Abstract

The paper examines whether common ownership along supply chains (i.e., vertical shareholding) is associated with better firm performance during supply chain shock. We find that supplier-customer dyads with vertical shareholding report higher combined monthly returns since the outbreak of the COVID-19 pandemic. The dedicated common investors have the most positive effects, followed by the common quasi-indexers.

Introduction

As overlapping institutional ownership becomes more prevalent, it has been heatedly debated whether it plays a positive role in market competition and corporate governance (e.g., Azar et al., 2018; Edmans et al., 2019; Schmalz 2021; Koch et al., 2021; Bebchuk and Hirst 2022). Most studies focus on its effects on industry rivals (i.e., horizontal shareholding). Compared to industry rivals, firms along the supply chain network have more collaborative and interdependent relationships. Hence, overlapping institutional ownership of supplier-customer dyads (i.e., vertical shareholding)1 may have distinct effects, especially in response to disruptive events that could jeopardize collaboration.

We utilize the COVID-19 pandemic as an exogenous supply chain shock to investigate whether vertical shareholding is associated with stock performance. Supply chain disruptions can be devastating to both upstream and downstream firms (e.g., Carvalho et al., 2021) and significantly exacerbate interfirm conflicts as opportunism and information asymmetry increase. Economic theories predict that cross-ownership can address inherent interfirm conflicts, such as the holdup problem (e.g., Klein et al., 1978; Grossman and Hart 1986). Institutional investors simultaneously holding shares of economically connected firms have incentives to prevent either contracting party from making a move that will hurt the value of their portfolios. Hence, they can influence the management of both upstream and downstream firms and thereby encourage information sharing and balance the bargaining power in supply chain contract renegotiations. For example, institutional investors can influence the supplier firms to prioritize the co-owned customer firms during supply shortages (Sheffi 2020).

Conclusion

We show that supply chain dyads with vertical shareholding have significantly better aggregate stock market performance since the COVID-19 pandemic. The effects vary with the incentives of common institutional shareholders. To our knowledge, we are one of the first empirical studies to show that vertical shareholding mitigates negative consequences of supply chain shocks. Our evidence sheds light on the debates over the roles common institutional investors play in interfirm relationships and supply chain risk management.

Highlights

Abstract

Keywords

Introduction

Disciplinary differences in the theoretical approach to IPV

Differences in expertise and objectives in conducting impact evaluations

Differences in analytic approaches, data sources, and indicator construction

Safety and ethical considerations

Forging a cross-disciplinary path

Funding and acknowledgements

CRediT authorship contribution statement

Declaration of competing interest

References

Abstract

Research on intimate partner violence (IPV) has progressed in the last decade in the fields of public health and economics, with under-explored potential for cross-fertilisation. We examine the theoretical perspectives and methodological approaches that each discipline uses to conceptualise and study IPV and offer a perspective on their relative advantages. Public health takes a broad theoretical perspective anchored in the socio-ecological framework, considering multiple and synergistic drivers of IPV, while economics focuses on bargaining models which highlight individual power and factors that shape this power. These perspectives shape empirical work, with public health examining multi-faceted interventions, risk and mediating factors, while economics focuses on causal modelling of specific economic and institutional factors and economic-based interventions. The disciplines also have differing views on measurement and ethics in primary research. We argue that efforts to understand and address IPV would benefit if the two disciplines collaborated more closely and combined the best traditions of both fields.

Introduction

Intimate partner violence (IPV) is a major global public health challenge with one in three women ever experiencing lifetime physical and/or sexual IPV (World Health Organization, 2021). In addition to causing physical injury and adverse health outcomes (Bacchus, Ranganathan, Watts, & Devries, 2018), IPV has been associated with adverse social and economic outcomes for women, households and communities (Heise, 2012). In the last decade, significant theoretical and empirical advances have been made (Wu, Chen, Fang, & Wan, 2020) that improve understanding of global IPV prevalence (Devries et al., 2013), underlying drivers (Yakubovich et al., 2018) and prevention (Ellsberg et al., 2015). IPV research has historically been undertaken by public health or feminist scholars, conceptualising IPV as a phenomenon driven by complex socio-ecological factors and using mixed methodologies (Heise, 1998; Jewkes, 2002). In particular, public health researchers often study complex interventions to shift individual attitudes, alongside factors at the community level (e.g., social norms condoning male authority) (Abramsky et al., 2014) or at the interpersonal level (e.g., poor communication, conflict negotiation skills; and alcohol abuse) (Dunkle, Stern, Chatterji, & Heise, 2020). However, IPV is increasingly being studied by diverse disciplines, including economics.

Economic literature on IPV emerged in the early 1990s, with an initial focus on developing and empirically testing theoretical models of the family that conceptualised how interactions between partners could lead to IPV and how economic or institutional factors affect these interactions and hence IPV (Farmer & Tiefenthaler, 1997; Tauchen, Witte, & Long, 1991). These inquiries have gradually expanded to evaluations of how diverse policies, institutions, and economic factors affect IPV, such as employment and wages (Aizer, 2010; Anderberg, Rainer, Wadsworth, & Wilson, 2016), divorce laws (Bowlus & Seitz, 2006), dowry (Bloch & Rao, 2002), or cash and asset transfers (Buller et al., 2018), with a continuing focus on the empirical estimation of quantitative relationships.

1- Introduction

2- Price effects of FTAs

3- The estimation results

4- Conclusion

References

Abstract

Most previous studies have examined extensively the economic effects of free trade agreements (FTAs) such as their effects on economic growth, income distribution across industries, price competitiveness for international trade, trade volume, and the price of a commodity, but not very much on inflation rates. This study empirically estimates how much FTAs affect domestic inflation rates. From the cross-country panel data analysis of 34 OECD countries over the period 1980-2014, the expansion of FTAs shows a significant negative effect on the Consumer Price Index inflation, which is more apparent than the traditionally used openness measure. The inflation reduction effect is more significant in countries with a low level of openness than those with a high level of openness.

Introduction

In recent decades, the inflation rates have been low in the OECD countries. In the 1980s, the average inflation rate of OECD countries was 10.6 percent, but it declined to 7.3 percent in the 1990s, 3.1 percent in the 2000s, and 1.8 percent in the 2010s.1 The reasons for this decline in consumer price inflation are likely to include various country-specific structural factors and external factors such as international commodity prices, crude oil prices, and low inflationary pressure from the slowdown of the world economy after the global financial crisis. Some argue that the expansion of low-priced Chinese exports produced with cheap labor may have contributed to the stabilization of global prices.2 Expansion of foreign trade means relatively low-priced imports of overseas goods, which stabilize domestic prices. The institutional change that has made a decisive contribution to the expansion of such trade is the proliferation of free trade agreements (FTAs). The main contents of bilateral or multilateral FTAs include the reduction of tariff rates and the easing or elimination of trade barriers, which could directly reduce the price of imported goods and contribute to the stabilization of consumer prices if imports were expanded. This study attempts to estimate quantitatively how much the expansion of FTAs, separately from the effect of international trade on inflation rates, affected the domestic consumer price inflation rate. The objective of this study is to analyze how much the expansion of FTAs affects the inflation rate of each country based on the panel data analysis of 34 OECD countries for 1980–2014. We examine how much additional FTAs have affected the inflation rate in addition to the effect of international trade on domestic inflation, which has traditionally been focused. This study will not only be an empirical study of one aspect of the benefits of the FTA, but it will also have an implication for predicting the future trend of inflation rate by examining quantitatively how much the inflation rate stabilizes down due to the FTA expansion. This paper is organized as follows. Section 2 examines the previous studies on the economic effects or the price stabilization effects of FTAs.

1- The shrinking upper tail

1-1- Better anchored or better feeling?

References

Abstract

Consumer inflation expectations are highly disperse, with some households reporting very high inflation forecasts. In recent years, disagreement in longer-run inflation expectations has fallen, reflecting compression in the upper part of the distribution. The 75th percentile of the distribution of longer-run inflation forecast has fallen 0.21 percentage points per year since 2012 and is at an all-time low. I show that the decline in long-run inflation expectations at the upper end of the distribution seems to reflect improvement in consumers’ general economic sentiment, rather than stronger anchoring of inflation expectations.

The shrinking upper tail

Panel A of Fig. 1 plots the 25th, 50th, and 75th percentiles of the distribution of longer-run inflation expectations over time. The 75th percentile shows the largest decline in the past few years, having decreased by an average of 0.21 percentage points per year since the inflation targeting announcement in 2012. The median and 25th percentile have fallen by an average of 0.07 and 0.05 percentage points per year, respectively, since 2012.3 Higher portions of the distribution have also fallen—e.g. the 90th percentile by an average of 6 percentage points per year since 2012, from above 60% to around 15%. Another way to see the movement away from extreme high forecasts is in Panel B, which shows the falling shares of forecasts above various thresholds. Panel C shows that the interquartile range (a proxy for disagreement) has declined to an all-time low of 2 percentage points.4 This decline is attributable to the shrinking difference between the 50th and 75th percentiles (π(75, 50)), as the difference between the 25th and 50th percentiles (π(50, 25)) has remained constant. Now π(75, 50) is smaller than π(50, 25), and the difference between mean and median expectations has fallen. As the upper tail of the distribution of inflation expectations is shrinking, so are the disparities in forecasts of various groups of consumers. Most notably, the forecasts of consumers with a college degree were nearly a percentage point lower than those of consumers with only a high school education in 2014, and only 0.15 percentage points lower in 2019.

1- Introduction

2- Theory: Predictions

3- Methods

4- Results

5- Conclusions

References

Abstract

We analyze the effect on inflation of doubling the minimum wage and cutting the value-added tax in half. Annual inflation decreased by 1.8 percentage points, suggesting that the minimum wage increase had a limited or even null effect on prices.

Introduction

Mexico elected a new government in the summer of 2018, in a context of high levels of violence in municipalities on the U.S. border and throughout the country, and of growing antiimmigration rhetoric in the U.S. For these reasons, and also because of concerns about migration from Central American countries, the newly-elected president of Mexico, Andrés Manuel López Obrador, announced his intention to generate better working conditions at the border to reduce incentives to cross to the U.S. The new government implemented a set of place-based policies that went into effect in the bordering municipalities beginning in January 2019. These included doubling the nominal minimum wage, halving the value-added tax (VAT) from 16 to 8 percent, reducing the corporate income tax by one-third, and adjusting the price of energy goods to make them comparable to those in bordering counties in the U.S. This set of policies is one of the boldest recent place-based policies in the world. In this paper, we restrict our analysis to the effects of these policies on inflation, taking advantage of their regional variation to estimate their joint effect. In particular, by using a rich dataset that includes the prices of goods with and without VAT in different cities throughout Mexico, we are able to shed some light on the magnitude of the effect on prices of doubling the minimum wage.

1- Introduction

2- Data and methodology

3- Empirical analysis

4- Robustness check

5- Concluding remarks

References

Abstract 

This paper investigates return and cash flow predictability via the decomposition of VIX. The squared VIX index is decomposed into expected return variations (ERV) and variance risk premium (VRP). Without imposing a strong assumption on the dynamics of the return variations, I examine the predictability via the generalized method of moments (GMM) approach with appropriately chosen instruments. Empirical analysis shows the short-term return predictability of VRP and the short- and long-term cash flow predictability of ERV.

Concluding remarks

This paper shows the short-term return predictability of VRP and the short- and long-term cash flow predictability of ERV. As argued by Bekaert and Hoerova (2014), the return predictability of VRP can potentially be explained in terms of the ‘‘habit’’ advocated by Campbell and Cochrane (1999). Their habit-induced counter-cyclical changes in risk aversion generate variations in risk premiums. On the other hand, one candidate theory for the cash flow predictability of ERV may be the long-run risk model (e.g., Bansal and Yaron, 2004). Economic uncertainty, which can be proxied by ERV, is a key variable in the long-run risk model. In a version of the model in Bollerslev et al. (2015), innovations in time-varying volatility and the long-run risk factor are correlated; thus, the long-run risk factor can have a persistent effect on dividend growth. This specification may drive the long-term cash flow predictability of ERV. It would be interesting to develop a version of the long-run risk model that leads to long-run cash flow predictability of economic uncertainty in future research.

1- Introduction

2- Epistemological issues

3- An exploratory study

4- Implications

References

Abstract

This research note raises the question of the lack of critical appraisal of the asset light model. Its purpose is to trigger an in-depth exploration of the determinants of performance of the implementation of such decision. To explore our argument, we used a longitudinal data design that combines both cross sections and time series to examine the effects of the asset light model on share returns, EBITDA and ROE of six leading U.S corporations over a 16-year period. We found that the model had no impact on financial performance. Our purpose is to a trigger debate within academia and practitioners on when and how the asset light model is a valid option. As well as which type of contract to rely on and how to build a differentiating strategy when implementing the asset light model.

Introduction

This research note raises the question of the lack of critical appraisal of the asset light model. Its purpose is to trigger an in-depth exploration of the determinants of performance of the implementation of such a model. The decision to divest properties and specialize in operations (i.e. the asset light model) has become a widespread practice amongst hotel corporations (Fig. 1) and is often presented as the best fit for the organization (The Economist, 2013; Nair, 2014; Host and Marriott 1994 annual reports). This practice, whose advantages have been widely reported throughout academia, is becoming the new norm (Fig. 1). The near universal acceptance of this model makes the dubious assumption that an asset-light strategy is the best fit, in terms of performance, for every organization in the hospitality industry. This assumption contradicts the fundamental principles of strategy, whereby unique choices (of competencies and positioning) are what drives outperformance (Wernerfelt, 1984, Porter, 1979). Blindly accepting the asset light model as the best option for lodging corporations leads practitioners and academia to overlook strategy fundamentals and ignore the consequences of the model on long-term performance. This blind spot in our approach to the issue prevents us from addressing key questions such as, for instance, how to position the company vis-à-vis differentiation. Unfortunately, the lack of critical appraisal of the model is an obstacle to acquiring this knowledge. Since it is accepted as the best choice for all companies, the asset light model and its effects on long-term performance is the subject of very few papers in tourism and hospitality research. These studies, with the exception of one (Low et al., 2015), converge to confirm that the model is beneficial to corporate performance, which begs the question: How can just one divestment model be beneficial to the performance of all companies? Not to mention that companies implement it to different degrees, so what are the contingent variables which moderate the effects? Why are some companies more successful in generating superior performance than others after the implementation of the asset light choice? It appears that academia and executives are wearing blinders when it comes to this model. We hope that this research note will trigger more discussion for a more critical view of the asset light decision with a view to providing valuable insights for both academia and corporate practices.

1- Introduction

2- Data and estimation strategy

3- Results

4- Conclusion

References

Abstract

This study examines the role of high frequency trading in price discovery and efficiency in the FTSE100 index tick changes. We find that there is no random walk when investors extract information at a millisecond to a second.

Introduction

In the last 30 years fast paced technological advancements and their rapid uptake in equity markets have markedly increased the importance of high frequency trading (HFT). Sahalia and Saglam (2014) show that HFT represents between 40 and 70% of the trading volume in the US and slightly less in the Canadian, European and Australian markets. As HFT may prevent all market participants from having equal access to information, there have been ongoing debates regarding its impact on making stock markets inefficient.1 Theoretical arguments and empirical evidence indicate many supposed benefits of HFT and algorithmic trading (AT), such as decrease in bid ask spread and transaction cost for equity investors (Jones, 2013; O’Hara, 2015), price discovery and efficiency (Hendershott, 2012), quality and liquidity Linton and Mahmoodzadeh (2018). In contrast, evidence also shows that HFT might contribute negatively to the development of stock markets; for instance, the May 6, 2010 flash clash and the October 15, 2014 bond market flash. This suggests that HFT may play a role in price instability and market volatility. It is therefore important to have a better understanding of the role of HFT with regard to price discovery and efficiency.

This paper finds that in portfolio choice where reference point arises endogenously in personal equilibria, investors behave as if they had a concave probability weighting function. This finding establishes a link between the reference-dependent utility and the rank-dependent utility theories.

Introduction

In a stimulating paper, Kőszegi and Rabin (2007) (KR, henceforth) explored how individuals with expectation-based referencedependent preferences make a risky choice. In their model, individuals care about consumption utility as well as gain–loss utility (i.e., utility over deviations from the reference), and the reference is the full distribution of the payoff reflecting individuals’ expectations. KR provide a solution framework for the formation of expectations-based reference, in which the individual knows exactly how he or she will behave in any future contingency and his or her reference point reflects this actual behavior. KR’s framework has inspired numerous applications. Among others, Heidhues and Kőszegi (2008) use this framework to study the Salop price competition; Herweg et al. (2010) apply it to re-design the employee compensation contracts; Karle and Peitz (2014) employ it to study the firm competition with asymmetric information regarding consumer tastes. In financial markets, Pagel (2016) explores the assetpricing implications of KR’s solution in a Lucas-tree model with dynamic asset allocations, while Pagel (2018) uses KR’s framework to solve a life-cycle portfolio choice problem in which the investor experiences loss-averse utility over news. Despite the existing applications, the implications of KR’s framework on optimal portfolio choice are not fully investigated. Pagel (2016, 2018) use KR’s framework in intermediate steps to solve the portfolio choice problems. However, she obtained the unique solution only for power and log utility functions under lognormal distributions for risky assets. It is not clear whether the solution would be unique for all concave utility functions and all continuous distributions, and how to characterize the optimal portfolio weights in KR’s framework in the more general setting. The purpose of our paper is to address these questions. Specifically, we offer an explicit characterization of the solution to the portfolio choice problem in KR’s framework under a general setting. KR introduces two specific solution concepts. One is the ‘‘unacclimating personal equilibrium’’ (UPE, henceforth), defined for the case where the choice is made based upon the reference, and equilibrium is achieved when the optimal choice coincides exactly with the reference. The other one is the ‘‘choiceacclimating personal equilibrium’’ (CPE, henceforth), defined for the case where the individual first sets the choice as the reference, and then optimizes over the choices. As KR argued, UPE arises in a context where individuals expect a choice only if they are willing to follow it through, while CPE applies in a context where individuals commit to a choice before outcomes occur. We show that investors in both UPE and CPE behave as if they had a concave probability weighting function, as axiomatized by Yaari (1987). This characterization is interesting because it establishes an equivalence between nonstandard utility under correct beliefs and standard utility under distorted beliefs. The equivalent concave probability weighting function implies that the choice in UPE is unique. Only with uniqueness, we are able to determine the UPE by virtue of the first-order condition and further translate this condition into a rank-dependent structure. This result is in contrast to the previous finding of multiple UPE choices under a discrete choice set, as shown in KR.1 The change in the property of UPE is driven by the change in the structure of the choice set: when the choice set contains only discrete strategies, the adjustment of reference in the UPE is likely to get stuck on some choice that is not globally optimal, yielding multiple UPE. In contrast, when the choice set contains the continuum of all possible strategies (as in the context of portfolio choice), the suboptimal equilibria are easily disturbed, and the UPE converges to a unique strategy. This result thus enriches our understanding on the implications of UPE.

In this paper, we show that the relation between expected investment and future stock returns (i.e., the expected investment-return relation) is negative and inconsistent with the multiperiod q theory. Further analysis reveals that the expected investment change measure of Hou et al. (2018a) is a poor proxy for future investment because of the mismatch of investment characteristics and the incorrect constraint imposed on the regression.

Introduction

In a multiperiod q theory framework, the first principle of investment implies that the investment return can be expressed as R I i,t+1 = (1 − τt+1) Πi,t+1 Ai,t+1 + 1 2 (1 − τt+1)at+1( Ii,t+1 Ai,t+1 ) 2 + δi,t+1τt+1 + (1 − δi,t+1) [ 1 + (1 − τt+1)at+1( Ii,t+1 Ai,t+1 ) ] 1 + (1 − τt)at( Ii,t Ai,t ) , (1) where RI i,t is the investment return; Πi,t, Ii,t, and Ai,t are the operating profits, investment, and productive assets of firm i in time t; δi,t is the exogenous depreciation rate of capital and is firmspecific and time-varying, τt is the corporate tax rate and is time-varying, and at > 0.3 This equation predicts that, all else equal, stocks with high expected investment should earn higher expected returns than stocks with low expected investment. Hou et al. (2018a,b) argue that decomposing Equation (1) leads to two different components: a “dividend yield”, (1−τt+1)Πi,t+1/Ai,t+1+(1/2)(1−τt+1)at+1(Ii,t+1/Ai,t+1) 2+δi,t+1τt+1 1+at(Ii,t/Ai,t) , that represents two welldocumented determinants of the expected returns (the profitability and investment-to-assets (I/A) variables) and a “capital gain”, (1−δi,t+1)[1+(1−τt+1)at+1(Ii,t+1/Ai,t+1)] 1+at(Ii,t/Ai,t) , that represents a third return determinant. They also find that their I/A growth proxy positively relates to future stock returns. In this paper, we focus on the expected investment-return relation, which is directly predicted by the multiperiod q theory, and call attention to the fact that Hou et al.’s decomposition is misleading. By construction, the I/A growth component contains two different anomalies in firm characteristics (the expected and current investment anomalies), where the latter is the same as that in the “dividend yield” component. Therefore, the I/A growth component is not a single but rather a composite anomaly that combines the well-documented asset growth effect of Cooper et al. (2008) with a new expected investment effect.4 In addition, although the results of Hou et al. (2018a,b) appear to be consistent with the multiperiod q theory, they tell us little about whether the documented positive linkage originates from the positive relation between the expected investment and expected returns, as suggested by the q theory, the inverse of the asset growth effect that is difficult to isolate with their proxy, or the mix of both. More importantly, by using the “capital gain” component as a determinant, Hou et al. (2018a,b) simply assume that the relation between expected investment and future stock returns is positive and consistent with the multiperiod q theory, which relies on certain theoretical assumptions that may or may not be valid, whereas the expected investment-return relation remains an open question. As such, we first construct a direct expected investment measure, denoted by Et [I/A], and form an implied expected investment component, denoted by Et [εI/A], by adding current I/A back to the Hou et al.’s (2018a) expected I/A changes (Et [d 1 I/A]), and then conduct both parametric and nonparametric tests to assess their predictive power. Our results reveal that Et [d 1 I/A] positively relates to future returns, which appears to be consistent with the multiperiod q theory. In contrast, we find opposite evidence that both Et [εI/A] and Et [I/A] are negative and statistically significant return predictors. The high-minus-low strategy earns -131 basis points per month on average with a Newey-West t-statistic of -7.977 and a Sharpe ratio of -1.192, which is greater than the average return of -0.817% (t = -5.210; Sharpe ratio = -0.841) in Et [εI/A]. After controlling for the well-known market, size, book-to-market, momentum, profitability, and investment factors discussed by Carhart (1997), Fama and French (1993, 2015), and Hou et al. (2015), we find that the difference between the returns on the decile portfolios with the highest and lowest Et [I/A] (Et [εI/A]) remains negative and highly significant, and the predictability of Et [I/A] is consistently stronger than that of Et [εI/A]. There are two potential reasons why the two expected investment measures and the expected investment change measure produce contrasting conclusions. First, we find that the return spread between the highest and lowest deciles on Et [d 1 I/A] earns positive profits largely because stocks in the lowest (highest) decile are those with high (low) current I/A instead of future I/A, which suggests that Et [d 1 I/A] is a poor proxy for future investment. In contrast, Et [I/A] is an appropriate proxy because it consistently aligns with firms’ future investment. Stocks with high Et [I/A] (decile 10) are those that invest aggressively in the future (0.264), whereas stocks with low Et [I/A] (decile 1) are those that invest conservatively (0.023). Furthermore, we show that the poor linkage between Et [d 1 I/A] and future investment and the weaker predictability of Et [εI/A] are due to the biased parameter estimates, which is constructed based on the incorrect constraint imposed in the regression. Running a regression using Et [d 1 I/A] as the dependent variable is equivalent to running a constrained regression of Et [I/A] and imposing the constraint that the slope of I/A must be one. However, we show that the estimated I/A slopes from the unconstrained regressions are much less than one, which indicates that the constraint imposed by Hou et al. (2018a) is inappropriate. Running a regression by imposing the incorrect restriction can lead to biased parameter estimates (Greene, 2012), hence biasing the fitted dependent variable and the associated Et [εI/A] derived from it.

The law and economics of default resolution in bankruptcy typically presume a single debtor that owes multiple creditors. But multiple debtors owe multiple creditors within interbank payment networks like Fedwire and CHIPS, as well as in decentralized networks created by complex interbank loan obligations. Nash Bargaining theory has previously been used to justify two popular rules for resolving default of a single debtor. I describe how to extend these rules to financial networks, but find that Nash Bargaining theory does not justify applying those rules to resolve defaults in those networks.

مقاله سال 2016

 مجله امگا – Omega

The development of modern technologies has created gaps between technologies used for civilian life purposes and military applications. Technologies that civilians use in the daily life (e.g., global positioning system and the Internet) are initially developed and applied for the purpose of national defense. In other words, the driving force prompting developments in the emerging industries such as the information and communications technology (ICT) and semi-conductor industries is the development of technologies in military. Overall, this trend leads to the development of dual-use technologies, i.e. technologies for civilians and militaries.

According to an analysis conducted by Benoit [1], a U.S. national defense economist, at least 40 percent of successful military technologies contribute in improving people’s economic wellbeing. Specifically, military technologies are able to enhance a country’s security and the overall national economy [2-4]. By incorporating military technologies into the private industries, countries not only transfer military innovations or inventions to civilian livelihood that create incomes, but also help upgrade technologies in the private industries, which in turn actively participate in the construction of national defense.

Because of limited budgets and resources, governments of various countries would stringently review the use of defense expenditures and actively conduct performance evaluation. For example, the United States announced the Government Performance and Results Act in 1993, stipulating that public departments (including institutions involved in the research and development (R&D) of technologies) must assess their performance; in 1997, Japan approved the Practical Guide to the Evaluation of National R&D, which serves as the basic architecture of technology evaluation, and proposes that technologies should be evaluated based on their level of importance. However, adopting traditional financial reports to measure the efficiency of implementing a dual-use technology development program (DuTDP) will cause evaluators to overlook external benefits. Note that resource allocation and long-term development of the social economy are dependent on the performance of DuTDPs. If the direct benefits for R&D institutions (e.g., academic and technological outcomes and R&D revenues) and indirect benefits for the social economy (e.g., the number of technology transfer, the output value generated from investments, and the number of treasury transfers produced by R&D results) are considered in assessing performance, the actual performance of DuTDPs can be more accurately assessed.