rafmacalaba/datause-encoder-data huggingface dataset:
|**HS**<br>**code**||||**Year**|||
|---|---|---|---|---|---|---|
|**2 digits**|**_Type of products_**|**2001**|**2002**|**2003**|**2004**|**2005**|
|||162|162|162|162|162|
|52||9.0|9.9|9.9|10.1|10.2|
||_COTTON_|<br>_(6.1)_|<br>_(6.0)_|<br>_(5.8)_|<br>_(5.8)_|<br>_(5.7)_|
|||43|43|43|43|43|
|53|_VEGETABLE TEXTILE_|7.0|7.3|7.3|7.3|7.1|
|||_(3.7)_|_(3.5)_|_(3.6)_|_(3.6)_|_(3.6)_|
|54|_MANMADE FILAMENTS_|89<br>9.8|89<br>9.8|89<br>9.8|89<br>9.5|89<br>9.5|
|||_(2.4)_|_(2.6)_|_(2.5)_|_(2.9)_|_(2.9)_|
|||170|170|170|170|170|
|55|_MANMADE STAPLE FIBRE_|7.7|7.8|7.8|7.8|7.8|
|||_(2.6)_|_(2.5)_|_(2.7)_|_(2.6)_|_(2.6)_|
|56|_WADDING FELT_|68<br>13.8|68<br>8.1|68<br>8.0|68<br>8.0|68<br>8.1|
||_,_|_(48.9)_|_(5.3)_|_(5.0)_|_(5.4)_|_(5.1)_|
|57||39<br>18.9|39<br>21.7|39<br>21.6|39<br>18.8|39<br>19.1|
||_CARPETS_|_(6.8)_|_(14.6)_|_(7.2)_|_(4.4)_|_(2.9)_|
|||56|56|56|56|56|
|58|_SPECIAL WOVEN_|19.6|19.7|19.6|19.8|19.6|
|||_(1.4)_|_(1.2)_|_(2.1)_|_(1.0)_|_(2.5)_|
|||43|43|38|38|38|
|59|_TEXTILE FAB COATED_|6.3|7.0|8.0|6.8|6.7|
|||_(4.0)_|_(5.9)_|_(9.8)_|_(4.7)_|_(4.1)_|
|||65|65|65|65|65|
|60|_KNITTED OR CROCHETED_|9.9|9.7|9.8|9.9|9.8|
|||_(0.5)_|_(1.2)_|_(0.8)_|_(0.4)_|_(1.1)_|
|||176<br>|176<br>|176<br>|176<br>|176<br>|
|61||20.8|24.1|26.9|22.4|20.2|
||_ARTICLES OF APPAREL KNIT_|_(7.8)_|_(14.7)_|_(15.0)_|_(11.7)_|_(4.7)_|
|||204<br>|204<br>|204<br>|204<br>|204<br>|
|62||22.1|23.9|28.0|20.4|19.9|
||_ARTICLES OF APPAREL_|_(15.6)_|_(20.9)_|_(27.6)_|_(6.4)_|_(4.8)_|
|||94|94|94|94|94|
|63||20.3|22.0|24.9|18.9|18.6|
||_OTHER TEXTILE ARTICL_|_(17.3)_|_(14.6)_|_(31.9)_|_(8.0)_|_(6.8)_|
|||82|82|82|82|82|
|64|_FOOTWEAR_|20.1|30.4|20.2|29.8|25.0|
|||_(9.2)_|_(20.1)_|_(13.6)_|_(21.4)_|_(9.3)_|

51
These results are at odds with the predominantly held view (of economists) that vocational schooling is a socially inefficient form of education, particularly in relation to traditional academic schools. Most follow-up evaluation studies show vocational schools to be cost-ineffective compared with academic schools. However, the present paper shows the importance of broadening the scope of these evaluation studies, particularly in relation to labor market outcomes. Too often such studies concentrate on earnings, without taking into account such intervening variables as type of occupation and its relevance to vocational studies pursued. In this case study, we have seen that such considerations are central to a proper underst.nding to the labor market outcomes of vocational schooling. Future evaluation studies will need to pay more attention to issues of curriculum (including the type and scope of vocational studies), as well as to the nature of the occupation followed and its relationship with prior courses of study pursued.

- ii -
the model assigns walking speeds to off‐road travel and vehicle speeds to on‐road travel. The routing algorithm therefore prefers road network routes when available.

# Calculation for on-road travel

Road network type, condition and geographic zone (Plain, Rolling, Hill, Mountain) determine the generic average vehicle speeds for each roadway type. Slope information from the DEM is attached to each element of road network data in order to categorize the speed of the vehicle by a combination of slope angle, road surface and road type.

Strategic Road Network (SRN) and District Road Core Network (DRCN) speeds (model Classes 1 and 2) are derived from the Road Classes II and III (half of the design speed) of the Nepal Department of Roads’ “Nepal‐ Road‐Standard‐ 2070”. Class 4 speeds were determined based on reported walking path travel times from consulted organizations and individuals (notably staff from the World Food Programme and Rural Access Programme) (WFP 2018, RAP 2018).

Strategic Urban Roads (SUR), Urban Roads (UR) and unpaved DRCN speeds (model Class 3) were determined using the results of the HRVS survey. The vehicle travel speed for each household was estimated by the below equation 3. The reported distance and time to financial institutions (banks) was employed to maximize the denominator and numerator of the equation and so reduce error. The estimated average vehicle speed was roughly 11 km/hour and the average on‐road slope was approximately 27% in Nepal (based on GIS calculations using DEM and road network data). These estimations were approximately half the speed for Class 2 roads in equivalent (mountainous) slopes.

Extrapolating from this, we set the speed for Class 3 to half of Class 2 for all slope categories. These speeds were then confirmed via consultations with partners.

## **_Equation 3_**

Where: Db: Distance to bank V: estimated vehicle travel speed of Dr: Distance to road V=<sup>Db −Dr</sup> Tb −Tr the household Tb: Time to bank Tr: Time to road However, DRCN / SUR / UR / VR road types can mislead as they indicate the relative importance, not the actual size or speediness, of a given highway segment. They may also reflect road future road development plans instead of the current road state. For example, DRCNs in some areas are paved whilst in others they are improved walking paths. The latter case is typical of mountainous areas and the hills and mountains of the Far West region in particular.

Therefore, some manual adjustments were made:

- All road segments above 2400m and all Far Western DRCNs in the Hills were assigned to class 4, walking paths, unless explicitly marked as paved within the RAI data set.

- Planned roads, VR and unpaved OSM highway segments tagged as smaller than “tertiary” were also assigned to class 4, paths.

- Similarly, SRNs in the Hill and Mountain areas of the Far West are usually rough dirt roads instead of paved highways. Therefore, they were assigned to class 3 instead of class 1.
framework<sup>26</sup> . However, that is only a solution for the first few years and the problem remains not only how to build in the necessary flexibility but also how to exit the constraints after the initial period.

Although the Prayas Report demonstrates clearly that there is a very long way to go before ERCs meet the standards of good regulatory governance, they do not argue that the effort has been a waste of resources. Indeed, they are (cautiously) optimistic about the potential development of the ERCs as effective regulatory agencies.

Given the starting circumstances, it is quite possible that the developments to date are a significant improvement on the previous implicit regulation by State Government agencies. (The Prayas Report dos not assess this.) They certainly seem to have provided some openness, division of powers, pressure for commercialization and open debate. Indian ERC development to date may be all that could reasonably have been expected. However, the key question unfortunately remains completely open. Is the glass half-empty or half-full? What are reasonable expectations in this area – for SubSaharan Africa, Russia and other CIS countries as well as for India and other Asian economies?

The fact that the Prayas Report provides the evidence with which to conduct such a discussion again shows the potential for using structured case studies to examine these issues. To assess the impact of regulatory arrangements on outcomes of course requires marrying data on investment, consumption, etc with regulatory data – but the Prayas Report confirms that this regulatory data must go a lot further than scrutiny of legal and related documents.

It is only unfortunate that the Prayas report did not code its findings (a) on regulatory actions and (b) on the formal requirements along the lines of the 1998 NERA report (and Stern-Holder). A sound and robust coding framework is the essential next step in further work of this kind.

# **3.3 The EBRD Study of Power Sector Regulatory Reform in Transition Economies and Similar Studies**

# **3.3.1 The EBRD 2003 Study**

This 2003 study by David Kennedy reports results from a survey carried out by EBRD in conjunction with the World Bank. Tables 2-4 of the Kennedy study report performance with respect to 6 governance criteria<sup>27</sup> . The criteria are:

- Separate (non-Ministry) regulator

- Fixed term appointment for regulator

- Funding by industry (eg by licence fee)

- Full tariff setting powers (ie tariff decisions made by regulator)

> 26 See, for instance, Bakovic, Tenenbaum and Woolf (2002) who argue this case for electricity distribution.

> 27

> See Kennedy (2003), pp 10-11

20
<mark>Schonlau, M., Van Soest, A., Kapteyn, A., & Couper, M. (2009). Selection bias in web surveys and the use of propensity scores.</mark> _<mark>Sociological Methods & Research</mark>_ <mark>,</mark> _<mark>37</mark>_ <mark>(3), 291-318.</mark> <mark>Skitka, L. J., & Bauman, C. W. (2008). Moral conviction and political engagement.</mark> _<mark>Political Psychology</mark>_ <mark>,</mark> _<mark>29</mark>_ <mark>(1), 29-54.</mark> <mark>Smyk, M., Tyrowicz, J., & Van der Velde, L. (2021). A cautionary note on the reliability of the online survey data: the case of wage indicator.</mark> _<mark>Sociological Methods & Research</mark>_ <mark>,</mark> _<mark>50</mark>_ <mark>(1), 429464.</mark> <mark>Steinmetz, S., & Tijdens, K. (2009). Can weighting improve the representativeness of volunteer online panels? Insights from the German Wage indicator data.</mark> _<mark>C&M Newsletter</mark>_ <mark>,</mark> _<mark>5</mark>_ <mark>.</mark> <mark>Steinmetz, S., Bianchi, A., Tijdens, K., & Biffignandi, S. Improving web survey quality– Potentials and constraints of propensity score weighting.</mark> _<mark>Online Panel Research: A Data Quality Perspective. Wiley</mark>_ <mark>.</mark> <mark>Szolnoki, G., and D. Hoffmann (2013), “Online, Face-to-Face and Telephone Surveys— Comparing Different Sampling Methods in Wine Consumer Research,” Wine Economics and Policy, 2, 57–66.</mark> <mark>UNESCO Institute of Statistics, 2022.</mark> Other Policy Relevant Indicators (OPRI) <mark>, 2022. Link:</mark> <u><mark>https://apiportal.uis.unesco.org/bdds. Last accessed on April 5, 2022.</mark></u> <mark>Valliant, R., & Dever, J. A. (2011). Estimating propensity adjustments for volunteer web surveys.</mark> _<mark>Sociological Methods & Research</mark>_ <mark>,</mark> _<mark>40</mark>_ <mark>(1), 105-137.</mark> <mark>Vehovar, V., Manfreda, K. L., & Batagelj, Z. (1999, May). Web surveys: Can the weighting solve the problem. In</mark> _<mark>Proceedings of the Section on Survey Research Methods, American Statistical Association</mark>_ <mark>(pp. 962-967).</mark> 27
# **5.1 Legal Status and Bargaining Power**

Employing an immigrant entails an expected cost as employers get fined if the employment contract is monitored and the immigrant does not have a valid work permit.<sup>22</sup> The data allows to explicitly test for the role of the legal status as about 8% of immigrants state in the search result that they have a work permit. One straightforward prediction of this mechanism is that within the group of immigrants, those with work permits receive more profile clicks. I test this hypothesis by including a dummy for workpermit ( _permit_ ) in specification (1).

Column 1 in Table 2 shows that among foreigners having a work permit in significantly _negatively_ correlated with the number of profile clicks in the pooled sample. Stating that one posesses a work permit reduces the number of profile clicks by about 7 percent.<sup>23</sup> While these findings are surprising at first glance, they are consistent with a theory that employers prefer job seekers that are exploitable. The DoL (2007) speculates about the reasons for anecdotal evidence showing lower unemployment among immigrants: “ _The usual advantages of irregular employment (low wages, vulnerability, exploitative conditions) may be at the core of this preference_ [for non-South African workers]”.

Consistent with this exploitation hypothesis is the finding that job seekers stating that they are willing to live with employers or expressing that they need a job urgently receive more profile clicks (Table 2). An alternative plausible theory is that employers infer from reading that somebody holds a work permit that the person demands a higher wage and reading that somebody is ‘urgently’ looking for work may signal that she is less likely to shirk on the job. However, results from the anonymous phone survey do not support this explanation. Undocumented immigrants do not have lower reservation wages are not willing to work longer hours than documented immigrants (Table 4).

Results in Table 4 point to an alternative explanation for the preference of undocumented over documented immigrants: bargaining power. South Africa instituted the Commission for Conciliation, Mediation and Arbitration (CCMA), an administrative tribunal at which employers can bring cases against employer mistreatment at no charge. If disputes are not resolved and no arbitration is reached, cases are referred to labor courts. About 70% of the formal workforce fall under the juristriction

> 22As per the Labour Act (Section 49), “ _anyone who knowingly employs an illegal foreigner or a foreigner in violation of this Act shall be guilty of an offence and liable on conviction to a fine or to imprisonment not exceeding one year_ .”

> 23This finding also provides evidence against the explanation that employers prefer South Africans because they are more likely to remain in the Western Cape area since, controlling for other factors, immigrants with a work permit can be expected to be more established and thus stay in South Africa for longer.

18
What then would be the "ideal" data collection scheme in a developing country, aimed at policy-relevant analysis of poverty and welfare? Base-line data would be provided by an integrated survey collecting full information on household expenditure and income and on basic needs fulfillment. The survey would incorporate methodological improvements particularly with regards to the reference and recall periods for expenditures, and the collection of income of non-farm household enterprises. This survey would be repeated every five years and cover a sample of three- to four-thousand households, using a sample design which ensures adequate representation of all important socio-economic and target groups. These groups would be identified before the design of the survey collaboratively by data producers and data users.

In the intervening years, poverty would be monitored with a lighter survey focusing on household expenditure and selected basic needs indicators. The sample could now be much larger if resources permit this and the survey would be repeated at least once a year, possibly more frequently if the country is undergoing rapid macroeconomic or structural change. Parallel to this, a small panel of households would be surveyed, drawn from the same master sample and runniig over the same period as the monitoring survey.

Both the integrated and the monitoring survey would have a well-integrated community module to obtain information on health and education facilities and other relevant infrastructure. At the same time, price collection in the country would be improved to cover more items, especially non-food, and be extended to the rural areas. In this way, the consumer price index would be useable as a deflator for household expenditure in the context of welfare analysis.

We do not know that any one country in Africa is currently embarked on this road, but several changes have been introduced recently in various countries which go in the right direction. We are particularly looldng forward to the analysis of the Priority Surveys which in the past twelve months have been started in more than a dozen SubSaharan African countries and which come close to the monitoring survey we have in 20 _
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All explanatory variables are measured at the beginning of the export spell (at time _t_ 0), except for the stock market liberalization dummy, which takes value 1 if the liberalization event occurs before the initiation of a spell or during the life of a spell.<sup>15</sup> Since the stock market liberalization variable varies across time and countries, we cluster the reported standard errors at the country-time level.

The main variable of interest in our difference-in-differences framework is the interaction between stock market liberalization and the product’s distance to comparative advantage ( _StMLibc,t_ 0 _∗ distanceck,t_ 0). A positive coefficient _β_ 2 would suggest a higher hazard rate when stock market liberalization dummy equals one and the exported product is far away from the exporting country’s comparative advantage. In other words, products disproportionately relying on scarce factors disappear from the country’s export portfolio at a faster rate after foreign investors are allowed to acquire equity stakes in domestic companies.

The liberalization dummy _StMLibc,t_ 0 is based either on the year of the official stock market liberalization or on the year of the first sign that stock market liberalization occurred. Alternatively, we also use two versions of a continuous variable capturing the intensity of stock market liberalization at the industry-country level. The stock market liberalization variable becomes in this case _StMLibic,t_ 0 (i.e., it varies both across industries and across countries) and the corresponding specification writes: A positive coefficient _β_ 2 would suggest that an increasing intensity of stock market liberalization in a given country and industry accelerates the demise of exported products far away from the exported country’s comparative advantage.

Depending on specification, the vector of control variables _Ycki,t_ 0 contains various interaction terms controlling for alternative channels that could be correlated with the mechanism captured by the main interaction term. We interact the stock market lib-

> 15 We slightly abuse the notation and use subscript _t_ 0 also in the case of stock market liberalization: _StMLibc,t_ 0

14
competition does not generate additional jobs through exciting non-winners to start businesses. It is possible the publicity and attention given to entrepreneurship motivates others to start businesses. Finally, note that this employment impact is the direct impact, and does not include any multiplier effects induced by the firms increasing demand for products of other firms, and by the firm owners and their employees increasing consumption of products made by other firms.

# **5.3 Cost Effectiveness for Employment Generation**

The first round gave the winners 9,240 million Naira (US$58 million) in grants. The cost of administering the first round of program including scoring the applications, running the 4-day business plan training, and administering the tranche payments is estimated at $2 million. At the end of the third year, the estimate from Table 5 is that 7,027 jobs were directly generated as a result of the program. Thus the cost per job created is US$8,538. The average wages of the jobs created in these firms is US$143 per month according to our survey reports, so the cost per job is equivalent to approximately 60 months of employment.

By way of comparison, the White House Council of Economic Advisors estimates that the cost per job-year created for different types of fiscal stimulus in the United States range from $92,136 for government spending to $145,351 for tax cuts.<sup>16</sup> Aggregating the job impacts over the three years in Nigeria, we have a cost of $3606 per job-year. Scaling for per-capita GDP differences would take this to $64,000. Thus even under the extreme assumption that all of the jobs created completely disappeared after our surveys, this program would be more effective at creating jobs than fiscal stimulus in the U.S.

The cost per job created also compares favorably to many job creation policy efforts in developing countries, which have struggled to find significant effects on employment. Appendix 8 provides some examples. The estimated cost per job created by wage subsidy and vocational training programs ranges from $11,000 to $80,000, with many of these studies not finding statistically significant impacts. Most studies of finance and training programs for microenterprises have at best found impacts on generating self-employment, but often offset by a reduction in wage employment of the potential entrepreneur (McKenzie and Woodruff, 2014), with little discernable

> 16 <u>https://www.whitehouse.gov/administration/eop/cea/Estimate-of-Job-Creation/</u> [accessed July 29, 2015].

24
# **Figure 9: Goods Exports of Low and Middle Income Countries 2009, Percentage Change (YTY)**

<!-- Start of picture text --> Europe and Central Asia East Asia & Pacific<br>‐35.7%‐31.1% ‐21.0% ‐2.8% 8.8%Bosnia‐Herzegovina ‐23.4%‐20.5%‐19.7%‐13.8% ‐1.2% China<br>‐41.6%‐36.5%‐29.0% Bulgaria ‐21.5%‐13.8%‐12.4% Hong Kong<br>‐13.0%<br>‐35.1% ‐31.9%<br>‐34.5% ‐26.2%<br>‐22.3% Czech Republic ‐19.3% Indonesia<br>‐5.4% 13.5%<br>11.3%<br>‐35.7% ‐28.9%<br>‐34.7% ‐33.3%<br>‐22.1% Hungary ‐26.4% Malaysia<br>‐30.0% ‐36.8%<br>‐36.2% ‐25.2% Poland ‐28.9%‐21.5% Philippines<br>‐5.5% ‐8.3%<br>‐47.9% ‐32.8%<br>‐46.1% ‐30.8%<br>‐39.7% Russian Federation ‐22.4% Singapore<br>‐22.3% ‐3.6%<br>‐32.6% ‐37.1%<br>‐33.3%‐24.8% Slovenia ‐32.5% ‐20.8% Taiwan<br>‐8.8% ‐5.0% 19.9%<br>‐35.7%‐33.2%‐30.4% Turkey ‐23.6%‐23.2%‐16.7% ‐1.2% Thailand<br>20.3%<br>2009‐I 2009‐II 2009‐III 2009‐Oct 2009‐Nov 2009‐I 2009‐II 2009‐III 2009‐Oct 2009‐Nov<br><!-- End of picture text --> <!-- Start of picture text --> Latin America & Caribbean<br><!-- End of picture text --> <!-- Start of picture text --> ‐26.0%<br>‐11.9%<br>‐34.9% Argentina<br>‐21.3%<br>‐19.4%<br>‐30.5%‐25.4% Brazil<br>‐23.9%<br>‐14.2%<br>‐40.6%<br>‐30.9% ‐16.0% Chile<br>‐3.2%<br>25.9%<br>‐12.5%<br>‐24.0%‐18.0% Colombia<br>‐43.3%<br>‐40.1%‐28.6% Ecuador<br>‐28.7%<br>‐31.6%<br>‐25.9% Mexico<br>‐10.1%<br>2009‐I 2009‐II 2009‐III 2009‐Oct 2009‐Nov<br><!-- End of picture text --> <!-- Start of picture text --> Other<br>‐16.8%<br>‐25.7%<br>‐16.7% Egypt<br>‐28.1%<br>‐30.0%<br>‐21.0% India<br>‐6.6%<br>‐17.8%<br>‐21.7%<br>‐13.9% Pakistan<br>8.2%<br>1.0%<br>‐26.8%<br>‐33.2%<br>‐28.7% South Africa<br>‐12.4%<br>‐30.9%<br>‐36.5%<br>‐27.7% Tunisia<br>‐17.5%<br>2009‐I 2009‐II 2009‐III 2009‐Oct 2009‐Nov<br><!-- End of picture text --> Source: Own illustration. Data: EIU Country Data.

18