rafmacalaba/datause-encoder-data huggingface dataset:
Table **8.** The Share of African Exports Covered by Nontarff Measures: AD OECD Countries and Major Indusrial Markts.

||199|2Value of Expo|rts (Smillion)|||Share|of Exports C|overed by N|TBs||
|---|---|---|---|---|---|---|---|---|---|---|
|Exporter|All OECD|EFTA|EU|Japan|USA|All OECD|EFrA|EU|Japan|USA|
|Angola|3,684|1|1,132|5|2,436|4.7|2.2|14.7|0.0|0.0|
|<br>Benin|76|1|60|2|10|1.3|7.6|1.5|0.0|0.0|
|Burkina Faso|53|-|41|5|-|12.3|32.8|13.1|0.0|20.8|
|Burundi|74|6|22|2|9|0.1|0.4|0.0|0.0|0.0|
|Cameroon|1,577|8|1,360|13|90|20.8|0.1|26.8|0.0|0.0|
|Cape Verde|7|-|6|1|-|39.5|0.0|60.8|0.0|0.0|
|Centrl African Rep.|102|-|99|-|1|0.1|0.0|0.1|0.0|0.0|
|Chad|66|1|42|_5_|-|0.1|0.1|0.1|0.0|0.0|
|Comoros|28|-|13|-|11|0.6|1.1|0.3|16.2|0.0|
|Congo|1,440|13|851|2|547|38.5|19.7|4|0.0|0.0|
|Cote D'Ivoire|2,258|49|1,623|16|206|14.1|2.4|16.1|0.0|3.6|
|Djibouti|4|1|3|-|-|5.2|46.2|3.2|0.0|0.0|
|<br>Equatorial Guinea|39|-|37|-|-|0.1|0.0|0.1|20.0|0.0|
|Ethiopia|160|6|67|44|9|1.8|1.3|3.3|0.0|0.0|
|Gabon|2,119|31|956|89|984|15.8|0.1|22.6|0.0|0.0|
|Gambia|<br>183|1|145|36|1|0.5|1.6|0.9|0.0|0.0|
|Ghana|886|42|442|65|102|0.4|3.7|0.4|0.0|0.0|
|Guinea|526|29|329|1|125|0.3|4.2|0.4|0.0|0.0|
|Guinea Bisau|8|-|7|-|-|1.6|0.0|1.7|0.0|0.0|
|Kenya|837|58|510|22|78|3.5|4.3|3.8|7.6|0.0|
|<br>Liberia|804|90|642|43|13|0.0|0.0|0.0|0.0|0.0|
|Madagascar|340|20|188|32|56|7.6|1.1|9.6|0.0|7.9|
|Malawi|393|20|130|68|64|29.6|13.5|18.2|99.5|13.4|
|Mali|117|1|95|_5_|2|0.4|2.4|_0.5_|0.0|0.0|
|Mauritania|413|-|241|151|10|1.3|1.6|2.3|0.1|0.0|
|Mauritius|1,279|23|950|4|147|61.6|43.0|59.9|2.0|79.9|
|Mozambique|<br>147|4|94|16|21|10.3|5.7|9.1|0.0|31.7|
|Niger|184|1|172|-|3|0.2|7.2|0.2|0.0|0.2|
|<br>Nigeria|11,379|510|3,720|2|5,299|14.4|0.1|38.2|0.0|0.1|
|<br>Reunion|<br>154|-|<br>151|1|<br>-|83.0|8.5|88.8|1.4|0.0|
|Rwanda|64|_|32|-|5|1.1|0.4|1.2|0.0|0.0|
|Sao Tome&Principe|_5_|-|3|-|-|17.2|0.0|18.4|0.0|0.0|
|Senegal|362|6|317|11|11|19.1|29.4|20.0|0.1|0.4|
|SeycheLes|37|_|34||I 1|91.4|7.1|95.5|0.0|0.0|
||(1.050)|(0.986)|(1.012)|(1.005)|(0.986)|(0.997)|(0.963)|(0.973)|(0.967)|
|---|---|---|---|---|---|---|---|---|---|
|Grit (Z score)|0.169|0.302|0.252|0.113|-0.059|0.038|0.083|0.012|0.052|
||(0.886)|(0.960)|(0.935)|(0.951)|(0.990)|(0.972)|(1.004)|(1.010)|(1.007)|
|Decision making (Z score)|-0.001|0.258|0.162|0.106|-0.138|0.000|0.079|0.054|0.068|
||(0.927)|(0.937)|(0.941)|(1.042)|(1.004)|(1.032)|(0.992)|(0.950)|(0.974)|
|Hostile attribution bias (Z score)|-0.081|0.060|0.008|-0.036|-0.016|-0.027|-0.011|-0.033|-0.021|
||(0.905)|(0.964)|(0.945)|(0.954)|(0.950)|(0.951)|(1.013)|(0.962)|(0.991)|
|Risk taking (Z score)|0.050|0.060|0.056|0.100|0.036|0.072|0.052|0.005|0.031|
||(1.022)|(1.037)|(1.031)|(1.106)|(1.012)|(1.066)|(1.038)|(1.010)|(1.026)|
|Time preference (Z score)|0.020|-0.001|0.007|-0.045|-0.147|-0.089|-0.006|0.000|-0.003|
||(0.971)|(1.001)|(0.990)|(1.009)|(0.873)|(0.953)|(0.993)|(0.993)|(0.993)|
|Metropolitan area of residence|0.496|0.451|0.468|0.351|0.397|0.371|0.251|0.280|0.264|
||(0.501)|(0.498)|(0.499)|(0.478)|(0.490)|(0.483)|(0.434)|(0.449)|(0.441)|
|Observations|236|399|635|481|370|851|900|686|1,586|
|||**Serbia**|||**Ukraine**|||**Vietnam**||
||(1)|(2)|(3)|(1)|(2)|(3)|(1)|(2)|(3)|
|VARIABLES|Male|Female|All|Male|Female|All|Male|Female|All|
|Log of hourly earnings in USD (trimmed)|1.282|1.426|1.357|1.374|1.006|1.139|1.240|0.942|1.066|
||(1.117)|(0.825)|(0.977)|(0.584)|(0.491)|(0.555)|(0.767)|(0.826)|(0.815)|
|Female|--|--|0.524|--|--|0.637|--|--|0.583|
||--|--|(0.500)|--|--|(0.481)|--|--|(0.493)|
|Age, years|39.842|40.300|40.082|37.445|40.857|39.619|39.518|38.754|39.073|
||(7.986)|(8.002)|(7.993)|(9.134)|(8.791)|(9.060)|(8.182)|(8.083)|(8.131)|
|# of years of education <=9 years|8.891|8.899|8.895|8.965|9.000|8.987|8.121|8.066|8.089|
||(0.436)|(0.582)|(0.517)|(0.565)|(0.000)|(0.340)|(1.931)|(1.942)|(1.937)|
|# of years of education 10-13 years|2.830|3.172|3.009|3.114|3.491|3.354|2.238|2.088|2.151|
||(1.182)|(1.080)|(1.142)|(1.005)|(0.837)|(0.919)|(1.762)|(1.790)|(1.780)|
|# of years of education > 13 years|0.476|0.888|0.692|0.969|1.267|1.159|1.076|0.948|1.001|
||(1.038)|(1.293)|(1.196)|(1.583)|(1.579)|(1.586)|(1.611)|(1.457)|(1.524)|
|Literacy assessment (plausible values Z score)|0.146|0.241|0.196|0.070|0.182|0.142|-0.006|-0.025|-0.017|
||(0.831)|(0.663)|(0.749)|(0.784)|(0.749)|(0.763)|(0.858)|(0.860)|(0.859)|
|Extraversion (Z score)|-0.005|0.140|0.071|-0.182|0.098|-0.004|-0.032|0.070|0.027|

27
above, we have already documented the particularly low levels of mean performance of students attending school in basically all the developing countries that have participated in at least one of the international student achievement tests. But of course, mean performance can hide a lot of dispersion that exists within countries, and the prior analyses of growth (Section 4.4) show that there is separate information at different percentiles of the test-score data.

Figures 5.2 and 5.3 depict the share of students in a country that surpasses the thresholds of 400 and 600 test-score points on the transformed scale of the combined international tests – the same measure and thresholds that we used in the growth analyses of Section 4.4. Figure 5.2 shows the sample of 50 countries which forms the basis of our growth analyses, and Figure 5.3 shows the remaining 27 countries that participated in one of the international student achievement tests but lack internationally comparable GDP data for the 1960-2000 period that would allow them to be included in the growth analyses.

When considering the basic educational achievement of students, we are interested in the share of students who surpass the threshold of 400 test-score points, which may be viewed as a rough threshold of basic literacy in mathematics and science. As is evident from the figures, this share varies immensely across countries. In countries such as Japan, the Netherlands, Korea, Taiwan, and Finland, less than 5% of tested students fall below this literacy threshold. By contrast, in many of the developing countries participating in the international achievement tests, more than half of the tested students do not reach this threshold of literacy. The countries with the largest shares of students who are functionally illiterate are Peru (82%), Saudi Arabia (67%), Brazil (66%), Morocco (66%), South Africa (65%), Botswana (63%), and Ghana (60%). In these countries, more than 60% of those in school do not reach a level of basic literacy in cognitive skills. It should be noted that the group of developing countries participating in the international tests is probably already a select sample from all developing countries, and, furthermore, the children enrolled in school at the different testing grades are probably only a select group of all children of the respective age in these countries.

53
Figure 4 shows job losses by occupation classified with the 1-digit ISCO. Job losses varied from 15 and 16 percent for managers and professionals, respectively, to 37 percent for machine operators and assemblers, and up to more than 50 percent for workers in elementary jobs. Since the ISCO classification sorts occupations by job complexity, from managers to elementary workers (Vosters 2018), Figure 5 suggests that job losses were decreasing in the complexity of the job. In the following section, we work with the 41 groups of the 2 digit ISCO classification, which implies a richer analysis.

# **3.5 An Index for Work from Home**

In order to analyze whether the differential impact of the pandemic was related to the potential for remote work in the pre-pandemic situation, we construct a variable for potential for work from home. We proceed in two steps; first we construct a measure of WFH at the occupation level (Dingel and Neiman 2020), and then adjust this measure for home internet access using the answers to the HFPS (Garrote-Sanchez et al. 2021).

The first step follows the widely used methodology of Dingel and Neiman (2020). They construct a variable of WFH at the occupation level using data on occupational characteristics from O*NET for the U.S. Some of these characteristics are suggestive of the difficulties of working from home (e.g. working directly with the public, outdoor activity, exposed to the weather). When at least one of these characteristics is assessed as "important" or "very important", the occupation is classified as not compatible with WFH.

Occupations in O*NET are classified according to the Standard Occupational Classification (SOC) System. The next step in the methodology is to map the results in the SOC-8 digits classification to the 2-digit ISCO classification, the one used in this paper.<sup>8</sup> The number of groups in the latter classification is smaller, so for each ISCO category we typically have several SOC occupations. As a result of this, our variable for WFH at the 2-digit occupation level is not a binary variable but a share: the share of SOC occupations included in an ISCO category that are compatible with WFH.

Some of the occupations that can be carried out at home require access to internet, a service that is not universal in Latin America. Given this concern, we adjust the measure of WFH for home internet access taking advantage of a relevant question in wave 1 of the HFPS (Garrote-Sanchez et al. 2021). We proceed in two steps. First, for each ISCO category we calculate the share of SOC occupations that require internet to be executed ( _ShrY_ ) and the share of occupations that do not ( _ShrN_ ).<sup>9</sup> Then, the share that requires internet is adjusted by home internet access at the individual level recorded in the first

> 8We follow Bonavida-Foschiatti and Gasparini (2020) for this step.

> 9We follow Garrote-Sanchez et al. (2021) and define the share of SOC occupations within each 2-digit ISCO occupation that requires internet using information from O*NET on the importance and frequency of computer and email use.

9
64

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private and a government school is staggering. An average female teacher in a government school earns Rs.5897 per month, which is not very different from the earnings for an average male (Rs.6408). Among private schools though, male teachers earn Rs.1789 per month (almost onethird!) while females earn only half as much at Rs.1069.

Two factors drive these salary differences between the public and private sectors: (a) differences in teacher characteristics and; (b) differences in the _returns_ to characteristics. Teacher characteristics in the public and private sector are clearly very different (Table 4). Private schools hire female teachers (76 percent compared to 43 percent in government schools) who are substantially younger (25 years versus 38 years for public school teachers), less likely to be married (77 percent are single versus 15 percent), twice as likely to come from the village where they are teaching (52 percent versus 25 percent), less educated (4 percent have a masters degree compared to 19 percent) and far less likely to have received any educational training (6 percent versus 71 percent).

The returns to characteristics are also different. Table 5 presents multivariate regressions of (log) teacher wages on observed characteristics including age, education, teacher-training, gender and place of residence (whether local or not). The first two columns show how different characteristics are compensated by public and private schools respectively, while the third column examines differences between public and private school compensation through interaction terms. Since the LEAPS data is based on a sampling scheme that guarantees multiple schools in every village, we can also look at the difference among teachers _in the same village_ ; the fourth column looks at differences within villages through a fixed-effect regression at the level of the village.

Salaries for public school teachers respond strongly to education and training but not to the gender of the teacher. Private sector salaries also respond to education, but not to teacher-training and females are paid considerably less. Surprisingly, there are few differences in the estimated coefficients in specifications with and without geographical controls (Column 3 vs. Column 4, Table 5). Using these regressions for the public and private sectors, Figures 4 and 5 highlight the separate roles of characteristics and returns to characteristics.

14
practices such as accounting and financial management, marketing, and human resource management.

In contrast, personal initiative training is a psychology-based approach that aims at creating a selfstarting, future-oriented, and persistent proactive mindset (Mensmann and Frese, 2017). The training contains exercises designed to foster innovation, as well as looking for opportunities to learn from setbacks and to differentiate oneself from other businesses. Another key component of the training emphasizes financial bootstrapping, through looking for ways to address financial challenges without taking on bank loans or using microcredit. Exercises have entrepreneurs brainstorm to think through approaches like securing advance payments from customers or more favorable terms from suppliers, and self-funding towards meeting a business goal. Campos et al. (2017) and Mensmann and Frese (2017) provide more details of the training content.

# **C. Data and Attrition**

Our short-term results in Campos et al. (2017) use the baseline survey taken in December 2013, and four rounds of follow-up surveys collected between September 2014 and September 2016. Averaging these rounds together gave profit and sales impacts over the first 2 years and five months after training. Response rates for these follow-up rounds ranged from 89 to 95 percent (Table A1). Long-term follow-up surveys to measure impacts 7 to 7.5 years post-training were conducted by the survey organization AdKontact Togo in 2021. We began with a phone survey in March-April 2021, and were able to interview 1035 entrepreneurs (69%). This was followed by an in-person survey in September-October 2021, which interviewed 1131 entrepreneurs (75%). Combining the two surveys, we have interviews for 1250 owners (83.3%), and in addition were able to ascertain the operating status (including closures) of a further 91 firms, so that the operating status of 1341 firms (89.4%) is known.

Although this overall response rate is high, Table A1 shows that it varies with treatment status, with response rates for the control group 7 to 8 percentage points lower than the personal initiative training group. We pursue several strategies to show that our results are unlikely to be driven by attrition. First, following Ghanem et al. (2023), we show that both the sample of non-atttritors (Table A2a) and of attritors (Table A2b) are balanced on baseline characteristics across all three groups. While not statistically different, if anything the control group firms that remain were doing slightly better on baseline profitability and capital stock than the personal initiative firms, 5
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39
**Figure 9.  Telecoms**

<!-- Start of picture text --> Telecoms<br>Constraint level, conditional means; quadratic fit; 5% robust CIs<br>BGD<br>AFG<br>LKA<br>BTN<br>PAK<br>MDV<br>NPL IND<br>6 7 8 9 10 11<br>Log(GDP p.c.) at PPP<br>4<br>3<br>2<br>1<br>0<br><!-- End of picture text --> 70
**Figure 5. Import Shipments from East Asia and North America**

<!-- Start of picture text --> 80%<br>70% 67%<br>60%<br>49% 51%<br>50%<br>40%<br>33%<br>30%<br>20% 18% 19%<br>10%<br>0%<br>Small Medium/Large Total<br>North America East Asia<br>Share of freight forwarders<br><!-- End of picture text --> Source: Authors’ calculations based on data from the Survey of Serbian Freight Forwarders. Note: the statistics shown are based on responses by all 153 freight forwarders for North America and 152 freight forwarders (140 small and 12 medium/large) for East Asia.

# **_Most overseas shipments are transited through Northern Adriatic ports_**

Freight forwarders that organize non-European shipments utilize several routes and transport options. Table 6 shows that for both East Asian and North American imports, the Northern Adriatic ports (Rijeka, Koper, Trieste) are the most common choice for the majority of freight forwarders. But there are differences across size on the main second route chosen for East Asian imports: the port of Bar for small freight forwarders and air transfer for medium/large freight forwarders (while no small freight forwarder uses air transfer). For both East Asian- and North American- origin imports, a single medium/large freight forwarder indicated that it most commonly moves intercontinental shipments by air.

The choice of Northern Adriatic ports over Thessaloniki for East Asian and Rotterdam for North American shipments may be interpreted as a choice of cost savings over speed by freight forwarders. According to international data on shipping distances and time, Serbian freight forwarders could move goods much more quickly through these more distant ports.<sup>12</sup> Shipments traveling from East Asia to Belgrade via the Suez Canal would arrive more than one day sooner if routed through Thessaloniki. Moving North American goods through Rotterdam to Belgrade, rather than through Northern Adriatic ports, would save three days of transit time. While there are multiple factors to consider when assessing land versus sea transport, a key

> 12 Data on international shipping distances and time was obtained from the website http://www.searates.com/reference/portdistance/.

14