rafmacalaba/datause-encoder-data huggingface dataset:
0 C **o** co O A m m **_C_ 0** =1 R w **0** OQ **0 0** a

**.**

|Table B6. Informal Ownership<br>from Informal Sector Surveys conducted by the author|<25 hours per<br>e<br>week<br>Taxicabs|45 hours<br>per week<br>Taxicabs<br>|Entire<br>Sample<br>Taxicabs<br>|Entire<br>Sample<br>Kiosks<br>|Entire<br>Sample<br>Stre<br>Markets<br>|Entire<br>Sample<br>et Services &<br>Vendors<br>|
|---|---|---|---|---|---|---|
|Number of Observations (actual number may vary depending<sup>on the</sup>|99|101|308|245|137|80|
|question)|||||||
|Officially Registered?|||||||
|Yes|20.2|74.0|44.6|100.0|33.6|51.3|
|Taxicab License Numbers|||||||
|Personal|71.7<br>|75.0<br>|74.8<br>|na|na|na<br>|
|Organization|28.3|25.0|25.3|na|na|na|
|Taxicab License Numbers|||||||
|Ulaanbaatar|96.9|99.0<br>|97.7<br>|na|na|na|
|Other|3.1|1.0|2.3|na|na|na|
|Ownership of vehicle/kiosk|||||||
|Self|64.7|58.4|62.3|64.5|na|na|
|Relative|5.1<br>|10.9<br>|9.7<br>|22.9<br>|na|nao<br>|
|Another private person|0.0<br>|4.0<br>|2.3<br>|9.4<br>|na|na<br>|
|Economic Entity|24.2<br>|26.7<br>|21.4<br>|2.9<br>|na|na|
|Budget Organization|6.1|0.0|4.2|0.4|na|na|
|Form of compensation if not self-owned (kiosks only)<br>|||||||
|Kiosk income goes to family|na|na|na|46.0<br>|na|na<br>|
|Paid a portion of the profits|na|na|na|26.4<br>|na<br>|na<br>|
|Paid based on time worked|na|na|na|16.1<br>|na|na<br>'|
|Other|na|na|na|11.5|na|na|
|Others who work at the kiosk...|||||||
|Spouse|na|na|na|35.1<br>|na<br>|na<br>|
|Other relatives<br>|na|na|na|53.1<br>|na<br>|na<br>|
|Unrelated people|na|na|na|5.7|na|na|
Table C15: Results: Network Details by University

||(1)<br>Family|(2)<br>Friends|(3)<br>Social Media|(4)<br>Schoolmates|(5)<br>Train mates|
|---|---|---|---|---|---|
|Offered Training|0.07<sup>_∗∗_</sup>|0.04<sup>_∗_</sup>|0.12<sup>_∗∗∗_</sup>|0.09<sup>_∗∗∗_</sup>|0.31<sup>_∗∗∗_</sup>|
||(0.03)|(0.02)|(0.03)|(0.03)|(0.02)|
|Offered * University degree|0.02|0.07|-0.05|0.03|-0.07|
||(0.06)|(0.05)|(0.06)|(0.06)|(0.05)|
|Control Mean: University degree|0.43|0.72|0.44|0.53|0.15|
|Control Mean: Diploma|0.42|0.69|0.44|0.49|0.21|
|Observations|2,597|2,597|2,597|2,597|2,597|
|p-value University|0.10|0.01|0.24|0.01|0.00|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level.

50
**Table 6: Cost-effectiveness of remedial education program using high school graduate wages from other countries, holding all else constant**

|**Country**|**Monthly salary of high school**<br>**graduate in base year currency**|**Base year**<br>**currency**|**Remedial tutor equivalent**<br>**salary in 2011 USD**|**Additional SD**<br>**per $100**|
|---|---|---|---|---|
|_India (Balsakhi remedial education program)_|7,500.00|2001 INR|13.24|3.07|
|**Neighbours**|||||
|Tajikistan|364.70|2009 TJS|90.23|0.68|
|**Similar GDP per capita**|||||
|Nicaragua|3,774.29|2009 NIO|196.08|0.33|
|Nigeria|20,165.13|2012 NGN|125.21|0.51|
|**Poorer**|||||
|Ethiopia|1,234.35|2011 ETB|73.48|0.82|
|Uganda|284,361.30|2011 UGX|114.11|0.55|
|**Richer**|||||
|Bulgaria|368.54|2007 BGN|277.47|0.24|
|Panama|556.47|2008 PAB|580.37|0.12|

Source: The monthly salaries of high school graduates in Tajikistan, Nicaragua, Nigeria, Ethiopia, Uganda, Bulgaria, and Panama are calculated using Living Standards Measurement Study data (World Bank 2014). Actual data for remedial tutor salaries in India are from J-PAL (2014). The exchange rate for India and average US inflation rates come from J-PAL (2014); average annual exchange rate data for all other countries are from OANDA (2014).

Notes: SD means standard deviations. The reported remedial tutor equivalent salaries presented in 2011 USD are calculated by converting the average monthly salaries of salaried workers who completed high school (and no further education) in local currency in the various base years (the year the data were collected) to USD in the given base year, before inflating to 2011 USD using the inflation <u>GDP deflator (annual %).</u> 22
**Figure 10. Workers with earnings between the current and the proposed minimum wage, % workers in selected categories (occupation and industry), 2018**

# **a. By occupation**

<!-- Start of picture text --> Armed forces 0.7<br>Elementary occupations 14.4<br>Machine operators 8.0<br>Craft workers 7.6<br>Skilled agricultural 0.0<br>Service and market sales workers 15.6<br>Clerks 3.1<br>Technicians 3.1<br>Professionals 1.2<br>Senior officials 1.2<br>0 2 4 6 8 10 12 14 16 18<br>Percent<br>b. By industry<br>Other Services, Unspecified 5.7<br>Public Administration 1.7<br>Financial and Business Services 10.3<br>Transport and Comnunications 3.1<br>Commerce 16.5<br>Construction 5.7<br>Public utilities 4.2<br>Manufacturing 10.2<br>Mining 4.5<br>Agriculture 14.9<br>0 2 4 6 8 10 12 14 16 18<br>Percent<br><!-- End of picture text --> _Source: Own estimates using 2018 Kosovo LFS._ Who are they? Decomposition of the affected group

**Certain groups of workers are more likely to be affected by the rising minimum wage, including youth, unskilled workers, workers with non-professional occupations, and informal-sector workers.** A large share of workers who earned between the current and the proposed minimum wage were young workers aged 20-34 (50.5 percent). Only 3.5 percent were aged 15-19, given teenagers’ low employment rate; 37.4 percent were aged 35-54 and 8.7 percent were aged 55-64.  There were more men than women in this group, given women’s low employment rate (72.8 percent of men vs. 27.2 percent of women). The majority of potentially affected workers had completed secondary education only (72.5 percent among those aged 15-34 and 55.0 percent among those aged 35-64). Among young potentially affected workers (aged 15-34), 16.7 percent had post-secondary education and the majority were market sales workers. Among those aged 35-64, the majority had secondary education and non-professional occupations. The large majority of affected workers were in the private sector (88.2 percent). Moreover, among young workers the majority of potentially affected workers were

18
_2_ 3 poIi'-', '4,~i. cr-,'~. iq ot thr :ush the intZFA rte- cha!nnel .L-th rcn!.-unmption and in.estmPnt d-mand. A third -hann,e (n.t allowei for in the previous estimatesi may be howe er as v ,-r if rati-na! Fri'.ate agents takie fully into a,-count the future ta< liatijities ass--ciated with bornd-financed defic~it. The impli-cati-n Is tha' a shift from ta< to debt finance of a given volume of public e<enditure should be neutral insoFar as it would be mat-hed bY offs`,tting priaete agents behaviour. This is because private agents would be perFectly aware of the future tax liabilities and the consequent reduction in their permanent disposable income that the increased deficit entails and would correspondingly reduce their consumption. This is known as the Ricardian equivalence hypothesis. There are several reasons whv this pr oposition may not hold, among which it is worth mentioning the possibility that private agents may discount the future at a different rate than the government and the presence of capital market imperfections which hinder the intertemporal smoothing of consumption by private agents. Evidence on these issues for developing countries is limited (see however Rossi, 1988, Nam 1989, Haque and Montie!, 1989, Deaton 1990 and Haque, 1990). In what follows we amend the previous specification to allow for the possibility that, by reducing perceived disposable income, government deficits may have a negative impact on consumption. We do not expect this effect to be particularly strong in Morocco, because of the absence of a consumer credit market and the likely pervasiveness of liquidity constraints on households. To capture this effect we introduce liquid assets (currency plus bank deposits) in our equation. We also allow for inflationary effects on the ground that a high level of inflation will lead to capital losses on liquid assets and impart a downward bias to the traditional measure of disposable income. An increase in inflation should also be associated with greater uncertainty and lead, through this channel, to an increase in precautionary saving. If we estimate this more complete model, we find that: A In Ct = .46 (In Y - In C,_ ) + .06 (n Mt - In Ct) -. 09 In X (.056) (.02) (.02) -.12 A In pt - .13 (deF/(CODP) (,07) (.05) R<sup>2</sup> = 83 DW = 2.16 SER .012
# **References**

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- Allen, Franklin, Jun Qian, Meijun Qian. 2005. "Law, Finance, and Economic Growth in China," _Journal of Financial Economics, Vol. 77_ , pp. 57–116."

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- Angrist, Joshua. 2001. "Estimation of Limited Dependent Variable Models with Dummy Endogenous Regressors: Simple Strategies for Empirical Practice," _Journal of Business and Economic Statistics_ 19(1), 2-16.

- Ayyagari, Meghana, Asli Demirguc-Kunt, and Vojislav Maksimovic, 2010. “Formal versus Informal Finance: Evidence from China.” _Review of Financial Studies_ 23(8): 3048-3097.

- Ayyagari, Meghana, Asli Demirguc-Kunt, & Vojislav Maksimovic, 2012. "Financing of firms in developing countries : lessons from research," Policy Research Working Paper Series 6036, The World Bank.

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- Beck, Thorsten, Asli Demirguc-Kunt, and Maria Soledad Martinez Peria, 2011. “Bank Financing for SMEs: Evidence across Countries and Bank Ownership Types.” _Journal of Financial Services Research_ , 39(1-2): 35-54.

- Berger, Allen N., Iftekhar Hasan, and Mingming Zhou, 2009. “Bank Ownership and Efficiency in China: What Will Happen in the Wrold’s largest Nation?” _Journal of Banking and Finance_ , 33(1): 113130.

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37
We question the notion that costs of transportation supported by developing countries are intrinsically high. Neither the distance covered, nor the unit cost of transportation services, are necessarily much higher in landlocked developing countries than in the wealthiest countries. Yet there are significant variations; for instance, Central and East Africa have higher unit costs than the EU but this is not the case of South and East Asia or other sub-regions in Africa. Unfortunately shippers also support a lot of non-essential overheads resulting from corruption, overregulation and private inefficiencies, the total of which may be very high. Furthermore, transportation costs only explain one part of the real impact of being landlocked. Delays and even more importantly low degree of reliability and predictability of services create massive disincentives to invest and higher total logistics costs.

Much of the cost supported by LLDCs may not be exogenous, as primary sources of cost are associated with poor performance of transit logistics resulting from a combination of (i) bad design or implementation of transit regimes, and (ii) unfavorable political economy of transit and particularly its vulnerability to rent seeking activities. These may be as or even more important in constraining the trading prospects of many LLDCs than poor infrastructure or political disputes.

Previous research is essentially based on macro-modeling and cannot identify the relative importance of possible sources of costs. In order to fill this gap, we propose to follow a supply chain model initially proposed by Baumol (1970). We use micro-level disaggregated data to identify the three components of transit costs: (i) transport costs, (ii) logistics costs and (iii) hedging costs incurred by shippers to cope with unpredictable delivery schedules. This paper identifies the various sources of logistics costs and provides examples worldwide. More detailed data (at shipment level) have been obtained from World Bank projects which we have contributed to appraise in East and Central Africa.

Section 2 reviews the literature on the nexus between geographic location, trade and growth and the induced policy conclusions. Section 3 reviews the transit framework and 4
**Annex 1B: Overview of Adoption of Washington Consensus Reforms in the Power Sector, 1990 – 2015**

|**Country**<sup>**20**</sup>|**Year of**<br>**establishment**<br>**of regulatory**<br>**entity**|**Restructuring timeline**|**Competition timeline**|**Year of**<br>**1st IPP**|**Private**<br>**generation share**<br>**(% of installed**<br>**capacity)**|**Years of private sector participation in**<br>**distribution/transmission companies (nr**<br>**of companies)**|
|---|---|---|---|---|---|---|
|**Afghanistan**|‐<sup>21</sup>|‐|‐|‐|9.5|‐|
|**Algeria**|2002|Partial vertical unbundlingin 2002|Single Buyer model since 2001|2004|9.6|‐|
|**Angola**|2007|Full vertical unbundlingin 2014|Monopoly plus IPPs model since 2015|‐|9.0|‐|
|**Argentina**|1992|Full vertical unbundlingin 1995|Retail Competition model since 1995|1992|69.3|1993(1),1995(4),1996(4),1997(2)|
|**Armenia**|1997|Partial vertical unbundling in 1995<br>Full vertical unbundlingin 1997|Single Buyer model since 1995|‐|68.5|2002 (1)|
|**Bangladesh**|2003|Full vertical and horizontal<br>unbundlingin 1996|Single Buyer model since 1995|1997|42.2|2006 (1)|
|**Belarus**|2000|Horizontal unbundlingin 1997|‐|‐|0.1|1995(1),2000(1)|
|**Benin**|2009|‐|‐|‐|0.0|‐|
|**Bolivia**|‐|‐|Monopoly plus IPPs model since 1998<br>Wholesale Competition model since 1996|1998|41.5|1995 (1), 2000 (1)|
|**Brazil**|1997|Horizontal unbundling in 1995<br>Full vertical unbundlingin 1998|Single Buyer model in 1995<br>Wholesale Competition model since 1998|1993|44.1|‐|
|**Burkina Faso**|2010|‐|Monopoly plus IPPs model since 1998|1998|0.8|2001(1)|
|**Burundi**|2011|‐|‐|‐|0.4|‐|
|**Cambodia**|2001|‐|Monopoly plus IPPs model since 1997|1997|94.9|‐|
|**Cameroon**|1998|‐|Monopoly plus IPPs model since 2009|‐|99.2|1993(1),1998(1)|
|**Central African**<br>**Republic**|2005|‐|‐|‐|0.0|1996 (1), 1997 (2), 1998 (1), 2006 (1)|
|**Chad**|‐|‐|‐|‐|100.0|‐|
|**Chile**|1978|Horizontal unbundling in 1982<br>Partial vertical unbundling in 1995<br>Full vertical unbundlingin 2000|‐|1992|99.5|1993 (1), 1998 (1)|
|**China**|2003|Partial vertical unbundling in 1995<br>Horizontal unbundlingin 2003|Single Buyer model since 1995|1992|36.8|1990 (1)|
|**Colombia**|1992|Partial vertical unbundlingin 1995|Wholesale Competition model since 1995|1993|69.2|1996(1),1997(2),1998(1),2006(1)|
|**Congo, Dem. Rep. **|‐|‐|‐|‐|0.0|‐|
|**Congo, Rep. **|2003|‐|‐|‐|0.0|‐|
|**Côte d'Ivoire**|1998|‐|Monopoly plus IPPs model since 1995|1994|100.0|1990(1)|

20 For a full list of the market players (regulatory entity, wholesale market and transmission system operators) please refer to the RISE project on rise.esmap.org 21 “‐“ denotes no reform 67
**Table 4**

|**Regression Results of deter**|**minants of Po**<br>|**wer Sector Perform**|**ance(Fixed E**<br>|**ffects)**<br>|
|---|---|---|---|---|
|_FIXED EFFECTS_|**Residential**<br>**Access (% )**|**Energy sold**<br>**(MWh/employee)**|**Tariff**<br>**(US$c/KWh)**|**Emission index**<br>**(CO2 ton/KWh)**|
|**D**|**EGREE OF U**<br>|**NBUNDLING**<br>***||***|
||-2.72*|-542.94|-0.46|0.02|
|**Partial unbundling**|(1.43)|(192.53)|(1.82)|(0.01)|
|**Full unbundling**|0.18<br>|-208.36*<br>|1.98***<br>|0.004<br>|
||(1.36)|(118.09)|(0.82)|(0.009)|
|**DEG**|**REEOF DISA**|**GGREGATION**|||
|**HHI generation (reverse)**|0.0008**<br>(0.0003)|||-0.00001***<br> (0.0000002)|
|**HHI distribution (reverse)**||0.63<br>|-0.006***<br>||
|||(0.40)|(0.002)||
|**DEGR**|**EE OF PRIVA**<br>|**TE OWNERSHIP**|||
|**Privatization in Generation (dummy)**|3.46***<br>(1.16)|||0.02***<br>(0.008)|
|**Privatization in Distribution/Total (share)**||9.57***<br>|0.37**<br>||
|||(3.47)|(0.17)||
||**REGULA**|<br>**TION**|||
|**Introduction of an autonomous regulator**|3.94***<br>(0.96)|299.34***<br>(88.19)|2.32***<br>(0.79)|-0.025***<br>(0.007)|
||**CONTR**<br>|**OLS**<br>|||
|**GDP per capita**|3.7***|190***|2.***|0.0003|
|**(thousand US$)**|(0.4)|(44.)|(0.2)|(0.002)|
|**Installed capacity**<br>**(MW thousand)**|0.6***<br>(0.08)|169***<br>(16.6)|0.303***<br>(0.067)|- 0.003**<br>(0.001)|
|**GDP per capita* Installed capacity**|-0.047***<br>(0.005)|-3.38***<br>(0.65)|-0.019***<br>(0.003)|0.0001<br>(0.00004)|
|**Financial crisis**|||-0.87***<br>(0.32)||
|**Capex**<br>**(US$/MWh)**|||0.28***<br>(0.11)||
|**Share of fossil fuels (access)/hydro (emission)**<br>**(% installed capacity)**|0.10<br> (0.07)|||-0.0007*<br>(0.0004)|
|**Ctt**|21.68***|3845.99|-60.63***|0.79***|
|**onsan**|(5.62)|(3453.8)|(17.76)|(0.03)|
|**N**|271|166|129|318|
|**F**|61.29***|223.77***|14.80***|8.95***|
|**Within R**<sup>**2**</sup>|0.64|0.90|0.53|0.23|
|**Between R**<sup>**2**</sup>|0.40|0.46|0.53|0.09|
|**Overall R**<sup>**2**</sup>|0.49|0.61|0.41|0.03|

_Note: *, **, *** indicate respectively level of significance of 10, 5 and 1 percent._ 12
14 prescribed order. The response rates in 1999, for example, vary from country to country with an average response rate of around 60 percent. Because EVS poses an identical set of questions to individuals in various European countries, the survey provides a unique opportunity to empirically examine tax morale changes over time. EVS has been designed as a wide-ranging survey, thereby reducing the danger of framing effects when compared with many other surveys that focus entirely on a specific question. This avoids the possibility of respondents reporting what they believe are the answers required by the researchers (Torgler & García-Valiñas, 2007). We will focus on the 10 Eastern European countries that have acceded to the European Union in 2004 or 2007. The general question to assess the level of tax morale in a society is: Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between: (...) Cheating on tax if you have the chance (% “never justified” = code 1 on a ten-point scale where 1=never and 10=always).

_Table 1_ presents mean tax morale values for all countries in 1999 and 2008, based on a scale from 0 to 3, were 3 is the highest tax morale (tax evasion is never justifiable) while value 0 integrates values 4 to 10. In general, extracting only a single question from the EVS also reduces problems of complexity inherent with the construction of an index, especially regarding the measurement procedure or a low correlation between the items. Even so, one should recognize that there are some good reasons to use a multi-item index instead of a single question to measure tax morale. Tax morale is likely to be a multi-dimensional concept, which may require a multi-item measurement tool, as in psychometric studies. In this context, a single-item measure like ours has some disadvantages compared to a multi-item index (Lewis, 1982; Jackson & Milliron, 1986). For example, a single-item measure may have difficulty in capturing adequately the interrelated facets of tax morale, and may also be adversely affected by random errors in measurement. Further, a multi-item index has the
Table 1: Some Indicators of Trade Regimes before and after Reform

|Country<br>|Pre-r<br>Post-re|eform'/<br>form year|Average u<br>legal ta<br>pre-reform|nweighted<br>riff rates<br>post-reform|Tarif<br>legal tar<br>pre-reform|f range<br>iff weight<br>post-reform|Covera<br>on i<br>preoreform|ge of QRs<br>mports<br>post-reform|Openn<br>econ<br>pre-reform|ess of<br>omy<br>post-reform|
|---|---|---|---|---|---|---|---|---|---|---|
|Argentina|1987|1991|**%C-**|la|**I1-L**|*-ss -,|J'ew||**3a.O**||
|Bolivia|1985|1991|12'|8|NA|S-10|NA|Minimal|57.5|83.9|
|Brazil|1987|1992|51|21|0-105|_06S_|39|Minimal|21.2|25.2|
|Chile|1984|1991|35|11|_35_|it|Minimal|0|44.9|_56.3_|
|Colombia|1984|1992|61|12|0-220|5-20|99|1|28.2|32.7|
|Costa Rica|1985|1992|**53'**|**15'**|0-1,400'|5-20|NA|0|58.7|79.0|
|Ecuador|1989|1992|37'|18|0-338|2-254|t00|0|48.7|50.8|
|Guatemala|1985|1992|50'|15'|5-90|5-20|6|0|31.3|35.6|
|Honduras|1985|1992.|41'|i5|_5-90_|5-20|NA|0|62.8|61.8|
|Jamaica|1981|1991|NA|20|NA|_045._|NA|0|10S.S|_163.5_|
|Mexico|1985|1990|24 <sup>L</sup>|**13**<sup>**k**</sup>|0-100|0-20|92<sup>b</sup>|2 0 <sup>b</sup>|22.6|34.3|
|Paraguay|1988|1991|NA|16|NA|3-86|NA|Few|51.0|63.1|
|Peru<br>Trinidad|1988|1992|NA|17|0-120|_5-25_|100|0'|30.4|41.6|
|and Tobago|<br>1989|1991|NA|41'|NA|0-103'|NA|Few|124.9|141.2|
|Uruguay|1987|1992|32|18|_10-55_|12-24|0|0|38.0|45.1|
|Venezuela|1989|1991|37|19|0-135|0-50|40|101|49.3|54.3|

- a. Including tarif surcharges.

- b. Percentage of domestic product.

- c. Import weighted average tariff.

d. Ecuador also has a specific tariff of 40 percent on automobiles, e. Percentage of domestic product, Guatemala has sigrificant QRs for health and safety reasons; pre-reform they covered 29 percent of domestic manufarturing production.

f. Including tariff surcharges.

g, Some QRs exist for health and safety reasons.

h. Production weighted average.

- i. Agricultural products only.

j. Another 8 percent of tariff items are restricted because of health reasons, per-reform the number was 5 percent. _Source:_ World Bank (various reports,.staff estimates and ANDRBX data).

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