rafmacalaba/datause-encoder-data huggingface dataset:
Canada, Australia, and, notably, the United States, stand out as being the primary receiving countries, when compared to their resident stock of inventors. On the other hand, Japan is, and has been over the years, one of the developed countries with a smaller share of inventor immigrant population.

<u>FIGURE 1. Share of Immigrant Inventors over Time, 1990–2010, by Selected Countries</u> <!-- Start of picture text --> Australia Canada Japan<br>US Europe World<br>1990 1995 2000 2005 2010<br>Year<br>20<br>15<br>10<br>5<br>% Inventors Migratory Background<br>0<br><!-- End of picture text --> Source: Authors’ analysis based on Miguélez and Fink (2013).

Meanwhile, technology-leading European countries, such as Germany or France, lag way behind compared to the United States (figure 2). The exceptional performance of the United States in attracting talent is even more notable when considering only immigrant inventors coming from low- and middle-income economies (see also figure 2). Appendix S4 shows the top 20 most populated inventor migration corridors, where again, the United States stands out as the most typical choice for destination country.

FIGURE 2. Immigration Rates of Iventors, 2001–2010, Receiving Countries were estimated around 1.8%, including 1.1% of immigrants among the unskilled population, 1.8% among populations with secondary education, and 5.4% among populations with tertiary education.

15
country (the climate is not kind to it), and was not purchased by middle and upper income families.

Despite these limitations, several Jakarta-based firms pioneered exports of rattan products in the 1970s; most were led by pribumi entrepreneurs. These pioneers were not, however, the prime movers behind the 'take-off" of Indonesian exports of rattan furniture which occured in the second half of the 198Os. In part, this take-off was a response to the broad policy reforms described earlier. But at least as crucial were bans on exports of rattan material.

Indonesia has long been a major supplier of raw rattan to the major rattan furniture exporting countries of Taiwan and the Philippines. In an effort to 'jump-start' the rattan products industry, the Indonesian goverment imposed bans on the export of unprocessed and semi-processed rattan, in 1986 and 1988-9 respectively. While from a public policy perapective its costs may well have exceeded its social benefits, the evidence seems unequivocal that the ban led to a major expansion in rattan furniture exports. The growth and changing size structure of the industry in the 1980s is revealed in Table I1 (although many smaller firms are probably missed). Output of rattan furniture and related rattan products has grown very fast over the period since about 1983 (accelerating in anticipation of the bans),-though the absolute level of output was not very large until the late lg80s and exports exceeded U.S. 100 million only in 1989.2 While in 1981, there were no establishments with more 2 We have not been able to obtain official export statistics for rattan furniture alone. total furniture exports, of which the bulk are of rattan, give a good idea of the tiends. World Bank data (World Bank, Report No. 9498IND, Indonesia: Develoninci Private Enternrise, May, 1991, p.138) are as follows (in U.S. millions): 1985 7.1 1986 9.2
Certain explanations of interindustry wage differentials could not be tested, as crucial variables aTe not part of the data set. In particular, differentials may reflect 'compensating' differentials, the testing of which would require information on risk of injury and other health hazards, non-standard weekly hours, or at least full-time versus part-time work.

Likewise, we cannot test an explanation which suggests (unmeasured) labor quality differences as a cause of wage differentials. Here, one needs longitudinal data to convtol for time-invariant unmeasured labor quality, possibly by estimating interindustry wage differentials estimated for a sample of industry changers and comparing them to those for a cross-section of workers.

Institutional explanations stress the extent of unionization across industries as a cause of wage differentials. If unions can raise wages in certain industries without suffering serious employment loss, this can lead to higher dispersion of differentials among unionized industries. The absence of information on unionization in the Guatemalan data set did not allow testing of this hypothesis. Results obtained by Krueger and Summers (1988) using United States data and Gera and Grenier (1994) using Canadian data did not provide support for this hypothesis, however.

Past research points to efficiency wages as the most promisng explanation of interindustry wage differentials. According to this explanation iwerindustry wage differentials are wage premiums which would not be Observed if the labor market was functioning according 6
||[0.024]|[0.925]|[0.024]|[0.923]|
|---|---|---|---|---|
|Number of prime adults (15-60yo) in HH|-0.022|0.087**|-0.022|0.087**|
||[0.604]|[0.015]|[0.602]|[0.015]|
|Number of elderly (60yo+) in HH|-0.103|-0.016|-0.102|-0.015|
||[0.499]|[0.897]|[0.502]|[0.905]|
|Household access to loan (0/1)|-0.042|0.329***|-0.042|0.329***|
||[0.674]|[0.000]|[0.671]|[0.000]|
|Normalized wealth index|1.813***|1.776***|1.810***|1.771***|
||[0.005]|[0.002]|[0.005]|[0.002]|
|Normalized TLU index|-2.788+|1.882|-2.791+|1.873|
||[0.117]|[0.192]|[0.117]|[0.194]|
|Total land area owned by HH in Acres|0.042**|-0.000|0.042**|-0.000|
||[0.011]|[0.794]|[0.011]|[0.788]|
|Household was affected negatively by some income Shock During the<br>last 12 months (0/1)|0.104|-0.064|0.104|-0.063|
||[0.364]|[0.501]|[0.364]|[0.510]|
|Household has a migrant network (0/1)|0.042|0.021|0.042|0.021|
||[0.711]|[0.819]|[0.713]|[0.821]|
|Rain - EA level CoV of Dec-Jan rainfall from 1983/84 - 2012/13|||-5.370***|1.022|
||||[0.002]|[0.454]|
|Average 12-month total rainfall (mm)|-0.001|0.002|-0.001|0.002|
||[0.571]|[0.265]|[0.570]|[0.266]|
|Annual Mean Temperature (∞C * 10)|0.002|-0.002|0.002|-0.002|
||[0.828]|[0.814]|[0.825]|[0.816]|
|HH Distance in (KMs) to Nearest Road|-0.014|-0.008|-0.014|-0.008|
||[0.318]|[0.544]|[0.316]|[0.548]|
|HH Distance in (KMs) to Nearest Population Center with +20,000|0.001|-0.001|0.001|-0.001|
||[0.936]|[0.860]|[0.938]|[0.871]|
|Value of daily_ganyu_wage salary in the EA|-0.000|0.000|-0.000|0.000|
||[0.544]|[0.329]|[0.544]|[0.322]|
|Price of fertilizer in the EA|-0.001+|-0.000|-0.001+|-0.000|
||[0.120]|[0.764]|[0.120]|[0.754]|
|Price of maize grains in the EA|-0.001|0.001|-0.001|0.001|
||[0.445]|[0.404]|[0.446]|[0.398]|
|EA neighbor’s wage employment participation|0.012***|0.006*|0.012***|0.006*|
||[0.003]|[0.091]|[0.003]|[0.087]|
|EA neighbor’s self employment participation|<br>0.007**|<br>0.016***|<br>0.007**|<br>0.016***|
||[0.028]|[0.000]|[0.028]|[0.000]|
|Time dummy (year 2010=1)|-0.029|-0.020|-0.029|-0.019|
||[0.852]|[0.878]|[0.852]|[0.883]|

48
At the same time, as in many other countries, tax-exempt investors have concentrated their purchases in the 2-, 5-, 7-, and 10-year maturities of the local yield curve. Thus the authorities have tried to reduce their refinancing risk and transfer risk to market participants, including dealers of government securities. All interest income on government securities is subject to a 20 percent income tax.

**Table 1. Outstanding government securities in the Philippines, August 2002–February 2003** **<u>(millions of U.S. dollars)</u>**

||||**2002**|||**20**|**03**|
|---|---|---|---|---|---|---|---|
|_Type of security_|_Aug_|_Sept_|_Oct_|_Nov_|_Dec_|_Jan_|_Feb_|
|**Treasury bills**|**4,424**|**4,410**|**4,513**|**4,770**|**4,288**<br>|**4,426**|**4,641**|
|91-day|862|1,203|1,353|1,199|639<br>|600|548|
|182-day|1,222|1,562|1,498|1,826|1,769<br>|1,985|1,968|
|364-day|2,340|1,645|1,663|1,745|1,880<br>|1,840|2,125|
|**Treasury bonds**|**13,164**|<br>**13,148**|<br>**13,418**|**13,627**|<br>**13,569**<br>|**13,422**|**13,074**|
|2-year|3,364|3,086|3,208|3,293|3,287<br>|3,070|2,611|
|3-year|366|607|678|677|671<br>|755|761|
|4-year|126|148|207|276|273<br>|325|328|
|5-year|3,837|3,869|3,888|3,951|3,920<br>|4,014|4,048|
|7-year|2,751|2,734|2,717|2,730|2,741<br>|2,582|2,545|
|10-year|2,235|2,221|2,226|2,209|2,189<br>|2,189|2,208|
|20-year|327|326|340|338|335<br>|335|420|
|25-year|158|157|155|154|152<br>|152|154|
|**Total**|**17,588**|**17,558**|**17,932**|**18,397**|**17,856**|**17,848**|**17,714**|

_Source:_ Bureau of the Treasury of the Philippines.

# _Developments in market infrastructure_

Government securities have been dematerialized since 1997, at first through contracts between the Treasury and dealers of government securities. Then, in 2000, the Securities Code and supporting regulations were updated to allow ownership of these securities to be transferred without physical delivery. Subsequently the Treasury created the Registry for Scripless Securities to register uncertified securities and conduct real-time gross settlement of trades in these securities. These developments saved the Treasury considerable time and cost in auctioning and redeeming its securities.

24
**Figure 6 Learning deprivation severity simulations results**

<!-- Start of picture text -->
1.6<br>1.4<br>1.4<br>1.2 1.2<br>1.0 1.0<br>0.8<br>0.6 0.6<br>0.5<br>0.4 0.4 0.4<br>0.3<br>0.2<br>0.1 0.1 0.1<br>0.0 0.0<br>WLD EAP ECA LAC MNA SAR* SSF<br>3.7 1 11 3.4 6.4<br>Baseline 0.3 1.2<br>Intermediate Optimistic Pessimistic<br>Change Learning Gap Inequality<br><!-- End of picture text -->

Source: Author’s calculation. All underlying numbers can be found in table A.2 in the annex. (*) all countries report LP using NLAs and there for and aggregated gap measures was not computed.

Tackling learning poverty will require strategies to identify and respond to the learning needs of individual students. The greater the learning gap and the inequality among the learning poor, the bigger and more heterogenous the challenge will be. COVID-19 will lead to greater inequality among the learning poor, tackling it will require strategies to Teach to the Right Level and to effectively design and deploy structured lesson programs. Figures 7 and 8 summarize these arguments by presenting the relationship between the _leve_ l of the learning deprivation gap and the learning deprivation severity (or inequality sensitive learning gap) and the _change_ in the respective measures across the different regions. Both Africa and the Middle East seem to have the biggest and more complex challenges to begin with (Figure 7), and those are also the regions in which both the learning deprivation gap and learning deprivation severity could increase the most (Figure 6).

This finding suggests that COVID-19 could qualitatively change the learning crisis on the African continent, since students will come out of this pandemic in a much deeper learning crisis than before, falling further behind the minimum proficiency levels established under SDG 4. This greater depth of the learning crisis in Africa and elsewhere will require qualitatively different policy responses of far greater complexity and cost.

29
<!-- Start of picture text --> 60<br>40<br>20<br>1960 1980 2000 2020<br>= BRA = CHN = ESP = IND TM World<br><!-- End of picture text --> <!-- Start of picture text --> 06<br>: ——<br>04<br>03<br>0.2<br>1990 1995 2000 2005 2010 2015<br>= BRA = CHN = ESP = IND TM World<br><!-- End of picture text -->
in GE models for environmental policy analysis in countries where electricity generation based on fossil fuels is one of the main sources of GHG emissions. The gross output of the electricity industry g is given as: XDg = β [ ω {Kg , φ (G g,1,..,G g,f )}, χ {L, μ ( γ (G g,1,..,G g,k ),G g,EL )}] (6) where ω is the composite of capital and the fuel aggregate used in electricity industry g and χ is the composite of labor and the material-electricity composite ( μ ). The demand and price variables in the case of the electricity industries are determined in a similar manner to the other sectors discussed above. Electricity generated with different types of technologies is aggregated as shown in Figure 2.

**Figure 2: Aggregation of Electricity Outputs Produced by Different Electricity Generation Technologies**

<!-- Start of picture text --> XDEL=CES (XDHY, κ ) Tier- 1<br>κ =CES ( υ , Ω , XDIC) XDHY Tier- 2<br>υ = CES (XDSTC, XDSTO, XDSTG) XDIC Ω =CES (XDCGO, XDCGG) Tier- 3<br>XD XD XD XD XD<br>STC STO STG CGO CGG<br><!-- End of picture text --> As can be seen from Figure 2, the total electricity output (XDEL) can be expressed as: XD EL = Λ [ κ { υ (XDSTC , XDSTO,XDSTG ), Ω (XD CGO , XDCGG ), XDIC}, XD HY ] (7)

> where Λ is the CES composite of the outputs of the hydropower industry (XDHY) and the thermal power industry ( κ ). κ is the CES aggregate of the outputs of the steam turbine electricity industry ( υ ), the combined cycle/gas turbine electricity industry ( Ω ) and the internal combustion electricity industry (XDIC). υ is the CES aggregate of the outputs of the coal fired steam turbine electricity industry (XDSTC), the oil fired steam turbine electricity industry (XDSTO) and the gas fired steam turbine electricity industry (XDSTG), while Ω is the

7
|With SGRY control<br>1.181_∗∗∗_<br>1.211_∗∗∗_<br>[0.256]<br>[0.263]<br>0.342_∗∗∗_<br>0.377_∗∗∗_<br>[0.096]<br>[0.119]<br>159,348<br>159,348<br>Yes<br>Yes<br>No<br>Yes<br>Yes<br>Yes<br>With SGRY control<br>-2.606_∗∗∗_<br>-2.422_∗∗∗_<br>[0.751]<br>[0.776]<br>-0.812<br>-0.576<br>[0.591]<br>[0.618]<br>159,348<br>159,348<br>Yes<br>Yes<br>No<br>Yes<br>Yes<br>Yes<br>RY from 2004 to 2007. Program expenditure<br>ssions include district and year-quarter fxed<br> from July 2004 to June 2005 and from July<br>dividual spends in public works. The depen-<br>s. All other controls are the same as in table<br>ets are clustered at the district level. SGRY:|
|---|
|**Panel A. Dependent Variable: Public Employment**<br>Without SGRY control<br>0.796_∗∗∗_<br>0.789_∗∗∗_<br>0.812_∗∗∗_<br>1.189_∗∗∗_<br>[0.257]<br>[0.256]<br>[0.257]<br>[0.257]<br>0.159_∗∗_<br>0.157_∗∗_<br>0.187_∗_<br>0.344_∗∗∗_<br>[0.069]<br>[0.069]<br>[0.097]<br>[0.096]<br>159,348<br>159,348<br>159,348<br>159,348<br>No<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>No<br>No<br>No<br>No<br>Yes<br>**Panel B. Dependent Variable: Private Employment**<br>Without SGRY control<br>-1.443_∗∗_<br>-1.706_∗∗_<br>-1.491_∗∗_<br>-2.330_∗∗∗_<br>[0.721]<br>[0.703]<br>[0.728]<br>[0.776]<br>-0.309<br>-0.387<br>-0.133<br>-0.727<br>[0.577]<br>[0.562]<br>[0.591]<br>[0.609]<br>159,348<br>159,348<br>159,348<br>159,348<br>No<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>No<br>No<br>No<br>No<br>Yes<br>ed on NSS 2004 – 2008 (Rounds 61 and 64) and program expenditure data for SG<br>ural Development, Government of India.<br>e order10_−_2. Each column presents results from a separate regression. All regre<br>es are in logs. The sample is composed of all women aged 18 to 60 interviewed <br>dent variable in panel A is the percentage of days in the past seven days that an in<br>percentage of days in the past seven days that an individual spends in private work<br>mates are computed using sampling weights. The standard errors in square brack<br>Yojana. NREGA: National Rural Employment Guarantee Act.<br>_<_0_._01|
|_NREGAd,t × Dry_<br>_NREGAd,t × Rainy_<br>Observations<br>Worker Controls<br>District Controls FEs<br>SGRY Controls<br>_NREGAd,t × Dry_<br>_NREGAd,t × Rainy_<br>Observations<br>Worker Controls<br>District Controls FEs<br>SGRY Controls<br>_Source_: Author’s analysis bas<br>data is from the Ministry of R<br>_Notes_: All estimates are of th<br>effects. All dependent variabl<br>2007 to June 2008. The depen<br>dent variable in panel B is the<br>2 in the main article. All esti<br>Sampoorna Grameen Rozgar<br>_∗p <_0_._10_,∗∗p <_0_._05_,∗∗∗p _|
CDF of bond issuances by developed countries, suggesting that capital is raised abroad at similar maturities.

To test whether the differences in the maturity between domestic and international bonds across countries are statistically significant, we regress the maturity of issuances on a developing country dummy. In an alternative specification, we also include a dummy variable indicating whether the bonds are issued in international markets and an interaction between the international market and the developing country dummies. All the specifications include industry-year fixed effects as well dummies for the use of proceeds. We also want to ensure that the finding that developing country firms issue abroad at relatively longer maturity does not simply reflect differences in other well-known factors. For instance, issuances abroad are typically denominated in foreign currency, and bonds denominated in hard currencies such as the U.S. dollar tend to have longer maturities. Hence, we include in the regressions a dummy for bond issuances denominated in foreign currency and another dummy for floating rate bonds.

Overall, the results reported in Table 4 show that firms from developing countries not only lengthen the maturity of their debt issuances by going abroad, but also do so relative to developed countries. The estimates show that bond issuances by firms from developing countries are about 2 years shorter than those by firms from developed countries when controlling for industry-year effects, use of proceeds, type of rate, and currency of denomination. Once the sample is restricted to international bond issuances, the differences in maturity at issuance decrease to about 7 months, implying a greater difference in bond maturity between developed and developing countries in local markets. Indeed, the estimations in column c of Table 4 show that domestic issuances by firms from developing countries are about 2.6 years shorter than domestic issuances by firms from developed countries (as captured by the developing country dummy). This specification also shows that in developed countries there are no significant differences in maturity between domestic and 15
18

# IV. HOUSEHOLD ENERGY SURVEY CHECKLIST

The following guidelines can be used as a quick checklist during household energy survey design:

1. Clearly define the objectives of your study and focus on the information that is essential for policymaking and follow-up project design.

2. Understand the characteristics of household fuels as well as how they are acquired and used.

3. Identify and target questionnaires to appropriate sub-groups.

4. Use stratified random sampling whenever possible.

_5._ Coordinate with local and regional officials and leaders.

6. Rely on local surveyors who are from the culture and linguistic group of the surveyed area. Use female interviewers when and if this facilitates access.

7. Choose educated interviewers, train them well beforehand, monitor field performance carefully and maintain morale.

8. Anticipate possible responses and pre-code the questionnaires to speed up data entry and anm 'sis.

9. Consider how to overcome possible problems with strategic, instrumental and hypothetical biases.

**10.** Make sure that respondents thoroughly understand the goals of the survey and the meaning of each question.

11. Take care to identify the most appropriate respondent in the household for a particular section of the questionnaire.

12. Separate consumption by end-use.

13. Convert commonly used units into standard ones by taking physical measurements, preferably by weight and moisture content (solid fuels), and volume (liquid fuels).

14. Focus on recent activities rather than distant events.

15. Break questions down into their smallest, simplest components.

16. Cross-check important answers, especially those pertaining to income, expenditure and quantities.

17. Investigate the reasons for variations in prices and quantities.

18. Examine land tenure arrangements to determine if they have a significant impact on consumption patterns.

19. Use computer analysis and statistical tests.

20. Anticipate delays and budget your resources (time, staff and money) accordingly.
Figure H.3. Labor Outcomes – Fuzzy RD Plots with Local-Quadratic Fitted Polynomial <!-- Start of picture text --> Labor Outcomes (Index) Formal Job<br>-200 -150 -100 -50 0 50 100 150 200 -200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018) Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 2 Sample average within bin Polynomial fit of order 2<br>Hours Worked (log) Job Satisfaction<br>-200 -150 -100 -50 0 50 100 150 200 -200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018) Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 2 Sample average within bin Polynomial fit of order 2<br>Reservation Wage (log) Self-Employed<br>-200 -150 -100 -50 0 50 100 150 200 -200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018) Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 2 Sample average within bin Polynomial fit of order 2<br>1.5<br>.15<br>1<br>.1<br>.5 .05<br>Formal Job<br>Labor Outcomes (Index) 0 0<br>-.5 -.05<br>4.2 .55<br>4.1 .5<br>4 .45<br>.4<br>3.9<br>.35<br>3.8 Job Satisfaction<br>Hours Worked (log) .3<br>3.7 .25<br>3.6 .2<br>.64 .5<br>.62<br>.45<br>.6<br>.58 .4<br>.56 .35<br>Self-Employed<br>.54<br>Reservation Wage (log) .3<br>.52<br>.5 .25<br><!-- End of picture text --> _Notes:_ Each graph depicts the bias-corrected estimator 200 days around June 8, 2018, when the RAMV census closed, and the mean squared error (MSERD) optimal bandwidths. Confidence intervals are at the 95% significance level.

88
would again yield an estimated treatment effect within their confidence interval.<sup>10</sup> AndersonMacDonald et al. (2014) use a 10 week course in South Africa, and obtain average increases in business practices of 26 percentage points. Based on Table 2 we would predict that this would lead to 17-21 percent increases in profits and sales. These predictions are lower than their point estimates, but lie within the confidence intervals for their estimated effects.

Taken together, we therefore view the experimental evidence on business training as being consistent with the strong associations we find between business practices and firm performance. The reason most business training studies struggle to find effects is that relatively short training courses do not lead to much of a change in business practices, leaving the studies with limited power for measuring impacts on sales and profits.

# **4. What explains the variation in business practices across firms?**

We have found that there is large variation within countries in terms of the business practices used by small firms, and that these differences are strongly correlated with firm performance and survival. This raises the question of what causes business practices to vary so much across firms. We consider several key factors, examining the extent to which factors identified as explaining differences in management practices among larger firms also play a role in determining business practices among small firms.

# **4.1 Human Capital**

In small firms the business practices used are to a large extent implemented by the firm owner. Without intermediate levels of management and workers, we expect the human capital of the owner to play a key role in determining which business practices are used. We have seen that our business practice measures retain significance even after controlling for multiple measures of human capital, suggesting that it is not that business practices are simply capturing owner ability. But it does seem likely that more educated and more able owners will find it easier to learn and adopt good business practices.

> 10 Note that their intervention combined the training with a grant, and they are unable to separate the effect of training from that of the grant. This may explain why our estimated effect based on Table 2 is near the bottom of a confidence interval for their effect size.

16