Category: Proposal & Literature Review


The opportunity costs of informal care giving can be measured in two ways. The first is the time forgone (Hassink and Bernard, 2011), namely:

(1)          H=b1C+b2X+u,

where H is the number of hours of paid work; C is the number of hours of informal care (or can be an indicator for whether providing care); X is the vector of control variables. b1 measures the opportunity cost, which is the average amount of time that could be devoted to work for each hour spent on giving care.

The other way to measure opportunity cost of care giving is forgone earnings, namely, the monetary value of informal care giving. It measures the monetary value that could be created from the production process and leisure without giving care (Berg et.al., 2006). That is:

(2)       log(V)=b1C+b2X+u, 

where V is the monetary value. It can be predicted by forgone paid and unpaid work and leisure:

(3)      V=nw+hs+lt,  

where n is hours of foregone paid work, h is hours of foregone unpaid work, l is the hours of foregone leisure; w is wage of paid work, s is the shadow price of unpaid work, and t is the shadow price of leisure. Most of the literature only looks at paid work. If someone is unemployed, then his wage can be imputed by either his reservation wage or the actual wage of similar individuals.

(As an alternative to using opportunity cost, there is another way to measure the monetary value of care giving. It is called the proxy good method (Berg et.al., 2006), which measures the value of the time spent on care giving of a close substitute. For example, the paid work by a professional caregiver.)

In practice, the predicted natural log of net (hourly) earnings can be generated by estimating standard Heckman selection corrected wage models by gender on the full sample of working-age individuals who are not in school (Stratton, 2012). Wages can be modeled as a function of age, education, region of residence, marital status and other community level and province level characteristics (such as unemployment rate).

To examine the casual effects of C in (1) and (2), a few studies have used the care needs as instrumental variables (e.g. see Ettner 1996). The main rationale for this is care need is correlated with hours of provided informal care, but it is not directly correlated with labor supply. Care needs can be measured by the health status (e.g. ADL and IADL, self-reported health) and marital status of the care recipients, the number of other caregivers (e.g. siblings), distance between the homes of caregivers and recipients.

In addition, there could be heterogeneity of opportunity costs for different types of care. They can be characterized in a number of ways. Common classifications are (Hassink and Berg, 2011):

  1. ‘household activities’ like cleaning and shopping;
  2. ‘instrumental activities of daily living’ like handling finances or administration;
  3. ‘activities of daily living’ like bathing or dressing or toileting;
  4. ‘surveillance’, like looking after the care recipient e.g. because this person cannot be left alone without involving safety risks;
  5. ‘medical related tasks’ like support with taking medicines or give injections.

Also they can be divided into co-resident care and nonresident care.

Reference:

Van den Berg, B., Brouwer, W., van Exel, J., Koopmanschap, M., van den Bos, G. a M., & Rutten, F. (2006). Economic Valuation of Informal Care: Lessons from the Application of the Opportunity Costs and Proxy Good Methods. Social Science & Medicine, 62(4), 835–45. doi:10.1016/j.socscimed.2005.06.046.

Stratton, L. S. (2012). The Role of Preferences and Opportunity Costs in Determining the Time Allocated to Housework. The American Economic Review, 102(3), 606–611. Retrieved from http://www.ingentaconnect.com/content/aea/aer/2012/00000102/00000003/art00107

Hassink, W. H. J., & Van den Berg, B. (2011). Time-bound Opportunity Costs of Informal Care: Consequences for Access to Professional Care, Caregiver Support, and Labour Supply Estimates. Social Science & Medicine (1982), 73(10), 1508–16. doi:10.1016/j.socscimed.2011.08.027.

Ettner, S. L. (1996). The Opportunity Costs of Elder Care. Journal of Human Resources, 31(1), 189–205. Retrieved from http://www.jstor.org/stable/146047

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Since the end of the Cold War, international election monitoring has been widely practiced by organizations all over the world. By directly observing the electoral process and evaluating the outcomes, it pressures governments to hold democratic elections. Studies have shown that election monitoring missions can play a positive role in facilitating democracy by deterring fraud, increasing confidence in the electoral process, and serving as third-party mediators (Bjornlund, 2004; Hyde, 2007). Monitors are able to effectively prevent election malpractices such as stuffing the ballot boxes and electoral violence (Hyde, 2007; Daxecker, 2012) on the election-days. However, there is another unnoticeable trend that might undermine the effectiveness of international monitoring, that is, the incumbents may strategically adapt their tactics by shifting away from overt election-day cheating to subtle manipulation, which is less likely to be observed and criticized (Daxecker, 2012) and which might be more harmful for the governance (Simper and Donno, 2012). International election monitoring, a widely accepted tool for democracy promotion, is now facing new challenges.

Not many economists have studied the issue on international election monitoring and manipulation, but it has attracted significant attention from political scientists, among whom there is a growing body of literature in an endeavor to measure or explain the effects of international monitoring.

A large volume of theories focuses on manipulation as a way to win elections. Elections generate public information that affects how the incumbent interacts with other elites and citizens (Little, 2012b), during which manipulation is considered as a hidden action to distort public information (Kuhn, 2012) and reports from international monitors of good reputation can help render election results credible (Magaloni, 2009; Ferson, 2011).

Other theories argue that international benefits such as international investment, foreign aid, preferential trade agreements and military support give electoral autocrats the incentive to invite international observers and manipulate elections to minimize international criticism (Beaulieu and Hyde, 2009; Hyde, 2011). Economic and political stability, transparency, and democratic political institutions are examples of valued and rewarded state-level characteristics.  For countries that are not perceived to possess the characteristic have an increased incentive to modify their behavior in order to gain more international benefits and to signal their commitment to skeptical or indifferent audiences (Hyde, 2011b).

Most of the existing theories adopt the signaling (Hyde, 2011b), decision-theoretic (Lehoucq, 2003; Fearon, 2011) or game-theoretic approach (Little, 2012b), in which inviting monitors are seen as a signal of a government’s commitment of democracy, and the incumbents face the tradeoffs between increasing their probability of winning elections and the chance that they will be captured for committing fraud and the associated decrease in legitimacy, credibility, or aid, so they strategically achieve an equilibrium in which the marginal cost of manipulation equals the expected marginal benefits of international support or winning the election. However, none of the models consider the more general equilibrium in combination of the short-run objective of election victory and long-run objective of development (such as gaining international benefits), or give out a benchmark that the international monitoring can be beneficial or harmful.

Regarding the empirical research, a lot works have been done to show the association between international monitoring, election manipulation and government outcomes, but few directly test the casual relationship. One possible reason is that any cross-national study attempting to examine the domestic effects of international observers would be plagued by endogeneity problems. At the aggregate level it is difficult to distinguish between an election that is clean because of the presence of international observers and an election that would have been clean regardless of their presence. One may use counterfactuals to make a persuasive argument, but demonstrating causality using cross-national evidence would be nearly impossible.

Nevertheless, there are still a handful of papers testing the casual effects of international monitoring on manipulation and a variety of other governance outcomes. For example, Hyde (2010) presented the first field-experimental study in international election monitoring, in which the international observers were randomly assigned in the 2004 Indonesia presidential election. By examining the micro level electoral data, she found that incumbent presidential candidate performed better in internationally-monitored villages and the presence of observers had a measurable effect on the votes cast. Simpser and Donno (2012) tested the effects of high-quality monitoring on governance, in which they instrumented for election monitoring by identifying sources of variation in the likelihood of monitoring. They used regional rate of high-quality election monitoring as IV, because changes in the operation of monitoring organizations are likely to affect the probability of monitoring in the region where the organization specializes, but unlikely to affect governance directly or via other channels. Both of the examples provide evidence of the casual relationship, but since they implicitly rely on the assumption that politicians manipulate the election in order to win more votes, they thus neglect other possible channels through which the international election monitoring can impact the governance.

References

Beaulieu, Emily, and Susan Hyde. 2009. “In the Shadow of Democracy Promotion : Strategic Manipulation, International Observers, and Election Boycotts.” Comparative Political Studies, 42: 392-415.

Bhirnlund, Eric. 2004. Beyond Free and Fair: Monitoring Elections and Building Democracy. Washington DC and Baltimore, MD: Woodrow Wilson Center Press and Johns Hopkins University Press.

Daxecker, Ursula. 2012. “All Quiet on Election Day? International Election Observation and Incentives for Violent Manipulation in African Elections.” Journal of Peace Research, forthcoming.

Fearon, James. 2011. “Self-Enforcing Democracy.” The Quarterly Journal of Economics, 126: 1661-1708.

Hyde, Susan. 2007. “The observer Effect in International Politics: Evidence from a Natural Experiment.” World Politics, 60 (1): 37-63.

Hyde, Susan, and Angela O’Mahony. 2010. “International Scrutiny and Pre-electoral Fiscal Manipulation in Developing Countries.” The Journal of Politics, 72 (3): 690-704.

Hyde, Susan. 2011. “Catch Us if You Can: Election Monitoring and International Norm Creation.”American Journal of Political Science, 55 (2): 201-462.

Hyde, Susan. 2011. The Pseudo-Democrat’s Dilemma: Why Election Observation Became an International Norm. Cornell University Press.

Hyde, Susan. 2012. “Does Information Facilitate Self-Enforcing Democracy? The Role of International Election Monitoring.” Working paper.

Lehoucq, Fabrice. 2003. “Electoral Fraud: Causes, Types, and Consequences.” Annual Review of Political Science, 6: 233-256.

Kelley, Judith. 2008. “Assessing the Complex Evolution of Norms: The Rise of International Election Monitoring.” International Organization, 62: 221-255.

Kelley, Judith. 2012. Monitoring Democracy: When International Election Observation Works, and Why It Often Fails. Princeton University Press.

Kuhn, Patrick. 2012. “To Protest or Not: The Election Losers’ Dilemma.” Manuscript.

Little, Andrew. 2012. “Elections, Fraud, and Election Monitoring in the Shadow of Revolution.” Quarterly Journal of Political Science, 7: 249-283.

Little, Andrew. 2012. “Fraud and Monitoring in Noncompetitive Elections”. Working Paper.

Little, Daniel. 2011. Varieties of Social Explanation. Westview Press.

Magaloni, Beatriz. 2009. “The Game of Electoral Fraud and the Ousting of Authoritarian Rule.” American Journal of Political Science, 54(3): 751-765.

Shleifer, Andrei, 2002. “The Memu of Manipulation.” Journal of Democracy, 13: 36-50.

Simpser, Alberto, and Daniela Donno. 2012. “Can International Election Monitoring Harm Governance?” The Journal of Politics, 74 (2): 501-513.

2011年春 北大CCER 经济学研究训练 作业

研究对象:CCER校外双学位项目提高校外学生的学费对学生的学习表现的影响。

研究背景:从2009年秋季开始,校外同学修CCER经双的学费从每学分400元涨到每学分800元,由此引来一些人们的不解和争议,讨论最多的是双学位的质量,学生学习的积极性和主动性等,即提高学费是否对经济学双学位的教学效果产生了影响。如果相关数据可得,通过经济学研究的方法,这种影响的存在性及影响的大小都是可以量化的。

研究思路:采用Difference-in-Difference方法,实验组是校外学生,对照组是校内学生,因为涨费不会影响到校内学生。以几门课上的所有学生(在2009年秋季前后都开设的课程才进入样本,而且相同的老师,相同的课程名才认为是同一门课)为基本观测点,数据的信息包括该学生在该门课上的最后总评成绩,是否来自校外,该同学入学年份(学号)和选这门课所在的学期等信息。由于校内学生是每学期交费,而校外修经双的学生是根据入学年份一次性收费,另外校外还有一定比例旁听生是每学期交费,所以需要同时根据学号和课程学期来确定该学生是交400元每学分的学费还是800元每学分的学费。每门课有两个学期的成绩信息,一个是离2009年秋季学期之前最迟开设该课程的学期,另一个是离2009年秋季学期之后最早开设该课程的学期,因为考虑到可能由于时间间隔的加长,同一个任课老师在不同学期上所做的教学改动会比较大,所以尽可能地选择了时间间隔最短的课来减少误差。

由于校外和校内学生在同一个教室上课,由同样的老师教授,使用同样的教材,做同样的作业,所以可以认为他们接受的经济学教育质量是相同的。经双最后的总评成绩都是通过正态的调整,因此各门课的平均成绩、优秀率、及格率理应是相近的,而且分数合理反映了一个学生的相对水平,不存在成绩偏高或偏低的情况。另外,本研究的三个假设是:1)在不同学期,相同的课程由相同的老师来教不会影响到校内和校外学生学习情况(成绩)的差异,变化的只是选课的学生,这里忽略了由于不同助教对校内外学生可能存在不同给分偏好和不同正态调整分数方法等因素带来的差异变化;2)同一门课,不同学期的学生能力/精力基本相同,所以即使认为校内和校外学生存在一定能力/精力差异,那么也认为这种差异是相对不变的;3)学生的学习之间不会相互影响,这就认为在2009年秋季后开设的课程中按400元每学分交费的学生不会对其他人的学习产生影响,学费上涨也仅仅对需要承担这部分学费的学生有影响,而不会影响到同班的其他人。

首先是比较2009年秋季学期之前校内与校外学生总评成绩的差异(差异1),这种差异可能来源于学费不同,学生能力、努力程度不同等。然后是比较2009年秋季及之后校内和校外学生总评成绩的差异(差异2),除了上述所说的原因,还有一个外生的影响因素是校外学生学费的上涨。通过比较差异1和差异2,找出差异之间的差异,如果这种差异是显著的,说明学费上涨对校外学生的学习效果产生了影响。

更多讨论:如果外校学生的平均成绩显著提高了,则一个可能的解释说学费上涨使得校外学生更加珍惜在北大学习的机会,更多的努力付出使他们的成绩比自己同校的师哥师姐们好;如果外校学生的平均成绩显著下降了,人们一个通常的解释是:学费上涨使一部分外校学生放弃了修读经济学双学位,于是经双入学考试的竞争变得不再像2008年之前那样激烈,而项目的校外招生人数不变,门槛的降低使得校外学生的平均水平有所下降,反映在较低的成绩上。然而,第二条解释却违背了假设2。当违反了假设2, Difference-in-difference的结果如果只用来衡量学习表现的话会得出由于变量缺失导致的有偏估计结果,成绩的差异其中还包括了涨学费的筛选效应从而导致了两批入学的校外学生之间能力的差异的影响。

在模型的三个假设中,我认为第2条是最强的假设,而且现实中假设2也是不太可能成立的,举一个真实的例子来说,有一所学校20072008年来修读经双的每年只有1-2位学生,而20092010年考入的每年都有5-6位。放松了第二条假设后,可以考虑在模型中加入更多控制变量能够反映一个人的能力,比如主修专业、院校、班级排名,甚至高考分数等。

如果违法了假设1,相关的数据往往很难获得,严重情况下Difference-in-difference不再适用,当然现实中我们也是不希望这种情况发生的。

对于假设3,不得不承认学习是一件具有外部性的活动。课后学习小组、小组课程项目、小组论文等等都会使学生的成绩之间发生相互影响,所以为了不违反假设3,在样本选取的过程中,应该尽量选择以考试为主要考察方式的课程。在监考严格的情况下,可以认为成绩最终反映的还是一个人的自身的学习结果。另外一方面,在随机抽样的条件下,也可以认为一个人与其他人的关系是随机分布的,最后不对结果产生系统性的影响。

另外,由于中国近年来的通货膨胀,校内学生虽然学费未涨,但实际上相当于学费是下降了,这种影响是否会影响到他们的学习努力程度,学习态度进而影响到他们的成绩还需要进一步考量。但尽管如此,这种下降的幅度远远比不上校外学生学费翻倍的增加,所以在一定的偏误范围内,仍然可以认为学费没有对他们造成影响。

个人认为中心可以对下一次学费上涨/下调时进行一次实验,即根据上涨年之前某一学年的课程设置,在上涨年之后某一学年设置相同的课程,配备相同的教师和每门课对应的助教,并且要求教师尽量和之前那个学期所开设的课程一致,使用同样的教材、布置同样的作业、并进行同样的考试(当然,实验的最大难度是需要事先对选课学生进行保密,以免他们会有向自己的学长“取经”的倾向,而且由于信息不对称,这方面往往是校内学生更占优势)。由于校外大多数同学都是按入学年份来交学费,这使得2009年秋季以后的课程中仍然有一部分校外学生进入不了实验组,这使得实验组的样本量太小,如果改为按学期来交费,就可以改善对照组和控制组样本量悬殊的情况,使他们的对比更加明显。