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, 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, 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.


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

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


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.


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






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。