rafmacalaba/datause-encoder-data huggingface dataset:
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43
of the traffic. Table 1 below shows data for the period from 2005 - 2009 for container ports in the region, highlighting Durban’s dominance in the container market.

**Table 1: Port Container Traffic - Eastern and Southern Africa (000 TEU’s)**<sup>**3**</sup>

|**Port**|**2005**|**2006**|**2007**|**2008**|**2009**|
|---|---|---|---|---|---|
|**Durban**|1 899.0|2 334.9|2 479.2|2 642.1|2 395.0|
|**Cape Town**|690.8|764.7|764.0|767.5|694.5|
|**Mombasa**|436.7|479.4|585.4|615.7|618.8|
|**Port Elizabeth**|369.7|497.2|422.8|423.8|441.4|
|**Dar-es Salaam**|258.4|272.7|334.0|373.5|473.7|
|**Walvis Bay**|71.4|83.2|144.9|170.5|Data|
|**Maputo**|54.0|62.5|80.3|92.2|107.0|
|**East London**|49.3|41.8|41.9|57.4|52.5|

_Sources: (TPT, MCLI, TNPA, OSC)_ The critical location of Durban on the North South Freight Corridor, an important route for transit traffic bound for Zambia, Zimbabwe, Malawi and a connection going as far north as Dar es - Salaam, puts it on the spot light and its performance is extremely vital for countries along the corridor.

The Durban Container Terminal (DCT) has benefitted substantially from major infrastructure investments and it now comprises a new terminal known as Pier 1 and the old terminal known as Pier 2. With a capacity of 720,000 TEU’s, Pier 1 has 3 berths with a 11,9m draft, 6 ship to shore gantries with 888 reefer points. The terminal operates RTG’s. The larger old terminal, Pier 2 is currently designed for a capacity of 2,9 million TEU’s and it boasts 6 berths over 14,000 ground slots with an average draft of 11,8m, 19 ship to shore gantries, 1117 reefer points. The terminal operates with straddle carriers’<sup>4</sup> .

containers. The National Port Authority Act includes clauses to terminate the control of nationalized ports, but the implementation is still not on the political agenda and it also face resistance from Transnet management and the DPE.

> 3 The port of Nqura has been excluded from the list as it mainly handles transshipments and started operations late 2009. Transhipments at the port make up approximately 60% of total traffic. In comparison with Durban, volumes at Nqura are significantly low.

> 4 The port operates 24hrs a day 365 days a year. During day light the ships are restricted to 243.8m length with a maximum width of 35m and a draught of 11.9m or 12.2m according to tide and harbor master service. The largest ship calling the in 2009 had a 6.742 TEU carrying capacity which is relatively small by world standards but probably the largest to call at any African port. See Annex 3 for port and container terminal layout and Annex 4 for berth data.

4
when payments come due. On the other hand, a debt maturity longer than asset life may create problems of finding new assets to support debt service."<sup>6</sup> Finally, another firm characteristic not suggested in the literature on agency cost is size. As argued **by** Demirgfii-Kunt and Maksimovic (1994), firm size may be relevant for financial structure, because access to financial markets may be a functior. **of** size.

The empirical literature on financial structure in developing countries emphasizes the influence of firms' characteristics, institutional factors, and the development of financial markets on the financing choices of firms. Aivazian, Booth, Demirgiiq-Kunt, and Maksimovic **(1999)** use a firm-level database to study whether the capital structure theory is relevant for developing countries with different institutional characteristics.<sup>27</sup> They study capital structure in ten developing countries and fccus on three main issues. First, they study the possibility that corporate leverage decisions differ between developed and developing countries. Second, they analyze the factors that affect cross-sectional variability within each country. Finally, they examine the possibility that capital structure models add insights to just knowing the nationality of the company. They focus on book and market debt ratios.<sup>8</sup> They regress these ratios against: (a) firms characteristics (tax rates, assets tangibility, business risk, size, returns on assets, and market to book ratios), **(b)** dummy variables for the different countries, and (c) aggregate financial indicators for each country (for cross country regressions).

See also Caprio and Demirgib;-Kunt **(1997).**

> There exist some case studies. Jaramillo and Schianl:arelli **(1996)** study the case of Ecuador, Schiantarelli and Srivastava **(1996)** and Samuel **(1996)** cover the case of India, while Gallego and Loayza (2000) analyze the case of Chile.

2 They focus on three different ratios: **(1)** the total book debt ratio, defined as total liabilities divided **by** total liabilities plus net worth, (2) the long book debt ratios, defined as long-term liabilities divided **by** long-term liabilities plus net worth, and **(3)** the long market debt ratio, **by** substituting the average equity market value for net worth.

41
the north. In 1998 the gap widened to 32 %. A variable on seniority was included in the model to see if there is any impact of recent changes in labor market. It is assumed that younger workers are more affected by the recent changes as they enter directly into a free market wage economy and higher levels of education can bring them a significant differences in earnings. The variable takes the value 1 if a worker entered the labour market for less than 5 years, and 0 otherwise. It turns out that other things being equal, it statistically does not make any difference in terms of earnings between young and senior workers.

An extended earning model was estimated to look at the rates of return for different levels of education. It estimates average private returns to different levels of schooling by converting continuous years of schooling (S) into a series of dummies variables. The dummy variables represent the completion of the respective level of general education or vocational training following general education. Following is the extended model: lnYi = a + 13IPRIMi + f32LSECi + f3 USECi + 134UNIV1 + I35VOCPi +I36 VOCLSi +P 7VOCUSi + P 8EXPj + I39 EXPi<sup>2</sup> + , I SEX1 + pII NORTHi + P312 I)RBANi + 1 3 SENIOR1 + **_Ce_** where PRIMi, LSECi, USECi, UNIVi are dummy variables indicating primary, lower secondary, upper secondary or university education completion by individual i; VOCPi , VOCLSi, **VOCUS1** reflect kind of vocational training that individual i took, whether it is vocational training after completing primary, lower secondary, or upper secondary. The private rates of return to one year at different levels of schooling then can be derived by dividing estimated rates of return to each level of education by correspondent number of additional years required at each level. For primary education, this is assumed to be equal to 1 as there will be no opportunity cost involved for a child at this level. In Vietnam a student is required to spend at least 4, 3, and 4 years at lower secondary, upper secondary and university education, respectively.

**Table 20: Extended earning functions based on level of education for private sector workers, 1993-98**

|||1993|||1998||
|---|---|---|---|---|---|---|
||Mean|Coefficient|t|Mean|Coefficient|t|
|Primary|0.66|0.153|3.75|0.67|0.143|3.86|
|L-second|0.31|0.118|2.64|0.34|0.072|2.14|
|U-second|0.08|-0.013|-0.18|0.09|0.122|1.90|
|University|0.00|-0.265|-1.01|0.01|0.940|4.17|
|P-Vocational|0.00|-0.334|-0.83|0.01|-0.025|-0.13|
|L-Vocational|0.01|0.063|0.44|0.03|0.068|0.65|
|U-Vocational|0.01|-0.027|-0.19|0.05|0.057|0.68|
|Years-experience|16.92|0.032|6.18|17.41|0.021|3.24|
|Experience-sq|444.18|-0.001|-6.29|440.22|0.000|-3.57|
|Sex(male=l)|0.65|0.389|11.65|0.65|0.300|10.81|
|North(North=1)|0.28|-0.269|-6.82|0.32|-0.369|-8.57|
|Seniority (Junior=1)|0.16|0.001|0.01|0.33|0.026|0.54|
|Urban(urban=1)|0.32|-0.048|-1.36|0.34|0.121|2.99|
|Constant||4.361|22.51||3.448|18.01|
|R-squared||0.200|||0.340||
|Numberofobservations||1285|||1897||

_Source:_ 1992-93 and 1997-98 VLSS 24
||**Table 4:**|**Government fu**|**nded materni**|**ty leave**|||
|---|---|---|---|---|---|---|
|Dependent variable: Female<br>workers|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Maternity leave (logs)*Govt.|**4.240****|**3.874****|**3.571****|**3.499****|**4.043****|**13.867*****|
|<br>Funded Y:1 N:0|**(2.107)**|**(1.676)**|**(1.659)**|**(1.641)**|**(1.628)**|**(4.512)**|
|Maternity leave (logs)|-0.923|-0.669|-0.426|-0.316|-0.952|73.381*|
||(1.181)|(0.979)|(0.996)|(0.989)|(0.978)|(40.344)|
|Govt. Funded Y:1 N:0|-12.516|-15.552**|-14.198*|-13.892*|-15.327**|-85.403***|
||(9.579)|(7.486)|(7.415)|(7.327)|(7.284)|(26.734)|
|GDP per capita (logs)||4.116***|4.301***|4.117***|2.852**|45.641***|
|||(0.586)|(0.578)|(0.573)|(1.396)|(11.361)|
|Growth rate of GDP||0.422**|0.427**|0.430**|0.325*|0.213|
|per capita (%, annual)||(0.181)|(0.176)|(0.176)|(0.166)|(0.174)|
|Females in adult||1.651***|1.691***|1.688***|1.484***|1.408***|
|population (%)||(0.462)|(0.462)|(0.459)|(0.434)|(0.399)|
|Number of workers||-0.073|-0.400|-0.493*|-0.459*|-3.901***|
|(logs)||(0.297)|(0.281)|(0.289)|(0.275)|(1.413)|
|Female owner present Y:1 N:0||4.989***|4.950***|4.907***|4.470***|2.604|
|||(0.499)|(0.493)|(0.489)|(0.491)|(3.101)|
|Top manager is female Y:1 N:0||13.330***|13.206***|13.198***|13.080***|13.123***|
|||(0.794)|(0.788)|(0.786)|(0.762)|(0.749)|
|Age of firm (logs)|||-0.208|-0.269|-0.359|-0.113|
||||(0.378)|(0.388)|(0.383)|(0.372)|
|Labor productivity (logs)|||-0.260|-0.299|-0.283|-0.268|
||||(0.198)|(0.204)|(0.195)|(0.196)|
|Exports (% of sales)|||0.030**|0.030**|0.024*|0.021|
||||(0.014)|(0.014)|(0.013)|(0.013)|
|Foreign ownership (%)|||0.020**|0.019**|0.019**|0.023***|
||||(0.009)|(0.009)|(0.009)|(0.009)|
|Firm offers formal training to|||2.165***|2.081***|1.822***|1.816***|
|<br>workers Y:1 N:0|||(0.565)|(0.562)|(0.559)|(0.549)|
|Inadequately educated workers:|||-0.235|-0.179|-0.120|-0.042|
|Obstacle severit (0-4 scale)|||||||
|y|||(0.188)|(0.203)|(0.205)|(0.205)|
|Firm registered when started<br>operations Y:1 N:0||||-0.405|-0.639|-0.400|
|||||(1.020)|(0.988)|(0.958)|
|Firm experienced losses due to||||<br>0.488|<br>0.503|<br>0.657|
|crime Y:1 N:0|||||||
|||||(0.595)|(0.599)|(0.597)|
|Access to finance: Obstacle||||-0.579**|-0.503**|-0.516**|
|severity (0-4 scale)|||||||
|||||(0.224)|(0.214)|(0.204)|

57
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20
The union membership variable equals 1 for respondents answering in the affirmative to the question, _jEstd usted qflhiado a alguna organizacion laboral o patronal?_ (Are you affiliated with a workers' or enmployers' organization?). The union membership variable indicates that 30 percent of the entire sample claims union membership. This figure closely approximates previous estimates of the proportion of the Mexican labor force unionized in the late 1980s (Nelson 1991). The service sector possesses the majority of unionized labor. The is largely due to the survey's inclusion of the public sector in the service sector category. The average natural log of hourly earnings, defined as the natural log of gross weekly wage rate, including payments in-kind, divided by hours worked per week, reveals substantially higher wage rates among the union than in the nonunion subsample. It is primarily this difference that will be examined in this study.

The _municipio_ indigenous percent variable and the northern state residence variables measure certain regional characteristics. The _municipio_ indigenous percent variable controls for the concentration of the indigenous population and reflects the prooability that a sampled individual is indigenous. Sinc= a specific question concerning indigenous origin was not included in the survey, the percentage of indigenous people recorded for each _municipio_ in the 1990 Census was substituted (INEGI ;990). The nonunion subsample _municipio_ indigenous percentage average of 3.0 percent is slightly higher than the union subsample at 2.9 percent (for further discussion regarding this variable, see Psacharopoulos and Patrinos 1993).

8
parameters and the unknown YijM. Therefore, the estimation of individual market income depends on a value of YijM that satisfies equations (3)–(6).

# **_Fiscal Interventions and Assumptions_**

## **_Direct Taxes/Contributions_**

The estimation of personal income taxes and social security contributions relies on the identification of individuals that belong to the formal sector. Formal workers in the private sector contribute to the provident fund and the GSLI; formal workers in the public sector contribute to the NPPF and the GIS, and farmers pay for the Rural Insurance. The BLSS contains information on employment and income sources, allowing us to identify the type of work and the applicable social security regime. Since the amount of taxes and social security paid cannot be directly observed in the BLSS, we combine information on the inferred formality status with the tax and social security rules to impute the values.<sup>20</sup> After discounting social security contributions, standard deductions apply to the payment of personal income taxes.<sup>21</sup> The last step is to calculate the final net taxable base using the statutory personal income tax rates shown in Table 4.

Table 4. Personal income tax rates by income bracket

|Minimum|Maximum|Rate|
|---|---|---|
|0|200000<br>|0%|
|200000|250000<br>|10%|
|250000|500000<br>|15%|
|500000|1000000<br>|20%|
|1000000||25%|

Source: Rules on the Income Tax Act of Bhutan 2001 (2017)

## **_Indirect Taxes_**

We include all types of indirect taxes—green taxes, custom duties, sales taxes and excise—in the CEQ analysis; however, the imputation of indirect taxes is restricted to the goods and services that are captured in the BLSS. Custom duty is applied on imported goods, while sales taxes and green taxes are applied on both domestic and imported goods. In 2017, excise is only levied on domestically manufactured alcoholic beverages (see Appendix for the applicable tax rates).

Since we use a household survey which only includes consumption by households, many goods and services with sales tax cannot be identified in the survey (for example, raw materials). In the BLSS, we can identify whether the food consumed is domestically produced or imported using the relevant question. However, unfortunately, this question was not asked for non-food consumption. We apply custom duty on imported

> 20 Furthermore, we also utilize information that describes the primary source income of the household.

> 21 Educational household expenses (up to Nu. 50,000) and donations are deducted from the taxable base of the highest-income individual in the household.

14
**Table 6: Principal Component Analyses Results**

|**PC Variables**<br>**Eigenvectors**|**MainObj**|**MinrObj**|**SuccObj1**|**SuccObj2**|**Facilities**|**SectrComposit**|**SectrUseShars**|**PrblmsBfr**|**PrblmsAftr**|**ExistUsrGrp**|**IncrmntImprv**|**ImprvRspons**|**UsrPay**|**BgtSpnt**|**BgtSrcs**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Var 1<sup>a</sup>|0.54915|0.43768|0.44982|0.55012|0.55127|0.49924|0.71619|0.34380|0.42071|0.57630|0.47983|0.40139|0.57733|0.57764|0.70103|
|Var 2|0.58167|0.62355|0.51375|0.58040|0.22062|0.52738|-0.45890|0.39082|0.42499|0.60196|0.05313|0.48740|0.57727|0.50808|-0.71268|
|Var 3|0.60007|0.64778|0.45340|0.60043|0.64978|0.50784|0.28144|0.45162|0.39011|0.55274|0.30532|0.49349|0.57745|-0.26670|0.02536|
|Var 4|||0.40379||-0.47457|0.37003|-0.44264|0.44197|0.41078||0.16372|0.40334||0.58057||
|Var 5|||0.40632|||0.27895|-0.03683|0.43838|0.39608||0.61470|0.44171||||
|Var 6||||||||0.37092|0.40569||0.28208|||||
|Var 7|||||||||||0.38031|||||
|Var 8|||||||||||0.21178|||||
|% of<br>Explained<br>standardized<br>variance by<br>the first eigen<br>vector|0.652|0.610|0.463|0.678|0.278|0.623|0.275|0.532|0.601|0.813|0.249|0.440|0.999|0.551|0.435|

aNote: Var 1-8 constitute different variables, depending on each Principal Component Analysis. For the specific variables in each analysis see Annex 3.

28