rafmacalaba/datause-encoder-data huggingface dataset:
In this case for example, the sampling strategy led to very few women being interviewed. Respondents report an average income that is almost twice as high as the national average in Nepal and they were also much better educated than the national population (18% had completed higher education compared to 10% nationwide). Further, only 14% undertook agricultural work<sup>39</sup> compared to 75% of the national population.<sup>40</sup> Leading to a sample that can hardly be considered representative.

There are several explanations for this misrepresentation. Most critically, the survey only covered 32 of 75 Nepali districts and was not able to reach the most remote areas. The highest response rates were achieved in Kathmandu, the capital, explaining why the sample is skewed toward the more educated and wealthy. Further, the sampling strategy was built around a community network which is heavily focused on men, contributing to the fact that the survey only reached very few women (87.6% of respondents were assigned male at birth, 12% female and 1% intersex).<sup>41</sup>

# _Methodological takeaways:_

- Extensive consultation and training are useful when surveying a population that is being marginalized and excluded. In this case, it helped avoid the problems that the Nepalese government encountered when conducting their census. One of the main findings of the survey was the diversity of terms used in Nepal to describe sexual orientation, gender identity, and expression. Providing respondents with locally relevant options to questions about sexuality and gender identity was essential. Designing the survey in a way that allows such diversity needs to be balanced with a way to aggregate data and allow some sort of generalization during data analysis.

- Snowball surveys like this provide an opportunity to get an in-depth understanding of discrimination and exclusion in a community (even if extrapolation to the population at large is difficult) that would be impossible to cover in a census, given space limitations.

- Sampling remains a critical challenge. Many LGBTI people in more rural parts of the country were unable to participate in the survey like women who were only partially reached. It’s also unlikely that the survey reached many sexual and gender minorities that hide their sexual orientation, gender identity and expression, and intersex characteristics since they would avoid the networks that were used to distribute the survey.  Similar issues have been faced with other hard to survey populations; in Section 6 we provide an overview of some of the lessons learned from those populations.

> 39 ibid.

> 40 CIA. <u>The World Factbook.</u>

> 41 UNDP; Williams Institute. 2014. Surveying Nepal’s Sexual and Gender Minorities: An Inclusive Approach.

13
Consequently, we also report both IV and GMM results, using both lags of righthand side variables as instruments and a number of appropriate excluded instruments for the endogenous regressors. If we allow for separate effects of foreign capital, employment, and trade flows from low and high income destinations, we have already six endogenous variables (in addition to import penetration at the sector level) that we need to instrument. For capital abroad, we use the following instruments: capital controls, distance, a dummy for the use of a common language, C02 emissions in metric tons per capita, the percentage of child labor, fixed line and mobile phone subscribers per 1000 people, internet users per 1000 people, and number of telephone main lines per 1000 people. The last three measures capture the ease with which parents are able to communicate with their affiliates and should be positively correlated with investment abroad. Emissions and child labor are also likely to adversely affect foreign investment, as firms now care increasingly about corporate responsibility. A dirty environment is increasingly regarded as a potential liability, in addition to the problems incurred in trying to manufacture high quality goods in a dirty environment.

For intra-firm trade, as instruments we use air transport (in million tons per kilometer), aircraft departures, and trade agreements. These all are correlated with bilateral trade but should be excluded from the estimating equation. Finally, candidate instruments for employment in high and low income locations include the total labor force in each affiliate location, the percentage of the labor force engaged in manufacturing, the percentage of national income spent on education, the local unemployment rate, and the number of PCs per 1000 people. The measures we use determine both the supply of labor available as well as the quality of that labor, yet should only affect U.S. labor market outcomes through their impact on the choice of employment in affiliate locations.

B. Imports and Exports using Prices instead of Quantities In Table 2 in the paper, we examine the impact of import penetration and the quantity of exports on worker’s wages. It is worth examining the impact of prices of imported or exported goods on workers wages, since most traditional models of trade presume that changing relative prices are the catalyst for wage effects. In Table A4, we replace our quantity measures for imports and exports with price series data for each. Our data on the prices of imports were generously provided by Robert Lawrence, which catalogue prices within manufacturing by 4-digit NAICS industry codes. We used a concordance to translate this into prices for each CPS industry. Our data on export prices
**Table 1: Estimated Production of Mineral Commodities**

|Commodity||2005|2006|2007|2008|2009|
|---|---|---|---|---|---|---|
|Aluminum|metric tons|--|--|--|10,600|13,000|
|Gold|kilograms|30|50|180|200|100|
|Iron ore, gross weight|thousand metric tons|60|88|58|62|99|
|Lead:|metric tons||||||
|Lead-zinc ore|metric tons|--|492,000|582,000|644,000|600,000|
|Metal,refined|metric tons|5,000|5,000|5,000|5,000|5,000|
|Tin:|||||||
|Gross weight|metric tons|1,700|1,818|229|240|230|
|Sn content|metric tons|1,300|1,400|180|185|180|
|Cement,hydraulic|thousand metric tons|2,700|3,300|4,700|5,000|5,000|
|Gypsum|metric tons|150,000|169,000|579,000|300,000|300,000|
|Coal,bituminous|metric tons|8,000|530,000|530,000|500,000|450,000|
|Naturalgas:|||||||
|Gross|million cubic meters|59,285|82,064|84,707|80,604|64,883|
|Dry|million cubic meters|36,282|53,479|57,400|58,793|46,895|
|Petroleum:|||||||
|Crude|thousand 42-gallon barrels|923,500|869,197|803,000|768,800|780,348|
|Refinery products:|thousand 42-gallon barrels|64,900|39,400|18,012|33,147|15,771|

_Source:_ USGS 2009.

8. Oil and gas production is concentrated in 9 out of 36 states in Nigeria—Abia, Akwa Ibom, Bayelsa, Cross River, Delta, Edo, Imo, Ondo, and Rivers, with the five core states accounting for more than two-thirds of production. The Niger Delta basin is the largest along the West African coast; it has 246 production fields and 3,446 active wells. According to the estimates by BP (2010), Nigeria has proven reserves of over 37 billion of petroleum barrels and more than 5 trillion of cubic meters of natural gas in 2008 (Figures 1-2). Nonetheless, while proven reserves of petroleum have risen steadily, petroleum production fluctuates during 1996-2008 and notably took a dip from 900 million barrels in 2004 to below 800 million barrels in 2008. For natural gas, proven reserves increase substantially from 1.2 trillion cubic meters in 1980 to 5.2 trillion cubic meters in 2008 which is in sharp contrast with the modest increase in other Africa‘s proven reserves during the same period.

9
|Slovenia|All|100%|No specific limit, unless specifically disclosed|
|---|---|---|---|
|Spain|All|100%|No specific limit (OECD). Limit for assets not admitted to be traded on a regulated market. Only investments in OECD<br>countries is permitted. In this case, offshore investing is forbidden by the Law.|
|Sweden|All|100%|Friendly societies: No specific limit (World) Life insurance undertakings: No specific limit (World) Providers of<br>occupational retirement pensions: No specific limit (World)|
|Switzerland|All|100%|No specific limit (World)|
|Thailand|All|100%|100% but subject to the following conditions: - Securities must be regulated by regulator that is an IOSCO member,<br>including Cambodia, Lao PDR and Myanmar; - Listed instruments must be listed on an exchange that is a full<br>member of the World Federation of Exchanges.|
|Turkey|All|100%|No specific limit (World)|
|United Kingdom|All|100%|No specific limit (World)|
|United States|All|100%|No specific limit; no additional limitations are applicable. (World)|
|Maldives|All|NA|No specific limit. Maldives Retirement Pension Scheme has not yet started investing in any foreign assets, hence no<br>limit has yet been set for foreign investments.|
||||Private pension funds under VPS - equity sub-fund: No specific limit (World) Private pension funds under the<br>Voluntary Pension System (VPS) - debt sub-fund: No specific limit (World) Private pension funds under the Voluntary|
|Pakistan|All|NA|Pension System (VPS) - commodity sub-fund: No specific limit (World) Other/Comments: Regulations are silent on<br>foreign investments (neither prohibit nor allow), however no pension fund manager has approached SECP for<br>investment in foreign assets. If a request is received, SECP will consider it on merit.<br>Pension System (VPS) - money market sub-fund: No specific limit (World) Private pension funds under the Voluntary|
|Tanzania|All|NA|According to the social security schemes investment guidelines 2015, Off-shore investment by the schemes shall be<br>in accordance with, and governed by the Foreign Exchange Act and Regulations, Directives and Rules issued by the<br>Bank from time to time, in this case offshore investments have only been allowed for east Africa region.|
|**1**|Limit refers to the p|ercentage of funds t|hat is allowed to invest overseas|
|_Source_|_Annual Survey of Inv_|_estment Regulation_|_s of Pension Funds and Other Pension Providers (OECD)_|
Table A.12: The Impact of Initial Conditions on Employment of Male Refugees by Years Since Migration

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|_Variable × Years since migration_|||||||
|Attitudes X 0-2 years|0.00680|0.0137<sup>_∗_</sup>|0.0106|0.00992|0.0172|0.0108|
||(0.00720)|(0.00697)|(0.00687)|(0.0124)|(0.0124)|(0.0125)|
|Attitudes X 3-5 years|0.0525<sup>_∗∗∗_</sup>|0.0450<sup>_∗∗∗_</sup>|0.0418<sup>_∗∗∗_</sup>|0.0481<sup>_∗∗∗_</sup>|0.0486<sup>_∗∗∗_</sup>|0.0439<sup>_∗∗∗_</sup>|
||(0.00695)|(0.00736)|(0.00713)|(0.0120)|(0.0123)|(0.0121)|
|Attitudes X 6-10 years|0.0374<sup>_∗∗∗_</sup>|0.0289<sup>_∗∗∗_</sup>|0.0276<sup>_∗∗∗_</sup>|0.0219<sup>_∗_</sup>|0.0172|0.0144|
||(0.00662)|(0.00716)|(0.00701)|(0.0112)|(0.0117)|(0.0116)|
|Attitudes X 11-15 years|0.0219<sup>_∗∗∗_</sup>|0.0159<sup>_∗∗_</sup>|0.0142<sup>_∗∗_</sup>|0.0133|0.0107|0.00807|
||(0.00675)|(0.00714)|(0.00693)|(0.0121)|(0.0129)|(0.0126)|
|Attitudes X 15+ years|0.0173<sup>_∗∗_</sup>|0.00538|0.00306|-0.00212|-0.00806|-0.0110|
||(0.00695)|(0.00755)|(0.00750)|(0.0144)|(0.0151)|(0.0148)|
|Unemployment X 0-2 years||-0.00559|-0.00670||0.00622|0.00283|
|||(0.00781)|(0.00771)||(0.0124)|(0.0122)|
|Unemployment X 3-5 years||-0.0240<sup>_∗∗∗_</sup>|-0.0244<sup>_∗∗∗_</sup>||-0.00278|-0.00482|
|||(0.00807)|(0.00789)||(0.0123)|(0.0120)|
|Unemployment X 6-10 years||-0.0251<sup>_∗∗∗_</sup>|-0.0265<sup>_∗∗∗_</sup>||-0.00929|-0.0116|
|||(0.00774)|(0.00754)||(0.0119)|(0.0117)|
|Unemployment X 11-15 years||-0.0220<sup>_∗∗∗_</sup>|-0.0231<sup>_∗∗∗_</sup>||-0.00701|-0.00892|
|||(0.00784)|(0.00771)||(0.0126)|(0.0125)|
|Unemployment X 15+ years||-0.0293<sup>_∗∗∗_</sup>|-0.0305<sup>_∗∗∗_</sup>||-0.0111|-0.0129|
|||(0.00870)|(0.00863)||(0.0149)|(0.0150)|
|Network X 0-2 years|||-0.0134<sup>_∗∗∗_</sup>|||-0.0171<sup>_∗∗_</sup>|
||||(0.00297)|||(0.00764)|
|Network X 3-5 years|||-0.0188<sup>_∗∗∗_</sup>|||-0.0159<sup>_∗∗_</sup>|
||||(0.00380)|||(0.00748)|
|Network X 6-10 years|||-0.00293|||0.000465|
||||(0.00328)|||(0.00680)|
|Network X 11-15 years|||-0.00687<sup>_∗∗_</sup>|||0.00358|
||||(0.00344)|||(0.00743)|
|Network X 15+ years|||-0.00561|||0.00471|
||||(0.00399)|||(0.00915)|
|Current year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country of birth FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Years since migration FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Arrival year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Canton of arrival FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Canton of arrival X Current year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Education FE|No|No|No|Yes|Yes|Yes|
|Observations|417214|417214|417214|72236|72236|72236|

Dependent variable is Employment dummy. Network is defined as log (nb of co-nationals+1). All regressions also include age, age squared, gender, marital status. Specifications (1) and (4) also include the variables Unemployment and Network, (2) and (5) also include Network. Standard errors are clustered at the level Canton of arrival _×_ Year of arrival. * p < 0.10, ** p < 0.05, *** p < 0.01 53
<!-- Start of picture text --> Change in Anchored Poverty (percentage points)<br>“13.4<br>( -11.52- -11.52- -8.26 ‘ i<br>[i() -8.26-5.46-- -5.464.01 =<br>[| 4.01 - -2.19<br>208<br><!-- End of picture text -->

<!-- Start of picture text --> Change in risk of poverty rate (percentage points)<br>Gl -5.31 - -5.12 cpeoee ne<br>[iyfp)[_} 4.91 -5.12 -1.35 - -- -1.35 4.91 -0.02 oy=——© 7<br>[_}-0.02 - 0.77<br><!-- End of picture text -->
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- Khan, A. (2000). Adolescents and reproductive health in Pakistan: a literature review. research report No. 11. Islamabad, Population Council and UNFPA.

Lloyd, C. B., C. Mete, et al. (2005). "The effect of gender differences in

- primary school access, type, and quality on the decision to enroll in rural Pakistan." Economic <u>Development and Cultural Change</u> **53** (3): 685–710.

- Maitra, P. and R. Ray (2002). "The Joint Estimation of Child Participation in Schooling and Employment: Comparative Evidence from Three Continents." Oxford Development <u>Studies</u> **30** (1): 41-62.

- Mumtaz, K. and S. Salway (2005). "I never go anywhere: extricating the links between women's mobility and uptake of reproductive health services in Pakistan." Social Science and <u>Medicine</u> **60** : 1751-1765.

- Sathar, Z., M. ul Haque, et al. (2003). Adolescents and youth in Pakistan 2001-2002: a nationally representative survey. Islamabad/New York: Populaion Concil.

- Skoufias, E. and S. W. Parker (2001). "Conditional cash transfers and their impact on child work and schooling." FCND Discussion paper No. 123. International Food Policy and <u>Research Institute. Washington DC.</u>

- Swaminathan, M. (1998). "Economic growth and the persistance of child labor: evidence from an indian city." World Development **26** : 1513-1528.

The World Bank Group (2002). Pakistan Poverty Assessment. Poverty in Pakistan: Vulnerabilities, Social Gaps, and Rural Dynamics. Report No. 24296-PAK. South Asia Region. Washington, DC.

The World Bank Group (2005). Pakistan Country Gender Assessment 2005: Bridging the Gender Gap Opportunities and Challenges. Washington DC, the World Bank Group.

- the World bank Group (2008). "Macroeconomics & Economics Growth in South Asia - Growth in Pakistan. Access at <u>www.worldbank.org</u> on September 9th, 2008."

- The World Bank Group (2008). Pakistan Learning and Educational Achievements in Punjab Schools (LEAPS): Insights to inform the education policy debate. Washington DC, the World Bank Group.

the World Bank Group (2008). "Pakistan Poverty Assessment. Forthcoming. South Asia Region. Washington, DC." 36
TABLE 3: CHINA IN DIFFERENT RANKINGS

|Ranking|World Bank (Aias)<br>(1988)|Pen<br>(1980)|Penn<br>(1985)|Penn<br>(1988)|
|---|---|---|---|---|
|16|Nigr<br>(260$)||||
|17|Uganda<br>(260S)||||
|18|China<br>(290$)||||
|19|Somaia<br>(290$)||||
|20|ToM<br>(290$)||||
|52||Bolivia (1,529Y|)||
|53<br>||Philppines (1,551$)|||
|54||China<br>(1,619$)|||
|_55_||Guyana (1,623$)|||
|56||Thaaind(1,694$)||<br>|
|62||||Botwana<br>(2,282)<br>|
|63|||_|SomonIs.<br>(2,282)<br>|
|64<br>|1|1||China<br>2,308|
|65||_|Algeria(2,142)|Jordan<br>(2,356)|
|66|||<br>Ecuador (2,387|Jamaica<br>(2,362)|
|67<br>||_hina|(2_444)||
|6S<br>|r|_|Turkey<br>(2,533)<br> <br>||
|69h|||Colomnbia<br>(2599|)|
|g||_<br>_<br>_<br>_|<br>||
|Soe:<br>lhe|World Bank ring<br>i take<br>ddkint years ver made b<br>Heston, 1988, 1991).|n from_Workd Deweop_<br>y the autho based on d|_ment_<br>_Repot 1989._The<br>ata mn The Penn Worl|Penn rnidngs in the<br>d Table (Sunmmrs and|

16. Changes in exchange rates either as a resul of a countrys devauation or as a consequence of its decision to allow its rate to float fieely, may lead to comparisons which are not related to relative real growth rates in the counties under compaison For example, per capita GDP of India in 1970 was $1 10 and that of China was $130 in the same year. During the next 20 years, per capita GDP in local currency grew at an arual rate of 5.5 percent in China and of 2.0 percent in India, raising the level of per capita GDP in China rlative to that of India by about 3.5 percent per year (World Bank, 1989b). However, if official exchange rates are used, the real per capita GDP in Clina relative to India did not increase at all ($360 for China and $350 for India in 1990, (World Bank, 1991c)).

8
interest, rainfall, should mainly have an effect in agricultural areas. The locations of rural clusters from each round are displayed in Figure 2. The surveys are based on interviews of women of childbearing age, i.e. between 15 and 49 years old, who were either permanent residents of the household or visitors present in the household on the night before the survey took place. We pool the two waves together to create a sample of about 11,500 child-level records that includes birth dates, and detailed child health characteristics.

# Figure 2

We have decided to focus on the younger children (i.e. those between 0 to 35 months of age) for several reasons. Firstly, young children are believed to be especially vulnerable to shocks that lead to growth faltering. Secondly, body size at adulthood is strongly correlated with stature at age three, and that after this age children from even very poor localities will grow as quickly as children in developed economies. In turn, adult height is correlated with earnings and productivity, poorer cognitive outcomes, and premature mortality due to increased risk of cardiovascular and obstructive lung disease (Hoddinott and Kinsey, 2001). In addition, children experiencing slow height growth usually perform less well in school, score poorly on test of cognitive function, have lower activity levels, and fail to acquire skills at normal rates. Finally, the evidence suggests that the period since birth to age three is the most critical period for the development of the immune system.

We removed biologically implausible values (most likely as the result of errors in measurement or data entry) using the cut-offs recommended by the WHO. Thus, data were excluded if a child’s HAZ was below -6 or above +6 (1,569 observations), or WHZ was below -5 or above +5 standard deviations (1,321 observations). Our final sample has about 10,000 children age 0 to 35 months living in rural areas.

The summary statistics can be compared with the international reference group, which has an expected mean Z-score of 0 for all normalized growth indices. In general, Z-scores 2 standard deviations below the reference are associated with severe growth retardation in the case of HAZ, and acute malnutrition for WHZ. The distributions of HAZ and WHZ for children below 36 months of age of both rounds of the Nigeria DHS are displayed in Figure 3. The distribution of height-for-age Z-scores is highly skewed to the left implying that growth retardation is very prevalent among young children of rural areas. The average child is 1.5 standard deviations shorter than the international reference mean, with the two frames might have used a different classification of EAs as rural or urban. However, our results are not qualitatively different if we only use the (larger) 2008 DHS.

14
<!-- Start of picture text --> Exports Imports<br>20 25<br>20<br>15 10.010.9<br>6.2 15<br>10<br>1.8 3.6 10<br>5 5 10.5<br>6.8<br>0 0<br>-5<br>-5<br>-10<br>-3.7<br>-10<br>-15<br>-15 -20<br>Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015 Q3 2015 Q4 2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018 Q1 2014 Q2 2014 Q3 2014 Q4 2014 Q1 2015 Q2 2015 Q3 2015 Q4 2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018<br><!-- End of picture text -->

# **4. Continued Challenges for the Indian Economy**

In this section we take a look at the past episodes of high growth rates in India, defined as those when growth reached 8 percent or higher, and ask whether similar levels of growth rates seem feasible in the near term, and what kind of challenges may need to be overcome for growth to move to a higher rate trajectory. We also discuss whether there is any room, or rationale, for countercyclical policies to support growth, and how is the external environment is poised to support a higher growth rate in India.

# **4.1   Past episodes of high growth**

A review of the data since 1971 reveals that there have not been many episodes when annual growth rate exceeded 8 percent. There have been six episodes over the last five decades, for a total of 11 years (including two years when the growth rate was 7.9 percent), when the growth rate in each year neared 8 percent or higher. With the exception of a five-year period, 2003–04 through 2007–08, most of these episodes of high growth did not sustain for more than a year (Table 4). Rather growth acceleration lasted for only one year, and corrected sharply a year later (Figure 15).<sup>36</sup> In some of these episodes, high growth was on account of an unusually good agricultural output (1976, 1989), due to the low base impact of slow growth in the previous year. In others, it was an outcome of stimulative fiscal or other macroeconomic policy (such as 2010–11), and hence proved to be unsustainable. The only durable episode that lasted from 2004 to 2008, was dependent on a comprehensive reform agenda, an unusual buoyancy in the global economy, and easy global liquidity.<sup>37</sup>

> 36 This is not unusual, as cross-country experience shows that a large percentage of high-growth episodes unravel within years (see Berg et al. (2008); and Pritchett and Summers (2014)).

> 37 See Panagariya (2018) for a discussion of how reforms undertaken in the 1990s and early 2000s translated into higher growth subsequently.

30