rafmacalaba/datause-encoder-data huggingface dataset:
security (the difference λ is due to exemptions, evasion, etc.). The model also takes into account the cost of the implicit cost of severance pay, using as proxy an implicit unemployment insurance rate. We have:

# **_The labor supply block_**

As mentioned above, one innovation in SELMA is the detailed projection of various skills levels based on repetition and drop-out rates at various levels of the education system, including 85 combinations of diplomas and specialisations in higher education and vocational training. There are 6 skills levels associated with primary education depending on the grade an individual completed. Similarly 3 skill levels are associated with low secondary education and another 3 skill levels with upper secondary education. There are 9 skill levels associated with vocational training depending on the level of vocational training<sup>7</sup> (and the length of the vocational training (1 or 2 years). In higher education the various areas of specialization,<sup>8</sup> combine with the length of study (varying between 2 to 7 years depending on the specialization) and whether the education is provided by public or private institutions of higher education, thus generating 64 distinct skill levels within the model.

At each time t, these skills are aggregated into 6 types of labor. Here we describe how we allocate the total supply of labour across economic sectors and formal/informal markets. In essence, there are three stages. In the first stage we model the migration process between the rural and urban labor markets. At the second stage, the urban labor market is divided into a formal and an informal segment. The informal labor supply is also then subdivided into independent workers and wage earners.

At the first level, following Cogneau et al. (1996), the migration process from rural to urban areas is modelled through an extended Harris-Todaro function. Thus, the relative supply of labor in

> 7 Certificat d'apprentissage, Spécialisation, Qualification, Technicien and Technicien  Spécialisé 8 10 specializations altogether: Médecine Dentaire; Médecine et Pharmacie; Enseignement Originel; Sciences Juridiques, Economiques et Sociales; Lettres et Sciences Humaines; Sciences; Sciences et Techniques; Technologie; Sciences de l'Ingénieur; Commerce et Gestion

10
estimates.<sup>23</sup>

# **(4.2) Causal Analysis and Estimates**

As noted earlier, a central issue in the literature has been whether most of the observed persistence can be accounted for by correlations in genetic endowments, leaving little room for economic forces to shape the opportunities faced by the children (see the discussion in Solon (1999) and Black and Devereux (2011)). Consider the following triangular model of a child’s schooling attainment and her/his parents’ economic status: Where _Di_<sup>_P_isanindicatorparents’socioeconomicstatus,inourcase,definedbyed-</sup> ucation and occupation, and the error terms capture unobservables such as ability and preference. If high ability parents produce high ability children, we expect that the correlation between the error terms is positive, _ρ_ = _Corr_ ( _νi, θi_ ) _>_ 0. This gives rise to classic “ability bias” where the effect of parental economic status _λ_<sup>ˆ</sup> 1 is overestimated by OLS or matching methods. Although the potential upward bias in the estimates of intergenerational persistence because of positive correlation in ability has been a central focus in the economics literature, the relative role of genetic factors in a child’s development outcomes has been controversial since the publication of Galton’s study in 1886 where he claimed the primacy of genetics. The pendulum has swung back and forth in Behavioral Genetics over the years, with the recent work attributing 50 to 60 percent variations in performance in academic test in UK (GCSE) to genetic differences (Shakeshaft et al. (2013)).<sup>24</sup> In con-

> 23This also means that we do not present any analysis of covariate balance which is important for matching estimates to have causal interpretation.

> 24The conclusions of Shakeshaft et al. (2013) are based on twins data as is standard in Behavioral Genetics. However, it is not clear to what extent these estimates can be treated as relevant for the wider

15
|9|Wholesale and retail trade; repair of motor vehicles,<br>motorcycles|1428|1128|79.0|
|---|---|---|---|---|
|10|Mining and quarrying|708|564|79.7|
|11|Other community, social and personal service activities|1476|1188|80.5|
|12|Fishing|1644|1392|84.7|
|13|Education|2256|1932|85.6|
|14|Construction|1344|1212|90.2|
|15|Public administration and defense|2256|2052|91.0|
|16|Real estate, renting and business activities|2052|1896|92.4|
|17|Health and social work|2388|2232|93.5|
|18|Transport, storage and communications|1680|1968|117.1|

Source: ILOSTAT database, International Labor Organization Table 8 shows that in majority of the sectors with higher share of female workers in Sri Lanka, women earn anywhere between 28-36 percent less than their male counterparts for doing the same job. In agriculture, hunting and forestry sector gender wage gap is around 28 percent, while employed women earn just 64 percent of their men counterparts in the manufacturing sector. However, the picture is not entirely bleak as greater gender parity in wages exists for women professionals in some service sectors such as education, health, and social work.

# 2.2.5 Nepal

Among all the South Asian countries, Nepal surprisingly has the highest female labor force participation. ILO reports that overall female labor force participation in Nepal stood at 80.1 percent in 2008. Though these numbers are encouraging, a detailed analysis at a sectoral level reveals a gloomy picture. As shown in Table 9, of the total women workforce employed in the registered sector in Nepal in 2008, almost 83 percent of women were engaged in agriculture, hunting and forestry activities, followed by other sectors such as electricity, gas and water supply (4.9 percent), hotels and restaurants (3.9 percent), and transport, storage and communications (1.6 percent). As highlighted by Tanaka and Muzones (2016), this is primarily because majority of the women entering the labor force in Nepal have a minimum level of education, thus ending up in low skilled and low paid jobs in the informal sector. Around 61 percent of employed females aged 15 and above had never attended school, 20.3 percent had attained less than primary to primary level education, and only 15 percent had attained lower secondary to secondary level education (Nepal labor force survey 2008).

Like other low-income countries, women in Nepal must work out of necessity and poverty In Nepal, majority of the women are burdened with household work and have less qualification in education and skills and mostly they are employed in informal sectors and do not have well-paying jobs in the formal sector. Thus, women’s share is large in all lower skill occupations working mainly in ‘service workers’ category and negligible in higher skill jobs. Under registered sectors, non-financial sector is the largest employer, followed 22
Barth, M. and Boriboonsomsin, K. “Real world CO2 impacts of traffic congestion”, Paper prepared for the 87<sup>th</sup> (2008) Annual Meetings of the Transportation Research Board, Washington, D.C. , November 15, 2007.

Beevers, S.D. and Carslaw, D.C. “The impact of congestion charging on vehicle speed and its implications for assessing vehicle emissions,” _Atmospheric Environment_ , 39, 6875-6884, 2005.

Davis, S.C. and Diegel, S.W. _Transportation Energy Data Book_ , _Edition 24_ , Oak Ridge National Laboratory, 2004.

Davis, S.C. and Diegel, S.W. _Transportation Energy Data Book_ , _Edition 26_ , Oak Ridge National Laboratory, 2007.

Dixit, A. and Stiglitz, J. “Imperfect competition and optimal product diversity,” _American Economic Review,_ 1977.

Gurjar, B.R., Butler, T.M., Lawrence, M.G., Lelieveld, J. “Evaluation of emissions and air quality in megacities,” _Atmospheric Environment_ , 42, 1593-1606, 2008.

Ingram, G.K. “Patterns of metropolitan development: what have we learned?”, _Urban Studies_ , 35(7), 1019-1035, 1998 IEA, 2008. CO2 Emissions from Fuel Combustion. International Energy Agency (IEA) Database Vol. 2008, Release 01. <u>http://caliban.sourceoecd.org/vl=213511/cl=25/nw=1/rpsv/iea_database.htm.</u> IAPT, 2007. _Millenium Cities Data Base for Sustainable Transport._ International Association of Public Transport, Brussels.

MCD, 2007. Millennium Cities Database for Sustainable Transport. International Association of Public Transport.

McFadden, D., “Conditional logit analysis of qualitative choice behavior”. In: Zarembka, P. (Ed.), Frontiers of Econometrics. Academic Press, New York, 1973.

Nicol, C.J. “Elasticities of demand for gasoline in Canada and the United States,” _Energy Economics_ , 25, 201-214, 2003 Parry, I.W.H. and Timilsina, G.R. “How should passenger travel in Mexico City be priced?” World Bank and Resources for the Future Working Paper, June 16 2008.

Saitoh, T.S., Yamada, N., Ando, D., Kurata, K. “A grand design of future electric vehicle to reduce urban warming and CO2 emissions in urban area,” _Renewable Energy_ , 30, 1845-1860, 2005.

8
_Table 7 The effect of other subjective indicators on the number of protests and strikes_

||(1)<br>Incl.<br>Freedom<br>of life|(2)<br>Incl.<br>Get<br>ahead|(3)<br>Incl. SoL|(4)<br>Incl. Sol<br>getting<br>better|(5)<br>Incl.<br>FS<br>index|(6)<br>Incl.<br>CB<br>index|(7)<br>Incl. all<br>variables|
|---|---|---|---|---|---|---|---|
|Freedom of life (%)|-0.018**<br>(0.006)||||||0.003<br>(0.005)|
|Get ahead (%)||-0.017*|||||-0.011|
|||(0.007)|||||(0.007)|
|Standard of living (SoL) (%)|||-0.016*<br>(0.007)||||-0.005<br>(0.008)|
|SoL getting better (%)||||-0.013*|||-0.000|
|||||(0.006)|||(0.006)|
|FS Index|||||0.008<br>(0.006)||0.018*<br>(0.008)|
|CB Index||||||-0.016<br>(0.010)|-0.008<br>(0.010)|
|Suffering (%)|||||||0.026**<br>(0.010)|
|Struggling (%)|||||||0.013<sup>+</sup><br>(0.007)|
|Constant|4.535|4.380|1.910|0.526|4.381|4.898|1.190|
||(4.312)|(4.488)|(4.260)|(4.467)|(4.424)|(4.147)|(4.359)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Control Var|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.270|0.267|0.266|0.258|0.258|0.259|0.293|
|Observations|847|855|861|788|856|847|814|
|Countries|118|118|118|118|118|118|118|

Note: Results are estimated with an OLS model. Robust clustered standard errors in parentheses. *** p<0.001, ** p<0.01, * p<0.05,<sup>+</sup> p<0.10 In the last column of Table 7 (Column 7) we include all domain specific variables and our overall subjective well-being indicators. _Suffering_ remains significant at the 5% level and s _truggling_ at the 10% level, whereas _standard of living_ , _freedom of life_ , and _get ahead_ cease to be significant, suggesting that our overall subjective well-being variables capture the variation in satisfaction in the individual capabilities and perceived standard of living, which in turn affects nonviolent uprisings. Interestingly, the FS index turns significant in the model, indicating that, when overall subjective well-being is controlled for, the percentage having food and shelter actually has a positive effect on nonviolent uprisings, supporting the idea that a certain subsistence level needs to be reached before citizens are willing to take to the streets.

34
**Table** 9

Cross-Country Savings Functions for 74 Countries, Average 1960-90

|Depend<br>(t-St|ent Variabl<br>atistics in p|e: Savings ra<br>arenthesis)|te||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|GDPpercapitagrowth|9.487<br>_(2.97)_|9.824<br>_(3.16)_|9.212<br>_(2.57)_|8.928<br>_(2.58)_|
|Dependency ratio|0.127<br>_(0.60)_|0.055<br>_(0.20)_|-0.044<br>_(-0.12)_|-0.109<br>_(-0.40)_|
|Overall Education Stock|0.370<br>_(4.94)_||||
|Primary Education Stock||0.374<br>_(5.02)_|||
|Secondary Education Stock|||0.156<br>_(2.09)_||
|Tertiary Education Stock||||0.124<br>_(2.45)_|
|AdjR<sup>2</sup>|0.404|0.412|0.323|0.333|
|Observations|74|74|74|74|

All variables are expressed in log (with the exception of the GDP growth rate). The TSLS have been used in all regression. All regression were estimated with intercept.
_14_ to customers at consumption sites. But the opening of natural gas supply to competition has multiplied the contractual relations between producers and suppliers. As more and more gas has become available for independent suppliers, trading has gradually concentrated where producers and suppliers have the best access to the gathering and transmission pipeline systems. Bilateral supply contracts are now based on delivery at the Bacton and St. Fergus terminals. And since the introduction of the Network Code, bilateral contracts with delivery at the national balancing point have developed (see the section below on on-system trading).

The opening of natural gas markets to competition often involves transition costs for incumbent firms. One example of such costs is the investment necessary to fulfill the regulator's requirement to secure reliable supply for all consumers in the franchise area, an investment that becomes useless in a competitive market. Another is the liability arising from contracts that are not fulfilled because the regulator has altered the contractual relations. A regulator might allow cancellation of long-term contracts between suppliers and consumers to promote development of a spot market, for example, British Gas Energy will face such transition costs as a result of the deregulation of the tariff market in 1998 (box 2).

_Box 2 Transition Costs of_ **_Openin_** _iNatural_ **_Gas_** _to_ **_Comptition: The. Case_** _of British Gas_ _Energy_ Long-term bilateral contracts oen become aburden for an incumtbent supplier after the opening of natural gas markets} to competition.- iTe incumbent ofen becomes stuck with takeor-pay contracts concluded under an "obliigation-to-serv regula regime, as many of its customers switch&to competing suppliers., British Gas Energy holds take-or-pay obligations tphase about 4.6iion cubic feet a day (bcfd) of gas from producers, but it cai sell onl at 4.35bcfd inconsumer market. This results in an estimated surplus fdtof tot take-or-pay ob ns, assuming that BGf maintains a 90pert s in tr n98. TeBGE's liability from this surplus amounfts t bout 28% milo o f t p nlue 2 basis, assuming that BGE maintains a 90 percenh in 1_ttatin 998.

Although BGE's surplus is not a significant volume, it represents; about 30 percent of recent spot market volumes. So if BGE delivers its surplus-gas to spot markets, it will push down spot market prices, enabling BGE's competition to attract more customers thrgh lower prices. Eventually, BGE may lose more than 10 percent ofits marklet e, whichVwould increase its take-or-pay liabilities.

|_Source._Royle_1996._|
|---|
**Figure 13: Haiti’s Domestic Seat Capacities and Direct Airport Pairs**

<!-- Start of picture text --> 200000 5<br>180000<br>160000 4<br>140000<br>120000 3<br>100000<br>80000 2<br>60000<br>40000 1<br>20000<br>0 0<br>Year<br>Estimated Seats Airport Pairs<br>Estimated Seats<br>Domestic Airports Served<br><!-- End of picture text --> _Source: Authors’ calculation based on data by Diio (2014)._

# _iii. Intra-Regional Routes_

## <u>Overview</u>

The largest concentration of islands, and intra-regional routes is in the Eastern part of the Caribbean, with the small island countries Trinidad, and Barbados forming the bulk of the routes. Trinidad has the main connections to Suriname and Guyana, with the route to Guyana being the heaviest traveled in the Caribbean. There are no scheduled intra-regional flights to and from Belize. In the East there are mainly three hubs, from the North to the South: Antigua (for LIAT), Barbados (also LIAT), and Trinidad (Caribbean Airlines). The routes are shown by country pair, which means no distinction is made as to the airport if multiple airports exist in a country, such as in St. Lucia.

**<u>Table 9: Intra Regional Traffic for Caribbean countries</u>**

|<br>**Route Type**<br>**Typology**|<br>**Country**|<br>**Seats**<br>**2013**|<br>**Share**|**Growth**<br>**2001-13**|**Growth**<br>**2008-13**|
|---|---|---|---|---|---|
||Antigua and Barbuda|209,261|7.9%|-3.7%|-9.1%|
||Dominica|78,481|3.0%|-3.6%|-6.6%|
||Grenada|174,226|6.6%|-4.6%|-5.8%|
|Small Island States|St. Kitts and Nevis|59,787|2.3%|-0.6%|-5.1%|
||St. Lucia|238,433|9.0%|-4.9%|-0.2%|
||St. Vincent and the Grenadines|122,642|4.6%|-5.4%|-12.9%|
||**Subtotal**|**882,830**|**33.3%**|**-4.3%**|**-6.6%**|
||Bahamas,The|19,032|0.7%|-10.7%|-18.3%|
||Barbados|409,279|15.4%|-6.5%|-11.2%|
|Intra-Regional<br>Hb Cti|Dominican Republic|54,652|2.1%|11.7%|2.4%|
|Routes<br>u ounres|Jamaica|68,315|2.6%|-7.4%|-11.6%|
||Trinidad and Tobago|788,723|29.7%|0.0%|1.4%|
||**Subtotal**|**1,340,001**|**50.5%**|**-3.1%**|**-4.7%**|
||Belize|N|o Reported|Capacities||
|Ctitl Cibb|Guyana|340,561|12.8%|-0.1%|2.5%|
|onnena arean|Suriname|86,996|3.3%|14.9%|-0.3%|
||**Subtotal**|**427,557**|16.1%|**1.4%**|**1.9%**|
|Fil S|Haiti|3,432|0.1%|7.4%|-5.8%|
|rage tates|**Subtotal**|**3,432**|**0.1%**|**7.4%**|**-5.8%**|
|**Total**||**2,653,820**|**100%**|**-3.0%**|**-4.5%**|

_Source: Authors’ calculation based on data by Diio._ 38
growth in incomes of over 285 percent between 1987/88 and 1999/00, resulting in annual PPP incomes of around P$160,000 per person.

# **...and that has meant rising inequality**

Figure 5 displays the trends in income inequality for the two countries. From a crosscountry perspective, India remains a relatively low-income inequality country (World Bank, 2005, 2006), although this is no longer true of China. The Gini index of income inequality for China rose from 28 percent in 1981 to 41 percent in 2003, though not continuously, and more in some periods and provinces.<sup>14</sup>

**_Figure 5: Trends in income inequality for China and India_**

<!-- Start of picture text --> 45.0<br>40.0<br>China (income)<br>35.0<br>30.0<br>India (consumption)<br>25.0<br>1978 1983 1988 1993 1998 2003<br>Gini coefficient of inequality<br><!-- End of picture text -->

**_Source:_** Authors’ calculations for India and Ravallion and Chen (2006) for China.

Note that the fact that the inequality measures for China use income while those for India use consumption (per capita) does not account for the difference in measured inequality as in figure 5. For a few years it is possible to measure inequality using consumption for China. When one does, the consumption-based inequality measure is only slightly lower than that based on incomes, and it is still appreciably higher than for India (Chen and Ravallion, 2006).

In the case of India, one finds that the Gini index rose in the 1990s, although the increase was less pronounced than in China (figure 5).<sup>15</sup> However, it is too early to say if India is undergoing a trend increase in inequality similar to what China has experienced. As can be seen from figure 5, on looking back over time, rising inequality in India is seen to be a recent

> 14 Note that the latter figure is somewhat lower than past estimates for China; this is because corrections have been made for changes in survey-valuation methods (as discussed above) and urban-rural cost-of-living differences, which have tended to rise over time because of higher inflation in urban areas (as price controls and subsidies were progressively removed on certain goods, including housing). Without these corrections, the estimate of the Gini index for 2003 rises to over 45 percent instead of 41 percent. 15 Figure 5 uses the NSS “thick samples” only. The thin samples for the 1990s also confirm the increase in inequality (Ravallion, 2000).

13
**Table 4: Probability of one-year income loss, total income and by source, Nigeria and Uganda**

||LOST INCOME|LOST INCOME|LOST FARM<br>INCOME|LOST NFE<br>INCOME|LOST WAGE<br>INCOME|LOST DOM.<br>REMITTANCES|LOST REMITT.<br>FROM ABROAD|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Female household head|-0.0333|-0.0342|-0.0304|-0.0920*|-0.102|-0.0738|-0.193|
||(0.0388)|(0.0396)|(0.0462)|(0.0484)|(0.0728)|(0.0699)|(0.277)|
|Age of household head|-0.000219|-0.000727|-0.000251|-2.56e-05|-0.000550|-0.00246*|0.00151|
||(0.000683)|(0.000639)|(0.000782)|(0.000841)|(0.00107)|(0.00132)|(0.00500)|
|Primary education|0.00353|0.00959|0.0564*|0.0275|-0.0145|-0.00280|0.0606|
||(0.0244)|(0.0244)|(0.0300)|(0.0343)|(0.0712)|(0.0630)|(0.230)|
|Secondary education|0.0400|0.0392|0.0166|0.0737**|0.0180|-0.0334|-0.00217|
||(0.0277)|(0.0283)|(0.0302)|(0.0346)|(0.0624)|(0.0683)|(0.263)|
|Tertiary education|0.0450|0.0490|0.0864**|0.0672*|-0.0513|0.0114|0.0802|
||(0.0310)|(0.0303)|(0.0351)|(0.0381)|(0.0683)|(0.0791)|(0.248)|
|Urban|0.0181|0.0202|0.0382|0.0145|-0.0325|-0.0123|-0.133|
||(0.0264)|(0.0275)|(0.0250)|(0.0270)|(0.0322)|(0.0571)|(0.131)|
|Children 0-4 present|-0.0213|-0.0283|-0.0212|-0.0421|0.00211|-0.0185|-0.0158|
||(0.0222)|(0.0217)|(0.0219)|(0.0277)|(0.0367)|(0.0653)|(0.172)|
|Female hh head*Children 0-4|0.0183|0.0195|0.0177|0.0291|0.0811|-0.0272|0.112|
||(0.0423)|(0.0409)|(0.0501)|(0.0599)|(0.0981)|(0.114)|(0.344)|
|Children 5-18 present|-0.0101|-0.00906|-0.00183|-0.00278|-0.0430|9.84e-05|0.147|
||(0.0324)|(0.0323)|(0.0327)|(0.0367)|(0.0461)|(0.0573)|(0.224)|
|Female hh head*Children 5-18|0.0908*|0.0935*|0.0507|0.116**|0.152|0.111|0.213|
||(0.0499)|(0.0492)|(0.0564)|(0.0550)|(0.0991)|(0.111)|(0.334)|
|Had farm income during previous||0.0408||||||
|12 months||(0.0336)||||||
|Had non-farm enterprises income||0.115***||||||
|during previous 12 months||(0.0267)||||||
|Had wage income during previous||0.0425||||||
|12 months||(0.0265)||||||
|Had domestic remittances during||0.178***||||||
|previous 12 months||(0.0315)||||||
|Had remittances from abroad<br>during previous 12 months||-0.0399<br>(0.0654)||||||
|Constant|0.403***|0.264***|0.323***|0.398***|0.317***|0.598***|0.286|
||(0.0531)|(0.0644)|(0.0553)|(0.0610)|(0.0847)|(0.0974)|(0.396)|
|Observations|2,888|2,888|2,227|1,752|827|599|65|
|R-squared|0.011|0.041|0.007|0.008|0.019|0.011|0.090|

Note: OLS estimation results, standard errors clustered at EA level in parentheses, *** p<0.01, ** p<0.05, * p<0.1. All estimations control for country fixed effects. Samples in col. 3-7 include only households receiving the relevant type of income before the pandemic. _Source_ : HFPS Nigeria R9 (Jan 2021) and Uganda R5 (Feb 2021).

19