Dependent Variable. Given the hypothesis that CDF deployment increases the likelihood of COIN victory, COIN outcome is the dependent variable. Drawing on Stam (1996) and Xxxxx and Xxxxxx (2009), the variable can take on three discrete values: win, draw, or lose. Operationally, insurgencies end either as a result of a military defeat or by way of a negotiated settlement. In the event of a military outcome, an incumbent is considered to have lost if its armed forces are destroyed or evicted from the capital. Likewise, an incumbent is considered victorious if the insurgent organization is destroyed or substantially weakened. Whenever a decisive military outcome is not observed at an insurgency’s end, either a compromise settlement has been negotiated or one of the participants has conceded all rival demands. An insurgency is considered to have ended in a draw if the warring parties reached a compromise agreement in which the government conceded some, though not all, insurgent demands. The second possibility is that an insurgent gives up its armed struggle having received no government concessions or hostilities end because the incumbent concedes all or nearly all rebel demands. In such cases, the outcome is coded as a victory and defeat, respectively. In coding outcomes, I use a narrow definition of draws in that the insurgent movement must obtain either a power- sharing or autonomous arrangement. The possibility exists that a rebel organization’s initial aim is limited to a power-sharing or autonomous arrangement, the subsequent achievement of which may strike some observers as a defeat for the incumbent rather than a draw. However, given that bargaining demands are endogenous to estimates of relative power and resolve, coding such cases as victories would get at a different conceptual definition—the accuracy of belligerents’ estimates. In coding the dependent variable, I rely primarily on the Correlates of War Project’s (version 4.0) descriptions of each case (Xxxxxxx and Xxxxxx 2010). I subse- quently substantiate each coding using a number of other reference materials, includ- ing Xxxxxxxx and Xxxxxxx’x (2005) Encyclopedia of Wars, Xxxxxxxxxx’x (2002) Warfare and Armed Conflicts, Xxxxxxx’x (2001) and Ackermann, Xxxxxxxx, Xxxxx, Xxxxxx and Xxxxxxxx’x (2008) encyclopedias of world history, Xxxx’x (2007) Dictionary of Wars, as well as a large number of individual case histories. A codebook documenting the coding decision for each observation is included in Appendix A. As far ...
Dependent Variable. Traditionally, studies in this vein would use the Political Terror Scale (PTS) (Wood and Xxxxxx 0000), Xxxxxxxxxxx and Xxxxxxxx (CIRI) (Cingranelli and Xxxxxxxx 1999), or Xxxxxx (Xxxxxx 2014) measures for human rights, all of which cover physical integrity rights – those rights generally conceived to include freedom from torture, extrajudicial killings, and political imprisonment. Each method naturally has its own set of drawbacks. Aside from the fact that each measure is based on yearly data, the PTS and CIRI are compiled from the US State De- partment’s Human Rights reports, which could be biased in their own ways and comment more favorably on the human rights performance of their allies or partners under IMF deals (Qian and Yanagizawa 2009). This paper will also introduce another way to measure human rights (more specifically, repression), as well as protests, by incorporating the data produced by ACLED – an initiative that produces high-quality, disaggregated and geo-located data on battles, conflicts, protests, riots, remote violence, and violence against civilians (Raleigh et al. 2010). These data also allow the user to disaggregate by perpetrator and target. For the purposes of this paper, this will allow me to examine specifically the actors and perpetrators of interest – in this case, the frequency with which the state committed human rights violations against civilians, since this is the mechanism suggested by previous work (Abouharb and Cingranelli 2006; 2009). ACLED will also be useful because it will allow me to use a more finely-tuned measure for examining the effects of loan implementation. Specifically, I will be able to see whether the month before and the months during implementation of the program were marked by significantly more unrest and protests (as predicted) and resulted in the predicted backlash by the repressive state. If IFIs induce economic stress or grievance indirectly, however, these effects may not be limited to protests just directed at these institutions. This is why I focus on protest and repression generally. People may take to the streets to protest unemployment, an economic downturn, wealth concentration, globalization, or a host of other issues when programs put forth by international financial institutions could be (purposefully or inadvertently) at the root of their collective problems. I do my best to control for these other underlying economic factors. There are certainly downsides to using ACLED as a measure ...
Dependent Variable. According to the study of previous literature, the structured air travel demand can be modeled as a function of Geo-economic and service-related characteristics of the Møre og Romsdal county. Such as the population of each airport within the located city, the travel frequency each airport offered, the size of airport etc. Moreover, the right side of this model includes those two types of variables that respectively influence the quantity of air travel demand for the four airports. Usually typical variables as the causing factors for air travel demand usually are population, income of passengers, employment of cities (metropolitan areas) and the distance between departure airport and destination airport. The air travel demand variation of alternatives in a city is mainly explained by affect factors’ characteristics of these alternatives. In other words, because of airport competition, air travel demand for an airport depends on the attractiveness of this airport’s Characteristics. Those characteristics can also affect the attractiveness to an airport compared to the competitor airport. Does the ground access distance and accesses time be beneficial and attractive factors for air travel demand? This will be examined in our study. Also there are some factors which affect the air travel demand commonly however not significant to the demand for a single airport city. Meanwhile there are some specific factors which can affect the air travel demand for a single airport city. According to the previous study of air travel behavior travel information in our dataset, we can observe that traveler behavior has a great impact on air travel demand and different regions have its own traveler behavior characteristic. Different Individuals use different decision-making process when choosing the best suited travel mode (Chou, 1992). In our study, the final demand function for the four airports should define the relationship between the travel frequency and certain factors. We will examine all the variables which are related to the travel demand and find the most significant variables. In other words, we will use the SPSS to examine the affecting factors’ significant level and do the adjustment for the model to get the most appropriate model for the air travel demand in Møre og Romsdal county.
Dependent Variable. The dependent variables were measured using a 113-item questionnaire, measuring a total of 6 constructs. The questionnaire was divided into three consecutive blocks: appropriateness-rating of the robot-group scenes, cultural- and personality background questions, and general demographic questions. In the first block, participants were asked to rate the 37 'robot approaches a family' scenes which have been described in the previous section. To avoid order-effects, all scenes were randomized. Participants were provided with the instruction: “The robot approached the family and has come to a halt between particular family members at a particular distance. Now it will interact with them”, and asked to indicate on a 7-point Likert scale whether or not 1 xxxx://xxxxx.xx//ergonomics/ Figure 4: F-Formation used. Dark grey indicates possible location of the robot. Grey: intimate zone, light grey: personal space zone. Participants standing in a circle with a diameter of 122 cm. Under review the position of the robot was considered appropriate. Another four items were included in this block to measure how participants themselves would approach the family. Two items were included to check the approach position- and distance manipulation. A final item was included in which we asked participants if they could indicate where they thought the family they had seen in the situations originated from. The second block of the questionnaire consisted of a series of validated scales measuring four dependent variables. Participants’ general attitude towards robot was measured by the Negative Attitude Towards Robots scale, a 14-item 7- point Likert scale. One way to explain cultural differences is by measuring individual vs. group self-representations. This was operationalized using 7 items, by Xxxxxx & Xxxx [1], and analyzed in a similar way as Xxxx et al. [22]. An indication of whether participants were members of a high- contact, or low-contact culture was assessed by measuring closeness. Five items from the IPROX (iconic proximity) questionnaire were used [7]. The final construct in this block was personality. We measured the Big Five personality traits using the 20-item Mini-IPIP scale [2]. The final block of questions included demographic questions like gender, age, nationality, and level of education. Social-demographic questions like nationality of ancestors, marital status and children were also included. Participants Participants were recruited from three different count...
Dependent Variable. Language Regime Choice The dependent variable is trichotomous. Specifically, it identifies whether the educational language regime of country j in year k is power-concentrating, power-sharing, or power-neutralizing. The variable Language Regime Choice is coded as power-concentrating (assigned a value of 0) if the education system is monolingual in the dominant group language; power-sharing (assigned a value of 1) if multilingual; and power-neutralizing (assigned a value of 2) if monolingual in a lingua franca. As with the dependent variable in the previous section, the data is from a mix of sources, xxxx Xxxxxxx (2007) providing the initial information for most entries. Since language regimes are political institutions, a lag is to be expected between the political shock and the actual choice of language regime. Some lags are short. In Brunei, for example, the National Program of Education was passed within one year of independence 26 For these latter cases, I consider this second round of political independence as a new observation. Anecdotal evidence suggests that even within the same country, dominant groups behave differently during the two periods given a different likelihood of remaining in power (see for exaxxxx, Raun 2001).
Dependent Variable. The measurement of compliance is the primary challenge of this analysis. In strict legal terms, the Annex I ratifiers of the Protocol are considered to be in compliance as long as (i) they make demonstrable progress by 2005 and (ii) their emissions meet the targets averaged over the five year period of 2008-201210. It is hypothetically possible that all ratifiers might achieve their five year total commitments only in that final year, in which case, they would all be considered in compliance regardless of any over-quota emissions produced during the preceding years. However, demonstrable progress is required by 2005 and, in any event, measures taken by governments to reduce emissions take years to produce significant reductions, so it is
Dependent Variable. The Center for Epidemiologic Studies for depression (CES-D) scale was used to measure depressive symptoms. CES-D measures depressive symptoms on a scale of 0 to 60. It categorizes depressive symptoms as absent (<16 CES-D), mild (16-21), moderate (21-25) and severe (26-60) (Xxxxxxx et al., 2016). We used greater than 16 to define depressive symptoms. There are 20 items in total, the participants’ answer for each item ranged from 0 to
Dependent Variable. English language learning. 19Calderón Xxxx, Xxxx. Los fundamentos curriculares en la enseñanza del inglés a distancia: un acercamiento de la teoría y de la reflexión de la práctica educativa. Revista Educación. Edición: 2009: No 29. San Xxxx.
Dependent Variable binary variable on the decision to integrate Our dependent variable is a firm’s propensity to transact an input in a particular source country within its boundaries. It is the outcome of the firm’s binary decision on whether to integrate or outsource the supply of the input from a given country. We define inputs at the 6-digit level product groups of the CN classification, which is in full compliance with the 6-digit Harmonized system (hereinafter HS) code. Transaction-level trade data provide us with information on the complete set of inputs sourced from abroad by a firm, while FDI data give the location of its dependent establishments. However, as most related studies, we do not have information on the extent to which the firm’s trade flows involve its dependent establishments (‘intra-firm trade’). Antr`as and Chor (2013) tackle this issue by exploiting available industry-level intra-firm trade data and using the share of intra-firm imports in total inputs as an indication of the propensity to transact a particular input within firm boundaries. The follow-up study by Xxxxxx et al. (2019) proposes an alternative solution based on the activities of establishments linked via ownership ties (net of subsidiaries of the ‘global ultimate owner’). While the former approach lacks information on the identity (activity) of the individual buyer, the latter does not use trade data and relies instead on input-output (‘I-O’) tables to determine the sets of integrated and outsourced inputs without information on their source countries. We build on the latter approach in defining as traded ‘intra-firm’ or ‘integrated’ the inputs a parent firm imports from an affiliate’s host country that are classified under the core activity of the affiliate, but we also exploit our detailed data to obtain the whole set of import transactions from different source countries. More specifically, inputs that a firm imports from its affiliate’s host country, if classified under the core activity of the affiliate at the 4-digit industry level, are regarded as ‘integrated’, whereas all other inputs that the firm imports from that country are considered as ‘outsourced’. Doing this also accounts for the fact that a firm may engage in both integration and outsourcing in a given country. If a firm has no FDI in a country, all imports coming from that country are regarded as ‘outsourced’. This allows us to estimate the regression model at the most disaggregated firm-input-country level. As we will...
