rafmacalaba/datause-encoder-data huggingface dataset:
|P133701|2014|2015|12/13/2014|1 Closed|Vanuatu|
|---|---|---|---|---|---|
|P133701|2014|2015|6/25/2015|2 Closed|Vanuatu|
|P133701|2014|2016|12/10/2015|3 Closed|Vanuatu|
|P133701|2014|2016|6/23/2016|4 Closed|Vanuatu|
|P133701|2014|2017|6/30/2017|5 Closed|Vanuatu|
|P133701|2014|2018|12/4/2017|6 Closed|Vanuatu|
|P133701|2014|2019|10/3/2018|7 Closed|Vanuatu|
|P133703|2014|2015|11/15/2014|1 Active|Uzbekistan|
|P133703|2014|2015|5/15/2015|2 Active|Uzbekistan|
|P133703|2014|2016|8/6/2015|3 Active|Uzbekistan|
|P133703|2014|2016|2/12/2016|4 Active|Uzbekistan|
|P133703|2014|2017|9/14/2016|5 Active|Uzbekistan|
|P133703|2014|2017|3/24/2017|6 Active|Uzbekistan|
|P133703|2014|2018|10/16/2017|7 Active|Uzbekistan|
|P133703|2014|2018|3/22/2018|8 Active|Uzbekistan|
|P133703|2014|2019|10/5/2018|9 Active|Uzbekistan|
|P133703|2014|2019|3/14/2019|10 Active|Uzbekistan|
|P133703|2014|2019|5/31/2019|11 Active|Uzbekistan|
|P133703|2014|2020|12/2/2019|12 Active|Uzbekistan|
|P133703|2014|2020|6/8/2020|13 Active|Uzbekistan|
|P133705|2014|2015|10/31/2014|1 Active|Armenia|
|P133705|2014|2015|5/13/2015|2 Active|Armenia|
|P133705|2014|2016|11/8/2015|3 Active|Armenia|
|P133705|2014|2016|5/31/2016|4 Active|Armenia|
|P133705|2014|2017|8/22/2016|5 Active|Armenia|
|P133705|2014|2017|2/22/2017|6 Active|Armenia|
|P133705|2014|2018|9/30/2017|7 Active|Armenia|
|P133705|2014|2018|4/30/2018|8 Active|Armenia|
|P133705|2014|2019|11/15/2018|9 Active|Armenia|
|P133705|2014|2019|6/7/2019|10 Active|Armenia|
|P133705|2014|2020|11/20/2019|11 Active|Armenia|
|P133705|2014|2020|5/4/2020|12 Active|Armenia|
|P133706|2013|2013||0 Closed|Myanmar|
|P133729|2013|2013||0 Closed|Indonesia|
|P133729|2013|2016|7/8/2015|1 Closed|Indonesia|
|P133731|2014|2015|11/26/2014|1 Active|Congo,Republic of|
|P133731|2014|2015|6/30/2015|2 Active|Congo,Republic of|
|P133731|2014|2016|2/10/2016|3 Active|Congo,Republic of|
|P133731|2014|2017|1/7/2017|4 Active|Congo,Republic of|
|P133731|2014|2018|10/17/2017|5 Active|Congo,Republic of|
|P133731|2014|2018|2/6/2018|6 Active|Congo,Republic of|
|P133731|2014|2019|11/14/2018|7 Active|Congo,Republic of|
|P133731|2014|2019|6/17/2019|8 Active|Congo,Republic of|
|P133731|2014|2020|2/13/2020|9 Active|Congo,Republic of|
|P133738|2014|2020|7/26/2019|1 Closed|Guatemala|
|P133777|2015|2016|11/30/2015|1 Closed|Nepal|
|P133780|2015|2015|12/1/2014|1 Closed|Uganda|
**Figure 3: Agricultural incomer per worker relative to economy average in Turkey and selected peers, 1995 to 2012.**

<!-- Start of picture text --> Share (%)<br>Spain<br>Italy<br>0.6<br>Brazil<br>Chile<br>Turkey<br>0.4<br>0.2<br>0.0<br>1995 2000 2005 2010 2012<br><!-- End of picture text -->

**Source: FAO (2014); World Bank (2014).**

**Figure 4: Composition of agricultural exports by processing category, 1993-2010**

<!-- Start of picture text --> Share of export<br>value<br>100%<br>Fresh fruit &<br>vegetables<br>80%<br>Nuts & spices<br>60%<br>Processed<br>goods<br>40%<br>20%<br>Raw & lightly<br>processed<br>0%<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text -->

**Source: FAO (2012) and World Bank staff calculations**

21
_Rural-urban gradient in Latin America and the Caribbean_ _page_ 12

# **Table 3.  Proportion of total population, by hours to city of 100,000 and low population density**

## **Hours to city of 100,000 people**

||**< 1**<br>||**1 - 4**<br>||**> 4**<br>||
|---|---|---|---|---|---|---|
||**All**|**< 150 /**<br>**sq. km.**|**All**|**< 150 /**<br>**sq. km.**|**All**|**< 150 /**<br>**sq. km.**|
|Argentina|50%|3%|29%|23%|21%|21%|
|Bolivia|20%|3%|37%|22%|43%|31%|
|Brazil|42%|4%|40%|31%|18%|18%|
|Chile|40%|7%|51%|28%|9%|8%|
|Colombia|42%|4%|46%|28%|13%|11%|
|Costa Rica|49%|2%|30%|23%|21%|21%|
|Cuba|50%|13%|49%|40%|1%|1%|
|Ecuador|49%|8%|43%|29%|9%|7%|
|Guatemala|35%|3%|59%|26%|6%|6%|
|Guyana|5%|2%|26%|8%|69%|52%|
|Honduras|33%|4%|62%|43%|5%|5%|
|Mexico|47%|5%|42%|28%|10%|10%|
|Nicaragua|35%|5%|47%|34%|17%|17%|
|Paraguay|45%|1%|20%|14%|35%|32%|
|Peru|43%|2%|22%|16%|36%|30%|
|Panama|26%|1%|26%|13%|48%|42%|
|Uruguay|37%|1%|23%|18%|40%|40%|
|Venezuela|51%|5%|40%|26%|10%|8%|
|_LCR Totals_|_43%_|_4%_|_40%_|_27%_|_18%_|_15%_|

Source: Population and density are from CIESIN Gridded Population of the World 3.

Notes: 1) LCR totals include all Latin American and Caribbean countries, including those that do not appear in the table.

2) There were a small number of coastal gridcells that were missing country codes, and were therefore omitted from the above table.

3) The denominator for the percentage was total population as given by the tabulated cells, and excluding those few cells with omitted country codes.
assessing the relationship between the psychology measures included in the baseline survey for business owners in the PI/BSED study with their business profits at baseline. We focus on the relationship between psychological variables and a business success indicator of above and below median profits at baseline.

The OLS regression specification for this relationship takes the form: Where Psychology0i is a psychology outcome (e.g. personal initiative, self-efficacy, error competence, entrepreneurial identity, locus of control and attitude to risk) of entrepreneur i measured at baseline. The coefficient β1 measures the correlation of the psychology outcome with the business success indicator variable BusinessProfit0i that takes the value 1 for profits above the median level at baseline and zero below the median at baseline. X′0i is a vector of control variables where we include controls for other measures of success ascribed at the individual and household level by including an indicator for educational attainment above secondary school level and an index of household asset wealth in the regression in addition to other demographic variables e.g. age, marital status and loan status at baseline. εit is the error term.

Table 4 shows that those women who exhibit higher levels of profitability (above the median) do score more highly across the range of psychology variables at baseline. We believe that the directional link in a business context is that the psychology is driving the economic success as opposed to the economic success is positively impacting psychology (Campos et al., 2017). It may well be possible that a profit windfall might generate a temporary boost in psychological outcomes, but we believe the shift in psychology a priori is what is necessary to change behavior that leads to an improvement in profits and economic success.

Since the PI and BSED trainings mainly attracted the business owners who were towards the lower end of the profit distribution in Ethiopia, perhaps those women could potentially benefit more from a business training since they have a deficit in these skills. They perhaps have pent up demand for these socioemotional skills by having more hardship or difficulty in their life or lacked opportunities to obtain the skills (e.g. less exposure to tertiary education or social networks) and therefore could possess a lower stock of these skills.
of disease at the baseline, and the perceived severity of disease at the baseline was positively correlated with the likelihood of receiving the vaccine. The second possible reason is the time effect of the intervention. For example, it is possible that the intervention only has a temporary effect on risk perceptions; that vanishes quickly over time, without affecting vaccination behavior. The interval from the time when a woman received the priming intervention to the time when she made a take-up decision may have been too short. As this interval is uniformly set at one week, we cannot investigate this potential reason.

# **References**

- Banerjee, Abhijit, Esther Duflo, Rachel Glennerster, and Dhruva Kothari. 2010. “Improving Immunization Coverage in Rural India: A Clustered Randomized Controlled Evaluation of Immunization Campaigns with and without Incentives.” _British Medical Journal_ 340, no. c2220.

- Barham, Tania, and John Maluccio. 2009. “Eradicating Disease: The Effect of Conditional Cash Transfers on Vaccination Coverage in Rural Nicaragua.” _Journal of Health Economics_ 28 (3): 611–621.

- Blencowe, H, J Lawn, J Vandelaer, M Roper, and S Cousens. 2010. “Tetanus toxoid immunization to reduce mortality from neonatal tetanus.” _International Journal of Epidemiology_ 39:102– 109.

- Commission, National Population, and ICF Macro. 2009. _Nigeria Demographic and Health Survey 2008_ . Abuja, Nigeria: National Population Commission and ICF Macro.

- ~~.~~ 2014. _Nigeria Demographic and Health Survey 2013_ . Abuja, Nigeria: National Population

- Commission and ICF Macro.

- Currie, Janet. 2006. Chapter The Take-up of Social Benefits of _Poverty, the Distribution of Income, and Public Policy_ , edited by Alan Auerbach, David Card, and John Quigley. New York: Russel Sage.

- Ehreth, Jenifer. 2003. “The global value of vaccination.” _Vaccine_ 21:596–600.

28
WPS3840 Estimating Trade Restrictiveness Indices* Hiau Looi Kee† Alessandro Nicita‡ Marcelo Olarreaga§

# Abstract

The objective of this paper is to provide indicators of trade restrictiveness that include both measures of tariff and non-tariff barriers for 91 developing and developed countries. For each country, we estimate three trade restrictiveness indices. The first one captures the extent to which trade policies at home affect domestic welfare. This follows the work of Anderson and Neary (1992, 1994 and 1996). The second index captures the impact of trade distortions on each country’s import bundle. This follows the work of Anderson and Neary (2003). The last index focuses on market access and summarizes the trade distortions imposed by the rest of the world on each country’s export bundle. All indices are estimated for the broad aggregates of manufacturing and agriculture products. Results suggest that poor countries (and those with the highest poverty headcount) tend to be more restrictive, but they also face the highest trade barriers on their export bundle. This is partly explained by the fact that agriculture protection is generally larger than manufacturing protection. NTBs contribute more than 70 percent on average to world protection, underlying their importance for any study on trade protection.

JEL classification numbers: F10, F11, F13 Keywords: Trade Restrictiveness Indices World Bank Policy Research Working Paper 3840, February 2006 _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._

*We are grateful to William Cline, Alan Deardorff, Michael Ferrantino, Bernard Hoekman, Jaime de Melo, Peter Neary, Guido Porto, Zia Qureshi, Phil Schuler, Dustin Smith, Javier Suarez, David Tarr, Dominique van der Mensbrugghe, Alan Winters and participants at seminars at the IMF, UNCTAD, the World Bank, the World Trade Organization, the Center of Global Development and the Central Bank of Uruguay for comments and suggestions.

†Development Research Group, World Bank, Washington, D.C. 20433, USA; Tel. (202)473-4155; Fax: (202)522-1159; email: hlkee@worldbank.org.

‡Development Research Group, World Bank,Washington, D.C. 20433, USA; Tel. (202)473-4066; Fax: (202)522-1159; email: anicita@worldbank.org.

§Office of the Chief Economist for Latin America, World Bank, Washington, D.C. 20433, USA, and CEPR, London, UK; Tel. (202)458-8021; Fax: (202)522-1159; email: molarreaga@worldbank.org
Second, we have no systematic information on the age of entry. It is therefore impossible to distinguish between immigrants who were educated at home and those educated abroad; for example, Mexican-born individuals who arrived in the US at age 5 or 10 and graduated from US universities are counted as highly-skilled immigrants. Hence, our definition of the brain drain is partly determined by data availability.

However, we do provide alternative definitions of the brain drain in order to distinguish between those immigrants who were educated at home from those educated abroad. These entail estimating the brain drain for various age categories, namely skilled migrants who left at any age, denoted as 0+ (i.e., all skilled individuals living abroad), those who left at age 12+, 18+ and 22+. For instance, individuals who have a university degree and emigrated at age 22 are assumed to have obtained their degree in their home country. This is an imperfect measure but is the best that can be done for a large set of countries.

# **2.1. Emigration Stocks**

It is well documented that statistics provided by migrants’ origin countries do not provide a realistic picture of emigration. When available, they are incomplete and imprecise<sup>6</sup> . Although detailed immigration data are not easy to collect on a homogeneous basis, information on emigration can only be captured by aggregating consistent immigration data collected in receiving countries. Information about the origin and skill of natives and immigrants is available from national population censuses and registers. More specifically, country _i_ 's census usually identifies individuals on the basis of age, country of birth _j_ , and skill level _s_ . Our method consists in collecting census or register data from a large set of receiving countries, with the highest level of detail on birth countries and (at least) three levels of educational attainment: _s=h_ for high-skilled, _s=m_ for medium-skilled, _s=l_ for low-skilled and _s=u_ for the unknowns. Let _M ti_ ,<sup>,</sup> _sj_ denote the stock of working-aged individuals born in _j_ , of skill _s_ , living in country _i_ at time _t_ .

Low-skilled workers are those with primary education (or with 0 to 8 years of schooling completed); medium-skilled workers are those with secondary education (9 to 12 years of schooling); high-skilled workers are those with tertiary education (13 years and above). The unknowns are either due to the fact that some immigrants did not declare their educational 6
**<u>Table 3: Composition of Pakistan’s Top 15 Exports to India</u>**

|||**20**|**00**|**20**|**05**|**20**|**10**|
|---|---|---|---|---|---|---|---|
|||**Export**||**Export**||**Export**||
|**HS**<br>**Code**|**Commodity Description **|**(US$**<br>**million)**|**Share***<br>**(%)**|<br>**(US$**<br>**million)**|**Share***<br>**(%)**|<br>**(US$**<br>**million)**|**Share***<br>**(%)**|
|0804|Fruits,fresh or dried.|25.11|36.07|24.76|14.92|59.55|18.57|
|2523|Cement|**|**|**|**|34.38|10.72|
|2710|Petroleum oils and oils obtained from bituminous minerals,<br>other than crude|**|**|**|**|23.34|7.28|
|7801|Unwrought lead|**|**|1.43|0.86|20.93|6.53|
|2711|Petroleumgases and othergaseous hydrocarbons|**|**|**|**|16.02|5.00|
|2917|Polycarboxylic acids, their anhydrides, halides,<br>peroxides andperoxyacids|0.18|0.27|7.21|4.35|13.51|4.21|
|2903|Halogenated derivatives of hydrocarbons.|||||12.26|3.82|
|5209|Woven fabrics of cotton|0.14|0.21|9.45|5.70|12.20|3.80|
|2902|Cyclic hydrocarbons.|**|**|**|**|11.75|3.66|
|2836|Carbonates; peroxocarbonates|0.02|0.03|0.00|0.00|10.46|3.26|
|5208|Woven fabrics of cotton|0.19|0.27|10.45|6.30|10.09|3.15|
|0713|Dried leguminous vegetables, shelled, whether<br>or not skinned or split.|2.42|3.47|64.90|39.11|9.72|3.03|
|5101|Wool,not carded or combed.|0.02|0.03|1.91|1.15|8.68|2.71|
|5205|Cottonyarn(other than sewingthread)|0.36|0.52|3.43|2.07|6.31|1.97|
|3923|Articles for the conveyance orpackingofgoods,ofplastics|**|**|0.69|0.42|6.13|1.91|
||**Total export from Pakistan to India**|**69.62 **|**40.86**|**165.95**|**74.86**|**320.73**|**79.61 **|

Note: *Share in total export ** No trade Source: Calculated based on UNCOMTRADE

**The composition of exports from India to Pakistan has been primarily limited to about 15 commodities, which on average accounted for around 64 percent of the total Indian exports to Pakistan in 2000, but increased to around 80 percent by 2010** (Table 2) **.** These commodities include sugar, raw cotton, synthetic fabrics, tea, petroleum products and chemicals, reflecting India’s more diversified industrial base. Shares of both raw cotton and woven fabrics in India’s export to Pakistan increased from almost zero in 2000 to more than 13 percent in 2010, whereas the share of oil-cake and other solid residues contracted from about 16 percent to 3 percent during the same period.

**The composition of official exports from Pakistan to India has also been limited to very few commodities.** These include fruits and vegetables, wool and products, petroleum products, chemicals, lead, and more recently cement. In 2010, the sectors with the largest shares in exports from Pakistan to India were of fruits (19 percent), followed by cement (11 percent), and petroleum products (7 percent) (Table 3).

**Trade between India and Pakistan is restricted by closed trade regimes.** Pakistan and India have amongst the most restrictive trade regimes (Figure 4). Bilateral barriers to trade are very complex in nature and “thick” at the India-Pakistan land border. India and Pakistan also perform poorly with their global peers on trade logistics. Non-price barriers (e.g., costs on account of

6
# **Figure 6: Level of education attained by income quintile**

<!-- Start of picture text --> Primary Secondary University Postgraduate<br>81.7%<br>68.0%<br>60.2%<br>49.7%<br>42.1%<br>38.4% 39.2%<br>24.9% 25.8%<br>19.5%<br>12.5%<br>11.1% 11.3%<br>5.3%<br>0.0% 0.0% 3.6% 3.8% 2.0% 0.9%<br>Q1 Q2 Q3 Q4 Q5<br>Source: ENIGH 2007<br><!-- End of picture text --> In order to estimate private pocket contributions to education, we may recur to national account figures<sup>7</sup> , where final private consumption in education amounted 17,147 RD$ million in 2005, which represented 1.37% of the GDP that year. As we argue, this may be a good proxy for private pocket expenditure in education (enrolment, fee for services, materials) as, by definition, it does not include capital formation (investment in infrastructures).

Overall, total private spending in education is believed to represent around 1.7% of GDP nowadays. In this mixed model (public and private centers), resources do not seem to be optimally allocated, as poor performance of Dominican children in tests demonstrates. Families have been contributing to the financing of education an average of 38% between 1995 and 2005, with enrolment in private schools amounting two thirds of expenditures in this field.

By looking at expenditure in education as a percentage of total family expenditure, it is possible to observe that households from different income levels allocate a similar share of their resources. For the poorer, meals and transportation represent most of their educationrelated expenditures, whereas the fourth and fifth quintiles are assigning proportionally more to enrolment fees and monthly payments<sup>8</sup> . This clearly confirms that the middle class and the richer are more willing and more able to pay for private education in order to obtain a perceived better quality of services. Still, in some cases, they just got better infrastructures

> 7 DR Central Bank. National Accounts 2005, “Supply-use matrix”. Thousands RD$

> 8 Calculations based in ENCOVI survey, 2004.

[12]
Table 6: Impact on household consumption by region

||**Tot**|**al**|**Im**|**ports**|
|---|---|---|---|---|
||Percent|US$ billion|Percent|US$ billion|
|South Asia|-4.4|-95|-47|-59|
|East Asia and Pacifc|-2.2|-54|-22|-91|
|Middle East and North Africa|-1.6|-35|-16|-81|
|Sub-Saharan Africa|-1.4|-20|-27|-66|
|Latin America and the Caribbean|-1.3|-56|-35|-133|
|China|-0.7|-29|-6|-9|
|Other Advanced Economies|-0.6|-44|-10|-69|
|European Union|-0.4|-50|-2|-38|
|United States|-0.4|-57|-5|-43|
|Europe and Central Asia|-0.2|-6|-11|-42|
|**Total**|**-0.9**|**-445**|**-12**|**-631**|

Source: Authors’ simulations.

Note: Results are reported relative to the baseline in 2020 in real consumption.

Union, where this decline is partially offset by the boost in the consumption of domestically produced products. These countries’ pronounced reliance on domestic markets have an important role in reducing the negative impact of the increase in barriers to trade. Nonetheless, household consumption declines markedly by US$57 billion in the United States and by US$50 billion in the European Union relative to the baseline by 2020.

The increase in barriers to trade translate into higher prices for consumers. CPI is estimated to increase by 8 percent in Sub-Saharan Africa and by close to 5 percent in MENA and ECA. Food prices are found to increase even more, by 15 percent in SSA and close to 10 percent in MENA. Consumers in low income countries in Sub-Saharan Africa that rely heavily on imports of machinery and capital goods are also significantly impacted by the close to 10 percent increase in the price of transport equipment and electronics and machinery.

# **5.2.3 Impact on trade**

All regions are expected to experience a pronounced decline in both exports and imports (Table 7). The strongest decline in imports is estimated in LAC, where the volume of 22
Portugal, we do not have data for the year 2000, and we therefore use data from 1999. For the United Kingdom we use data from 1998. In what follows, we refer to this interest rate spread as SPREAD1. While the advantage of this proxy for lending spreads is given by the wide scope of coverage, the problem is that a number of countries do not collect comparable average rates on either deposits and/or loans. For countries with a large component of dollarized assets or liabilities it is difficult to verify whether the reported rates are referred to domestic currencies or to the average of domestic and dollar rates. Also, the reference group for which IFS reports interest rates differs across countries. For some countries, the lending rate reflects the average rate for prime borrowers in the country, while for other countries it reflects the average rate for a borrower of average quality.

Our second proxy is given by average interest rate spreads computed at a banklevel using data from Bankscope and successively aggregated at a country level. Bankscope reports balance sheet and income statement data for a large number of banks and countries. The advantage of using bank-level data is that it allows for comparison of interest rates charged by similar types of banks in different countries, while the IFS data merely reflects the spread for the average bank in the country. Using Bankscope data, we will be able to compute analytically the value of the average rate charged by an homogeneous sector of the banking system, therefore enhancing the comparability across countries. The challenge when using bank-level data on balance sheets and income statements, such as the one provided by Bankscope, is that interest rates have to be imputed from information on interest income, as data on bank-level interest rates are not reported. The problem is that in many countries, banks are not obliged to report interest 6