rafmacalaba/datause-encoder-data huggingface dataset:
28 proportionally little on the pension system in coming years. Healthcare costs as a share of GDP would also be expected to decline.

The Australian government is the only one known to systematically monitor and project the fiscal impact of refugees compared to other visa categories employing a model over a 20‐ year period. While immigrants across all visa categories other than “parent” and “humanitarian” make a positive fiscal contribution within a year or two of arrival, that of refugees is negative, especially over the first 3 years, and only turns positive after 20 years. It is notable that the employment rate of Australian born and of those born in mainly English‐ speaking countries is essentially the same. It is also notable that migrants born in a mainly English=speaking country are more likely to be employed than those born in not English speaking countries and that the difference is especially pronounced among the female population.

**Table 5 ‐ Employment status of civilian population aged 15 years or more, by birthplace, 2010–11**

|Category||Employment<br>rate(%)|Unemployment<br>rate(%)|Participation<br>rate(%)|
|---|---|---|---|---|
|Australian born males||71.1|5|74.8|
|Australian born females||59.2|5.1|62.4|
|Australian born||65.1|5|68.6|
|MESC (Mainly English<br>Countries) born males|Speaking|71.7|3.9|74.6|
|MESC born females||58.5|5|61.6|
|MESC born||65.3|4.4|68.2|
|NESC<br>(Not<br>English<br>Countries) born males|Speaking|63.5|4.9|66.8|
|NESC born females||47.6|6.6|51|
|NESC born||55.3|5.6|58.6|
|Total||63.3|5.1|66.6|

Source: Labor Force, Australia (6291.0), Australian Bureau of Statistics, Cited by Trends in Migration: Australia 2010–11

# <u>Fiscal Impact at the Local Level</u>

The previous sections have dealt with the fiscal impact of refugees at the national level, but refugees settle in specific localities. What happens to the budget when refugees arrive in large numbers at the local level? One example comes from Hagstrom’s examination of the Mohawk Valley (2000). This example may not be representative of situations where the refugees’ arrival is unplanned and refugees are still to be vetted, it is nevertheless instructive. Between 1975 and 1999 the Mohawk Valley (which includes Oneida County and within it the city of Utica in New York State), resettled 8,759 refugees, mainly from Bosnia, the former
# **The Evolution of Deep Trade Agreements**<sup>**1**</sup>

Aaditya Mattoo, Nadia Rocha, Michele Ruta World Bank, Washington DC, United States

## 1. Introduction

This paper takes a first look at new data on the content of all Preferential Trade Agreements (PTAs) that have been notified to the World Trade Organization (WTO), and highlights the emergence of Deep Trade Agreements (DTAs).<sup>2</sup> The detailed description of the data and the methodology used to collect it are discussed in the Handbook of Deep Trade Agreements (Mattoo, Rocha and Ruta, 2020) -henceforth, “the Handbook”.

DTAs are reciprocal agreements between countries that cover not just trade but additional policy areas, such as international flows of investment and labor, and the protection of intellectual property rights and the environment, among others. While these legal arrangements are still referred to as trade agreements,

> 1 This paper is based on the Introduction of the Handbook od Deep Trade Agreements (Mattoo, Rocha and Ruta, 2020). We would like to thank Petros Mavroidis for inviting us to present our work at Columbia Law School and Tom Prusa for his comments. We are highly indebted to Alvaro Espitia for providing background research for this paper and more broadly for his outstanding contribution to the production of the Handbook. Kazusa Yoshimura and Edith Laget worked on the quantification exercise using machine learning techniques in Section 4.

> 2 In the international economics and law literature, PTA is an umbrella term encompassing several types of reciprocal agreements between trading partners: Regional Trade Agreements (RTAs), Free Trade Agreements (FTAs), and Customs Unions (CUs). This definition differs from that of the World Trade Organization (WTO), which defines PTAs as agreements that grant _unilateral_ (i.e., non-reciprocal) trade preferences such as the Generalized System of Preferences schemes, under which developed countries grant preferential tariffs to imports from developing countries.

> This study, following the definition from international economics and law, uses the term PTA to refer to all types of trade agreements, both within and across regions, and uses DTA to refer to PTAs that contain provisions aimed at deepening economic integration between trading partners.
WPS4174

# **_Asian Century or_**

# **_Multi-polar Century?_**

## **_By David Dollar*_**

## **_World Bank Country Director, China and Mongolia_**

### **_Summary_**

The “rise of Asia” is something of a myth. During 1990–2005 China accounted for 28% of global growth, measured at PPP. India accounted for 9%. The rest of developing Asia, with nearly a billion people, accounted for only 7%, the same as Latin America. Hence there is no general success of Asian developing economies. China has grown better than its developing neighbors because it started its reform with a better base of human capital, has been more open to foreign trade and investment, and created good investment climates in coastal cities. China’s success changes the equation going forward: its wages are now two to three times higher than in the populous Asian countries (India, Pakistan, Bangladesh, Vietnam, Indonesia), and China will become an ever-larger importer of natural resource and labor-intensive products. Developing countries need to become more open and improve their investment climates to benefit from these opportunities. China itself faces new challenges that could hamper its further development: unsustainable trade imbalance with the U.S., energy and water scarcity and unsustainable use of natural resources, and growing inequality and social tension. To address the first two of these challenges, good cooperation between China and the U.S. is essential. I conclude that we are more likely to be facing a “multi-polar century,” than an Asian century.

### World Bank Policy Research Working Paper 4174, March 2007

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

**_*I thank Pieter Bottelier, Lant Pritchett, Wang Shuilin, Xue Lan, and Ernesto Zedillo for helpful comments on an earlier draft, which was prepared for the GDN annual conference in Beijing, January 2007._**
Assume that the indirect utility of an individual i depends on the usage of constructed

> NMT road and other explanatory variables. Let _q_ 1 and _q_ 2 represent the utility levels associated with and without the NMT project, y is income, _W_ * is the amount of money an individual is willing to pay, X represents the vector of socioeconomic characteristics or other factors that may affect WTP. The WTP that equates the two indirect functions under initial condition without project and under improved situation with project can be written as:

_v_ [( _q_ 1, _y_  _W_ *, _X_ ,  )]  _v_ [( _q_ 0 , _y_ , _X_ ,  )] (1)

> Where  represents uncertain factors which are not reflected in y, q, W.

> Solving for the equation, W*  = _WTP_ ( _q_ 1, _q_ 0 , _y_ , _X_ ,  ) . Suppose that each individual has his or her own willingness to pay _Wi_ * and _Wi_ * follows some form of cumulative distribution function F(t). Although we do not directly observe _Wi_ * from payment card responses, we know

_Wi_ * for individual _i_ lies somewhere between _WiL_ and _WiU_ , where _WiL_ is the lower bound that individual _i_ would vote for, and _WiU_ the upper bound that individual _i_ would not vote for. Thus the probability for individual _i_ ’s WTP falling between the interval [ _WiL_ , _WiU_ ] is We may assume F(t) a specific distribution function, for example, normality, with unique mean<sup></sup> _i_<sup>and standard deviation</sup>  _i_ for each individual. The likelihood function for estimation therefore is 15
- 3.1 Energy Security

3.1.1 Performance Energy security had been a major concern for the Philippines in the early 1990s, but vigorous action had provided a short-term solution through the entry of several IPPs made possible by emergency legislation. Figure 5 shows that post EPIRA there was initially slow growth (1.9 percent per annum) in total capacity until 2012, while since then capacity has expanded rapidly (5.9 percent per annum).

**Figure 4: Total Installed Generation Capacity (MW)**

<!-- Start of picture text --> Total generation capacity (MW)<br>25000<br>20000<br>15000<br>10000<br>5000<br>0<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> _Source_ : Philippines Department of Energy.

A measure of the adequacy of the generation capacity is given by the ratio of peak demand to dependable capacity. The highest monthly peak demand is identified for each of the three main geographic areas (Luzon, Visayas and Mindanao). Adding these together gives the total noncoincident peak demand, which is the upper bound of the highest coincident peak for the three areas. The ratio of peak demand to dependable capacity is shown in Figure 6. Over the period for which data are available, the ratio worsened at the beginning of the period when peak demand reached almost 80 percent of dependable capacity thus reducing the reserve margin to a critical level, but the rapid growth of capacity in the last few years has seen the ratio drop back to a safer level.

Figure 5: Ratio of Peak Demand to Dependable Capacity ( percent) 26
- Tauchmann, H. (2014). Lee (2009) treatment-effect bounds for nonrandom sample selection. _The Stata Journal 14_ (4), 884–894.

UNHCR (2023a). Jordan. Technical report, United Nations High Commissioner for Refugees.

- UNHCR (2023b). Syria. Technical report, United Nations High Commissioner for Refugees.

- UNHCR (2023c). Unhcr global trends. Technical report, United Nations High Commisioner for Refugees.

- Woerner, W., B. Fleitlich-Bilyk, R. Martinussen, J. Fletcher, G. Cucchiaro, P. Dalgalarrondo, M. Lui, and R. Tannock (2004). The strengths and difficulties questionnaire overseas: evaluations and applications of the sdq beyond europe. _European child & adolescent psychiatry 13_ , ii47–ii54.

- Zhou, Y.-Y. (2019). How refugee resentment shapes national identity and citizen participation in africa. In _APSA 2018 Annual Meeting Paper_ .

37
36 Besides the production-related farm institutions and distribution-related market institutions, there are also major influences from national level policies and laws such as those related to farm subsidy, rural industrialization, poverty alleviation, and wage rates and working conditions. But, at the same time, customary institutions related to cultivation practices and common grazing lands have significant effects on crop choice and livestock development. Notably, customary tendencies towards paddy cultivation, though a serious constraint for crop diversification, have a positive effect on the supply side of food security. To what extent changes in the performance of these rural institutions could affect the ultimate goal can be evaluated in terms of chain functions capturing how a marginal change in any of the institutions leads to a series changes within the equation systems and culminates finally in the marginal change in food security. Similarly, how impact synergies among development programs contribute to the final goal can also be evaluated in terms of the marginal changes in one or more of the variables characterizing various impact chains. Sensitivity analysis of this nature can provide valuable information for policy makers in prioritizing institutions and sequencing development programs.

From an overall perspective, the econometric property and theoretical consistency of the model results also suggest the proposed methodology of institution-impact interaction to be highly robust and the empirical approach of using perception-based information to be very reasonable. While the methodology and empirical approach are intuitive and the results provide considerable insights into the internal dynamics of and institutional roles in impact transmission, one cannot ignore the limitations of the present attempt and the scope for further refinements. Some of the limitations are obviously related to the specification and structuring of equations. The unexpected signs for and insignificance of crucial variables in some equations are also problems that can be avoided with a more refined specification of equations. Although only the perception-based qualitative data is used here to provide an empirical demonstration of evaluation approach, it is possible to explore ways for using observed and quantitative data from secondary sources and household surveys for as many variables as possible. In this case, a mix of quantitative and qualitative data can be used to estimate the model. More
**Evidence from the Delhi Primary Survey** The results on female time use so far are based on the CPHS data which does not separate travel times into purpose of travel (i.e., commuting to work vs non-work related trips). The Delhi Survey, on the other hand, reports the purpose of travel and the average travel distance for each respondent. While this data does not report employment status, the survey answer ‘ _travel for work_ ’ is meant to capture travel _to work_ in the case of employed women and travel _to search_ for work in the case of unemployed women.

We use the Delhi data to compare the purpose of travel and the average travel distance between new and continuous users of bus transit in the post-implementation periods. We acknowledge that the new users are induced into using the buses because they are free and hence are selected relative to incumbent users. The comparison group of continuous users is matched on observable characteristics to the new users as discussed in Section E.2. While this does not address the selection issue completely, this comparison is still revealing and useful. With this disclaimer in mind, we estimate the following regression: where _Yi_ is a dummy variable for travel purposes which is equal to one if it is for work or a dummy variable for average travel distance via buses which is equal to one if the average distance exceeds 10 kilometers. _I_ new users is a dummy variable set to one for new users and zero for continuous users. _X_ is a vector of control variables including an individual’s marital status and education level. The results are reported in Table A8.

In column 1, we find that new bus users are 6.8% more likely to travel for work than continuous users. The estimated coefficient decreases slightly to 6.6% after conditioning on marital status and education. We then restrict the matched sample to users whose travel purpose is work, which includes travel to work or job search depending on unreported employment status. In columns 3 and 4, we observe that, on average, new users are 18.2%-19.8% more likely to travel longer than 10 kilometers compared to continuous users. These results provide suggestive evidence that free passes enable women to travel greater distances, potentially allowing them to work in more remote jobs or facilitating their job search efforts.

# **8 Conclusion**

In the face of a literature that costs often re- large documenting increasing commuting duce women’s work hours and labor force participation, there is an increased policy 19
disclosure, terms of both quality and ease of retrieval, such as through IIFS websites and annual reports. Of particular importance would be, inter alia, the SSB annual report and its _fatwas_ . Informing IIFS’s stakeholders on the composition, powers, competence and decision-making of SSBs would also enhance the transparency of the processes leading to _Shariah_ pronouncements.

# **III. External Arrangements for** **_Shariah_ Compliance**

Under the prevailing current decentralized system of _Shariah_ advisory, the flexibility in _Fiqh_ opinions has encouraged innovation in Islamic financial products, contributing significantly to the dynamism of the industry. Nevertheless, because of the very nature of jurisprudence, interpretation of the _Shariah_ by a large number of independent scholars, notably in the various SSBs gives rise to inconsistencies in the _fatwas_ across SSBs, or even over time in the same SSB. Internal systems and procedures could be developed in IIFS to limit inconsistencies and explain different pronouncements. External arrangements, including mechanisms of market discipline can provide complementary channels inducing compliance with rulings and their harmonization. The following first analyzes existing external arrangements and then considers options to strengthen them.

## _A._ _<u>Existing External Arrangements</u>_

The broad _Shariah_ governance framework may feature (a) arrangements put in place by regulators, and (b) the presence of providers of financial information services external to the firms. Among regulatory arrangements, _centralized SSBs_ are the most noteworthy. While there are significant differences across countries, centralized SSBs are usually concerned with ex-ante monitoring, mostly understood as standardization of _Shariah_ interpretation, and ex-post monitoring of _Shariah_ compliance. They also offer arbitration to settle _Shariah_ disputes arising between members of the same SSB. In addition, a few countries have set up public rating agencies that assess financial instruments and institutions. These are meant to create a positive climate for _Shariah_
<mark>Crowdsourcing for agricultural applications: A review of uses and opportunities for a farmsourcing approach.</mark> _<mark>Computers and Electronics in Agriculture</mark>_ <mark>,</mark> _<mark>142</mark>_ <mark>, 126-138.</mark> **[2]** <mark>Missirian, A., & Schlenker, W. (2017). Asylum applications and migration flows.</mark> _<mark>American Economic Review</mark>_ <mark>,</mark> _<mark>107</mark>_ <mark>(5), 436-40.</mark> **<mark>[</mark> 1]** <mark>Morrissey, J. W. (2013). Understanding the relationship between environmental change and migration: The development of an effects framework based on the case of northern Ethiopia.</mark> _<mark>Global Environmental Change</mark>_ <mark>, 23.6 , 1501-1510.</mark> **<mark>[</mark> 1]** Mueller, V., Gray, C., & Hopping, D. (2020). Climate-Induced migration and unemployment in middleincome Africa. _Global Environmental Change, 65, 102183_ . **[1]** <mark>Mueller, V., Gray, C., & Kosec, K. (2014). Heat stress increases long-term human migration in rural Pakistan.</mark> _<mark>Nature Climate Change</mark>_ <mark>,</mark> _<mark>4</mark>_ <mark>(3), 182-185.</mark> **<mark>[</mark> 1]** <mark>Mueller, V., Schmidt, E., & Lozano-Gracia, N. (2015, July). Household and Spatial Drivers of Migration Patterns in Africa: Evidence from Five Countries. In</mark> _<mark>Urbanization and Spatial Development of Countries Research Workshop, World Bank, Washington, DC</mark>_ <mark>.</mark> **<mark>[</mark> 2]** <mark>Mueller, V., Sheriff, G., Dou, X., & Gray, C. (2020). Temporary migration and climate variation in Eastern Africa.</mark> _<mark>World Development</mark>_ <mark>,</mark> _<mark>126</mark>_ <mark>, 104704.</mark> **[1]** <mark>Muller, C. L., Chapman, L., Johnston, S., Kidd, C., Illingworth, S., Foody, G., ... & Leigh, R. R. (2015). Crowdsourcing for climate and atmospheric sciences: current status and future potential.</mark> _<mark>International Journal of Climatology</mark>_ <mark>,</mark> _<mark>35</mark>_ <mark>(11), 3185-3203.</mark> **<mark>[</mark> 3]** <mark>Myers, N. (2002). Environmental refugees: a growing phenomenon of the 21st century.</mark> _<mark>Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences</mark>_ <mark>,</mark> _<mark>357</mark>_ <mark>(1420), 609-613.</mark> **<mark>[</mark> 1]** <mark>Nawrotzki, R. J., Riosmena, F., & Hunter, L. M. (2013). Do rainfall deficits predict US-bound migration from rural Mexico? Evidence from the Mexican census.</mark> _<mark>Population research and policy review</mark>_ <mark>,</mark> _<mark>32</mark>_ <mark>(1), 129-158.</mark> **<mark>[</mark> 1]** <mark>Neumann, B., Vafeidis, A. T., Zimmermann, J., & Nicholls, R. J. (2015a). Future coastal population growth and exposure to sea-level rise and coastal flooding-a global assessment.</mark> _<mark>PloS one</mark>_ <mark>,</mark> _<mark>10</mark>_ <mark>(3),</mark> 47
(Aitchison and Brown 1966, chapter 11). Likewise, changes in the Lorenz curve itself can be mapped into changes in the Gini coefficient.

On a cross-country basis, what is usually available to the researcher is some summary information on the shape of the Lorenz curve. One such summary is provided by the income shares of the different quintiles of the population: Given the one-to-one mapping between the Gini coefficient and the Lorenz curve that follows from (1) and (2), under lognormality there must be also a one-to-one mapping between the Gini coefficient and the quintile shares (3). Thus, a test of the null hypothesis of lognormality can be based on the comparison of the empirical quintiles, say _E20j_ , with their Gini-based theoretical counterparts _Q_ 20 _j_ . Following this approach, a formal lognormality test can be performed on the basis of the regression model: where _j=_ 1,2,3,4 denotes the income quintile; _i=_ 1,2,..., _N_ is a country index, and _t=_ 1,2 _,...,Ti_ denotes the date of each income (or expenditure) survey available for country _i_ . In general _Ti_ will differ across countries, resulting in an unbalanced sample. In (4), the theoretical quintiles _Q_ 20 _it j_<sup>are constructed on the basis of the observed Gini coefficients</sup> _G_<sup>_it_</sup> , as implied by (1)-(3): Testing for lognormality in (4) is equivalent to testing the joint null hypothesis: α=0; β=1. (6) We should note that the precise null hypotheses entertained here is that the size distribution of income is described by a _two-parameter_ lognormal function. Strictly speaking, rejection of (6) does not quite amount to rejection of lognormality more generally, since the distribution of income might still be characterized by a _threeparameter_ lognormal density. This could happen, for example, if per capita income follows a displaced lognormal distribution -- i.e., it is lognormal over the range above some unknown minimum level τ (where τ is expressed as a ratio to average per capita income). In such case,<sup>9</sup> the variance of log income, and hence the Gini coefficient, remain unaffected, but from Aitchison and Brown (1969, p.15) it follows that

> 9 Explored, for example, by Metcalf (1969), who fits a displaced lognormal to U.S. personal income.

-5-