rafmacalaba/datause-encoder-data huggingface dataset:
At 54 percent, a relatively high share of respondents in Tajikistan said they were either already vaccinated or planned to be in July 2021.<sup>6</sup> Only about 17 percent of respondents said they either probably or definitely would not be vaccinated. By March 2022, vaccination intentions had risen to 92 percent, with only 4 percent responding that they would not be vaccinated.

Following the introduction of vaccination mandates in Uzbekistan and Tajikistan and a new wave of infection that rapidly spread in Central Asia in July and August 2021, all three countries later achieved significantly higher vaccination rates, with Tajikistan highest among them as of June 2022. By June 2022, about 9.29 million people were fully vaccinated in Kazakhstan (49.5 percent of the population including children), 15 million in Uzbekistan (43.9 percent), and nearly 5 million in Tajikistan (52.2 percent).<sup>7</sup>

# **Figure 1: Self-Reported Vaccination Intentions**

# _(A) Kazakhstan_

<!-- Start of picture text --> Self-Reported Vaccination Intentions in Kazakhstan<br>May-22<br>Apr-22<br>Mar-22<br>Feb-22<br>Jan-22<br>Dec-21<br>Nov-21<br>Oct-21<br>Sep-21<br>Aug-21<br>Jul-21<br>Jun-21<br>May-21<br>Apr-21<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Definite or Already Lean yes Lean no Definite no<br><!-- End of picture text -->

> 6 Due to differences in fieldwork and questionnaire updating protocols, questions on vaccine hesitancy were added in July 2021 in Tajikistan.

> 7 All estimates from the _Our World in Data_ dashboard: https://ourworldindata.org/covid- <u>vaccinations?country=OWID_WRL , retrieved June 8, 2022.</u>

5
38 Appendix Table A4: Value of textile production with and without protection,<sup>a</sup> by region, 2001 <u>(percent)</u>

||Region’s sh<br>textile p<br>|are of global<br>roduction<br>|Share of texti<br>region’s tota<br>prod<br>|les in value of<br>l merchandise<br>uction<br>|
|---|---|---|---|---|
|Region<sup>b</sup>|Under<br>protection|Under<br>free trade|Under<br>protection|Under<br>free trade|
|China|24.5|26.0|8.7|9.2|
|United States|17.3|15.6|2.5|2.3|
|EU25|14.8|14.0|2.0|1.9|
|Korea & Taiwan|5.7|7.3|5.6|7.1|
|Japan|5.5|6.0|1.6|1.8|
|India|4.5|4.5|7.5|7.5|
|Other Eastern Europe & CA|4.5|4.0|4.6|4.1|
|Mexico|2.5|2.1|3.5|3.0|
|Other Latin Amer & Carib|2.3|2.0|3.8|3.4|
|Middle East & Nth Africa|2.0|1.6|2.2|1.8|
|Indonesia|1.8|2.1|7.6|8.6|
|Pakistan|1.8|1.9|25.2|26.5|
|Turkey|1.5|1.5|9.5|9.1|
|Brazil|1.5|1.4|3.0|2.9|
|Thailand|1.4|1.6|7.1|7.8|
|Hong Kong & Singapore|1.3|1.9|6.1|7.5|
|Other SE Asia & Pacific|1.1|1.2|8.4|8.9|
|Canada|1.0|0.9|1.6|1.3|
|Bangladesh|0.9|0.8|14.7|12.9|
|Malaysia|0.5|0.6|2.5|3.1|
|South Africa|0.4|0.4|3.0|2.6|
|Australia|0.4|0.3|1.4|1.2|
|Argentina|0.4|0.3|2.0|1.8|
|Philippines|0.3|0.4|2.7|3.2|
|Russia|0.3|0.3|0.8|0.8|
|EFTA|0.3|0.3|0.9|0.8|
|Other Southern Africa|0.3|0.2|4.7|4.0|
|Other Sub-Saharan Africa|0.3|0.3|2.2|2.1|
|Sri Lanka|0.2|0.2|10.8|10.3|
|OtherSouth Asia|0.2|0.2|8.8|8.6|

a “Without protection” refers to what production would be without subsidies and tariffs in the cotton, textile and clothing markets globally, as generated by the GTAP model b Same list of countries as in Appendix Table A3 and sorted according to region’s share of global textile production in 2001. Regions with less than 1 percent share of the global market are not shown. They are Malawi, Mozambique, Nigeria, Other SACU, Singapore, Uganda, Viet Nam, and Zambia.

Source: GTAP database 6.05 and authors’ GTAP model simulation results
The last term of equation (4.2) captures the instructional loss due to health shocks. Formally, where _q_ is infection of one of the household members with COVID-19, and _d_ is death of a household member due to COVID-19. The probabilities _P_ ( _q_ =1) and _P_ ( _d_ =1) are estimated based on the number of COVID-19 infections and deaths per inhabitant in the country multiplied by the household size.<sup>9</sup> Parameters <sup>_q_</sup> and <sup>_d_</sup> are the respective days of schooling lost due to the occurrence of the two events. Like NLT we set τ<sup>q</sup> to the average days of symptom duration (5 days of schooling), and τ<sup>d</sup> to a three-week loss of instructional time (15 days).<sup>10</sup> Finally, we follow NLT in computing α as one minus the parental factor of substitution, which is a function of parental education. Formally, where _ei_ measures parental educational background in the household of student _i_ and max(e) its maximum value in the sample ( _i.e_ . the years of education associated with tertiary education). Parental educational background is defined as the years of education of the parent in the household with the highest level of education.

# **4.2. Predicted educational losses**

In this section we illustrate the results for 13 Latin American countries: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador Mexico, Peru, Paraguay, and Uruguay. These countries represent 89% of the total population of the region, and 87% of the population at

> 9 In this point we depart from NLT who take the average country-level household size and use data for 2020 to estimate the average household size for each country.

> 10 The results are robust to changes in the number of days.

13
**V.2. Policy Recommendation II -** **_Strengthen Financial Infrastructure for Financial Inclusion_**<sup>33</sup>

**Improving financial infrastructure, especially the improvement of current credit informational system, should be the priority item in the policy agenda of OIC countries.** Deficiencies in financial infrastructure are one of the major obstacles for further SME lending in MENA region, as confirms by the survey result in

Figure **7. Percentage (%) of Banks responding that Obstacle is Very Important or Important for SME Financing: GCC vs. Non-GCC** . Improving financial infrastructure will entail expanding the range of collateral, improving registries for movables, and improving enforcement and sales procedures for both fixed and movable assets. It also entails upgrading public credit registries, and more importantly, introducing private credit bureaus capable of significantly expanding coverage and the depth of credit information.<sup>34</sup> Financial infrastructure improvements will reduce the information asymmetry that constrains access to credit and raises the costs and risk of financial intermediation.

**Core components of the financial infrastructure such as credit information, investors’ rights, insolvency regimes, etc. are essential irrespective of type of financing, i.e. conventional or Islamic.** For this purpose, it is suggested that, first, regulated financial entities should be required to share credit information, ideally through national credit registries. Sharing borrower information is essential to lowering costs and overcoming information constraints. As mentioned earlier, lack of access to credit information and low coverage ratio of credit history of individuals are two main features that contribute to the financial exclusion in OIC countries especially for SME financing. By integrating _Shariah_ -compliant products and customers information into credit bureaus, the coverage and utility of data in the credit bureaus can be improved. Financial institutions of all types can extend access to new customers, while managing risks and costs more effectively.  This will also help _Shariah_ -compliant financial institutions to expand their funding source and enhance their risk-sharing mechanism, as institution with its clients’ credit information available to the public can establish its reputation much easier than those with an informal credit history system.

Second, data on financial inclusion and unbanked markets/customers (especially those due to religious reason) is also needed to improve to underpin a sustainable expansion in access

> 33 For more details, please refer to (Pearce 2010)

> 34 (Rocha, et al. 2011)

44
**Table 2.  Simulated coefficients with and without skew in the second-stage error**

||Norma<br>|l error<br>|Skewe<br>|d error<br>|
|---|---|---|---|---|
||δf|δm|δf|δm|
|RM Linear LIML|1.60<br>(0.050)|1.60<br>(0.053)|1.61<br>(0.054)|1.61<br>(0.052)|
|PK (linear first-stage)|1.00<br>(0.067)|1.01<br>(0.064)|1.00<br>(0.88)|0.99<br>(0.089)|
|PK (Tobit first-stage)|1.00<br>(0.051)|1.00<br>(0.051)|1.05<br>(0.081)|1.04<br>(0.083)|
|PK (Tobit first-stage, skew<br>in non-choice errors only|n.a.|n.a.|0.99<br>(0.051)|0.99<br>(0.053)|

Note: Means of 100 replications with standard deviations in parenthesis. True parameters are 1.00 in every case. Skew and normal second-stage errors are as specified in RM (2014). The δf and δm values for each simulated data set are themselves the means over the three program types.

**Table  3 .  The effect of Roodman and Morduch’s use of linear LIML on estimates of program effects using simulated PK data (200 simulated datasets)**

|**Program effect δ**|**True**<br>**value**|**PK method**|**RM in**|**struments**|
|---|---|---|---|---|
|||**maximum likelihood**|**2SLS**|**Linear LIML (RM**<br>**method)**|
||(1)|(2)|(3)|(4)|
|Male group 1|.250|.253<br>(.037)|.482<br>(.248)|1.146<br>(7.754)|
|Male group 2|.250|.253<br>(.036)|.504<br>(.214)|-.386<br>(6.693)|
|Male group 3|.250|.248<br>(.036)|.500<br>(.222)|.861<br>(15.100)|
|Female group 1|.750|.747<br>(.038)|.526<br>(.232)|.338<br>(18.614)|
|Female group 2|.750|.750<br>(.034)|.513<br>.246)|.469<br>(8.057)|
|Female group 3|.750|.748<br>(.035)|.531<br>(.235)|1.161<br>(17.022)|

Note: Standard deviation in parenthesis. Source: PK (2012), Table 8.

23
part is called the ‘foreign content of exports’ ( **FC** _sr_ , Koopman et al. 2014): In the same way gross exports are decomposed by country of origin in (2), they can be classified according to the country of ultimate absorption in final demand. **E** _sr_ consists of final goods ( **Y** _sr_ ) and intermediate inputs for the production of gross output in country _r_ ( **X** _r_ ): where **Y** _sr_ indicates the _N ×_ 1 vector of final goods completed in _s_ and consumed in _r_ . The intermediate inputs imported by country _r_ ( **X** _r_ ) can be linked with the country of final completion and the market of ultimate demand. According to basic I-O accounting relations (see equation A.2 in the Appendix), all the remaining (and potentially infinite) stages of production are accounted for by the Leontief inverse matrix **B** : where _k_ and _l_ might be the exporting country _s_ itself, and the generic **Y** _ij_ indicates the final goods completed in country _i_ and finally sold in country _j_ .

Relationships in (2) and in (4) add relevant information to traditional gross trade statistics (i.e. the very origin and the ultimate destination of exports). Nevertheless, it is still ‘gross accounting’, in the sense that these measures include the items that cross country _s_ ’s borders several times along the production process. Only by isolating these double counted items, we can measure the ‘net’ production (value-added) embedded in exports, akin to countries’ GDP.

Johnson and Noguera (2012) contribute to filling this gap by proposing a way to measure the share of a country’s GDP that is absorbed abroad. In particular, in a global input-output framework the ‘net’ value-added produced by _s_ and absorbed abroad (the so-called ‘value-added export’, **VAX** _s_ ) can be computed as follows: 7
**Kenyan Exports and Imports of Dixit-Stiglitz Goods and Other Products in 2007**

|||Trade|value($'00|0)||T|rade Share||||
|---|---|---|---|---|---|---|---|---|---|---|
|Product<br>C|OMESA15|EAC5|EU27|ROW|WLD|COMESA15|EAC5|EU27|ROW|WLD|
|**EXPORTS**||||||**E**|**XPORTS**||||
|All goods|664,849|952,788|1,084,812|1,378,351|4,080,800|0.163|0.233|0.266|0.338|1.000|
|Beverages|46,796|47,692|11,535|61,085|167,109|0.280|0.285|0.069|0.366|1.000|
|Food manu|79,712|98,905|106,990|31,678|317,284|0.251|0.312|0.337|0.100|1.000|
|Printing and|9,987|41,596|129|3,635|55,347|0.180|0.752|0.002|0.066|1.000|
|<br>Mineral fue|15,225|86,515|139|72,263|174,143|0.087|0.497|0.001|0.415|1.000|
|Chemicals|68,878|175,389|1,057|106,367|351,691|0.196|0.499|0.003|0.302|1.000|
|Metals and|129,528|198,787|11,782|80,253|420,350|0.308|0.473|0.028|0.191|1.000|
|Non-metall|10,513|87,666|5,697|10,639|114,515|0.092|0.766|0.050|0.093|1.000|
|Other manu|45,774|88,777|26,412|32,468|193,431|0.237|0.459|0.137|0.168|1.000|
|Agriculture|211,253|29,739|877,333|627,966|1,746,291|0.121|0.017|0.502|0.360|1.000|
|Other good|47,183|97,723|43,737|351,997|540,640|0.087|0.181|0.081|0.651|1.000|
|**IMPORTS**||||||**I**|**MPORTS**||||
|All goods|332,205|191,598|1,812,340|6,653,119|8,989,262|0.037|0.021|0.202|0.740|1.000|
|Beverages|11,958|27,881|15,716|13,650|69,204|0.173|0.403|0.227|0.197|1.000|
|<br>Food manu|73,603|19,352|38,219|436,903|568,077|0.130|0.034|0.067|0.769|1.000|
|Printing and|30,462|7,634|69,199|88,868|196,163|0.155|0.039|0.353|0.453|1.000|
|Mineral fue|45,727|427|60,393|1,811,868|1,918,415|0.024|0.000|0.031|0.944|1.000|
|Chemicals|58,989|4,172|322,652|754,982|1,140,796|0.052|0.004|0.283|0.662|1.000|
|Metals and|60,085|12,273|958,236|2,461,164|3,491,757|0.017|0.004|0.274|0.705|1.000|
|Non-metall|5,118|491|30,219|90,373|126,201|0.041|0.004|0.239|0.716|1.000|
|Other manu|7,117|2,616|152,026|257,025|418,784|0.017|0.006|0.363|0.614|1.000|
|Agriculture|33,340|96,683|64,962|328,230|523,215|0.064|0.185|0.124|0.627|1.000|
|Other good|5,804|20,070|100,720|410,055|536,649|0.011|0.037|0.188|0.764|1.000|

Source: Based on UN COMTRADE Statistics.

86
For youth in the labor force, unemployment is a big problem. Youth unemployment topped in 2002 at 38.8 percent in urban areas according to a nationally representative household survey (PNAD); with a higher rate for female than for male youth (Justesen and Verner 2007b). Lack of jobs is an important reason for youth not to be employed, but youth also perceive lack of experience, low age, lack of education, and the lack of social connections to be important issues (Table 3.8). Thus, social relations including memberships in sport clubs, etc., can be a protective factor easing the transition into the labor market for youth, as well as benefiting the well-being of youth in other ways. Few youth find discrimination to be a reason for being without a job. This is partly explained by less than 5 percent of the sample being indigenous; a group particularly vulnerable to (racial) discrimination. Exposure to discrimination is an obvious risk factor for marginalization.

|Table 3.8: Perceived Reason for N<br>ArgentineYouth,2005 (percent)|ot Working,||
|---|---|---|
|Reason|Female|Male|
|Not enough experience|30.8|23.0|
|Not enough education|11.1|13.5|
|Not have specific requirement|1.7|2.7|
|Not have social connection|7.7|6.8|
|Not have characteristics required|2.6|2.7|
|Not enough work|12.8|13.5|
|Discrimination|3.4|2.7|
|Too young|15.4|16.2|
|Too old|0.9|0.0|
|Other reasons|13.7|18.9|
|Total|100.0|100.0|
|_Note_: Only youth looking for a job are inclu<br>_Source_: Own calculations on ESCVJA(four|ded.<br>urban areas)||

# **3.4 Substance Abuse**

Substance abuse is an undesirable outcome for youth, because it is associated with negative effects on work ability, human capital accumulation, health, and school dropout (e.g., Chatterji and DeSimone 2005).

Frequent drinking and smoking is common among Argentine youth and generally more so for young males than for females (Figures 3.5 and 3.6). While 4 to 6 percent of 15 year old Argentines drink frequently, the proportion quickly increases, and at the age of 18, 17 and 33 percent of females and males, respectively, drink frequently. It appears that after a few years of experimenting around the legal drinking age (18 years), the use of alcohol and tobacco drop to a lower level. Toward the end of youth, a new increase in the use of alcohol and tobacco appears.

12
# **References**

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Informal Employment and Worker’s Wellbeing in Russia / June 2019 28
**Table 5: Sectoral gravity model**

|||**OLS**|||**PPML**||
|---|---|---|---|---|---|---|
|VARIABLES|(1)<br>FDI Stock<br>(Logged)|(2)<br>FDI Stock<br>(Logged)|(3)<br>FDI Stock<br>(Logged)|(4)<br>FDI Stock<br>(Logged)|(5)<br>FDI Stock<br>(Logged)|(6)<br>FDI Stock<br>(Logged)|
|IPA Sector Targeting|0.192***|0.164***|0.114***|0.198***|0.133***|0.0987***|
||(0.0558)|(0.0278)|(0.0290)|(0.0387)|(0.0208)|(0.0201)|
|GDP Source Country (Logged)|0.798***|||0.582***|||
||(0.0292)|||(0.0171)|||
|GDP Host Country (Logged)|0.390***|||0.301***|||
||(0.0348)|||(0.0216)|||
|Source-Host Distance in Km (Logged)|-0.371***<br>(0.0566)|||-0.292***<br>(0.0396)|||
|Common Language|0.686***|||-0.0913|||
||(0.204)|||(0.0872)|||
|Common Colonizer|1.306***|||0.371|||
||(0.305)|||(0.505)|||
|Common Religion|0.549***|||0.571***|||
||(0.165)|||(0.104)|||
|Regional Trade Agreement|0.0216|||0.0540|||
||(0.126)|||(0.0964)|||
|Year F.E.|YES|NO|NO|YES|NO|NO|
|Country-Pair F.E.|NO|YES|YES|NO|YES|YES|
|Source-Year F.E.|NO|YES|YES|NO|YES|YES|
|Host-Year F.E.|NO|YES|YES|NO|YES|YES|
|Sector-Year F.E.|NO|NO|YES|NO|NO|YES|
|Host-Sector F.E.|NO|NO|YES|NO|NO|YES|
|Constant|-19.45***|1.080***|1.143***|-15.72***|0.960***|1.150***|
||(1.060)|(0.0354)|(0.0369)|(0.742)|(0.0267)|(0.0258)|
|Observations|85,718|92,830|92,830|85,718|85,982|85,982|
|R-squared|0.367|0.620|0.696|0.399|0.481|0.569|

Note: Author’s calculations. Robust standard errors in parentheses, clustered at the country-pair-sector level. FDI stock is defined at bilateral, sectoral level in USD Million (Logged) in constant 2018 terms. OLS = Ordinary Least Squares. PPML = Poisson pseudo-maximum likelihood estimation. FDI = Foreign Direct Investment. IPA = Investment Promotion Agency. KM = Kilometers. GDP = Gross Domestic Product. F.E = Fixed Effects. *** p<0.01, ** p<0.05, * p<0.1 To further gauge the relative effectiveness of IPAs, we use the model from equation 6, and include an additional country-interaction effect with the IPA sectoral targeting coefficient. To maintain the anonymity of respondents in the WB-WAIPA survey, we will not make public the names of the specific countries, but instead focus on aggregate results only. Their results are summarized in Table 6 and presented in full detail in Table 7. These findings suggest that there is considerable heterogeneity between IPAs in their ability to affect FDI stocks. Overall, 10 countries have positive and statistically significant results (which we will classify as “high performing IPAs” in the next section). An additional 13 IPAs have statistically insignificant results, while 3 IPAs have negative and statistically significant results (which we will jointly classify as “other IPAs” in the next section). Finally, 10 IPAs fell out of the country-interaction equation as they either did not have sectoral targeting or exhibited no variation in the timing of their 15