Data and Variables Clause Samples
Data and Variables. This paper’s objective in propensity score matching was to investigate individuals’ labour supply decisions during the period leading up to UI benefits exhaustion. Utilising ▇▇▇▇▇▇ et al. (2015) dataset, this paper primarily focused on individuals whose unemployment duration was covered by UI benefits up until four weeks before expiration. ▇▇▇▇▇▇ et al.’s(2015) datasets are derived from the United States CPS data in 2008-2011 and 2012-2014:6. These datasets have been utilised in two published research: ▇▇▇▇▇▇▇▇▇ (2011), a working paper accepted by NBER; ▇▇▇▇▇▇ et al. (2015), a journal article ac- cepted by American Economic Review. This endorsement of two well-known economic research institutions has increased the creditability and reliability of the dataset. Using CPS data to study UI effects has advantages and disadvantages compared to using other datasets. The advantages of using the CPS data include large observations and a wide range of demographic variables. Following the same set-up as ▇▇▇▇▇▇ et al. (2015), this study incorporated the following factors as determinants of labour force transition: unem- ployment duration, ethnicity, gender, education level, number of dependants and state unemployment status. The outcome variables, which served as dependent variables, in- clude the binary variable for exiting unemployment, the dummy variable for returning to employment and the Zero-one integer for exiting the labour force. All these variables have been well defined in ▇▇▇▇▇▇ et al. (2015) dataset. Another important advantage of using the dataset of ▇▇▇▇▇▇ et al. (2015) is that specific adjustments were imposed to lessen the likelihood of spurious labour force transition prevalent on individual-level census datasets (Abowd & ▇▇▇▇▇▇▇ 1985). The limitation of utilising the CPS data, which is a common issue in UI benefit stud- ies such as ▇▇▇▇▇▇▇▇▇ (2011), is that an individual’s employment status is self-reported. Therefore, the employment status might be misreported due to the interviewee’s ▇▇▇▇▇- derstanding of CPS questions. In order to minimise this impact, this research mainly focuses on the individuals who are currently enrolled in the Unemployment Insurance Scheme whose UI benefits are approaching expiration within four weeks. This approach relies on government assessment on individuals’ eligibility for UI benefits. This assess- ment conducted by the government is prudential and rigorous. Applicants must submit documents to verify their unemploym...
Data and Variables. We consider 112 developing countries between the years of 1965 to 2004. The level of analysis is the country-year and dependent variables are dummies, PTA and BIT, which are coded as 1 if a country had signed any number of PTAs or BITs in the current year, and zero otherwise (PTA data is drawn from ▇▇▇▇▇▇▇▇▇ and ▇▇▇▇▇▇ (2012), while BIT data comes from ▇▇▇▇▇ and ▇▇▇▇▇ (2010)). The primary independent variable is the Polity score which measures the degree of democracy and it runs from 0 to 20. We use Polity as a proxy for the regime type (▇▇▇▇▇▇▇▇, Jaggers and ▇▇▇▇ 2000). A probit model is used to estimate the likelihood of countries signing an international commercial agreement in a given year. We control for the usual covariates: trade as a percentage of GDP and logged population or logged GDP to proxy for market size in the PTA estimations. In the BITs estimation, we control for Foreign Direct Investment (FDI) as a percentage of GDP and for the cumulative number of PTAs already signed.2 Additionally, we control for economic variables such as GDP per capita, GDP per capita growth rate. The macroeconomic data comes from World Bank’s World Development Indicators (WDI). We also include a time trend of signings in order to control for the systemic diffusion of these agreements over time. These trend variables are created by aggregating the signing behavior across countries and then generating the non-parametric trend variable over the years using lowess smoother method. Figure A.1 in Appendix shows that signing PTA was popular in 1960’s but has slowed down since then, while signing BITs has become increasingly fashionable in recent years. Moreover in all models we control for region fixed effects (based on coding in Cheibub, Ghandi and ▇▇▇▇▇▇▇▇ (2010)). There is usually a delay between the actual decision to sign and the official signing itself, so the polity score and all the other control variables are lagged in the estimations.