due to the smoothing of the consumption and production. Denizer et al. (2002) focused their research solely on the financial development and found that the way in which the financial system develops is also of influence. They state that the most important financial factor is the credit supplied to the to the public sector.
The price variability and flexibility are associated with greater volatility in GDP growth (Easterly, Islam & Stiglitz). This is because inflation is a major cause of recessions (Bruno & Easterly, 1998). The higher the inflation the more volatile a country’s GDP growth becomes.
The policy volatility focuses on the standard deviation of the fiscal balance, inflation and money growth. These are all factors that, to a certain point, can be influenced by the political regimes. These factors are associated with higher volatility.
Fisher (1993) shows that GDP growth volatility is negatively associated with inflation and positively associated with good fiscal performance and undistorted financial markets. These factors depend on the current political regime. A regime that is more conservative and focused on the long run might stabilize these factors and reduce the GDP growth volatility. This is the mentality often seen in family businesses.
3. Method
3.1 Quantifying Family Businesses
For measuring the effect of Family businesses on the volatility of GDP growth we need data on the estimated GDP contributed by family businesses. However this data is not available. Perrow & Rogolsky (1988) state the reason for this is that family businesses where long not seen as a different business entity and therefore no data was collected. This is why the number of family businesses is estimated in academic research. Researchers use various methods to estimate the number of family businesses. Bjuggren et al. (2011) state that in 2006, 76% of all companies were family owned, making use of tax data. Lansberg, Jorissen et al. (2001) and others use survey data retrieved from a variety of industries (Abdellatif, 2010; Astrachan & Kolonko, 1994; Klein, 2000). National samples are rarely used for research, which makes it difficult to get data on the number of family businesses in a country (Winter et al., 1998). There is data on legal forms and turnover available and this is why legal forms can be used to estimate and quantify the number of family businesses (Shanker & Astrachan, 1996; Astrachan & Shanker, 2003; Bjuggren et al., 2011).
The quantification of family businesses in this paper is based on the following steps (Shanker & Astrachan, 2005; Bjuggren et al., 2011), which will lead to a percentage of the GDP contributed by family businesses and will be explained in this section.
• Finding yearly data of the turnover or GDP share per legal forms
• Estimating the percentage of businesses being a family businesses per legal form based on literature for the three definitions
• Using these estimates to calculate the GDP contribution of family businesses per legal form
• Summing the GDP contribution of family businesses per legal form and dividing it by the total GDP will lead to Percentage Family Owned (PFO)
The legal forms used are Sole Proprietorship, Private Limited Company, Public Limited Company and Partnerships. A Sole Proprietorship is a business with a fully independent founder and owner. This can either be an owner’s principal occupation or mere a registered business without activity. Private Limited Company and Public Limited Company are legal entities. In case of a Private Limited Company shares are sold privately. In case of a Public Limited Company these shares are sold publicly and the company will become a listed company (Starting own Business, 2014). In both forms one family could buy these shares. A Partnership is a partnership between two or more business partners.
What is needed is the estimates of the percentage of businesses being a family business for all four legal forms, which are based on previous research and are further, altered in the middle and narrow definition models of PFO.
In the majority of the European countries and the United States a Sole Proprietorship is presumed to be a family business (Family Business Issues, 2008). This makes the estimated percentage of Sole Proprietorships that are a family business 100%,
of which 65% of the Sole Proprietorship are assumed to be the sole proprietor’s principal source of income.
Researchers estimated the number of Private Companies being a family business. The research of Burch (1972) shows that in 47% of the listed firms a family or an individual holds 4-5% or more of the voting stock and is represented in the board of directors, making the family in control of the business and it strategic direction. Jetha (1993) shows that 37% of the companies on the Fortune 500 of 1992 qualify as family businesses. Both the definition corresponds with the broad definition of PFO. Shanker and Astrachan (1996) add that smaller firms are not taken into account in these estimates. These smaller companies are, according to these researchers, more likely to be under family control. Therefore Shanker and Astrachan’s (1996) add percentages to the aforementioned estimates to get an estimate of 60% of Private Companies being a family business. They used this estimator in their paper of 2003 (Astrachan & Shanker, 2003) and is also used by Bjuggren et al. (2011). This estimator will be used for the Private Companies being a family business.
Porta, Lopez-De Silanes and Sheifer (1999) identified the ultimate controlling shareholders of the Public Companies in 27 wealthy economies. They find that families or the state mostly control these firms and participate in management. Porta et al. (1999) published a list of country specific estimators, which indicate that on average 25% of the large listed companies are family owned. Anderson and Reeb (2003) analyzed the S&P 500 for family businesses. They found that 35% of the companies on the S&P 500 are family owned. This paper will take the average of these two estimators to come to 30% of Public Companies being a family business.
Kirchhoff & Kirchhoff (1987) state that it is safe to assume that many Partnerships have direct family involvement. Researchers used the same estimator for Partnerships as for Private Companies corresponding to 60% of all Partnerships being family businesses (Shanker & Astrachan, 1996; Astrachan & Shanker, 2003; Bjuggren et al., 2011).
The previous researches show that the number of family businesses can be estimates using the legal forms. They show an estimation of businesses that meet the conditions to be a family business. These estimates will remain static over time. The broad PFO is calculated based on the aforementioned broad estimators (100% SP, 60% PS, 60% PR, 30% PU). The estimators vary along the other family business definitions. (Astrakhan & Shanker, 1996; Shanker & Astrachan, 2003; Bjuggren et al., 2011, Bureau of Economic Analysis).2
The broad definition In this case, 100% of Sole Proprietorships are included as family business. The ownership of the Sole Proprietorship implies control over strategic direction and it is considered normal that family members help in the Sole Proprietorship. This can be unpaid or paid, in the latter this is mostly not reported (Kirchhoff & Kirchhoff, 1987; Astrachan & Shanker, 2003).
PFOBroad = (1.0 * SP + 0.6 * PS + 0.6 * PR + 0.3 * PU) / GDP
2 SP corresponds with the GDP from Sole Proprietorships, PS with the GDP from Partnerships, PR with the GDP of Private Companies and PU with the GDP of Public Companies. GDP reflect the total GDP.
The middle definition Here includes only the principal occupation Sole Proprietorships as family business. Astrachan and Shanker (2003) believe that without the Sole Proprietorship being the owner’s principal occupation, the owner can never have the intention to keep the firm within the family. They conclude that 65% of all Sole Proprietorships are the principal income of the owner and are therefore a family business.
PFOMiddle = (0.65 * SP + 0.6 * PS + 0.6 * PR + 0.3 * PU) / GDP
The narrow definition This definition requires a payroll, to be able to have more than one family member in management. We can assume that every Public Company has a payroll. Partnerships can be seen as a company of two companies meaning there are at least two people working for the Partnership, which implies that all Partnerships have a payroll. Data shows that 50% of the Sole Proprietorships have a payroll and 65% of the Private Companies.
The second requirement in this definition is that multiple generations are involved. This is only possible when the business survives and reaches the next generation. The papers of Ward (1987) and Andersen (1995) show that 35% of the family businesses survive and reach the second generation. Knowing this means that of the family firms that have a payroll, 35% are defined as a family business under the narrow definition. Gathering all relevant information, the narrow definition leads to 17.5% (100%*50%*35%=17.5%) of the Sole Proprietorships, 21% (60%*100%*35%=21%) of the Partnerships, 13.65% (60%*65%*35%= 13.65%) of Private Companies, and 10.5% (30%*100%*35%= 10.5%) of the Public Companies, be counted as family business.
PFONarrow = (0.175 * SPPR + 0.21 * PSPR + 0.1365 * PRPR + 0.105 * PUPR) / GDP
3.2 Regression Equation
This paper follows the method of Denizer et al. (2002) and the researches on GDP mentioned earlier. In the paper of Denizer et al. (2002) they use an ordinary least square regression to test the influence of financial development on the volatility of GDP growth. For the purpose of this research Percentage Family Owned (PFO) is added to their model.
V
t= λ + β
FINDEVFINDEV
t+ β
PFOPFO
t+ β
2X
t+ ν
tIn this model Vt is the standard deviation of per capita GDP growth, λ is the constant, X includes additional control variables that may help to explain volatility and νt is the residual. PFO is the percentage of GDP contributed by family businesses as calculated in the section above. FINDEV is represented by the base money (M2) as a percentage of the GDP.
The X variable as written in the paper of Denizer et al. (2002) includes the real per capita GPD growth and the level of per capita GDP at time t-1. These variables are included because more developed or faster growing economies exhibit less variability. The mean and standard deviation of inflation are included because these are correlated with output growth variability. The mean and standard deviation of government spending as a share of GDP are taken into account to control for the effects of changes in
government spending on macroeconomic fluctuations. X also includes the standard deviation of exchange rate changes and the degree of openness of the economy included. The last is measured by the ratio of exports plus imports to GDP. These variables got included to control for the effects of external shocks on domestic macroeconomic volatilities and interact on the theory that exchange rate volatility should affect investment, consumption and output volatility differently in economies that are open. Last an index of the type of political regime is included, which may also affect stability due to certain policies.
3.3 Data Description
This research used data of the United States from 1959 until 2011 on yearly frequency. The data on turnover per legal form comes from the Bureau of Economic Analysis of the United States and the data on M2 was extracted from the IMF International Financial Statistics. The rest of the data comes from the Penn World Table 8.1.
Figure I – Growth Volatility line graph
The data shows some interesting phenomena. The growth volatility shows a spike at 1958, this visualizes the recession of 1958. Also the first and second oil shock of 1973 and 1979 are visible. The volatility of the United States balances around 0.5 most of the time.
As visualized in Figure II, Percentage family owned follows a declining trend, which starts with 68% in 1959 and balances around 60% from 1970 until 2011.
0 2 4 6 8 VO L 1940 1960 1980 2000 2020 t
Figure II – Percentage Family Owned scatterplot3
Figure III shows that there is an exponential increase in turnover of both Sole Proprietorships and Partnerships/Companies. In 1959 the turnovers of both groups are similar, but deviate over time. This figure shows that over time Partnerships and Companies contributed more percent of the GDP in comparison to 1959. Because the estimate for Partnerships and Companies are lower than that of Sole Proprietorship, this should lead to a decrease of percentage family owned. This decrease is visible until 1980. From that moment the PFO starts rising and falling, showing that the ratio Sole Proprietorships and Partnerships/Companies is changing after 1980.
Figure III – Turnover of Sole Proprietorships and Partnerships/Companies
3 The data of the broad definition of PFO was used in this graph. The PFO data of the other definitions show the same trend.
.55 .6 .65 .7 .75 PF O b 1940 1960 1980 2000 2020 t 0 2000 4000 6000 8000 1940 1960 1980 2000 2020 t SP PS/CO
Macroeconomic data often shows an upward slope or trend and is not stationary. For a time series regression it is important that all variables are stationary (Brockwell & Davis, 2002). All seasonal and trend components have to be removed for the results to be of any value. Of the variables used in this paper and the GDP per capita and the openness are not stationary 4. Figure II suggests that also PFO is not stationary. The data on these variables suggests a trend5. There are different methods for removing a trend in data and make it stationary. This paper will make use of the Hodrick-Prescott (HP) filter to transform the data (Hodrick & Prescott, 1980). The HP filter is often used in real business cycle literature. It is a two-sided symmetric moving average filter. It will first linearly detrend the data and then apply the HP filter to deviations from trend (Cogley & Nason, 1995). It will measure the difference between the trend value and the real value making the data stationary. The transformation of PFOb to stationary data PFObGAP is visible in Figure IV. The same is applied for GDP per capita and the openness.
Figure IV – Percentage Family Owned scatterplot (HP filter applied)
4 The other data in this paper is already stationary, which means there is no
transformation needed.
5 PFO is downward sloping and GDPPC and OPEN are upward sloping.
-. 0 4 -. 0 2 0 .02 .04 PF O b (G AP) 1940 1960 1980 2000 2020 t
Table I shows the correlation between the variables. The GDP per capita (GDPPC) is correlated with the GDP growth (GROWTH), the financial development (M2GDP) and highly correlated with government spending (GOVMEAN), the openness of a country (OPEN), and the three PFO variables. This leads to collinearity in models where these last variables are used in combination with GDPPC. The three PFO variables are highly correlated.
Table I – Correlation between variables
4. Results
Table II presents the results of the estimation of the equation for the standard deviation of the GDP.
(See Table II)
In general the results in Table II suggest that family businesses are associated with more volatility in GDP growth however the results are not significant. It is surprising that the result might suggest that family businesses have a positive effect on the GDP growth volatility. The literature shows that family businesses should be less volatile. The reason this differs from the proposed hypothesis could have several explanations.
Firstly it could be due to the measurement of Family Businesses. Although this follows the method of Shanker and Astrachan (2005) and Bjuggren et al. (2011), it is possible that this method is not suitable for time series. A possible reason could be the use of static estimators.
A second reason for these results could be that family businesses truly have a positive effect on GDP growth volatility. Family businesses are prone to nepotism and inflexible management. It is likely that when corporate control is passed on to the next generation, the heir is less able than the founder making the company less likely to succeed. The low percentage of family businesses that survive more than one generation indicate that. FINDEV 0.3056 0.3338 0.3458 -0.5956 0.0866 0.2924 0.1200 0.1828 0.8615 -0.3790 -0.6121 1.0000 OPEN -0.4349 -0.4942 -0.3980 0.9642 -0.0133 -0.4222 -0.1097 0.1503 -0.7806 -0.0824 1.0000 GOVSTDEV 0.0764 0.0706 -0.0182 -0.0945 -0.2356 0.0474 -0.1019 -0.4862 -0.2787 1.0000 GOVMEAN 0.4286 0.4718 0.4602 -0.7518 0.0513 0.3133 0.1219 0.0392 1.0000 INFSTDEV -0.2435 -0.2656 -0.1959 0.1027 0.4001 -0.1490 0.3810 1.0000 INFMEAN -0.2965 -0.2514 -0.3298 -0.2895 0.4828 0.3436 1.0000 GROWTH 0.1820 0.2500 0.1265 -0.5002 -0.2013 1.0000 VOL -0.1051 -0.1036 -0.0940 -0.1029 1.0000 GDPPC -0.4405 -0.5045 -0.3919 1.0000 PFOn 0.9897 0.9715 1.0000 PFOm 0.9899 1.0000 PFOb 1.0000 PFOb PFOm PFOn GDPPC VOL GROWTH INFMEAN INFSTDEV GOVMEAN GOVSTDEV OPEN FINDEV
Since none of the estimators for PFO is significant, a third reason could be that there is no effect of family businesses on the growth volatility of a country. However, as visible in Table II the models with PFO (1, 2, 3) do have a higher r-square than the model without PFO (4). This indicates that the models with PFO predict the volatility better then the models without PFO, suggesting that PFO is relevant for the prediction of GDP growth volatility.
Besides PFO it is interesting that most of the other estimates are not significant but have the same sign as assumed in the literature. Only INFMEAN is slightly significant in al four models. The positive sign of the estimator follows previous research stating that inflation is correlated with output growth volatility when the aggregate supply curve is upward sloping (Erceg, Henderson & Levin, 2000). that economies with less fluctuation grow faster (Denizer et al., 2002). Table II shows a positive estimator for OPEN. This is, although not significant, in line with the research of Easterly, Islam & Stiglitz (2000). They found that states were more volatile when they were more open. This is because when a state is more open the state is more prone to foreign shocks making the area that can affect the country bigger. It is also interesting that GROWTH and GDPPC have a negative estimator. Economic theory shows that more GPD growth leads to more GDP growth volatility. However these results imply the opposite. Easterly, Islam and Stiglitz (2000) also found a negative estimator for GDP growth. There is no real reason for this estimator to be negative. The HP filter could be a reason for the flipping of the sign. There could however be a reason for GDPPC to have a negative estimator. The bigger a country the more likely it is able to absorb a shock. GDPPC is highly correlated with OPEN, meaning that the bigger the GDPPC the bigger the OPEN. This could lead to a more diversified economy, which leads to less vulnerable and volatile country.
The different models make a comparison possible. Comparing the models makes it visible that there is not much difference in estimators between the different definitions of PFO. The middle definition has a slightly lower p-value but they are all far from significant. There is no clear conclusion to be made about which estimator is the best.
Another interesting finding found by comparing the models is that the model (4) without PFO is relatively similar to the models (1, 2 & 3) with PFO. The two main differences are the effect of the GROWTH and the effect of OPEN. The estimated effect of GROWTH in model 4, is smaller then the estimated effect in the other models. This indicates that when family businesses are included in the model a country’s GDP growth volatility is estimated to be more influenced by GDP growth. The other remarkable estimator is the estimator for openness. This estimated effect of OPEN, the openness of a country, on the volatility of a country is bigger in the model without PFO than in de models with PFO. This implies that family businesses decrease the effect of openness on the GDP growth volatility. A reason for this is that family businesses are more nationally orientated. The decrease of the impact of OPEN in the models with PFO suggests that countries are less prone to foreign shocks.
The estimates in this paper are not significant. This makes it hard to interpret these findings and possibly make the estimates biased. However the non-significant estimators corresponds with the results of Denizer et al. (2002). They found two of the estimates significant. The N of their research was almost 200 and data was gathered from several countries. A reason for this research to not find significant estimates could be
because the low N and only one country. It was not possible to make the research into a panel data analysis because the data was not available.
To conclude, the results stated in this section state no clear evidence on what definition of PFO is better and show no significant effect of the percentage of GDP contributed by family businesses on the GDP growth volatility of a country. If there would be an effect these results suggest that family businesses have a positive influence on the GDP growth volatility and make a country’s GDP growth more volatile.
Table II - Estimates on Volatility + p<0.1, * p<0.05, ** p<0.01 p-values in parentheses R-sq 0.374 0.374 0.372 0.369 N 53 53 53 53 (0.323) (0.314) (0.322) (0.296) _cons 1.465 1.389 1.432 1.383 (0.425) (0.417) (0.438) (0.445) FINDEV -3.524 -3.335 -3.339 -2.964 (0.535) (0.522) (0.523) (0.464) OPENGAP 1.227 1.262 1.267 1.406 (0.384) (0.375) (0.380) (0.354) GOVSTDEV -61.85 -58.99 -60.53 -59.06 (0.395) (0.385) (0.420) (0.484) GOVMEAN 11.53 11.20 10.77 8.962 (0.384) (0.370) (0.386) (0.377) INFSTDEV 0.154 0.160 0.152 0.155 (0.066) (0.064) (0.070) (0.073) INFMEAN 0.156+ 0.157+ 0.153+ 0.143+ (0.198) (0.191) (0.226) (0.460) GROWTH -0.0889 -0.0913 -0.0837 -0.0668 (0.163) (0.159) (0.176) (0.144) GDPPCGAP -10.60 -10.78 -10.50 -9.807 (0.761) PFOnGAP 3.666 (0.664) PFOmGAP 4.859 (0.699) PFObGAP 4.419 VOL VOL VOL VOL (1) (2) (3) (4)
5. Conclusion
There are generally three things that can be concluded from this paper. The first
conclusion and answer on the general question in this paper is that, based on the data of the United States from 1959 till 2011, there are at this moment no reasons to think that family businesses have an effect on the GDP growth volatility in the United States. Family businesses might bring some advantages to a country’s stability. They are more risk averse and their strategy is based on long-term preservation of the business to be able to pass it to next generation. However this mentality also leads to less innovation and limits the possibility to find a good heir for the business, which makes family businesses more volatile.
The second conclusion is that there does not seems to be a big difference between the three definitions of PFO when testing on GDP growth volatility. The most convincing reason for this is the use of the static estimators, which is hard to avoid when estimating a variable based on time series data.
The third conclusion is that this paper has shown that models including
Percentage Family Owned are better at predicting GDP growth volatility than the model tested without this variable.
These conclusions imply that there are no practical implications for governments trying to stabilize their economy and that there should not be extra governmental policies on family businesses. However there is more research needed to find the precise effect of family businesses on the GDP growth volatility.
There is however more research needed to know the real effect of family
businesses on the GDP growth volatility. To make this possible there needs to be better data on family businesses. The start for better data is to acknowledge family businesses as a separate business form. This will lead to new studies focused on the macro-economic effect of family businesses.
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