rafmacalaba/datause-encoder-data huggingface dataset:
**_<u>________________________________________________________</u>_**

The Excel workbook provides these data both at a regional level and by pilot site. It is based on the methodology described in details in the <u>TTFSE Manual. (Please see Annex F</u> for definitions) The methodology entails measurement of processing times at pilot sites, usually on a monthly basis, and of the overall Customs performance on an annual basis. Performance at pilot sites are measured by the National Customs Administrations of the countries concerned with support of US Customs consultants under the TTFSE program.

_-_ _<u>Global Express Association Country Market and Customs Barriers Reports</u>_ The Global Express Association represents the international express delivery companies that serve over 215 countries, carrying over 30 million packages each day, all of them guaranteed to be delivered within specified time frames. The report is a summary of information about 150 countries based on a detailed questionnaire. Questions asked on market access restrictions include restrictions on entry to the express delivery sector, foreign investment, and the use of local services. Questions asked on customs restrictions are categorized into transparency, customs efficiency, and post-release processes.

# _<u>CSCMP: Annual US State of Logistics Report</u>_

Delivered each June by Rosalyn Wilson, _The US State of Logistics Report_ is published annually by The Council of Supply Chain Management (CSCMP), formerly called the Council of Logistics Management (CLM), which is the association for individuals involved in supply chain management.

Total logistics cost normally includes trade and internal logistics. This often is cited as 10% of GDP for advanced countries, and 20% or more for developing countries. This report is a compilation of statistics about transportation and business logistics, and includes statistics on logistics costs/GDP and inventories.

The 16<sup>th</sup> annual report published in June 2005 is titled _Security Report Card—Not Making the Grade._ _–_ _<u>World Customs Organization (WCO) Time Release Study</u>_ In 2002, the WCO outlined a methodology to help customs administrations measure the time required for the release of goods (normally from the time of arrival of goods at the port/airport/land border until their release to the importer or a third party on his behalf). The methodology can also be used to measure the time necessary for clearance given by customs after the payment of duties and taxes. This could be useful in identifying bottlenecks in the process of clearing goods, reasons and possible solutions to any problems -improving the efficiency and effectiveness of clearance procedures. The study can be used as a basis for future to identify where measures are required to simplify procedures. The methodology chosen can assist a Customs administration in comparing the results obtained by means of the standardized system with previous studies, especially when introducing changes in Customs procedures.

18 __________________________________________________________________________________________________________
**_Global Value Chains in the Electronics Industry                       Sturgeon and Kawakami_**

Linden, Greg, Kenneth Kraemer, and Jason Dedrick. 2007. “Who Captures Value in a Global Innovation System? The Case of Apple's iPod.” University of California at Irvine, Personal Computing Industry Center (PCIC) working paper, June.

Linden, Greg, and Deepak Somaya. 2003. “System-on-a-Chip Integration in the Semiconductor Industry: Industry Structure and Firm Strategies.” _Industrial and Corporate Change_ 12 (3): 545–57.

- Ling, Zhijun. 2006. _The Lenovo Affair: The Growth of China’s Computer Giant and Its Takeover of IBM-PC_ . Singapore: Wiley.

- Mann, Catherine, and Kirkegaard, Jacob. 2006. _Accelerating the Globalization of America: The Next Wave of Information Technology._ Washington, DC: Institute for International Economics.

- Mathews, John. 2002. _Dragon Multinational: A New Model for Global Growth_ . Oxford, UK: Oxford University Press.

- McKendrick, David, Richard Doner, and Stephen Haggard. 2000. _From Silicon Valley to_

- _Singapore: Location and Competitive Advantage in the Hard Disk Drive Industry._ Stanford CA: Stanford University Press.

- Nikkei Electronics. 2007. “Kanabi Shijo wo Sekken suru PND” (PNDs Sweeping the Car-navi Markets), May 21.

- Nolan, Peter. 2001. _China and the Global Business Revolution_ . Basingstoke, UK: Palgrave Macmillan.

- Ogawa, Koichi. 2007. _Wagakuni erekutoronikusu sangyo ni miru purattofomu no keisei mekanizumu: akitekucha-besu no purattofomu keisei niyoru erekutoronikusu sangyo no saiko ni mukete_ (Mechanism of platform formation in the Japanese electronics industry: towards a resurgence of the electronics industry based on the formation of an architecture-based platform). MMRC Discussion Paper 146. Manufacturing Management Research Center, Tokyo.

- Principe, A., A. Davies,  and M. Hobday (eds.). 2003. _The Business of Systems Integration_ . Oxford, UK.

Sayer, Andrew. 1986. “Industrial Location on a World Scale: The Case of the Semiconductor Industry _._ ” In _Production, Work, Territory,_ ed. A. Scott and M. Storper, 107–24. Boston: Allen and Unwin.

Shih, Willy, Chintay Shih, Hung-Chang Chiu, Yi-Ching Hsieh, and Howard H. Yu. 2008. “ASUSTeK Computer Inc. EeePC (A)” Harvard Business School Case Study 9-609-111, Harvard Business School, Cambridge, MA.

Song, Lei. 2007. “Modularization, Modularity Traps and Competitiveness: Towards an Architecture Analysis of China’s AV Industry.” ITEC workshop presentation, October 24 , Kyoto, Japan.

49
Table A.14: Marriage market outcomes and partner characteristics

|Full Sample<br>Rural<br>(1)<br>(2)|Urban<br>(3)|
|---|---|
|**Panel A: Husband’s years of schooling**||
|Fraction of female leaders<br>-0.174<br>-0.698|1.585|
|(0.570)<br>(0.624)|(1.211)|
|Observations<br>44,714<br>31,479|13,235|
|Outcome mean<br>7.548<br>6.851|9.204|
|**Panel B: Schooling diference between husband and wife**||
|Fraction of female leaders<br>-0.505<br>-0.305|-0.441|
|(0.493)<br>(0.560)|(1.013)|
|Observations<br>44,714<br>31,479|13,235|
|Outcome mean<br>1.542<br>1.788|0.957|
|**Panel C: Age diference between husband and wife**||
|Fraction of female leaders<br>-0.624<br>-0.622|-0.419|
|(0.576)<br>(0.664)|(0.835)|
|Observations<br>44,714<br>31,479|13,235|
|Outcome mean<br>4.684<br>4.592|4.904|
|**Panel D: Husband worked last week**||
|Fraction of female leaders<br>0.056<br>0.059|0.088|
|(0.078)<br>(0.097)|(0.084)|
|Observations<br>44,713<br>31,478|13,235|
|Outcome mean<br>0.915<br>0.902|0.947|
|State fxed efects<br>x<br>x|x|
|District characteristics<br>x<br>x|x|
|Individual characteristics<br>x<br>x|x|

_Notes_ : Data are from the 2015-16 National Family Health Survey (NFHS) of India. The table presents 2SLS estimates using the fraction of female leaders in a district in close elections as an instrument. The dependent variables are husband’s years of schooling (Panel A), the difference in years of schooling between husband and wife (Panel B), the age difference between husband and wife (Panel C), and an indicator for whether a husband worked last week (Panel D). Column (1) presents estimates for the full sample, while columns (2) and (3) restrict the samples to those living in rural and urban areas, respectively. We define close elections as those in which the victory margin is less that 3 percent of votes. All regressions include state fixed effects and control for the share of seats in the district that had close elections between women and men. Individual controls include age, years of schooling, and indicator variables for living in a rural area, religion, and caste. District-level controls include the share of female population, the share of urban population, and the share of SC/ST population in the district, and the female and male literacy rates. Robust standard errors are clustered at the district level. ***, **, and * denote significance at the 1, 5, and 10 percent levels.

41
Figure _5_ <!-- Start of picture text --> Return on Assets<br>- IAS Data -<br>1990 1990 #of<br>pre-tax post-tax InstiutIons<br>Total 3.2% 1.8% 7<br>Large Banks 3.1% 1.8% 4<br>Small & Medium Banks 6.1% 5.6% 2<br>Savings Banks 3.2% 1.5% 1<br>Sources: National Bank of Hungary and Bank Staff Estirates<br>Leverage<br>- IAS Data -<br>1990<br>Total 21.0<br>Large Banks 21.0<br>Small & Medium Banks 5.6<br>Savings Banks 25.8<br>Sources: N.tlonal Bank of Hungary and Bank Sal Estinates<br>Return on Equity<br>- IAS Data -<br>- Nominal - - Real -<br>1990 1990 1990 1990<br>pre-tax post-tax pre-tax post-tax<br>Total 68.0% 37.4% 30.2%/ 6.5%<br>Large Banks 65.9% 37.5% 28.6% 6.6%<br>Small & Medium Banks 34.2% 31.3% 4.0% 1.8%<br>Savings Banks 82.6% 38.9% 41.6% 7.7%<br>1990 Consumer Price Inflation 29.0%<br><!-- End of picture text --> <!-- Start of picture text --> Souroes: National Bank of Hungary and Bank Staff Estrnates_><br><!-- End of picture text --> lla
**<u>Context</u>**

Poor hygiene and recurrent disease have detrimental effects on adults, but it is important to emphasize why children are more vulnerable than adults. It is primarily the rapid development of the brain and cognitive functions in early years that make children suffer most in the presence of recurrent disease and poor hygiene. Young children’s development is especially sensitive to negative effects such as early under nutrition, deprivation of care, and illness. Walker et al. (2007) estimated that at least 200 million children under the age of 5 fail to reach their potential in cognitive and socio emotional development. The number of affected children is calculated based on the prevalence of early childhood disease, stunting and the number of people living in absolute poverty as indicators of poor development. Poor sanitation and hygiene become an important source of disease and under nutrition among children.

Many studies have confirmed that poor hygiene behavior and inadequate sanitary conditions are key contributors to a number of infectious diseases in developing countries (Vivas et al. 2010). In this respect, young children are the most defenseless, as their immune systems have limited exposure to morbid conditions, and their hygiene behaviors are more likely to be careless. Located in West Africa, Senegal is a country with a population of approximately 14 million inhabitants, of which about 45 percent were reported to be under the age of 14 years in 2012 (World DataBank). Anecdotal evidence suggests that Senegalese are likely to share and ingest food using their hand due to cultural and social norms<sup>3</sup> . In fact, a study conducted in Sub-Saharan Africa that included Senegal, revealed that only 17 percent of the participants washed their hands with soap after using the toilet (Vivas et al., 2010). Lacking appropriate hygiene habits are likely to have negative health consequences, which might not only be experienced right away, but may manifest in the future.

When parents and caregivers of children have low exposure to critical hygiene and sanitation information, outcomes of children can be affected: recurrent episodes of

> 3 The fecal‐oral route is the main mode of transmission of intestinal parasites like _Blastocystis_ and other gastrointestinal parasites, that affect predominantly children (Tan, 2008). Also, Senegal has the highest prevalence of _Blastocystis_ in the world, based on El Safadi et al. (2014).

5
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the Yemeni population earn their livelihood from farming. However, about 70 percent of the population lives in rural areas, and 34.8 percent of Yemenis live below the poverty line in 2005/2006 (World Bank 2007). Estimates suggest that poverty and food insecurity (as a measure of extreme poverty) has increased substantially due to the food crisis impact of 2008. Breisinger et al. (2010) estimate that poverty increased to 42.8 percent in 2009. Ecker et al. (2010) and WFP (2010) show consistently that 32 percent of the Yemenis suffer from food insecurity in 2009, with negative implications on current and future generations’ health and productivity.

The Government of Yemen has taken some first steps to initiating comprehensive petroleum subsidy reform by increasing the price of fuel by about 25 percent so far in 2010. In order to contribute to the ongoing debate of whether and how to design continued reform, this paper assesses the economic and social impacts of further reform and alternative options for the allocation of resources saved. More specifically, this paper discusses two major scenarios: one accelerated reform path (Reform Option 1) where all subsidies are cut within one year, and one more gradual reform alternative (Reform Option 2) that phases out subsidies over a period of three years. It also considers the impacts of using the savings from reform for budget deficit reduction, direct transfers to households, and investments. The rest of the paper is organized as follows: Section 2 analyzes the role of petroleum subsidies and petroleum products for the government budget, producers, and consumers. Section 3 describes the computable general equilibrium (CGE) model, which serves as analytical tool for assessing the impact and reform options. Section 4 presents the model results, and Section 5 concludes with a summary and policy implications.

# **2. The Role of Subsidies in the Economy and Options for Reform**

## **The Role of Subsidies**

To understand and assess the potential impacts and options for reform, it is important to analyze the linkages between subsidies, the government budget, production, consumption and households.

Petroleum subsidies in Yemen make up more than 20 percent of the government budget, more than total spending on education, health and social transfers in 2007 combined (Table 1).<sup>6</sup> The share of the subsidy within the category of “economic affairs” has increased dramatically over the past years, up from about 45 percent of total economic affairs expenditures in 1999 to 85 percent in 2007. This expansion of cost for the petroleum subsidy comes at the expense of other sectors. For example, fiscal resources for social protection remained fairly low, only 0.2 percent of total government spending was used for social protection and programs.

> 6 It is important to note that the overall annual fiscal costs for the energy subsidy depends on international prices for petroleum products, as the domestic price is fixed. In general, the higher the international price for petroleum products, the higher the costs for the subsidy.

4
network would be higher in areas with a larger migrant network and more children, it is perhaps important for telecom providers to internalize the extra benefits for providing telecommunications in such villages.

# **References**

- Adam, Richard Jr. and John Page. 2005. “Do International Migration, Remittance Reduce Poverty in Developing Countries”, _World Development_ 33, 1645-1669

- Alan, de Brauw and Scott Rozelle. 2008. “Migration and Household Investment in Rural China”, _China Economic Review_ 19, 320-335

- Aker, Jenny C., Michael A. Clemens, and Christopher Ksoll. 2011. “Mobiles and Mobility: The Effect of Mobile Phones on Migration in Niger”, working paper

- Angelucci, Manuela. 2012. “Migration and Financial Constraints: Evidence from Mexico”, working paper

- Bao, Shuming, Bodvarsson, Örn B., Hou, Jack W. and Zhao, Yaohui. 2011. "The Regulation of Migration in a Transition Economy: China’s Hukou System", _Contemporary Economic Policy, Western Economic Association Internationa_ 29, 564-579

- Barr, Abigail and Abena Oduro. 2002. “Ethnic Fractionalization in an African Labour Market”, _Journal of Development Economics_ 68, 355-379

- Differences Estimates?”, _Quarterly Journal of Economics_ 114, 249–275

- Borjas, George. 1987. “Self-Selection and the Earnings of Immigrants”, _American Economic Review_ 77, 531-553

- Braga, Michela. 2009. “Dreaming Another Life. The Role of Foreign Media in Migration Decisions: Evidence from Albania”, working paper

- Clegg J., Kamall S. and Leung M.. 1996. “European Multinational Activity in Telecommunications Services in the Peoples’ Republic of China: Firm Strategy and Government Policy”, _Management International Review_ 36, 111-137

- Cai, Fang. 2001. “Institutional Barriers in Two Processes of Rural Labor Migration in China”, working paper series No. 9, Institute of Population Studies, Chinese Academy of Social Sciences

- Clemens, M.. 2011. “Economics and emigration: Trillion-dollar bills on the sidewalk?”, Working Paper 264, The Center for Global Development

- Deshingkar, P. and Grimm, S.. 2005. “Internal Migration and Development : a Global Perspective”, IOM migration research series, No. 19

- Fafchamps, Marcel and Forhad Shilpi. 2009. “Determinations of the Choice of Migration Decision”, working paper

23
39 Table 9. Recruitment of Workers in Sample Rural Enterprises in China by Selected Categories (number of workers)

|Category|Allocation|Voluntary|Total|
|---|---|---|---|
|By county||||
|Wuxi|270|191|461|
|Jieshou|84|209|293|
|Nanhai|99|154|253|
|Shangrao|78|66|144|
|Total|531|620|1,151|
|By age||||
|Under 20|40|114|153|
|20-29|233|320|554|
|30-39|168|117|258|
|40-49|71|57|128|
|50+|19|12|31|
|Total|531|620|1,151|
|By ownership||||
|Public|398|380|778|
|Private|33|111|144|
|Total|431|491|922|
|By pay (yuan per year)||||
|Less than 625|82|180|262|
|625-1,249|180|263|475|
|1,250-2,499|173|142|315|
|2,500-4,999|44|31|75|
|Total|531|620|1,151|

- Notes: Chi square 73.4 for counties, 54.9 for age, 38.9 for ownership, and 51.5 for pay. In all cases, these are significant at 0.1 percent.

Source: Fieldwork of World Bank/Institute of Economics of the Chinese Academy of Social Sciences research project.
A summary of descriptive statistics from SA reveals no significant differences between the predefined treated and non-treated samples for a large number of variables (see Appendix, Table A-3 – first two columns), suggesting that the random design of the experiment in terms of the _intended_ treatment and control groups was successful.<sup>33</sup> However, the match between the intended groups with _actual_ BDH recipients is imperfect – transfers went to about 78 and 42 percent of households in the so-called treatment and control groups, respectively. A comparison between _recipients_ and _non-recipients_ of BDH transfer shows significant differences between the two groups for variables like child enrollment, parental education and household size (Appendix, Table A-3, third and fourth columns). Thus selection into the BDH program appears to be non-random.

In order to avoid misspecification due to bias from the contamination of both treatment and control groups, SA computed the “treatment-on-the-treated” or TT estimator using the Two Stage Least Square method. The TT estimator suggests that the BDH increased enrollment rates among compliers by 9.7 percent, with the statistic being only weakly significant (i.e. significant at the 10 percent level but not at 5 percent). Notably, around 25 percent of households in the sample believed that compliance with the condition of school attendance was mandatory for receiving the cash transfer (to be called “conditioned households” henceforth), despite the fact that this conditionality was not enforced by BDH administrators. Out of these conditioned households, 55 percent were from treatment and 45 percent from control groups. Following the re-weighting scheme of Hirano et al (2003), SA reported the Difference-in-Difference or DD estimator, adding a dummy variable for “conditioned” and an interaction term of the “conditioned” dummy variable with the treatment group dummy variable.

As previously mentioned, our BFL model estimator is a type of “Intend-to-treat” estimator, ITT. In this case, it is easy to transform the _ex post_ TT estimator from SA (results from Table 3 in SA) for comparison with our estimator, as follows:

> 33 Children from the intended treatment and control groups are indistinguishable in the baseline data in terms of their enrollment rate, grade attainment, labor participation, gender, per capita expenditure, assets, parental education and household size,

27
_line z such that Xz_ = _XQ_ ( _{u}_ ) _and a poverty score function p such that for all_ ( _y,_ _<u>y</u>_ <u>)</u> _,_ ( _y_<sup>_′_</sup> _,_ _<u>y</u>_<sup>_~~′~~_</sup> ) _∈ Xz,_ _Proof._ See online Appendix S7.

■ We emphasize two implications of Proposition 2 for the properties of the global poverty line. First, the global line corresponds to the indifference curve passing through the reference bundle (see Figure 4). That is, the global line yields, for each individual (separately), the same utility in all countries.<sup>28</sup> Second, the global line yields a _perfect identification_ of the welfare poor; that is, all individuals below the global line are welfare poor and all individuals above the global line are not welfare poor. This is because all individuals hold the common utility function.

The trade-offs that the measure makes between own income and relative income can be graphically illustrated by means of its iso-poverty-score map (see Figure 4). An iso-poverty-score map is a collection of iso-poverty-score curves, which are sets of bundles that yield a constant poverty score. The global line is one iso-poverty-score curve, or, more precisely, it is the frontier of a ‘thick’ iso-poverty-score curve. Below the global line, iso-poverty-score curves exactly correspond to indifference curves. The two properties (fairness and welfare-consistency) completely characterize the trade-offs made by the measure; that is, the comparison of any two bundles is determined.

# **4.3 General Impossibility and Weak Pareto**

In many settings with heterogeneous preferences, the Pareto principle conflicts with the Domination principle (Fleurbaey and Trannoy, 2003; Brun and Tungodden, 2004). This conflict also arises in our setting, as we show in Proposition 3. Intuitively, the reason is that the Pareto principle requires that the poverty score function (negatively) represents individual preferences, while the Domination principle requires that the poverty score function does not depend on preferences.

We say that the set _U_ is _heterogeneous_ if there exist two _u, u_<sup>_′_</sup> _∈ U_ and some ( _y,_ _<u>y</u>_ <u>)</u> _∈ X_ with _<u>y</u> ≥_ _<u>y</u>_<sup>_~~z~~_</sup> such that ( _y,_ _<u>y</u>_ <u>)</u> _∈ XQ_ ( _u_ ) and ( _y,_ _<u>y</u>_ <u>)</u> _∈/ XQ_ ( _u_<sup>_′_</sup> ).<sup>29</sup>

> 28Our assumption of homogeneous ordinal preferences does not imply interpersonal levelcomparability of utility.

> 29Our definition of a heterogeneous set _U_ is not the complement of that of an homogeneous set _{u}_ . For instance, set _{u, u_<sup>_′_</sup> _}_ with _u_ = _u_<sup>_′_</sup> does not meet our definition of an heterogeneous set

24