rafmacalaba/datause-encoder-data huggingface dataset:
Table 10: Sensitivity of changes in tariff and trade tax revenue losses, by scenario

||**Tarif **|**revenu**|**e**|**T**|**rade ta**|**x reve**|**nue**|
|---|---|---|---|---|---|---|---|
||**E**|**USAA **|**with n**|**o exc**|**lusion **|**list**||
|||_σ_<sup>_ES_</sup>||||_σ_<sup>_ES_</sup>||
|_σ_<sup>_P_</sup>|1.0<sup>*</sup>|1.5|3.0|_σ_<sup>_P_</sup>|1.0|1.5|3.0|
|0.3|-23.8|-24.8|-27.5|0.3|-14.5|-15.1|-16.7|
|0.5|-23.3|-24.3|-27.0|0.5|-14.0|-14.7|-16.3|
|1.0|-22.1|-23.1|-25.7|1.0|-12.9|-13.6|-15.2|
|||**T**|**arif o**|**ption**|**A**|||
|||_σ_<sup>_ES_</sup>||||_σ_<sup>_ES_</sup>||
|_σ_<sup>_P_</sup>|1.0|1.5|3.0|_σ_<sup>_P_</sup>|1.0|1.5|3.0|
|0.3|-25.3|-25.4|-25.8|0.3|-15.3|-15.4|-15.5|
|0.5|-24.8|-24.9|-25.2|0.5|-14.9|-14.9|-15.0|
|1.0|-23.5|-23.6|-23.9|1.0|-13.8|-13.8|-13.9|
|||**Compr**|**ehensi**|**ve sc**|**enario **|**A**||
|||_σ_<sup>_ES_</sup>||||_σ_<sup>_ES_</sup>||
|_σ_<sup>_P_</sup>|1.0|1.5|3.0|_σ_<sup>_P_</sup>|1.0|1.5|3.0|
|0.3|-26.9|-27.1|-27.5|0.3|2.5|2.4|2.0|
|0.5|-27.3|-27.5|-27.9|0.5|1.3|1.2|0.9|
|1|-28.4|-28.5|-28.8|1|-1.8|-1.8|-1.9|

* Numbers reported in percentage terms.

tariff revenue losses. Since tariffs are part of the tax base of the spending tax and the revenue tax, the fall in tariff revenues also decreases revenues for the latter. An increase in the demand elasticity generates a stronger increase in imports as a response to a given tariff liberalization. This increases both tariff and trade tax revenues. The reasoning is similar for the case of a reduction in other trade taxes. The key difference is that trade taxes do not form part of the tax base for tariffs, such that the fall in revenues from, say, the spending tax or the revenue tax does not directly reduce tariff revenues.

# **4 Policy Directions for Trade Reform in Syria**

Between 1960 and 2000, the Syrian economy largely operated as a socialist economy, with extensive central planning and significant state intervention. In part due to this economic history, many Syrian officials favor a deterministic, engineered approach with regard to the state’s policy reform efforts. Credible, successful policy reform in Syria 24
compared to the benchmark in 2010, while the losses are less than 2% for Khulna, Rajshahi and Barisal (Figure 4a). This is because the East is projected to suffer larger losses in remittances _and_ labor incomes compared to the West (see Annex 2, Table G). The fact that remittances are more common in the East also implies that loss in remittances affects a larger share of the population than in the West.

Consistent with the projected income losses, the impact on poverty is higher in the East as well compared to the West. The percentage increase in poverty rate due to the crisis (compared to the benchmark) is projected to be the highest in Chittagong followed by Sylhet and Dhaka, and smaller for the other divisions (Figure 4b). The percentage increase in extreme poverty rate (not shown here) is high for Chittagong and Sylhet divisions, and much lower for the other divisions including Dhaka. Chittagong and Sylhet are the highest recipients of international remittances by a large margin.<sup>26</sup> As a result the projected income loss in 2009 and 2010 due to the crisis (relative to the benchmark) is the highest in Chittagong and Sylhet, which in turn leads to high poverty impact in these divisions.

Even after taking into account the significantly higher impact of the crisis in the East, the East‐West gap in poverty and income levels would continue to remain large in 2010. This is because the gaps in 2005 – the baseline year for all the simulations – were large to start with (Figure 5). Khulna, Rajshahi and Barisal are projected to remain as the poorer divisions of the country due to a slow rate of poverty reduction during 2005‐2010. The crisis is however expected to moderate the rapid poverty reduction that would have otherwise occurred in Sylhet, Chittagong and Dhaka due to rapid industrial growth and rise in remittances.

<!-- Start of picture text --> Figure 5: Poverty headcount rates for divisions (%) Figure 6: Poverty headcount rates for urban/rural<br>2005 estimates & 2010 projections 2005 estimates & 2010 projections<br>60 50<br>50 40<br>40<br>30<br>30<br>20<br>20<br>10<br>10<br>‐<br>‐<br>Rural Urban<br>2005 2010 benchmark (no crisis) 2010‐crisis<br>Poverty HCR (%) Poverty HCR (%)<br>Chittagong Dhaka Sylhet Barisal Khulna Rajshahi<br><!-- End of picture text --> Source: Estimates and simulations based on HIES (2005)

**Rural‐urban differences in impact.** Our model does not project stark differences in the income and poverty impact of the crisis between rural and urban areas. The loss in average household income due to the crisis, as a percentage of benchmark income, is slightly higher in rural areas (2.5%) than urban areas (2.3%). The main source of income loss is the drop in remittances, which is slightly higher (as a percentage of benchmark remittances) in rural areas than in urban areas. The projected loss in labor

> 26 For example in 2005, 24% and 16% of households in Chittagong received remittances, compared to 9% of households nationally (World Bank 2008).

22
30

**Figure 5**

# **Kernel density of remittances over GDP**

_Panel A: raw data_ <!-- Start of picture text --> 1988 1996<br>0 5 10 15 20 25 0 5 10 15 20 25<br>Remittances/GDP Remittances/GDP<br>.3 .3<br>.2 .2<br>Density Density<br>.1 .1<br>0 0<br><!-- End of picture text --> _Panel B: net of country dummies and year dummies_ <!-- Start of picture text --> 1988 1996<br>-4 -2 0 2 4 -4 -2 0 2 4 6<br>Remittances/GDP Remittances/GDP<br>1 1<br>.8 .8<br>.6 .6<br>Density Density<br>.4 .4<br>.2 .2<br>0 0<br><!-- End of picture text -->
surprising results as to the existence of hunger in the city and the pattern of unemployment. Second, using the quantitative data, the survey allowed us to match the geographical distribution of expenditures in the city with the distribution of poverty at the sub-municipal ( _comuna_ ) level. A strong anti-poor bias for all expenditure categories emerged. Third, combining the qualitative and quantitative information, we found that service satisfaction levels of the population varied significantly across services but very little by income group. This was important as it allowed for the development of broad, integrative social policies. Fourth, budget priorities of the population showed clear winners and losers, with education and health at the top and sport arenas and police at the bottom of the list of services deserving more funding. The large and costly metro project – a cornerstone of Cali’s development plan at the time – was given a rather low priority rating by the population.

The paper is structured as follows. The next section provides background information on Cali, including how city developments fit into the national urban picture. Section 3 describes the survey instrument used. Section 4 highlights some of the results of the survey of particular relevance to city policy makers. Section 5 concludes.

# 2. The Setting: Developments in Cali, Colombia

Cali, with over two million citizens the second largest city in Colombia, is the capital and economic hub in its _departamento_ , the Valle del Cauca. Cali’s GDP was around 6 billion dollars at the end of the 1990s, representing 7% of national GDP and accounting for more than half of GDP of the department. The most important sectors in 1998 were manufacturing (25 percent of city GDP), communal services (22 percent), real estate services (15.9), financial services (11), construction (11), and commerce (9). The city population grew rapidly throughout the 1990s and continues to do so today with a large number of migrants fleeing political and drug-related crime in the rural areas.

Administratively, Cali’s metropolitan area is divided in 21 urban _comunas_ (where 94 percent of the population live) and 4 rural _comunas_ . Decentralization in Colombia increasingly shifts implementation of programs from municipal governments to _comunas_ but broad expenditure and revenue policy is still largely determined by the municipal government and legislature (the Municipal Council). Over two-thirds of Cali’s revenues stem from local taxes 3
In the next section, we review some problems that have occurred when high-powered regulation and high leverage have met bankruptcy-shy governments or regulators.

**Box 2: Gaming and capital structure in UK Private Finance Initiative projects**

<mark>A review of the United Kingdom’s PFI (UK Government 2001: 156) noted, “in most PFI projects, equity is invested as a blend of share capital and Junior Debt. In calculating Authority Default compensation, many projects have distinguished between Junior Debt and equity. Typically, Junior Debt has been repaid in full (together with interest) while compensation for equity has been based either on its market value or on a base case return.... Since in most PFI projects, the substantial majority of “equity” is invested as Junior Debt, the effect has been to give the Contractor the opportunity of equity upside (through the market value compensation) but insulate it from downside (since Junior Debt is repaid in full).” Even for termination in the case of “Contractor Default”, there have been “some contracts that virtually guaranteed (implicitly or explicitly) full</mark> <u><mark>payout of Senior Debt. (UK Government 2001: 167).”</mark></u> 13
# **How Do Multinationals Report Their Economic, Social, and Environmental Impacts?**

## **Evidence from Global Reporting Initiative Data**

**Deborah Winkler**<sup>**1**</sup>

JEL: F2, F6, M4 Key words: Multinational enterprises, sustainable development goals, sustainability reporting, economic, labor, social, environmental, governance.

> 1 Senior Consultant, Trade & Competitiveness Global Practice, World Bank Group, 1818 H St NW, Washington, DC 20433, USA. eMail: dwinkler2@worldbank.org. This paper is part of a larger project on the development footprints of multinationals in global value chains. The author thanks Alyson Slater and Bianca Covlescu from the Global Reporting Initiative for making the data set available, and Jack Boulter, Olivier Cattaneo, Cyril Chalendard, Koen van Bommel, Bob Rijkers, Alyson Slater, Daria Taglioni, Kriti Toshniwal, and the participants of the Show and Tell seminar at the World Bank Group on July 25, 2017 for their valuable comments and advise. The views expressed in this paper are those of the author and should not be attributed to the World Bank Group, its Executive Directors or the countries they represent.
A Duration Analysis of CONALEP

# **Selected References**

> Carnoy, B., and others. 2000. _Aprendiendo a trabajar: Una revisión del Colegio Nacional de Educación Profesional Técnica y del Sistema de Universidades Tecnológicas de México_ . Processed.

- CONALEP (Colegio Nacional de Educación Profesional Técnica). 1994. _Encuesta de Empleo a Egresados del CONALEP, Cohorts 1991, 1992 and 1993_ . Final Report.

- ———. 1999. _Encuesta de Empleo a Egresados del CONALEP, Cohorts 1991, 1992, 1993, 1994 and 1995_ . Final Report.

- Hobart, B. 1999. “Globalization and its Impact on VET.” _Review of Research_ . Adelaide. NCVER.

> Lane, J., and H. Tan. 1996. _Evaluación del Programa DGETI_ . Processed.

Lee, K. W. 1998. “An Alternative Technical Education System: A Case of Study of Mexico.” _International Journal of Educational Development_ . Oxford.

Lopez-Acevedo (2002). “A Reassessment of Technical Education in México.” Mimeo.

SEP (Secretaría de Educación Pública). 1997. _Informe de Labores_ .

———. 1998. _Informe de Labores_ .

# ———. 1999a. _Informe de Labores_ .

# ———. 1999b. _Compendio Estadístico por Entidad Federativa_ .

- OECD (Organisation for Economic Co-operation and Development). 1997. _Reviews of National Policies for Education: Mexico Higher Education_ . Paris.

———. 2000. _Education at a Glance_ . Paris.

19
# **Table 7: Cross-country covariates associated with deposit volume**

The results of OLS regressions of the natural logarithm of value of deposit accounts in commercial banks over GDP on different country characteristics.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|GDP it l|0.219***|0.178***|0.177***|0.231***|0.179***|0.241***|0.135|0.153**|0.189***|0.199***|0.200***|
|per capa (og)|(0.029)|(0.049)|(0.052)|(0.047)|(0.036)|(0.032)|(0.100)|(0.072)|(0.045)|(0.029)|(0.045)|
|Poulation densit (lo)|0.158***|0.148***|0.067|0.120**|0.102*|0.161***|0.177***|0.210***|0.173***|0.117***|0.165***|
|p y g|(0.040)|(0.040)|(0.079)|(0.056)|(0.056)|(0.038)|(0.045)|(0.045)|(0.040)|(0.034)|(0.044)|
|Branches per adult (log)||0.104<br>(0.067)||||||||||
|Branches per km<sup>2</sup>(log)|||0.090<br>(0.064)|||||||||
|Expected real economic growth||||0.053<br>(0.131)||||||||
|Inflation|||||-0.025***<br>(0.008)|||||||
|Deposit insurance||||||-0.148<br>(0.118)||||||
|Electricity consumption|||||||0.068<br>(0.097)|||||
|Land line and cell phone users||||||||0.128<br>(0.128)||||
|Absence of violence|||||||||0.066<br>(0.083)|||
|Offshore financial center||||||||||0.824***<br>(0.245)||
|Interest spread|||||||||||-0.031**<br>(0.014)|
||-3.326***|-1.981*|-2.122**|-3.352***|-2.514***|-3.433***|-3.187***|-3.612***|-3.135***|-3.025***|<br>-2.932***|
|Constant|(0.309)|(1.014)|(1.037)|(0.437)|(0.394)|(0.312)|(0.355)|(0.363)|(0.420)|(0.287)|(0.510)|
|N<br>|118|113|109|119|113|116|99|84|113|118|77|
|Adjusted R<sup>2</sup>|0.349|0.388|0.381|0.191|0.266|0.351|0.294|0.410|0.343|0.417|0.394|
|Robust standard errors in brackets<br>***p<0.01,**p<0.05,*p<0.1||||||||||||

28
recurrent expenditures on social sectors as well as other budget categories) grows at the rate of planned expenditures by the Government of Ghana or, in the absence of this information at the disaggregated level, is assumed conservatively to grow at the same rate as real GDP.<sup>10</sup> As a result, government consumption grows at an average yearly rate of 5.7 percent (Table 4) and the share of public expenditure in GDP declines over the model horizon. Significantly, this scenario assumes the continued fiscal prudence of the government that has been demonstrated in recent years as well as its ability to contain public expenditure pressures in the medium term.

**Table 4 Ghana:  Projected macro variables in the three model scenarios**

||2004|Baseline|Accelerated<br>growth|MDG<br>achievement|
|---|---|---|---|---|
||_Bln. Cedis_|_Averag_|_e 2004-2015 grow_|_th rate (%)_|
|Real GDP at market prices|79,888|6.9|7.4|7.8|
|Private consumption|63,396|7.6|7.7|8.2|
|Government consumption|9,724|5.7|5.8|7.6|
|Private investment|12,782|7.8|8.2|9.9|
|Public investment|9,888|5.7|8.5|13.3|
|Exports|32,329|6.6|7.4|6.5|
|Imports|48,231|7.5|8.0|9.4|
|Real GDP per capita (thou Cedis)|3,688|4.2|4.7|5.1|
|Real Exchange rate (Cedis per USD)|1.00|-0.3|-0.1|-0.8|
||||_2015 level_||
|Trade-to-GDP (%)|100.8|103.7|104.9|106.3|
|Investment-to-GDP (%)|28.4|28.5|31.2|41.3|
|Private|16.0|17.6|17.4|19.8|
|Public|12.4|11.0|13.8|21.4|
|External debt-to-GDP (%)|55.5|26.8|25.9|22.9|
|External debt service-to-exports(%)|2.9|1.4|1.3|1.3|

Figure 2 shows the contributions of labor, capital, and productivity in sustaining the growth of real GDP at factor cost. It is clear in order to attain the baseline growth path described in the previous paragraph, Ghana’s productivity growth must further accelerate from the recent (and already high by historical standards) growth of 1.6 percent per year to 2.6 percent per year. At this rate, the contribution of productivity to total GDP growth will be just under 38 percent. There is only one Sub-Saharan African country— Botswana—that has been able to sustain higher TFP growth rates over the 1981-2002 period (3.7 percent), although TFP in Uganda has been growing at an average annual rate of 2 percent during the same period (Akinlo, 2005).<sup>11</sup> It should be noted, however, that much faster productivity growth rates have been observed in other regions, most recently in the European and Asian transition economies (Alam et al., 2008). In terms of sectoral growth, manufacturing and services are the most dynamic sectors, growing at 7.9 and 7.6 percent per year, respectively. Agriculture grows at 6.0 percent per year, while the public

> 10 For more details see Table 9 and Table 10 in the Annex.

> 11 Note that the sample in Akinlo (2005) does not include South Africa.

10
What are the implications of these production and consumption trends for food availability and prices? In 2000 South Asia had a production surplus of almost 11 million MT of rice and 7.5 million MT of wheat. By 2008 the wheat surplus had vanished and converted into a deficit of about 6 million MT. The rice surplus also fell to around 5 million MT. The production shortfalls in wheat were met mostly by drawdown of reserves (Figures 5 and 6). South Asia’s largest grain producer, India, had built up a huge foodgrain stock, partly for food security reasons and partly to provide price support to farmers. Thus in 2001/02 India had accumulated stocks of 25 million MT of rice and 25 million MT of wheat. These reserves were drawn down over the coming years, mostly to meet the consumption gap in wheat but also to reduce fiscal cost and physical wastage through rice exports. By 2005/06 the wheat stock had fallen to only 2 million MT causing a reversal in policy to build up reserves. Rice stocks also started rising in 2004/05 recovering from the low levels of 9.5 million ton. By 2008/09 stocks had recovered to 8 million MT for wheat and 15 million MT for rice. This obviously added <!-- Start of picture text --> Figure 5: End Year Stock for Wheat<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2000/01 2001/02 2002/03 2003/04 2004/05 2005/06 2006/07 2007/08 2008/09<br>Period<br>South Asia India<br>Million MT<br><!-- End of picture text --> _Source: USDA Database_ 9
<!-- Start of picture text --> 7<br>68<br>rac<br>ol 66<br>2<br>6.4<br>6.2<br>-2 0 2 4<br>z_ndvi2018<br><!-- End of picture text --> <!-- Start of picture text --> 74<br>73<br>7.2<br>“|<br>°<br>D<br>Ke)<br>7.4<br>7<br>69<br>-2 0 2 4<br>z_ndvi2018<br><!-- End of picture text -->
mental disability, age and gender of the person, education, occupational and marital status of the household head, as well as household income.

Research by Abayasekara and Gunasekara (2019) using 2016 Labour Force Survey data reveals some of the risk factors that predispose young people to become NEET in Sri Lanka. Using binomial and multinomial logistic regression models, the results indicate that the risk factors center on being female, belonging to an ethnic and religious minority, being in the 20–24 age group, having very low or very high levels of education, being English-illiterate, belonging to a low‐income household or to a male-headed household, having young children, and living in a more remote area. The authors also offer important policy recommendations for how to reduce Sri Lanka’s NEET rate and engage more youth in education and in the labor force.

Looking at developed countries, Quintano, Mazzocchi and Rocca (2018) analyze the determinants of the NEET condition in Italy through a step-by-step procedure. They first determine the main characteristics of being NEET, then focus on specific homogeneous clusters of NEETs. The decomposition of the clusters into different probabilities of being NEET enables the effect of various personal characteristics to be verified. Using a bivariate selection probit model based on the propensity to look for a job against the condition of being inactive, the authors assess the influence of unobserved factors on the professional condition of young people. The results confirm the crucial role of education, as well as the importance of economic and social disparities between men and women in the Italian territorial districts.

# 4.  Data and Methodology

## 1. Data and descriptive statistics

<mark>In this paper, we make use of the “</mark> _<mark>Enquête nationale sur l’emploi</mark>_ <mark>”, a nationally representative labor force survey conducted by the Moroccan</mark> _<mark>Haut-Commissariat au Plan</mark>_ <mark>(HCP). Its main objective is identifying the volume of active population as well as the main demographic, cultural and socioprofessional characteristics of workers. The data set may also be used to measure the Moroccan population’s access to basic social services.</mark> <mark>The survey has been conducted every year since 1999, using a comprehensive questionnaire covering both urban and rural areas. The sampling frame follows a two-stage stratification strategy in the country’s urban and rural areas and regions, which in 2013 were consolidated into 12 from an original 16. On average, every year the sample comprises about 80,000 households, of which 60,000 reside</mark> 9
times each year and recorded whether each child was present. From these multiple visits, an annual perchild attendance average is collected. The 2002 Pupil Questionnaire was carried out in mid-January 2002 before uniforms were distributed, and socioeconomic data were gathered on all children who were present at school that day.

The Pupil Attendance data set has uniquely defined pupil identification numbers, and the 2002 Pupil Questionnaire and the 2010 follow-up survey use those same numbers. However, the data sets with information about the students who won the lottery, those who were present on the day of the lottery and those who received uniforms do not include those pupil identification numbers. Therefore, names were matched manually and by computer. Some pupils in the various other data sets did not match any pupil in the Pupil Attendance data set. This may stem from the fact that sometimes children have several names and may only give a subset of those in one data gathering exercise and a non-overlapping subset in another exercise. It is also common for students to spell their names differently in different contexts and to change their names, further challenging the matching process. In total, we matched attendance data for 805 pupils out of the 932 pupils who had been fitted for uniforms (86 percent). In 2010, eight years after initial uniform distribution, we collected follow-up information on the students in our sample. Field officers were sent to the 12 program schools with a roster of students from the start of the program. They asked head teachers, teachers and older students whether or not the student had finished primary school, whether they were still in school and the highest grade completed. Field Officers also examined official Ministry of Education Records at the school to get results from the Kenya Certificate of Primary Education (KCPE) exam, which students take at the end of 8<sup>th</sup> grade.

Overall, the tracking rate was high; we were able to collect information for about 95 percent of the students in our sample. Since the students usually live within close proximity of the school, in many cases teachers and older students knew what had happened to the students. School officials reported that 10