rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A3. Determinants of Business Failure**

|All<br>(1)|Male<br>(2)|Female<br>(3)|
|---|---|---|
|Interest in training<br>-0.081**|-0.083*|-0.098*|
|(0.033)|(0.044)|(0.053)|
|Ever in business<br>-0.032|0.008|-0.059|
|(0.035)|(0.046)|(0.054)|
|Self-Employment (1=Yes)<br>0.024|0.052|-0.009|
|(0.035)|(0.041)|(0.061)|
|Age<br>-0.001|-0.002|0.000|
|(0.002)|(0.002)|(0.003)|
|Education<br>0.004|0.006|0.003|
|(0.005)|(0.006)|(0.007)|
|Risk Tolerance<br>-0.001|-0.007|0.005|
|(0.006)|(0.008)|(0.009)|
|Digit Span Recall<br>-0.010|-0.028**|0.013|
|(0.009)|(0.012)|(0.015)|
|Knowledge of Competition index<br>0.008|-0.004|0.023|
|<br>(0.016)|(0.021)|(0.023)|
|Decision-making power (0-8)<br>-0.002<br>|-0.009<br>|0.013<br>|
|(0.006)|(0.007)|(0.010)|
|Female (1=Yes)<br>0.094**|||
|(0.042)|||
|Female Mobility index||-0.002|
|||(0.020)|
|No Purdah index||0.029*<br>|
|||(0.015)|
|Trust index<br>0.010|0.017|0.013|
|(0.012)|(0.015)|(0.022)|
|Stress index<br>-0.003|0.008|-0.012|
|(0.013)|(0.019)|(0.020)|
|Log HH Expenditure<br>0010|-0002|0044|
|<br>.<br>(0.032)|.<br>(0.043)|.<br>(0.051)|
|Credit Constraints (1=Yes)<br>0.056*|0.058|0.049|
|(0032)|(0043)|(0050)|
|.<br>Household member has held hereditary or political office (1=Yes)<br>0.000|.<br>0.058|.<br>-0.123|
|(0.049)|(0.056)|(0.088)|
|Months in CO<br>0.000<br>|0.000<br>|0.000<br>|
|(0.001)|(0.001)|(0.001)|
|Fi f CO b<br>0073|0027|0225*|
|racton o memers same zaat (caste)<br>.|.|.|
|(0.057)<br> <br>|(0.068)<br>|(0.124)<br>|
|Holds office in CO (1=Yes)<br>-0.023|-0.047|0.013|
|(0.041)|(0.058)|(0.065)|
|Land<br>-0.004*|-0.006***|-0.001|
|(0.002)|(0.002)|(0.005)|
|Distance<br>0.001|0.001|0.001|
|(0.002)|(0.003)|(0.004)|
|Member has Business (1=Yes)<br>-0001|-0009|0011|
|<br>.<br>(0.053)|.<br>(0.060)|.<br>(0.110)|
|Eligibility for Loan Lottery<br>-0.026|-0.025|-0.032|
|(0.033)|(0.044)|(0.052)|
|Mean of Dependent Variable<br>0.39|0.34|0.46|
|<br>N Obti<br>949|529|420|
|. servaons<br><br>R-Squared<br>0.03<br>Notes: Dependent variable takes value 1 if the main business at baseline had failed at follo<br>baseline businesses in COs that were not offered Business training. 1Only the index of Kno<br>included in the Business Knowledge index as the other variables were only collected at foll<br>definition of variables. All regressions include branch fixed effects. Standard errors are clu<br>following symbols *,** and *** denote significance at the 10, 5 and 1 percent level, respec<br>definition of variables.|0.05<br>w-up. Sample in<br>wledge of Com<br>ow-up. See App<br>stered at the CO<br>tively. See App|0.05<br>cludes all<br>petition is<br>endix B for<br>level.The<br>endix B for|
In telecom, the accelerated growth requirements of the order of some US$4 billion are very significant and it is not clear whether they can be met within the current industry and market structure set up. It will, therefore, need to be considered in the context of the broader regulatory reform of the sector to tap the potential efficiency gains from greater competition and higher investments.

# **Conclusion**

The following main conclusions arise from this and related analyses.

First, new investments in infrastructure are required in South Africa to reverse the investment decline from the past several years and build the new capacity required to support accelerated growth, competitiveness, poverty reduction and more rapid economic and social integration of the society.

Second, our previous work on international benchmarking of infrastructure performance (Bogetić and Fedderke (2005 a) suggests solid service at reasonable quality compared to relevant benchmarks and, in some cases, very competitive prices. But there remain significant shortfalls relative to benchmarks in all infrastructure sectors, largely related to limited access and less than expected quality, especially in rural areas where most of the poor reside. It also shows that access to electricity in urban areas, for example, in South Africa (84%) remains below that of its main comparator group of countries––upper middle income group (90%). Access in rural areas is rather low (37%) although higher than the average in the same group of countries (30%), reflecting recent major efforts towards electrification.

Third, we estimated a long-run dynamic model of demand for electricity and telecom using panel data on 52 countries for the period 1980-2002; the model performs well in characterizing historical demand. We use the model to project demand forward in the 2003-2010 period for South Africa in order to derive preliminary estimates of the cost of required new investments in electricity and telephone lines under two growth scenarios for South Africa’s economy.

Fourth, the estimates of required investments to meet demand for electricity and telephones under the current growth scenario of 3.6% per annum over the medium term are as follows: Electricity production: US$0.5 billion per year or 0.2% of GDP per year Telephone lines: US$1.98 billion per year or 0.75% of GDP per year Fifth, the accelerated growth path of 6% per annum would result in approximately doubling of these investment requirements.

20
29 Table 5. Cost Structure: DeRosits of Syndicate Bank: 1975 (as percentage of total deposits under each category)

||||Deg|osit|Category||
|---|---|---|---|---|---|---|
|Cost Item<br>Deposits|Current|Pigmy|Savings|Fix|ed & Cummulative|' Total|
|Interest<br>Costs|-|0.48|4.30||11.70|6.86|
|Incentives<br>&<br>Commissions|-|3.94|0.09||0.01|0.14|
|Administrative<br>Costs|0.20|1.10|2.57||1.30|1.55|
|Total|0.20|5.52|6.96||13.01|8.55|

Source: The same as in Table 1.

Large Metropolitan Interest Costs 6.50 Bank (Bombay) Other costs 3.29 Total 9.79 Source: The Bank.
||(2.421)|(4.113)|(1.698)|(3.639)|(1.992)|(3.561)|(2.724)|(4.306)|
|---|---|---|---|---|---|---|---|---|
|Home FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Sector FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Observations|1,852|1,852|2,508|2,508|2,508|2,508|1,852|1,852|

Until now, however, we have ignored an important potential problem when looking at the association between trade and investment agreements and FDI flows: the possibility of endogeneity. If we find a statistically significant positive effect of the BIT and PTA variables, before concluding that signing trade and investment agreements causes an increase in FDI inflows, we should be able to demonstrate that the causality does not in fact run the other way, i.e., that higher FDI flows may lead countries to join more of these agreements. For example, multinational corporations that invest in a country may press their governments to sign BITs to ensure greater protection for their investments. If they are part of global value chains of international production networks, they may also demand trade agreements to gain access to lower cost inputs and to facilitate re-exporting goods back to their home country.

In Table 4, we report the results of the analysis employing an IV-GMM approach, with _BITall_ and _PTAall --_ the number of BITs/PTAs a host country has concluded with third countries, subtracting the one with the home country—as instruments. Model 11 includes both instrumental variables but the coefficient of _PTAall_ is not statistically significant, most likely due to high correlation between the two instruments (0.68). Table 4 also reports the first stage Shea R-square, which tells us the percentage change in the R-square associated with changes in each of the instruments. For the _BITall_ the Shea R-square is 0.21, which seems reasonable, but for _PTAall_ it is 0.07. In Models 12 and 13, therefore, we include _BITall_ and _PTAall_ in separate regressions. Model 12 shows that the instrument for BIT is positive and statistically significant at the 99% level. The Shea R-square increases to 0.31, suggesting it is a relevant instrument. We also examine the validity of the instruments using the Hansen J-test for overidentifying restrictions. For Model 6, the p-value of the J-test (0.73) shows that we cannot reject the null hypothesis that the overidentification restrictions are valid (or put differently, that our instrument is correlated with the error term). Model 13 only includes the instrumented PTA variable. Although the coefficient is statistically significant, the low value of the Shea R- square and the p value for the J-Hansen test suggest our instrument is not very strong.

**Table 4** . IEAs and FDI. GMM estimator

||(5)|(6)|(7)|
|---|---|---|---|
|VARIABLES|IVREG GMM|IVREG GMM|IVREG GMM|
|_GDPpcj_|-0.0280*|-0.0349**|-0.0342*|
||(0.0165)|(0.0148)|(0.0190)|
|_Populationj_|-0.00464|-0.00764|-0.0179|
||(0.0115)|(0.00814)|(0.0116)|
|_Tradeij_|6.07e-08***|6.32e-08***|5.65e-08***|
||(1.73e-08)|(1.64e-08)|(1.73e-08)|
|_Distanceij_|-7.99e-06***|-7.59e-06***|-6.97e-06***|
||(2.57e-06)|(2.36e-06)|(2.57e-06)|
|_Comlangij_|0.0791**|0.0717**|0.0214|
||(0.0326)|(0.0314)|(0.0323)|
|_Poll stabilityj_|-0.0123|-0.0143|-0.0320*|

15
- (b) Common shorter/longer/different ways (train vs railway, rail vs railway)

4. Add types

- (a) If landmark ends with: stage, bus stop or bus station, add “stage” as type (we preference certain types, hence we do this).

5. Remove parallel landmarks if only 1-2 characters long, and add rest to gazetteer

# **F. Remove landmarks**

1. If it has a stop word and is 2 or less words, remove

2. If landmark contains/begins with/ends with:

- (a) If landmark contains: road or rd, remove

- (b) If landmark begins with a stop word or preposition, remove

- (c) If landmark ends with road word (street, st, avenue, ave), remove

3. Remove common English words

- (a) Remove one word landmarks that are also English words (spelled correctly according to an English spell checker)<sup>4</sup> but are not nouns<sup>5</sup> or categorized as a bus/ transit station.<sup>6</sup>

- 4 We use Hunspell, a commonly used

- spellchecker

- 5 We use spaCy, an open source nat-

- ural language processing library, to determine the part of speech of each landmark

- 6 We keep bus/transit stations as

- users often reference matatu stages when describing crash locations

S6
Table D7: Ethnicity randomization checks 4

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Pokot|Judge plur. Somali|Judge plur. Turkana|
|Pla. plur. Pokot|-0.00444<br>(0.00458)|||
|Def. plur. Pokot|0.00327<br>(0.00832)|||
|Pla. plur. Somali||0.00474<br>(0.00516)||
|Def. plur. Somali||-0.00715<br>(0.00518)||
|Pla. plur. Turkana|||0.0000974<br>(0.000118)|
|Def. plur. Turkana|||-0.000940<br>(0.00102)|
|DV mean|0.00482|0.00964|0.000468|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|14944|14944|14944|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

43
# **Table 1b: Estimates of Adjusted Net National Income Using a Modified Hicksian Approach**

||**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|
|---|---|---|---|---|---|---|---|---|---|---|
||**_aNNI: $m, 2000_**<br>|**_Percentage_**|**_growth in (r_**|**_eal) aNNI p_**|**_er annum_**||||||
|Angola|$2,758|29.0%|43.8%|25.0%|23.0%|-15.7%|46.8%|91.3%|-2.9%|82.7%|
|Azerbaijan|$2,474|20.3%|38.8%|16.5%|9.8%|13.8%|29.1%|49.7%|67.4%|10.3%|
|Ecuador|$10,935|43.6%|18.2%|10.9%|4.2%|5.8%|7.0%|6.4%|21.5%|4.3%|
|Kazakhstan|$12,345|29.5%|8.3%|20.7%|30.6%|19.7%|34.0%|27.1%|17.4%|-6.7%|
|Norway|$120,049|4.3%|12.1%|15.5%|10.3%|12.3%|7.7%|13.9%|9.0%|-13.7%|
|Russian Federation|$198,931|22.5%|10.9%|19.7%|34.1%|22.0%|16.4%|31.3%|36.1%|-26.4%|
|Saudi Arabia|$143,325|-3.7%|4.1%|5.5%|5.6%|16.8%|9.2%|5.3%|13.7%|-9.1%|
|Sudan|$8,916|8.8%|7.5%|26.5%|15.3%|26.6%|24.5%|14.4%|20.4%|-0.7%|
|Trinidad & Tobago|$5,776|11.6%|0.0%|20.2%|14.9%|14.6%|5.1%|20.5%|27.7%|-29.9%|
|Venezuela|$92,094|4.9%|-27.6%|-11.9%|26.3%|26.3%|26.5%|29.0%|36.2%|4.3%|
|||**_Percentage_**|**_contribution_**|**_of depletion_**|**_to (real) aN_**|**_NI growth_**|**_per annum_**||||
|Angola||36.1%|-11.7%|-11.7%|-36.2%|-77.0%|-71.2%|-33.8%|-63.3%|88.8%|
|Azerbaijan||10.8%|28.5%|-3.3%|-6.4%|-36.5%|-56.5%|-45.6%|-33.5%|23.5%|
|Ecuador||3.4%|1.0%|-2.3%|-7.8%|-7.6%|-5.1%|-1.2%|-1.6%|10.3%|
|Kazakhstan||4.8%|-3.4%|-3.7%|-8.5%|-7.8%|-5.5%|-2.8%|-7.7%|6.8%|
|Norway||3.7%|0.1%|-2.0%|-4.1%|-4.9%|-2.0%|0.5%|-5.0%|9.0%|
|Russian Federation||2.6%|-0.8%|-3.9%|-6.5%|-6.9%|-4.0%|-1.7%|-4.8%|6.6%|
|Saudi Arabia||4.3%|0.8%|-6.6%|-8.0%|-13.4%|-4.1%|-0.1%|-14.4%|20.7%|
|Sudan||0.8%|-2.4%|6.6%|-3.8%|-3.7%|-4.0%|-8.7%|-4.8%|7.3%|
|Trinidad & Tobago||3.7%|-1.4%|-4.0%|-3.0%|-8.8%|-3.9%|2.2%|-5.1%|7.3%|
|Venezuela||2.7%|1.1%|0.8%|-6.3%|-5.6%|0.0%|2.0%|1.8%|2.4%|

Source: authors’ own calculations and adapted from BP (2011), World Bank (2011a), Gelb _et al_ . (2012) 15
Figure A.1 shows that rural scores were slightly higher than urban classrooms across all three domains— Emotional Support, Classroom Organization, and Instructional Support—although only by a tenth of a point. With only 18 teachers and six schools for each category, it is difficult to draw any significant conclusions when the data are so similar. While the local counties are able to differentiate what urban and rural schools look like within their particular context, from the viewpoint of Beijing and Shanghai, all schools observed would likely be considered rural. With that in mind, it is understandable that there is not a significant difference in observed teacher behaviors.

A breakdown of scores by district for elementary and middle schools shows a similar trend in relative scores between the three counties.

**Figure A.2: Average Elementary School CLASS Scores by County**

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>2 Dianbai<br>1 Lianjiang<br>0 Wuhua<br><!-- End of picture text --> Source: Guangdong class observations; authors’ calculations.

In figure A.2, Dianbai stands out, particularly in Instructional Support, where it outscores the other two counties in several dimensions.

25
with those reported in income surveys, assuming that tax evaders have no incentives to conceal their true income when responding to an income survey. It is also possible to investigate tax evasion based on data on food spending and estimates of the link between income and food spending (Engels curve). For example, Kukk & Staeher (2014) show that in Estonia, the reported total income of households with business income above 20% of total income must be multiplied by 2.6 to attain the same propensity to food consumption as households of wage earners. In this sense, households with business income underreport 62% of their ‘true’ total income. The comparison of consumption patterns of the public sector employees (where envelope wages are not an issue due to legal constraints) and the private sector is another method. Kiełczewska et al. (2021) apply this method to Poland. They find that 12% of the employees receive envelope wages and that the most important problem is near the minimum wage threshold. Lopez-Luzuriaga et al. (2023) apply this method in Ecuador using income tax and online billing data. They find that the underreporting of income among private sector workers is 7%-9%, corresponding to 3% of the GDP. Envelope wages are most common in small companies. Alternatively, discrepancies in economic statistics can also reveal information about tax evasion. This approach involves comparing actual tax revenue with the National Accounts, which can help identify discrepancies indicative of tax evasion. Recently, a novel strand of the literature on tax evasion developed based on leaked lists of tax evaders (e.g., Alstadsæter et al., 2019).

**Studies based on Eurobarometer data show that in 2007, the share of employees receiving envelope wages in Romania stood at 22%, the highest among transition economies.** However, the study based on Eurobarometer data for 2019 shows that approximately 5% of the employees receive envelope wages in Romania, and this share is medium compared to other CEE economies (Horodnic & Williams, 2021). A survey conducted in 2020 on a representative sample of students revealed that 31% of the Romanian students working during their studies received envelope wages, and 38% would accept envelope wages when having job offers. Moreover, 37% of students prefer envelope wages to obtain higher take-home income (Horodnic et al., 2020). Barth & Onegdal (2018) present evidence that approximately 5% of employees in EU countries receive envelope payments (part of their income is not reported to the tax authority).  Di Nola et al. (2019) find high cross-country heterogeneity in the share of employees receiving envelope payments. According to their results, this share is 15% in Romania,

6
corresponding average share for employed men (KILM, 4th edition). Overall, men are more likely to be self-employed (34.4 versus 27.8 percent), and women are more likely to be working as unpaid family labor (14.6 versus 7.0 percent). The shares of employed men and women in wage employment are roughly identical (see Table 1).<sup>10</sup>

**Table 1.  Distribution of male and female workers by type of employment: 91 countries, most recent year**

||Wage and salaried|Self-employed|Contributing family|Total*|
|---|---|---|---|---|
||workers|workers|workers||
|Women|57.3|27.8|14.6|99.7|
|Men|57.7|34.4|7.0|99.1|

Notes: * Totals do not sum to 100 because of rounding. Shares are unweighted cross-country averages. The sample is restricted to developing countries. Source: Authors’ calculations based on KILM, 4th edition.

In terms of the likelihood of being engaged in informal employment, a World Bank (2001) report notes that there are countries in which women’s share in informal employment is less than their share in total employment (Burundi, Costa Rica, Egypt, Kenya, Korea, Mali, Panama, Tanzania and Vietnam), countries in which women’s share in informal employment is greater than their share in total employment (Botswana, Brazil, Colombia, Ghana, Honduras, Indonesia, Jamaica, Malaysia, Peru, Philippines, Zambia and Zimbabwe), and countries in which the two shares are roughly similar (Congo, Fiji, Gambia, Mexico, Thailand, Uruguay and Venezuela). For the 80 developing countries in the KILM database that provide data on own-account workers, on average, 29 percent of male workers are own-account workers versus 23.6 percent of female workers.

Finally, it is true that women are more likely than men to work as domestic workers and unpaid workers in family enterprises, and that men are more likely to work as employers.<sup>11</sup> On average, 14.6 percent of employed women work as unpaid family workers, while only 7 percent of men do so. A key question is whether women prefer to work at home or in family-owned businesses because of the location or the flexibility of work hours, which allows women to more easily combine work, domestic chores and care work. Or, on the other hand, do prevailing gender norms condition women to assume this triple workload and/or to restrict their mobility (Carr and Chen, 2004)? Alternatively, is this pattern a result not of supply considerations, but rather of the gendered demand for labor, which presumably reflects existing societal gender norms?

> 10 All reported shares are unweighted mean shares across the developing countries in the sample.

> 11 Employed men are more than twice as likely as employed women to work as employers: 4.6 percent of employed men do so, versus 2.1 percent of employed women (KILM, 4th edition)

13
Brown, R., 2020. The Intergenerational Impact of Terror: Did the 9/11 Tragedy Impact the Initial Human Capital of the Next Generation? _Demography_ , pp.1-23.

Case, Anne, and Christina Paxson. 2008. Stature and Status: Height, Ability, and Labor Market Outcomes. Journal of Political Economy 116(3): 499-532.

- Carrillo, B., 2020. Early Rainfall Shocks and Later-Life Outcomes: Evidence from Colombia. _The World Bank Economic Review_ , _34_ (1), pp.179-209.

Center on the Developing Child at Harvard University. 2009. Maternal Depression Can Undermine the Development of Young Children: Working Paper No. 8. _<u>Http://www.Developingchild.Harvard.Edu</u>_ <u>, December 2012.</u> Centers for Disease Control. Complete reference - <u>http://www.cdc.gov/nccdphp/dnpa/growthcharts/resources/biv cutoffs.pdf</u> Chowdhury, Gopa 1991. A comparison of covariance estimators for complete and incomplete panel data models. "Oxford Bulletin of Economics and Statistics" 53, 88 - 93.

- Cogill, S. R., H. L. Caplan, et al. (1986). Impact of maternal postnatal depression on cognitive development of young children. _British Medical Journal_ ; 292:1165-1167.

- Cueto, S. and J. Leon. 2009. Psychometric Characteristics of Cognitive Development and Achievement Instruments in Round 2 of Young Lives. Young Lives Technical Note 15. Oxford, United Kingdom: University of Oxford.

Cunha, Flavio, James Heckman and Lance Lochner. 2006. Interpreting the Evidence on Life Cycle Skill Formation.‖ In Eric Hanushek and Finis Welch, eds., Handbook of the Economics of Education. North Holland: Amsterdam, pp. 697–812.

- Cunha, Flavio, and James Heckman. 2007. The Technology of Skill Formation. _American Economic Review_ 97(2): 31-47.

- Currie, J., and D. Thomas. 1999. Early Test Scores, Socioeconomic Status and Future Outcomes. WP 6943, NBER Cambridge, MA.

- Dunn, Lloyd M., Delia E. Lugo, Eligio R. Padilla, and Leota M. Dunn. 1986. Test de Vocabulario en Imágenes Peabody. Circle Pines, MN: American Guidance Service.

Engle, P., M. Black, J. Behrman, M. Cabral de Mello, P. Gertler, L. Kapiriri, R. Martorell, M. Eming Young, and the International Child Development Steering Group. 2007. Strategies to avoid the loss of developmental potential in more than 200 million children in the developing world. _The Lancet,_ 369, 229-242.

21