rafmacalaba/datause-encoder-data huggingface dataset:
Total TEU’s handled across quay in 2010 for both piers was 2,5 million, still below the 2008 peak of 2,6 million.

Most of the containers going through Durban are destined for Johannesburg and transported by road while traffic volumes on rail have remained static over the years. A review study of the Durban Inland Intermodal Terminal and Logistics Hub conducted in 2008 reveals the turnaround time within the port area was too long and had major implications on efficiency thus dwell time (Arup, 2008).

Even though, from interviews, port performance usually emerges as the main culprit for long delays along transit corridors, disentangling port inefficiency is scarcely carried out, especially in SubSaharan Africa. However, in a country with relatively higher trade value, such as South Africa, this is of even more importance.

Therefore, this study seeks to identify what has been done at the Port of Durban in the last decade in Durban in the effort to reduce cargo dwell time within the last decade. It will also demonstrate the impact of reduced cargo dwell time on port operational capacity. This paper especially describes the of public sector actors, such as Customs, the port authority and the port operator.

The paper is based on interviews and data collected from a field visit to Durban carried out in January 2011. During the mission, extensive consultations with port stakeholders involving port authorities, shipping lines, freight forwarders, customs brokers and various cargo owners were undertaken. Data on dwell time was also obtained from a number of sources including Transnet Port Terminal (TPT) and the private sector port community<sup>5</sup> .

The main lessons for Sub-Saharan Africa, which can be drawn from Durban port, are that cargo dwell time is mainly a function of the characteristics of the private sector, but the onus is on public sector players such as customs, the port authority, etc. to put pressure to change the behavior of the private sector port users to better comply and reduce cargo dwell time. In this regard, prohibitive charges for storage, coupled with strict enforcement, and the possibility to pre-clear with Customs with advantages attached to it and service level agreements binding both parties are critical tools for the reduction of cargo dwell time The outline of the paper is as follows: section two presents descriptive data on cargo dwell time in Durban and compares it to several ports in Sub-Saharan Africa and decomposes the aggregate cargo dwell time figure. Section three presents the technical measures, such as cargo storage fees changes,

> 5 Because of lack of detailed data and the limited share of transit traffic in the total traffic, the paper focuses on domestic cargo.

5
19 iv) **Economic structures and speed of reform** The differences between the Baltics and the rest of the FSU in the pace of reform and level of development may result in uneven gains and losses from integration. The Baltics have the highest per capita incomes in the former FSU, the fastest pace of reform and the most success in exporting to the West. The latter is often a good barometer of success in transition (Table 6). In many cases elsewhere in the world, maintenance of a union has required a successful system of transfers among members to compensate the less developed ones. The European Community, for example, has important transfer mechanisms in favor of its less developed but small southern partners. Depending how the FSU union would be constructed, the Baltics, that are relatively well-off members of the FSU, risk having to provide transfers to less developed republics. This can be politically very difficult and would be bad news for small Baltics supporting big Russia, Ukraine, etc.

Table 6: Economic Indicators of the Baltics and **some** other **FSU** Countries

||GDP/capita<br>1991 ,US$|Agriculture<br>in GDP 1991 (%)|Exports to non-FSU as<br>% of total, 1992|
|---|---|---|---|
|Estonia|3830|15|53|
|Lavia|3410|18|50|
|**Lithuania**|2710|30|28|
|Russia|3220|17|32|
|Belaras|3100|16|6|
|Ukraine|2500|24|10|
|Kazakhstan|2470|34|13|

Source: World Ban (1992b), PlanEcon (1993).

Different pace of reform among the members of a (temporary or pennanent) preferential trading area can even disrupt the process of reform. As for example Estonia has now largely liberalized most of its prices, competition from slower reforming Russia on subsidized goods could undenmine new investment at 'world prices", especially if export taxes in Russia were eliminated. The subsidies can be implicit in controlled prices, or in production of state
The relationship between expectations and schooling outcomes follows a more bended arc shape. Expecting to achieve a bachelor’s degree rather than a high school degree is associated with grades that are 0.06 standard deviation higher and with missing 0.17 fewer days of school per month. Expectations are associated with higher grades and absenteeism until between 15 and 16 years of expected education (about the equivalent of a bachelor’s degree) after which the trend reverses. Students who expect to complete high school have similar grades and absenteeism as those who expect to achieve a PhD. Perhaps students with highest grades and high-but-not-highest aspirations express that they expect a bachelor’s rather than master’s or PhD out of higher carefulness —being conscious that possible difficulties and hard work lie ahead—or because part of the students who do not have the top grades would express expecting a PhD without really understanding the requirements and efforts to achieve it. Boys have a stronger relationship between their expectations and their schooling outcomes than girls, suggesting that boys may be particularly motivated by dreams that seem achievable.

Aspirations appear to motivate students despite the constraints they face, except when the gap between aspired and expected education levels surpasses 7 years. Expecting to achieve an education level that is less than the aspired one is positively correlated with grades and school attendance, likely because students who have lower expectations than aspirations are also those who aspire to the highest levels. However, the relationship between current schooling outcomes and the number of years difference between aspired and expected education levels is non-linear. Students who aspire to a PhD but expect at most a secondary-school level have similar or worse current schooling outcomes than those who do not have a gap between their aspired and expected education levels. This suggests that aspirations motivate students despite expected constraints unless their expectations fall very short of their aspirations.

These non-linear patterns are aligned with theoretical and empirical studies that explore the differences between aspirations and current situations. Foundational theory of aspirations in economics argues that aspirations motivate individuals up to a point after which aspirations become too far off one’s initial situation and are no longer motivating, coining the term “aspirations gap” to refer to the difference between someone’s aspirations and his current situation (Ray 2006, Genicot and Ray 2017). Panel data for Indian adolescents confirm that educational outcomes at age 19 correlate positively with the size of their aspirations gap of at age 12—the difference between the wages corresponding to their aspired job and education, and the wages corresponding to their parent’s occupation—up to a point corresponding to about seven times the average parents’ wage, twice the average aspiration gap, and one and a half times the average aspired level (Ross 2019). Similar nonlinear relationships were 4
Table <u>5. PARTII. Regression of Number of Utilizations and Log of Total Expenditures</u>

||PARTII, Number of<br>Utilization|PARTIII. LOG (Total<br>Expenditures)|
|---|---|---|
|Welfare (reference= quintile 5)|||
|Quintile 1|0 (-0.06, 0.06)|-0.07 (-0.13, -0.00)*|
|Quintile 2|0.03 (-0.03, 0.09)|-0.08 (-0.15, -0.02)*|
|Quintile 3|0.01 (-0.04, 0.07)|-0.03 (-0.10, 0.03)|
|Quintile 4|0.01 (-0.05, 0.07)|-0.04 (-0.11, 0.02)|
|Age (reference=30-40 years old)|||
|18-30|-0.05 (-0.12, 0.02)|-0.05 (-0.13, 0.03)|
|40-50|0.21 (0.15 , 0.28)**|0.03 (-0.05, 0.11)|
|50-60|0.32 (0.25 , 0.39)**|-0.01 (-0.09, 0.07)|
|60-70|0.18 (0.11 , 0.25)**|0.04 (-0.04, 0.12)|
|70-80|0.06 (-0.01, 0.13)|-0.04 (-0.12, 0.04)|
|Female (=1 if female)|0.05 (0.02 , 0.09)**|-0.24 (-0.28, -0.19)**|
|Education (reference: no formal education)|||
|Primary|0.08 (-0.12, 0.28)|-0.18 (-0.41, 0.05)|
|Secondary|0.16 (-0.03, 0.36)|-0.1 (-0.32, 0.12)|
|high school or equivalent|0.08 (-0.15, 0.31)|-0.16 (-0.41, 0.10)|
|above high school|0.26 (0.05 , 0.46)*|-0.08 (-0.32, 0.15)|
|Education of head (reference: no formal edu|cation)||
|Primary|0.08 (-0.13, 0.29)|0.05 (-0.18, 0.29)|
|Secondary|0.06 (-0.15, 0.26)|0.06 (-0.18, 0.29)|
|high school or equivalent|0.15 (-0.13, 0.43)|0.11 (-0.21, 0.42)|
|above high school|0.05 (-0.17, 0.26)|0.01 (-0.24, 0.25)|
|Employment (=1 if employed)|-0.07 (-0.11, -0.03)**|-0.09 (-0.13, -0.04)**|
|Household size|-0.01 (-0.02, 0.00)|0 (-0.01, 0.01)|
|Relationship to head (reference=head)|||
|Wife|0.02 (-0.03, 0.07)|-0.02 (-0.07, 0.04)|
|Children|0.01 (-0.05, 0.07)|0.04 (-0.02, 0.11)|
|Other|-0.01 (-0.09, 0.07)|0 (-0.09, 0.10)|
|Urban (=1 if urban)|-0.03 (-0.07, 0.01)|-0.05 (-0.10, -0.01)*|
|Region (reference = Imereti )|||
|Tbilisi|0.27 (0.21 , 0.34)**|-0.07 (-0.14, 0.00)|
|Guria|0.03 (-0.06, 0.12)|-0.27 (-0.38, -0.17)**|
|Racha-Lechkhumi, qvemosvaneti|-0.01 (-0.11, 0.10)|-0.22 (-0.35, -0.10)**|
|Kakheti|0.16 (0.09 , 0.22)**|-0.26 (-0.34, -0.19)**|
|Mckheta-Mtianeti|<br>0.16 (0.08 , 0.25)**|<br>-0.1 (-0.20, 0.00)|
|Samegrelo|<br>0.05 (-0.01, 0.11)|<br>-0.21 (-0.28, -0.14)**|
|Qvemo Qartli|0.19 (0.11 , 0.26)**|-0.14 (-0.23, -0.05)**|
|Sida Qartli|0.22 (0.15 , 0.29)**|-0.41 (-0.49, -0.33)**|
|Achara|0.07 (0.00 , 0.14)*|-0.18 (-0.26, -0.11)**|
|Observations|16,434|16,423|

95% confidence intervals in parentheses * significant at 5%; ** significant at 1% 26
<!-- Start of picture text --> Walk ; we<br>wn<br>co)<br>Qa<br>fea)<br>°<br>=<br>o l<br>2<br>(mean waiting time)<br>Public Transport) | wary<br>0 20 40 60 80<br>Percent Loss of Job Accessibility<br><!-- End of picture text -->

<!-- Start of picture text --> Percent loss of job access A Percent loss of job access A<br>0.0 - 0.10 ® NORTH 0.947 - 5.00 NORTH<br>0.11 - 1.0 5.01 - 10.0 f<br>Mi 1.1-20 MM 10.1 - 15.0<br>MM 2.1 -3.0 MM 15.1 - 50.0 p<br>HMM 3.1 - 4.0 HB 50.1 - 92.7<br>onof DEN) a Sa, x oem a say<br>d“—=<br>02.55 10KM | 02.55 10KM<br>poe H++4H<br>Percent0.0 loss of jjob access A PopulationP Densityy (2017 (2017) " A<br>WM 11-50- 1.0 “<> NORTH 510.0 -- 250 50 ePa NORTH<br>MM 5.1-7.0 MM 260 - 500<br>Wi 7.1 -10 HE510 - 1000<br>Ml 11-20 HM 1100- 1400 Wee<br>» Re y ’ | AAS > y .<br>> i a2” 7 Pea & ef wn Be<br>02.55 10KM 02.55 10KM<br>K+++H4 -K++++H4<br><!-- End of picture text -->
**Figure 5.  Effects of Business-as-Usual Approach on Labor Outcomes in the Last Seven Days by Wealth Quintile**

<!-- Start of picture text --> Any crop work Livestock<br>Non-farm enterprise: ownership/management Non-farm enterprise: supporting work<br>Wage employment Ganyu employment<br><!-- End of picture text -->

**<u>Notes:</u>**

The quintiles are for the wealth index, as explained in footnote 11. q1 = lowest wealth quintile, q5 = highest wealth quintile. The points in the graph represent coefficients from linear probability models of participation on the business-as-usual survey approach, for each wealth quintile, controlling for district, month and enumerator fixed effects. Lines represent 95 percent confidence intervals. The regressions are weighted by the response weight.

22
National Center for Energy Economics, 2022). In each of these baselines, emissions are expected to rise to just above 40 GtCO2 yr<sup>_−_1</sup> before declining. It has been argued that higher emissions scenarios are unlikely (Hausfather and Peters, 2020). We therefore assume that, without abatement, emissions will maintain their peak levels in each of these emissions scenarios, i.e., at _∼_ 40 GtCO2 yr<sup>_−_1</sup> . This describes our “business-as-usual” scenario<sup>9</sup> ; we probe the impact of assuming an increasing emissions baseline in Section 2 of the _Supplementary Materials_ . Thus, in this calibration, once all emissions are abated, society is entirely decarbonized. We set the time horizon for decarbonization to 2100; this translates to solving the model for 80 years.

# **2.3.2 Marginal abatement and investment costs**

Throughout the main text, we use the low cost, linear marginal cost calibration shown in Figure 1; we explore the impact of the other two alternative calibrations given in Figure 1 in Section 3 of the _Supplementary Materials_ . In this calibration, we assume that our cost functions _ci_ ( _xi,t_ ) and _γi_ ( _ai,t_ ) are quadratics, such that We calibrate these cost functions using IPCC marginal cost data (Intergovernmental Panel on Climate Change, 2022). Marginal abatement costs are given by, which implies that marginal abatement costs are linear in abatement potential. We translate the marginal abatement cost into the marginal investment cost by requiring that between the two approaches (MAC and INV) the relative cost between each sector are equal, such that for each unique pair ( _i, j_ ). This defines marginal costs up to one remaining parameter. The final cost coefficient is calibrated by demanding that the total discounted costs between both approaches are equal for a given target (Grubb, 1997) under the assumption of certainty about the carbon budget. Therefore, any differences in total policy costs once we add in the effects of uncertainty must come from the uncertainty itself, as the models are calibrated to be equally costly in the certainty case. See Table 1 for a full summary of our calibration results.

> 9Note that some green technologies will be cost-competitive with their dirty equivalents, and therefore not necessarily require a carbon price to be assimilated into the economy; indeed, these “costless” abatement technologies are a key source of uncertainty in projecting the costs of the green transition (Kotchen et al., 2023). Therefore, our assumption that emissions will remain at peak levels without a carbon price is conservative, as all abatement in our approach is spurred by the presence of a carbon price.

10
allow to instrument access to markets excluding zones that are more likely to be prone to an endogeneity bias due to non-random road placement. Second, in order to avoid the recursion problem, we substitute market access indicators with measures of access to infrastructure that do not involve population or income. These measures include the number of roads intersecting a circle of a given radius around the centroid of each locality, or the number of kilometers of roads within a given distance to that centroid. Third, we use these access to infrastructure variables as instruments for market access indicators.

Our paper makes three principal contributions. First, we construct a new panel of geo- referenced roads data in Mexico over three decades (for the period 1986 2014). Second, we also take advantage of the panel nature of our data to estimate time-varying measures of market access for more than 2,000 localities (whereas previous studies, either just used a few dates or had a sample with a limited number of observations). Third, to the best of our knowledge, our paper is the first to study the effect of market access on specialization.

Our key findings are that transportation improvements over the last three decades increased access to markets in Mexico—which we measure in concurrent ways—and that these improvements in accessibility had a positive impact on local employment and economic specialization. The effects are large and heterogeneous across sectors. A 10 percent increase in market access results in a 1.6 to 2.1 percent increase in employment and a large increase in locality specialization. The effect of our market potential measure is slightly larger. A 10 percent increase results in a 2.9 to 6.5 percent increase in employment, and a 13.0 percent increase in output specialization. We also effects across sectors with in commerce and find heterogeneous employment services benefiting more than manufacturing from road improvements.

The rest of the paper is structured as follows. Section 2 presents the Mexican context. Section 3 describes the data used, explains the construction of our market access and market potential measures, and provides information on the trends in industrial concentration and specialization in Mexico. Section 4 presents our empirical methodology and discusses identification strategies. Section 5 discusses our empirical results regarding the effects of market access and market potential on employment and specialization, and Section 6 concludes.

# **2 The Mexican context: Lessons from the literature**

## **2.1 The concentration of industries and specialization of localities**

Economic activity in Mexico is highly concentrated in the Mexico City Metropolitan Area, which in 2010, contributed to a quarter of the national Gross Value Added even though it covers less than 0.3 percent of the national territory. In the wake of Michael Porter’s work (Porter, 1998), there has been increasing policy interest in Mexico to facilitate the industrial concentration and specialization of localities and the development of industries, so as to ultimately 2
change the incomes to their postfiscal levels of �1,3� and �2,2�. Following Higgins and Lustig (2016), the fiscal impoverishment is the same for the two fiscal systems, although with the former the poorest individual is fiscally impoverished and with the latter, the richest individual is fiscally impoverished. This is because the current measure of fiscal impoverishment focuses only on the absolute values of change in incomes, and of course, this is informative.<sup>4</sup> Yet if one wants to rank the impoverishment of each individual on the basis of the change in individual welfare levels, the law of diminishing returns would argue that the fiscal impoverishment of the second fiscal system is more desirable than that of the first. The advantage of being consistent with the law of diminishing return is that one can give more importance to the way the poorer individuals within a society are being affected by the fiscal system in measuring the total impoverishment effect.

Given the limitations of current practices in measuring the actual welfare losses and gains induced by a fiscal system, Section 3 proposes a family of measures that aim to evaluate the various dimensions of fiscal losses and gains.

# **3. Methodological Framework**

The intervention of a fiscal system modifies the incomes of people in a society. This may happen either through the taxes they are paying, the social spending they are receiving, or both. Thus, during a given time, some people may have their incomes lowered due to the fiscal system, while others may have their incomes increased.

Consider, as an illustration, a theoretical economy of five individuals. Their prefiscal and postfiscal incomes, as well as the fiscal system in place, are known for one given year, as reported in Table 1.

**Table 1. Effects of Fiscal Policy on a Theoretical Population of Five Individuals**

|individuals|1|2|3|4|
|---|---|---|---|---|
|Pre‐fiscal Income|5|7|4|10|
|Taxes|2|3|1|5|
|Transfer|3|1|1|2|
|Post‐fiscal income<br>_Source:_The authors.|6|5|4|7|

As can be seen in Table 1, in the absence of the fiscal system, the incomes of the five people are stated as 5, 7, 4, 10, and 8, for individuals 1, 2, 3, 4, and 5. After the intervention of the fiscal system, their incomes change, and are now stated as 6, 5, 4, 7, and 9. The incomes of individuals 2 and 4 have worsened due to the fiscal system, while the income of individual 3 remains unchanged, and the incomes of individuals 1 and 5 have improved.

For any given economy, the intervention of a fiscal system may have one of the three effects described in the example given on the incomes of the individuals. The following notations are used

> 4 The measures of fiscal impoverishment violate the transfer principle first formulated by Dalton (1920), which states that transfers from a richer to a poorer person should improve the measure of welfare.

6
WPS3993

# **ASSESSING BENEFITS OF SLUM UPGRADING PROGRAMS IN SECOND-BEST SETTINGS**<sup>*****</sup>

Basab Dasgupta and Somik V. Lall _Development Research Group, The World Bank, Washington DC 20433, USA_

## **Abstract**

Slum upgrading programs are being used by national and city governments in many countries to improve the welfare of households living in slum and squatter settlements. These programs typically include a combination of improvements in neighborhood infrastructure, land tenure, and building quality. In this paper, we develop a dynamic general equilibrium model to compare the effectiveness of alternative slum upgrading instruments in a second-best setting with distortions in the land and credit markets. We numerically test the model using data from three Brazilian cities and find that the performance of in situ slum upgrading depends on the severity of land and credit market distortions, and how complementary policy initiatives are being implemented to correct for these problems. Pre-existing land supply and credit market distortions reduce the benefit-cost ratios across interventions, and change the rank ordering of preferred interventions. In the light of these findings, it appears that partial equilibrium analysis used in typical costbenefit work overstates the stream of net benefits from upgrading interventions, and may in fact propose a misleading sequence of interventions.

## **JEL Classification: R21, R31, R38, I31.**

**Key Words:** Slum upgrading, <u>preexisting distortions, second best policies</u>

## World Bank Policy Research Working Paper 3993, August 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* This research has been funded by a World Bank Knowledge for Change (KCP) program grant “Dynamics of Slum Formation and Strategies to Improve Lives of Slum Dwellers.” We thank Paulo Avila, Bob Buckley, Dean Cira, Mila Freire, Sonia Hammam, Hyoung Gun Wang and Christian Zimmerman for helpful comments. Author for correspondence: Somik Lall; Senior Economist, Development Research Group, World Bank. Email: slall1@worldbank.org.