The second category focuses on the country and time period of the study. The wage impact of immigration differs by country. This might be because either the host country under study is big and hence can easily adjust to the immigrant influx or it is small and therefore react more to the increased immigration. The size of a country under study does contribute to the impact of immigration. The third category relates to how the labour market conditions of a country under study can affect the wage impact of immigration. The economic conditions during the time period under evaluation might also have something to do with the variations in the results of different papers. Labour market conditions in the host country may influence individuals’ decision on when and where to migrate. Under a simple demand and supply model, less advantaged groups such as immigrants, less educated or young workers are particularly sensitive to economic slowdowns. When economic conditions are adverse, immigrants are likely to get laid off because immigrants frequently lack extensive social networks and are typically unfamiliar with local social norms and culture (Wang and Sakamoto 2016). These workers are not just directly affected by the slow eco- nomic activity during recessions but are also indirectly replaced by high skilled workers who move down the skill chain (Devereux 2004). Low skilled immigrants are affected more than the low skilled natives during rough times because they face additional difficulties such as language barriers, tend to have less social capital and are relatively ill-informed about the labour markets. For each coun- try and time period under study, a variable using the unemployment series from the World Bank Database is used as one of the indicators of labour market conditions and is included in the esti- mation in order to better understand the variation in the results. The average immigrant share in working population for each country from World Bank Database for the time period under study is also created and included in the estimation in order to examine whether the size of immigrant stock in total population in a country matters.
Proponents of school choice argue that the structure of the public educational system – where education is mainly provided by government with substantial monopoly power and largely no competition – leaves educational consumers with limited choice among schools. They further suggest that this may result in a disconnect between school quality and parents’ preferences. There is a growing literature in economics that suggests that expanding school choice could improve educational outcomes by increasing disadvantaged children’s access to high quality schools, and by causing underperforming schools to become more effective or to shrink as families “vote with their feet” (Friedman 1955; Becker 1995; Hoxby 2003; Belfield and Levin 2003). 1 These ideas have gained strong currency in education policy circles, leading to policy innovations such as open enrollment systems, magnet and charter schools, private school vouchers, and expanded public school choice for students in poorly performing schools.
In this paper, I have argued that own-account workers and employers are differ- ent and need to be treated separately. Having a large share of self-employment and small business owners does not mean a country is more entrepreneurial. I have argued that financial intermediation can help explaining the cross coun- try differences in occupation shares. A lower financial intermediation efficiency leads to a higher cost of borrowing and lower return on savings. Agents who save with financial intermediaries are more likely to seek alternative occu- pations to manage more wealth. Wage workers are more likely to become an own-account workers, operating small businesses to manage their wealth. Agents who need to borrow to become employers choose to be own-account worker instead. The result is less capital and labour intermediated through the market. The quantitative results presented here has shown that, by varying financial efficiency, the model can account for over 70% of the cross country variation in the share of own-account workers.
Table 2.2 provides the summary statistics for the non-disabled students used in the re- gressions, and, for comparison purposes, the summary statistics for disabled students who also have non-missing suspension outcomes and student and peer controls. The summary statistics for disabled students are separated into three categories: special education, undi- agnosed, and declassified. The table shows that, as expected, disabled students have lower academic achievement than non-disabled students. Since the most severely disabled students always receive special education services, special education students have the lowest math and English scores, almost 1.5 standard deviations below those of non-disabled students. Undiagnosed and declassified students fare better but still score about 1 and 0.5 standard deviations lower than non-disabled students, respectively. These summary statistics also suggest that disabled students may be more disruptive; while the overall rates of suspension are relatively low, less than 3% for non-disabled students, suspension rates are twice as high for special education and declassified students, and over three times higher for undiagnosed students. Disabled students also tend to be male, are disproportionately black and Hispanic minorities, and are more likely to receive free lunch and English Language Learner services. Table 2.2 also reports the characteristics of students’ peers in their school and grade. About 7% of the peers of non-disabled students receive special education services, 1.6% have undiagnosed disabilities, and 0.4% have been declassified. Compared to non-disabled students, undiagnosed and declassified students are only slightly more likely to have spe-
This table reports abnormal returns for equal- and value-weighted Democracy-Dictatorship hedge portfolios using the Carhart four-factor model. Both Democracy and Dictatorship portfolios are divided into three terciles based on the three transparency proxies deflated by either lagged share price or lagged assets per share or absolute value of forecast mean: forecast dispersion, forecast error, and revision volatility. Then we form a Democracy-Dictatorship hedge portfolio for each transparency tercile every month and regress the monthly excess returns to each hedge portfolio on the market factor (RM RF ), the size factor (SM B), the book-to-market factor (HM L), and the momentum factor (U M D). The estimated intercept α is interpreted as the abnormal return of the trading strategy. Forecast error is defined as the absolute value of the difference between the actual annual earnings per share (EPS) and the mean of analyst forecasts. Forecast dispersion is defined as the forecast standard deviation across all analysts following the same firm in the same year. Revision volatility is computed as the standard deviation of the changes over the fiscal year in the median forecast from the preceding month. Panel A reports the α when transparency proxies are scaled by lagged share price. Panel B and Panel C show the results when transparency proxies are scaled by lagged assets per share and absolute value of forecast mean, respectively. The last two columns show the value-weighted abnormal returns to the Democracy (Long) and Dictatorship (Short) portfolios for the high transparency groups (i.e., lowest terciles). The sample period is from September 1990 to December 1999. t-statistics are reported in parentheses under the estimation coefficient. The significance levels 1%, 5%, and 10% are denoted by ***, **, and *, respectively.
The unit o f analysis and the unemployment measure seem more important in the models that add Medicaid eligibility and the unemployment*Medicaid interaction on the right hand side (lower panels o f Tables 2, 4, and 5). This is an appealing finding since the benefits o f using county-level data should be the largest in models which already have a state-level variable - Medicaid eligibility - on the right hand side. It is noteworthy, however, that the differences in results across the three model specifications are again not large. In particular, among black women, the interacted results do not reach statistical significance in any o f the model specifications but the coefficients mostly have the same sign. Among white women, unemployment per se decreases prenatal care use and the Medicaid ‘safety net’ increases it across all three specifications. Not surprisingly, the results are most significant when county-level cells are used (lower panels of Tables 2 and 4). In fact, the Medicaid ‘safety net’ is associated with significant benefits to infant and maternal health only when county-level cells are used (lower panels of Tables 2 and 4) and the results are most consistent across outcomes studied when county-level unemployment is also employed (lower panel of Tables 2).
Columns 1 through 3 show that peers’ BMI does not seem to influence student’s daily meals. The results may be due to the responses themselves being not quite relevant; admit- tedly, eating dinner together with or without family could not affect student’s BMI much. Nevertheless, Columns 1 and 2 indicate that peers would not affect student’s number of having meals or appetite. Columns 4 through 6 show that peers’ BMI does not affect the student’s time use in indoor or outdoor activities. The dependent variable in Column 6 is the sum of minutes spent per day on doing homework, reading books, and self-study. Though we regress time use in those three study related activities separately, we do not find any significant effects. These results are in line with Yakusheva, Kapinos and Eisen- berg (2014)’s finding that their peer effect on weight gain is not driven by peer’s exercise habits or eating disorder symptoms.
This version of the paper focuses on men, since early retirement rules differ for men and women. Results for women are available upon request and yield a qualitatively sim- ilar picture. We select all male UI-entries between 1980 and 2010 who qualify for their age-specific maximum PBD based on their working histories. This leaves a five year win- dow before the first year in the data (1975) and a three year window after the last (2013), allowing us to calculate UI eligibility for all individuals and unemployment durations for up to three years after UI entry. Since some of the requirements for maximum PBD eli- gibility, such as the duration over which claims could be accumulated, changed over the study period, the restrictions set on this duration differ slightly over time. We summarize these restrictions in Appendix Table B.4. Additionally, we exclude mining and steel con- struction from our analysis, since both sectors are known to have specific early-retirement rules for at least some of the periods. For other specific subgroups which face some, but less clear or pronounced early retirement rules we do not exclude cases a priori, but ad- dress them throughout the analysis. For the selected individuals, we construct detailed biographical information such as experience tenure or past exposure to unemployment.
Switzerland is a highly decentralized country composed of three levels of government. Indeed, the Swiss Federation has a unique scal system that makes this country an outstanding scenario to develop our study. In 1998 the federal government reformed its corporate tax schedule by introducing several modications. For instance, since then, capital is not taxed at the federal level and corporate taxes shifted from a non-at to a at tax rate. The federal government currently taxes prots at a at tax rate of 8.5% and does not tax capital at all. The lower tiers of government (cantons and municipalities) have important degrees of freedom concerning their scal competencies. Cantons are free to tax personal income and wealth as well as corporate prots and capital. Similarly, municipal governments have an important autonomy in levying taxes on either of these items. The total tax revenue raised is roughly equally divided among the three levels of government. Moreover, while the federal government collects the main part of its tax revenue from indirect taxes, the VAT and specic consumption taxes like the mineral oil tax; cantons and municipalities strongly depend on tax revenues coming from personal and corporate income and wealth taxes. In both cases, personal income tax accounts for the biggest portion of total tax revenue ( 61% for cantons and 68% for municipalities) whereas corporate taxes on prot and capital represent 18% ( 16% ) and wealth taxes only 8% ( 9% ) of cantonal (municipal) tax revenue.
To the extent that the presence of monitoring substitutes mutes the effect of board independence, one would expect the decrease in pay to be concentrated in noncomplying firms without monitors. Here we modify the empirical model of Table 1.1 to allow the effect of noncompliance to differ between the presence and absence of substitute monitors. Blockholder is a binary variable that equals one if a firm has any non-employee directors who own more than 5% of the company’s shares and zero otherwise. High concentration is a binary variable that takes the value of one if a firm’s institutional ownership concentration falls into the top quartile. The other concentration variables — upper middle, lower middle, and low concentration — are also binary variables indicating the lower three quartiles of institutional concentration. Note that low concentration encompasses the bottom quartile in columns 1 to 3, and the bottom three quartiles in columns 4 to 7. Concentration of institutional ownership is the proportion of institutional investor ownership accounted for by the five largest institutional investors in the firm. See Section 1.5.2 for more details. All other variables are defined in Table 1.1. The numbers in parentheses are heteroskedasticity-robust standard errors, clustered at the firm-period level. ***, **, and * indicate statistical significance at the 10%, 5%, and 1% levels.
with a stock price below the equivalent of USD 2 are excluded. The measure for foreign ownership used here is “NOSHFR” in Datastream, which is the percentage of total shares in issue held by institutions domiciled in countries other than that of the firm. While several firms have no foreign ownership, the dataset also includes a large number of firms with no information of foreign ownership. These are consequently also excluded in the analysis. The observations of the three most represented countries, USA, Japan, and Korea, make up 38.3%, 22.4%, and 9.8%, respectively, of the sample. In terms of market capitalization in the sample, the shares are 54.7%, 7.4%, and 2.7%, respectively. In comparison and according to WorldBank data for 2014, the shares of global market capitalization are 41.6%, 6.9%, and 1.9%. The most underrepresented country is China with only 1.29% market capitalization in the sample compared to a 9.5% share in the global market. The statistics are based on the observations for the firms from each country for the period between Jan 2006 and Dec 2014 as this period is ultimately used for the analysis. MV is the market valuation in US dollar and Turn is the monthly turnover in billion of local currency. The share of free floating shares in foreign ownership (FO ) is shown as the average and standard deviation for the firms which have at least some foreign ownership. Finally, share DO provides the average share of firms in a given country which were held solely by domestic investors. From the descriptive statistics it is clear that the assumption of foreign investors holding the market portfolio does not hold. Apart from Hong Kong, for over half of the observations from each country, domestic investors hold all floating shares. For USA, Japan, China, and Turkey the share is over 90%. Even when looking at the firms with some foreign ownership, there is a large di↵erence between firms in each country. The average foreign ownership in this sample is 4.5%. 79.3% of the firms have no foreign ownership. When excluding these firms, the average foreign ownership is 21.8%.
4.3. INSTITUTIONAL BACKGROUND and Scoppa (2011); Zinovyeva and Bagues (2011); Bagues et al. (2014)). This avoids endogene- ity due to the possible existence of unobservables that may be correlated with commissions’ and candidates’ characteristics. The exogenous variation in the gender composition of the commission enables the estimation of the impact of one more female commissioner on the likelihood of a female candidate to be selected. Although the papers share the same methodology, the evidence is mixed. De Paola and Scoppa (2011) examine 1,000 candidates in Chemistry and Economics for the Italian qualifications to Associate and Full professorship held in 2008 and document same-sex preferences, while Bagues et al. (2014), analyzing 66,000 applications to Associate and Full Professorships in all academic fields in Italy in 2013, report the opposite result: namely, each additional female com- missioner decreases the success rate of female candidates by 2%. A mixed and different result is provided by Zinovyeva and Bagues (2011) on Spanish data from all academic fields: opposite-sex preferences are found in competitions for Associate Professor, whereas female evaluators tend to prefer female candidates in competitions to become Full Professor. The authors explain their results with the internalization of the glass ceiling effect in academia by female evaluators, who may tend to discriminate against potential future female competitors. However, little is known on the gender dynamics operating at the start of a research career.
The previous difference-in-differences results show that, on average, there exists a relative increase in prices for houses sold in locations scheduled for redistricting. To gauge the magnitude of this redistricting plan on aggregate house values and tax revenue, I use property assessment data in 2013, one year before the start of redistricting process, to calculate the total gain in property values. I focus on houses that will be redistricted to the new school catchment area and multiply the 2013 value by the corresponding coefficient I find in the bottom line in Table 4.6. The results are listed in Table 4.7. The total increase in the housing stock value in Bryan Station is more than 85 million dollars from around 8,000 houses that will be redistricted into the proposed area. Henry Clay also has substantial increase around 15 million dollars. Though Lafayette has the largest estimate (10.4%) from previous section, but due to lower average house value and fewer houses, the total gain is less than the other two catchment areas. But the aggregate impact of the new school is large. The total value of housing stock in the three catchment areas amounts to more than 2 billion dollars and change of value is around 108 million dollars, an increase, on average, of $9016 per house. If I annualize the benefits over a 15 year period at a discount rate of 3.5%, this is a benefit of $783 per year per household. The estimated construction cost of the new high school was 82 million dollars 11 though this cost does not include any additional costs associated with maintenance of new facilities or any other costs not strictly a function of enrollments. As discussed in Section (4.3), to the extent that adding the new high school (Frederick Douglass) affects educational quality in the other high schools, the change in value is a measure of the relative benefits of the new school, not the absolute benefits.
I want to thank all the people who have supported and helped me while writing this disser- tation. In particular, I am indebted to my supervisor C. Katharina Spieß who "adopted" me as an external PhD student among her doctoral candidates at the Education and Family De- partment at DIW Berlin. I very much appreciate the extra work she put into guiding me through this dissertation over the years. Her valuable feedback and sometimes critical comments have considerably improved my work. Most importantly, however, I always felt that she believed in me completing this dissertation and gave much-needed encouragement. I am also grateful to everyone else at the Education and Family Department that I had the chance to work with, discuss with, and in general spend time with. The somewhat coin- cidental decision two or three years into this project to regularly work at DIW Berlin (I wanted direct access to SOEP regional data instead of SOEP remote) instead of my home environment proved to be one of the best strategic course-settings. The informal learning that took place on these occasions was invaluable to me. Apart from this, I always enjoyed the atmosphere at the department. I felt welcome at our monthly doctoral colloquiums from day one. In particular, I would like to thank Maximilian Bach, my co-author of Chapter 4, as well as Felix Weinhardt, Frauke Peter, Jan Marcus, and Jan Bietenbeck (although not a DIW employee) who have read parts of this dissertation and provided valuable feedback. Jan Marcus gets another thank you for agreeing to be my second supervisor.
While our argument will be much stronger if we can support our hypothesis with a consistent and continuous data set of visible and invisible trade costs, such data set is rare. 3 Alternatively, we turn to case studies with OECD’s aid for trade dataset to see whether the efforts to boost bilateral trade are strengthened when debt rene- gotiation happens. For the purpose of the paper, we restrict our attention to the categories of aid which are directly related to trade policy adjustment (See Table 7 for details). Figure 3.A.1 plots the change in aid (only for trade policy purposes) around the default period for the following three cases: Honduras in 2004, Congo in 2008 and Burundi in 2009. In the years of sovereign defaults, creditors double or triple their expense in trade-related aid to help defaulters out. They are generous with trade benefit instead of strict with harsh trade punishment. The case studies serve as indirect evidence for our hypothesis that creditors lower their trade costs with debtors.