rafmacalaba/datause-encoder-data huggingface dataset:
**EMPLOYER VOICES, EMPLOYER DEMANDS, AND IMPLICATIONS FOR PUBLIC SKILLS DEVELOPMENT POLICY**<sup>**1**</sup>

Wendy Cunningham and Paula Villaseñor Key Words: Skills, Labor Demand, Cognitive, Non-Cognitive, Behavioral skills, Competences, Employer surveys, Skills Policy, Education Policy, Training Policy JEL Classification: J23, J24

> 1 Many thanks to Monica Parra and Mariana Escalante for their contributions to the paper and to Stephen Close, Margaret Grosh, Katia Herrera, Noel Muller, Jan Rutkowski and Alexandria Valerio for their critical review and suggestions.
**Table 11: Factor Decomposition All** Variables

Earnings Residual Replacing Earnings

|||F|actor|||C|ommunality|
|---|---|---|---|---|---|---|---|
||1|2|3|4|_5_|6||
|Tax|0.71|--|--.|0.36|--|--|0.64|
|Site|0.71|--|--|0.35|--|--|0.64|
|Hours|0.66|--|--|--|--|--|0.44|
|K/L|0.53|-0.10|--|--|0.26|--|0.37|
|Experience|--|0.87|--|0.10|--|-0.14|.080|
|Education|0.22|-0.77|--|--|--|0.15|0.67|
|Finn life|0.11|0.69|--|-0.12|--|0.28|0.58|
|Move/stay|--|0.13|0.91|--|--|--|0.87|
|Expand|--|--|0.90|0.12|0.11|--|0.86|
|Workers|0.21|--|--|0.69|--|0.13|0.54|
|Benefits|--|--|--|0.60|--|0.10|0.38|
|Clients|0.12|--|0.10|0.47|0.10|-0.27|0.34|
|Noproblems|--|--|--|0.20|-0.61|--|0.43|
|Credit problems|--|0.10|-0.12|-0.11|-0.59|0.12|0.40|
|Financing (new)|0.11|--|--|0.30|0.57|0.11|0.44|
|Financing (initial)|0.13|--|--|0.16|0.48|--|0.28|
|Voluntary|0.19|-0.11|--|--|--|0.71|0.56|
|Residual|-0.13|--|--|0.24|--|0.68|0.54|
|Eigenvalue|2.87|1.94|1.60|1.23|1.13|1.03||
|% of variation|15.95|10.79|8.87|6.81|6.27|5.75||
|_Moments and mixing p_|_arameter (p)_|_offactor dis_|_tributions'_|||||
|Pi|-0.70|-0.85|--|-0.36|--|-0.54||
|Pu2|0.46|0.49|--|1.09|--|0.54||
|**a,**|0.37|0.58|--|0.58|--|1.05||
|Cr2|1.02|0.86|--|1.19||0.56||
|p|0.40|0.37||0.75||0.50||
|<br>LR|-767.9**|160.3**|-|1124.3**||-415.9**||

**Statistically significant at the 5% level. Kaiser-Meyer-Olkin Measure of Sampling Adequacy = 0.65 Factors 3 and 6 are clearly tri-modal so moments could not be estimated.

33
19 cost supplier will be whichever country signs the RTA with the large country. That country will become significantly more attractive to investors than the remaining low cost countries. This has smaller countries scrambling to enter RTAs with larger partners and making significant concessions in order to do so. Bhagwati (1991), for example, cites then President of Mexico Salinas as stating that a factor pushing Mexico towards a RTA was the fear that European investment would be diverted to Eastern Europe once it is integrated with the European Community. As Salinas put it at an early stage of the NAFTA negotiating process: What we want is closer commercial ties with Canada and the United States, especially in a world in which big regional markets are being created. We don't want to be left out of any of those regional markets.20 The insurance explanation is less convincing in the context of agreements between developing countries. For example, in the event of a global trade war or of a resurgence of protectionism in the major developed export markets of the CARICOM or Mercosur countries, they would all be in the same boat; to a lesser extent this is likely to be true of any major macroeconomic shock. They are thus not particularly logical candidates to "insure" each other nor do either of the domino theories explain why these countries would enter into agreements with one another.

# **_E. Bargaining Power_**

The desire to increase bargaining power with respect to third parties is often cited as a reason why countries may wish to join a RTA. The reasoning behind this is the belief that these countries should in fact have greater bargaining power combined than separately; and that the RTA is the good way for them to coordinate their positions (in other words, that it reduces the transaction costs involved in reaching an optimal negotiating position). This explanation is more convincing for a customs union, which has a common external tariff, than a free trade area, which does not. A good example may be Mercosur; the incentive here to coordinate is large because the vast bulk of Mercosur's trade is still extra- rather than intra-regional, so there could be significant gains to greater coordination of trade policies with respect to third countries.

20 As cited in Perroni and Whalley (1994), p. 4, from The Globe and Mail, April 10, 1990, p. Bl.
These metrics condense large and diverse industrial structures for cities into manageable statistics of local industrial conditions. The metrics do have limitations, though. First, we do not capture potential interactions that exist beyond the local district, but factor and product markets can be wider (e.g., Rosenthal and Strange 2001, Kerr and Kominers 2010). Second, the metrics do not consider final consumers. In unconditional estimates, we separately model city populations and gender ratios. Third, the metrics do not measure quality differences across districts in inputs like worker or input quality beyond basic traits like education levels. Finally, these metrics can suffer from omitted variable biases should another district-industry factor jointly determine both incumbent structures and entry rates. We will use lagged industrial conditions as instruments to partially address this concern.<sup>10</sup>

# **Analysis of Relative Female Entrepreneurship Rates**

We first characterize relative female entrepreneurship rates through a series of unconditional linear regressions with the above determinants as explanatory variables. Table 4 considers the 2000 and 2005 manufacturing surveys using a specification of the form: Female Entry%dit = ηit + β∙Xd + γ∙Zdit + εdit.

The outcome variable Female Entry%dit is the ratio of female-run youngestablishments in the district-industry-year to the sum of female- and male-run young establishments. This outcome variable is the most intuitive, and we focus on a share outcome that is independent of the overall level of entrepreneurship in the district-industry. We find similar results when using variants like the log female share or examining the log levels of female entrepreneurshipdirectly. We exclude district-industries where we do not observe entry.<sup>11</sup> While presenting results for female entrepreneurship ratios, we find very comparable patterns when examining female business-ownership ratios. We focus on the entrepreneurship rates since we can exclude entrepreneurs from the incumbent structures and directly circumvent 10 There are several factors that we do not consider in this study: natural cost advantages (e.g., Ellison and Glaeser 1999), local industrial diversity (Jacobs 1970), knowledge flows (Marshall 1920, Porter 1990), and entrepreneurial culture (Hofstede 2001, Florida 2005). These exclusions are in part due to data constraints for India and in part due to our desire to maintain a consistent empirical framework between manufacturing and services.

11We also exclude in the manufacturing estimates the district-industries that are not present in 1994. This is done to keep a consistent sample size between the least squares and instrumental variable specifications. We find very similar results when using the full sample of potential district-industries where entry occurs.

13
# **Box 3: Togo's Training and Employment Observatory**

In 1990, Togo established a Training and Employment Observatory (OEF) as part of a World Bank-supported technical and vocational training project. The OEF was located in the Ministry of Technical Education and Vocational Training. Its purpose was to gather information on labor and training markets to inform public and private decisions involving training and employment. To avoid the expensive process **of** gathering information already available from other sources, the OEF was expected to focus first on existing sources of data in Togo before generating new data. The information gathered was supposed to cover the public sector, parastatals, modern private sector, and informal urban and rural sectors. Training information was to be collected from training centers, apprenticeship programs, and other sources.

Changes in public sector employment by occupation, pay grade, ancd salary were to be studied every six months using administrative data on the civil service from the Directorate of Finance. Special ad hoc surveys were to be used to study the changing match between training and employment. Social security information on employment would be the main source of time series information for parastatals and modern private sector enterprises. This would form the basis for a sample of firms for regular surveys of pre-empl^yment and in-service training needs. Regular analyses of the informal urban and rural sectors were to be carried out using periodic censuses, profession-specific studies (10 per year), and in-depth studies of some 30 firms in the 10 professions studied each year.

One of the main sources of information for the OEF was to be the regular use of tracer studies of graduates from training centers and programs. This information would inform resource allocation decisions in both the public and private sectors. A household survey conduicted as part of Togo's Social Dimensions of Adjustment project would be used to study patterns of education, training, employment, and income. New training needs would be identified by studying information on investment project dossiers submitted to the Ministry of Planning and applications from 'ilrms to participate in the Free Trade Zone. The OEF was expected to produce three kinds of products:

- charts summarizing the training and employment situation in Togo to provide a firm foundation for resource allocation decisions by a training fund,

- the collection and analysis of data and studies on training and employment for publication, and

- the distribution of the publications to as wide an audience as possible, including schools, training centers, and employers.

Malaysia. The rapid adjustment of real wages back in line with productivity growth, restoring export competitiveness, was important, he argues, to the economy's quick recovery from its mid-decade recession.

15
suggestive evidence of the negative impact of unreliable electricity provision on the entry of new firms in an economy.

# **6 Discussion and Conclusion**

Many African countries are confronted with rising unemployment. At the same time, many economies in the region grapple with unreliable provision of electricity to households and industry, with potential implications on economic performance. The extent to which unreliable provision of electricity contributes to the growing unemployment in the region is a question central to development policy in the region.

This paper presents evidence of how electricity shortages constrain job creation in Africa. To this end, I assemble an array of household and firm data across several SSA countries, along with unique data on greenfield FDI projects to: (i) estimate the extent to which outages affect employment, and (ii) the channels through which these impacts arise. Findings from the paper indicate that electricity outages are a major contributory factor to the growing unemployment in Africa. Skilled jobs and employment in nonagricultural sectors are the most affected. Specifically, from the cross-country analysis, the results from an IV regression estimation suggest that outages are associated with a 13.5 pp decrease in the probability of employment. Estimates from country-case studies in Ghana and Nigeria suggest that outages are associated with a 4.7 pp and 5.7 pp reduction in the probability of employment respectively. Factors such as cross-country heterogeneity and differences in the measure of employment and outages across the various datasets could account for the differences in the point estimates. Nonetheless, the estimates fall in a similar ballpark, suggesting that outages reduce employment by between 4.7 and 13.5 pp.

Further, the paper provides suggestive evidence of two plausible mechanisms. First outages constrain firm entry via: a reduction in firm density, and foreign direct invest46
35 clw stories **about** portfolio strategies and other **servies unrelated** to performance. They avoid Ixiexat{on, as this would reduce their own day to **day** responsibilities, as well as internal asset management, as this would give them too great a reonsibility for errors. The authors suggest these **aency** costs are additional to the difficulties **_(u_** noted above) which arise between a (rational profit-m&ximzig) sponsor and the fund manager, W **that a sift** to defined contribution plans would help overcome the difficulties.

The main implication of these analyses is that IdenIo, as dicussed below, will be optimal for mot pension fund assets, with the caveat that te beefits arising from active management are likely to incrase with the number of funds adopting such predictable passive strategies, and witb the inefficiency of the market more generally. As discussed below, widespread indexation may also weaken incentives to monitor corporate management. Meanwhile, to the extent indexation is not used, a reliable measure of fund managers' performance as a means of control is seen as essential. Performance measurement services - whose data generated the results reported above - are well developed in the AngloAmerican countries but vestigial in Continental Europe and Japan. Indexation does not, of course, remove the need for selection of **aset** categories in the light of liabilities and expected returns, as well as choice of national markets. To the extent these activities remain profitable, they suggest that there is greater efficiency within thn between markets.
> <sup>38</sup>

ethnic cleavage to another suggests that they would have had difficulty making credible appeals to pursue the collective interests of any particular ethnic group, however. The Zambian responses to the Afrobarometer survey are consistent with this. All the Zambian ethnic groups comprising more than 5 percent of respondents, the Bemba, Tonga, Lozi, Chewa and Nsenga, meet the lax definitions of clustering. None meet any stricter definition of clustering that requires some degree of “monogamous” sorting among parties and ethnic groups. All express high rates of partisan indifference, 47 percent on average.

# **Conclusion**

Analyses of ethnicity argue that development outcomes deteriorate when political competition is a contest between the collective interests of different ethnic groups. Such a contest cannot emerge, however, if ethnic groups are not collectively organized. The evidence here indicates that political parties in 16 African countries do not, with a few exceptions, facilitate that collective organization. Numerous patterns in the data support this conclusion.

First, ethnic clustering of partisan support is far from a universal phenomenon; by the strictest standards of clustering, only 25 percent of respondents to the Afrobarometer surveys belong to ethnic groups that cluster their partisan support. Second, members of ethnic groups that cluster their partisan support are not substantially more likely to express a partisan preference. In most countries with both types of groups, there is no significant difference in rates of partisan indifference between clustered and unclustered groups. Absolute rates of partisan indifference are high even where clustered groups are more likely to express a partisan preference than non-clustered groups. Finally, clustering has a smaller
38 Table 16 Annual Level of Immigration, Emigration and Net Migration by Sex, Canada, 1980-81 to 1986-87

||I|mmigra|nts|E|migran|ts||Net||
|---|---|---|---|---|---|---|---|---|---|
|Year<sup>1</sup>|Total|Male|Female|Total|Male|Female|Total|Male|Female|
|||||(|in thou|sands)<br>2||||
|1980-81|129|64|<br>65|44|22|<br>21|86|42|<br>44|
|1981-82|135|66|<br>69|45|23|<br>22|90|43|<br>46|
|1982-83|105|51|<br>55|50|26|<br>24|55|25|<br>30|
|1983-84|88|41|<br>47|49|25|<br>24|39|16|<br>23|
|1984-85|84|40|<br>44|46|24|<br>23|33|16|<br>22|
|1985-86|88|42|<br>46|45|23|<br>22|43|20|<br>24|
|1986-873|123|63|<br>60|41|21|<br>20|82|42|<br>40|

-----------------------------------------------------------------------

__----__---------'From June 1 of year t to May 31 of year t+l.

2Due to rounding, the data do not always add up to the totals.

3These are preliminary estimates. Emigration estimates are subject to revision.

Source: Immigration: Data provided by Employment and Immigration Canada. Emigration : Data estimated by Statistics Canada, Demography Division. Estimates produced by using family allowance files and Revenue Canada tax files.
61

# **Appendix D: Full List of Characteristics of At-Scale Programs and Evaluated Programs**

# **Table D1**

_Overarching Aspects – Descriptive Statistics_

||All Eva|luated Program<br>Standard|s||At-scale Progra<br>Standard|ms|
|---|---|---|---|---|---|---|
|OverarchingAspects|Mean|Deviation|Obs|Mean|Deviation|Obs|
|Designed by Government|0.152|0.364|33|0.795|0.405|132|
|<br>Designed by NGO or social enterprise|0.394|0.496|33|0.068|0.253|132|
|Designed by researchers|0.424|0.502|33|0.136|0.344|132|
|Implemented by Government|0.273|0.452|33|0.896|0.309|48|
|Implemented by NGO or social enterprise|0.394|0.496|33|0.250|0.438|48|
|Implemented by researchers|0.333|0.479|33|0.125|0.334|48|
|<br>Design not based on diagnostic|0.121|0.331|33|0.479|0.505|48|
|Design based on informal diagnostic|0.242|0.435|33|0.383|0.491|47|
|Design based on formal diagnostic|0.333|0.479|33|0.563|0.501|48|
|Targeting by geography|0.533|0.507|30|0.125|0.334|48|
|Targeting by subject|0.300|0.466|30|0.188|0.394|48|
|Targeting by grade|0.806|0.402|31|0.313|0.468|48|
|Targeting by years of experience|0.067|0.254|30|0.125|0.334|48|
|Targeting by skill gaps|0.033|0.183|30|0.208|0.410|48|
|Targeting by contract teachers|0.100|0.305|30|0.063|0.245|48|
|Participation has no implications for teacher<br>status, salary, or promotion|0.364|0.489|33|0.417|0.498|48|
|Participation has status implications only|0.061|0.242|33|0.250|0.438|48|
|Participation has implications for salary or<br>promotion|0.303|0.467|33|0.250|0.438|48|
|Teachers are not evaluated|0.212|0.415|33|0.563|0.501|48|
|Positive consequence if teachers are well<br>evaluated|0.121|0.331|33|0.375|0.489|48|
|Negative consequence if teachers are poorly<br>evaluated|0.061|0.242|33|0.167|0.377|48|
|Program provides materials|0.867|0.346|30|0.958|0.202|48|
|Program provides textbooks|0.214|0.418|28|0.292|0.459|48|
|<br>Program provides storybooks|0.321|0.476|28|0.125|0.334|48|
|<br>Program provides computers|0.143|0.356|28|0.125|0.334|48|
|Program provides teacher manuals|0.552|0.506|29|0.625|0.489|48|
|Program provides lesson plans/videos|0.321|0.476|28|0.542|0.504|48|
|<br>Program provides scripted lessons|0.241|0.435|29|0.333|0.476|48|
|Program provides craft materials|0.107|0.315|28|0.333|0.476|48|
|Program provides other reading materials|||||||
|(flashcards, word banks, reading pamphlets)|0.357|0.488|28|0.208|0.410|48|
|Program provides software|0.276|0.455|29|0.188|0.394|48|
|<br>Number of teachers trained|655.7|1,514.9|19|8,514.5|37,582.2|139|
|Number of schools in program|95.6|149.5|28|6,367.3|18,281.7|29|
|Program age (years)|2.6|2.8|25|3.8|4.5|138|
|Dropouts in lastyear|0.5|0.5|15|58.9|346.9|43|

Obs refers to the number of PD programs in each sample (top performing, evaluated programs, and at-scale programs) that report whether or not they have a given characteristic.
**Table 3: Data Transparency and Sovereign Bond Spreads: Channels of Transmission, fixed effect**

|||||dependent va<br>1995|riable: sprea<br>-2018|d||
|---|---|---|---|---|---|---|---|
|Variables||[1]|[2]|[3]|[4]|[5]|[6]|
||GDP per capita (constant 2010 US$)|-0.634***|-0.614***|-0.606***|-0.417**|-0.526***|-0.543***|
|||(0.196)|(0.199)|(0.193)|(0.196)|(0.194)|(0.196)|
||Growth (annual %)|-0.0319***|-0.0321***|-0.0316***|-0.0276***|-0.0312***|-0.0309***|
|||(0.00622)|(0.00623)|(0.00624)|(0.00611)|(0.00609)|(0.00624)|
||CPI Inflation (average consumer prices)|0.0564**|0.0526*|0.0543*|0.0792***|0.0610**|0.0563**|
|||(0.0282)|(0.0280)|(0.0280)|(0.0278)|(0.0274)|(0.0280)|
||Primary Balance to GDP (%)|0.00405|0.00424|0.00343|0.00642|0.00669|0.00281|
|||(0.00759)|(0.00760)|(0.00762)|(0.00747)|(0.00745)|(0.00766)|
||Current Account Balance to GDP (%)|0.00198|0.00203|0.00210|0.00163|-0.000339|0.00247|
|||(0.00455)|(0.00456)|(0.00457)|(0.00445)|(0.00449)|(0.00457)|
||Public Debt to GDP (%)|0.0124***|0.0124***|0.0125***|-0.0120|-0.0648**|-0.00373|
|||(0.00157)|(0.00157)|(0.00158)|(0.00766)|(0.0308)|(0.00861)|
||VIX Index|0.513***|0.512***|0.507***|0.557***|0.531***|0.520***|
||US 10-year Treasury Bond Yield|(0.0569)<br>-0.384***|(0.0571)<br>-0.382***|(0.0572)<br>-0.381***|(0.0560)<br>-0.288***|(0.0558)<br>-0.293***|(0.0574)<br>-0.359***|
|||(0.0823)|(0.0825)|(0.0822)|(0.0827)|(0.0827)|(0.0835)|
||ICRG Index|-1.926***|-1.983***|-1.921***|2.566|19.35***|0.211|
|||(0.391)|(0.395)|(0.391)|(2.113)|(6.657)|(2.843)|
||Methodology Assessment|0.103|||4.143**|||
|||(0.0983)|||(2.055)|||
||Periodicity and Timeliness Assessment||0.0663|||19.10***||
||||(0.267)|||(6.335)||
||Data Source Assessment|||0.123|||1.784|
|||||(0.0993)|||(2.582)|
||Methodology x ICRG||||-1.093**<br>|||
||||||(0.495)|||
||Periodicity and timeliness x ICRG|||||-4.863***<br>||
|||||||(1.504)||
||Data source x ICRG||||||-0.470<br>|
||||||||(0.641)|
||Methodology x public debt||||0.00677***<br>|||
||||||(0.00193)|||
||Periodicity timeliness x public debt|||||0.0177**<br>||
|||||||(0.00696)||
||Data source x public debt||||||0.00405*|
||||||||(0.00210)|
||Constant|16.96***|17.17***|16.62***|-1.881|-67.24**|8.374|
|||(2.508)|(2.546)|(2.550)|(8.951)|(28.18)|(11.62)|
||Observations|560|560|559|560|560|559|
||R-squared|0.440|0.439|0.440|0.471|0.468|0.445|
||No. Countries|57|57|57|57|57|57|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 25