rafmacalaba/datause-encoder-data huggingface dataset:
- 43 -

will the utilization of such physical assets be dampened because of the prevailing operational constraints, it is also not assured that the modal choice coincides with market preferences.

# VIII. AN AGENDA FOR REFORM

102. First and foremost, the country needs a strategy for adjusting its logistics system to the changing environments in international trade and transport markets. The container will be central to such a strategy. At the same time, a plan has to be developed which will provide for necessary system adaptations so that the efficiency potential of containerization can also be tapped for improving the domestic trades. To achieve this, two of the presently existing shortcomings in this context have to be rectified. Firstly, it is the high degree of fragmentation in the public administration with regard to systems planning, control and management. Secondly, it is the limited involvement of the private sector in problem assessments and decision-making as regards the development and management of trade logistics networks.

103. Given the urgency of required system adjustments in line with market demands, a joint public-private sector task force should be appointed to establish an agenda for market surveys and system analyses which will identify shortcomings and improvement needs. Taking it from there, the task force should formulate an action plan aimed at correcting existing constraints and streamlining processes related to cargo handling and transport. This group should be organized along the lines of the Simplification of International Trade Procedures Boards (SITPRO) which have been established by many governments<sup>1 0 7</sup> to spearhead trade logistics system adjustment programs. There have been attempts in India to follow this route, and a body called INDPRO was established some time ago. TNDPRO's self-proclaimed objective is **'to** act as freight/trade facilitation organization dealing with the rationalization and modernization of international trade procedures in India, and the development of electronic data processing techniques'. But INDPRO has been ineffectual, largely because support by the public administration was lacking. To succeed in any effort to improve trade logistics in India, the full and unequivocal commitment and support of the Government will be needed. This should be one of the highest priorities in managing the national economyl 104. Following the establishment of a national trade logistics management strategy, the role and function of each element in the system should be defined. Based on such definition, strategic plans will have to be formulated for the development and management of each subsystem, as they relate to the provision of trade logistics services -the shipping, railway, road and air transport sectors, as well as the port and 107/ Great Britain **was the first** country **to take these steps,** most **other European countries have followed,** but **also developing countries have been sJccessful** in **taking this route (e.g. Sri Lanka).**
# Figure 4. Global inequality frontier and global inequality, 1820-2002

# Gini

<!-- Start of picture text --> Theil<br><!-- End of picture text --> <!-- Start of picture text --> inequality possibility frontier<br>2002<br>1980<br>1950 1960<br>19131929 inequality possibility frontier<br>1870<br>1850<br>1820<br>2002<br>1980<br>1950 1960<br>19131929<br>182018701850<br>1 6 11 16 21 26 1 6 11 16 21 26<br>global income to subsistence global income to subsistence<br>100<br>400<br>80<br>300<br>60<br>200<br>globa l gini<br>40 global theil<br>100<br>2 0<br>0 0<br><!-- End of picture text --> Note: All global average incomes, including for 2002 expressed in 1990 PPP (see note to Table 3). [From summary_utrecht.dta. ] 22
**Table 8.  Average (Year on Year) GDP growth in the First Four Quarters of Sudden Stops**

||(1)|(2)|(3)|
|---|---|---|---|
|Capital Flows (% of GDP, Average of past 8 quarters)|-1.800**|1.080|1.727|
||[2.14]|[0.68]|[1.11]|
|Capital Flows (% of GDP, Average of past 8 quarters)*||||
|dummy 2003-2014||-3.305*|-3.861**|
|||[1.80]|[2.12]|
|Other Flows/Total Flows|-0.677||-3.819|
||[1.09]||[1.40]|
|(Other Flows/Total Flows)* dummy 2003-2014|||3.235<br>[1.16]|
|Dummy for 2003-2014||5.145*|4.790*|
|||[1.99]|[1.85]|
|Constant|2.018*|-2.494|-2.045|
||[1.71]|[1.12]|[0.92]|
|Observations|41|41|41|
|R-squared|0.241|0.281|0.309|
|Adj. R-squared|0.201|0.223|0.211|

Robust t statistics in parentheses. **,** and * indicate significance at 1, 5, and 10% levels, respectively

**Table 9. Policies during Sudden Stops 1991-2014**

25
<!-- Start of picture text --> —A— CE ai<br>ecco diagonal = ae Q<br>a“ 2<br>oe NS<br>4 ©<br>ao 2) g<br>a a<br>= °<br>0 Pil 2 3 4 5 6 EC 8 9 1<br>six<br><!-- End of picture text --> <!-- Start of picture text --> —A— CE wt lo<br>ecco diagonal = ae Q<br>a 2<br>“z- NS<br>o ©<br>a- hde<br>a a<br>“ °<br>0 05 1.15 .2 .25 3 35 4 45 5 55 6 65 .7 .75 8 85 9 95 1<br>p<br><!-- End of picture text -->
# **Small Cash Rewards for Big Losers: Experimental Insights into the Fight against the Obesity Epidemic**

Boris Augurzky Thomas K. Bauer* Arndt R. Reichert Christoph M. Schmidt Harald Tauchmann _JEL Codes:_ I12, I18, D03, C93 _Keywords:_ field experiment, weight cycling, sustainability, incentives

* Boris Augurzky, RWI; Thomas K. Bauer, RWI, Ruhr‐Universität Bochum, IZA Bonn; Arndt R. Reichert, The World Bank, RWI; Christoph M. Schmidt, RWI, Ruhr‐Universität Bochum, IZA Bonn, CEPR London; Harald Tauchmann, RWI, CINCH. – The authors are grateful to the “Pakt für Forschung und Innovation” for funding, and to Rüdiger Budde, Viktoria Frei, Karl‐Heinz Herlitschke, Klaus Höhner, Julia Jochem, Mark Kerßenfischer, Lionita Krepstakies, Claudia Lohkamp, Thomas Michael, Carina Mostert, Stephanie Nobis, Margarita Pivovarova, Gisela Schubert, and Marlies Tepaß for research assistance. We also gratefully acknowledge the comments and suggestions of Alec Brandon, Alfredo Paloyo, one anonymous World Bank Research Group staff, and participants of the following: participants of the research workshop “Empirical Economics” at the Ludwig Maximilian Universität München. We explicitly thank the medical rehabilitation clinics of the German Pension Insurance of the federal state of Baden‐ Württemberg and the association of pharmacists of Baden‐Württemberg for their support of and dedication to this experiment. In particular, we acknowledge the support of Susan Eube, Michael Falentin, Ina Hofferberth, Marina Humburg, Silke Kohlenberg, Thomas Krohm, Max Lux, Tatjass Meier, Monika Reuss‐Borst, Constanze Schaal, and Wolfgang Stiels. – All correspondence to Thomas Bauer, RWI, Hohenzollernstr. 1‐3, 45128 Essen, Germany, E‐Mail: Thomas.Bauer@rwi‐ essen.
**Figure 3: Simulated enrollment impact of placing schools in every cluster**

<!-- Start of picture text --> Simulation: Simulation:<br>Primary school in every cluster Primary and secondary schools in every cluster<br>1.0 45 degree line 1.0 45 degree line<br>Cameroon<br>0.8 0.8 Uganda<br>Madagascar<br>0.6 0.6 C.A.R<br>C.A.R Benin<br>Niger 98<br>0.4 0.4 Chad<br>Chad Mali<br>Mali<br>0.2 Niger 98 0.2<br>Niger 92<br>0.0 0.0<br>0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0<br>Actual enrollment Actual enrollment<br>Simulated enrollment Simulated enrollment<br><!-- End of picture text --> _Note_ : The vertical distance from the 45<sup>o</sup> line shows the change in proportion enrolled associated with reducing the distance to school to zero. Labeled countries are those for which the absolute value of the change is greater than .04 for the left panel, and 0.1 for the right panel.

_Source_ : Author’s calculations from DHS data.

Despite the seemingly larger point estimates in Table 3, the large increase in school availability implied by building a primary and secondary school in every cluster would still not typically go very far toward achieving full enrollment. Even in Burkina Faso and Senegal, with some of the lowest enrollment rates, the simulation of having both schools in all clusters is associated with no increase in enrollment.

_Can a reduction in distance reduce inequalities in enrollment?_ The low average impacts of school availability on enrollment could be consistent with heterogeneity across children. For example a small overall impact might be the average of no impact on boys but a relatively large impact on girls. Or no impact on children from richer families but a large impact on those from poorer families. To investigate this possibility, the probit model (with distance specified as a simple linear function) is run separately for four subsets of each data set: girls from the poorest 50 percent, girls from the richest 50 percent, boys from the poorest 50 percent, and boys from the richest 50 percent. Table 4 reports the average marginal effects of the distance to primary and secondary schools, as well as the statistical significance of the underlying probit coefficients, for each of these samples.

The overall patterns are consistent with those from the aggregate model reported in Table 1. In particular, in the Western and Central African countries where overall enrollment is low, the distance to primary school tends to be statistically significant for all the subgroups. The marginal effects of distance on enrollment are very similar for boys and girls. These results are therefore not consistent with a substantial difference in the responsiveness of enrollment to the availability of schools by gender.

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# **Who Are the Poor in the Developing World?**

Andrés Castañeda Dung Doan David Newhouse Minh Cong Nguyen Hiroki Uematsu João Pedro Azevedo World Bank Data for Goals Group<sup>1</sup> JEL Classification: I32, O10 Keywords: Global poverty, Poverty Measurement

> 1  Poverty and Equity Global Practice, World Bank Group. This report is a background paper for the Poverty and Shared Prosperity Report 2016. It is based on data harmonized by the Data for Goals Group, which comprises approximately 20 staff and consultants from the World Bank’s Poverty and Equity Global Practice and is led by Andrew Dabalen, João Pedro Azevedo, and Nobuo Yoshida. Pablo Suarez-Becerra provided invaluable assistance in data processing and validation. The team thanks Kathleen Beegle, Jose Cuesta, Andrew Dabalen, Samuel FreijeRodriguez, Pedro Olinto, and Prem Sangraula for constructive comments, and Ana Revenga and Carlos SilvaJauregui for their support. Correspondence should be directed to dnewhouse@worldbank.org. The views expressed here do not necessarily reflect the views of the World Bank Group or its executive board, and should not be interpreted as such.
filled the short-term survey. This should mitigate the concern that the results could be driven by over-exposure to the questionnaire and its interaction with the treatment (e.g., recall bias). To recover the average treatment effects of the two series in the short and medium terms, we estimated the following linear model via OLS: where _i_ is the individual, and _t_ stands for the survey wave, namely, baseline ( _t_ = 0), short term ( _t_ = 1) and medium term ( _t_ = 2). Treatment 1 is a dummy indicator equal to 1 if the individual was assigned to the humorous drama with implicit messaging and 0 otherwise, while Treatment 2 equals 1 if the individual was assigned to the docuseries with explicit messaging and 0 otherwise. Y indicates the outcomes of interest, specifically, the general and topic indexes described previously as well as variables measuring information-seeking and posting behaviors.

Most of our outcomes were only collected at follow-ups. Given the experimental design, the lack of baseline values should not affect the causal interpretation of the results as both the treatment and control groups are identical in expectations (Bruhn and McKenzie, 2009). As shown below, this is further confirmed by observed balance for the pre-treatment values of observable characteristics and attitudinal self-reported outcomes. As such, we show plain estimates from a parsimonious specification with no controls. Nevertheless, to improve the efficiency of the estimator, we also report the results controlling for a series of socio-economic indicators measured at baseline, represented by vector **X’** in (1).<sup>19</sup> Among them, we always include an index measuring the baseline stance of the individual towards gender norms and VAW. Specifically, we rely on those few knowledge and attitudinal variables that were – – measured at baseline and aggregate them into an index i.e., the Baseline Stance Index which captures the ex-ante progressiveness of the respondents’ view.<sup>20</sup> Finally, when the baseline values of standalone outcomes of interest were available, we also included them on the RHS of equation (1) to explicitly account for potential pre-existing imbalances in those outcomes among arms.

For individuals assigned to the control group, both the drama and documentary dummies are simultaneously equal to 0. Hence, the _β_ and _γ_ coefficients capture the ATE of being

> 19Selected controls were theoretically associated with outcomes or showed pretreatment imbalances and included age, gender, education, education of household head, religion, caste, occupation, relationship status, self-assessment of the English language, frequency of watching videos online, indicators for being a student, having sisters, having male friends beating partner or female friends beaten, and city-of-residence fixed effects.

> 20Table A32 describes the individual items used to construct this index, their factor loadings and the Cronbach’s alpha.

11
# **Table 18: Impacts on Number of Firms from African FTA (% change from benchmark)**

**No initial rent capture case**

||Tanzania|European<br>Union|Africa|Rest of the<br>World|
|---|---|---|---|---|
|**Business Services**|||||
|Telecommunication|-0.5|-1.9|7.5|-2.2|
|Insurance|-7.9|-13.6|43.2||
|Banking|-0.2|-0.4|41.1|-0.8|
|Professional business services|0.0|-0.4|11.7|-0.5|
|Air transport|0.2|0.6|1.8|0.7|
|Road transport|-0.1|-0.1|6.4|-0.2|
|Railway transport|0.1|||0.6|
|Water transport|-1.1|-4.4|29.8|-5.2|
|**Dixit-Stiglitz Goods**|||||
|Mining & quarrying|0.0|0.2|0.2|0.5|
|Grain milling|0.0|0.3|0.3|0.6|
|Processed food|0.0|0.2|0.2|0.6|
|Beverages & tobacco products|0.1|0.1|0.1|0.2|
|Textile & leather products|0.0|0.2|0.2|0.4|
|Manufacture of basic & industrial chemicals|0.1|0.1|0.0|0.1|
|Manufacture of fertilizers & pesticides|0.0|0.2|0.1|0.2|
|Petroleum refineries|0.0|0.0|0.1|0.1|
|Glass & cement|0.0|0.1|0.1|0.2|
|Iron steel & metalproducts<br>Source: Authors' estimates.|0.0|0.0|0.0|0.1|

86
Table 5: The impact of PAL on learning

|_Outcome =_|(1)<br>Math<br>(z-score)|(2)<br>Math<br>(z-score)|(3)<br>Spanish<br>(z-score)|(4)<br>Spanish<br>(z-score)|(5)<br>3rd subject<br>(z-score)|(6)<br>3rd subject<br>(z-score)|
|---|---|---|---|---|---|---|
|In-kind|-0.044|-0.049|-0.016|-0.026|-0.022|-0.029|
||(0.080)|(0.073)|(0.088)|(0.081)|(0.078)|(0.071)|
|Cash|-0.193**|-0.182**|-0.144|-0.156*|-0.173**|-0.156*|
||(0.086)|(0.086)|(0.095)|(0.093)|(0.082)|(0.080)|
|State FE|NO|YES|NO|YES|NO|YES|
|Year FE|NO|YES|NO|YES|NO|YES|
|Grade FE|NO|YES|NO|YES|NO|YES|
|Pre-program controls|NO|YES|NO|YES|NO|YES|
|Observations|11,006|11,006|11,006|11,006|10,432|10,432|
|**_Effect size:_ In-kind - Cash**|0.15|0.13|0.13|0.13|0.15|0.13|
|**_H 0:_ In-kind = Cash (p-value)**|0.05|0.06|0.10|0.07|0.03|0.05|
|**_H 0:_ In-kind = Cash (Randomization**<br>**Inference p-value)**|0.06|0.09|0.14|0.10|0.05|0.08|

Notes: *** p<0.01, ** p<0.05, * p<0.1 (1) Standard errors are clustered at the village level.

(2) Pre-program controls include gender, age, and indicators for whether the household head is married, whether the house has running water, and whether the closest school offers morning shift.

(3) The 3rd subject was not administered in 2007, and it covered Science in 2008 and 2012, Ethics and Civics in 2009 and 2013, History in 2010, and Geography 2011.

34
# Sheet2

**Table 8. Regions with highest and lowest levels of budget subsidies per capita. Data from 1995 budgets, in 1991 rubles**

|Lowest levels of Nat. economy|expenditures|Highest levels of Nat. econom|y expenditures|
|---|---|---|---|
|1 Aginskiy Buryatskiy AO|75.97|1 Magadanskaya oblast|2860.78|
|2 Adygeya republic|94.64|2 St.Petersburg|2104.11|
|3Kalmykia republic|95.94|3 Chukotskaya AO|1934.57|
|4 Chitinskaya oblast|123.96|4 Evenkiyskiy AO|1566.02|
|_5_Northern Osetia rep.|125.01|5 Moskovskaya oblast|1403.18|
|6 Dagestan republic|125.17|6 Yamalo-Nenetskaya rep.|1278.93|
|7 Mordovia republic|141.75|7 Koryakskiy auton. okrug|1201.57___|
|8 Vladimirskaya oblast|145.87|8 Sakha (Yakutia) republic|1157.37___|
|9 Ust-Ordynskiy Buryat. AO|149.79|9 Komi republic|701.09|
|10 Komi-PeTmyatskaya AO|153.09|10 Tyumenskaya oblast|695.51_|
|Lowest levels of Housing expe|nditures|Highest levels of Housing exp|enditures_<br>___|
|1 Kalmykia republic|46.85|1 Evenkiyskiy AO|1410.31|
|2 Aginskiy Buryatskiy AO|47.38|2 Chukotskaya AO<br>_|1369.66|
|3 Adygeya republic|56.41|3 Moskovskaya oblast|1128.76|
|4 Komi-Permyatskaya AO|58.20|4 St.Petersburg<br>_|1124.17|
|5 Mordovia republic|62.00|5 Yamalo-Nenetskaya rep.|1064.47|
|6 Chitinskaya oblast|68.84|6 Magadanskaya oblast|1050.71|
|7 Dagestan republic|75.04|7 Koryakskiy auton. okrug|955.90|
|8Northern Osetia rep.|77.76|8 Komi republic|533.3651|
|9 Tambovskaya oblast|78.56|9 Sakha (Yakutia) republic|500.70|
|10Kirovskaya oblast|84.13|10 Khanty-Mansiyskiy AO|436.26|
private sector innovation and efficiencies. The challenge is encouraging such innovation while maintaining on the project company the risk of performance of the works.

- Certainty of time and cost – a greenfield project, with limited interface with other works, flexibility in design and a well defined scope of works, as is often the case for Build projects, makes it easier for the project company to enter into a fixed price and time construction contract (also known as an engineer-procure-construct (EPC) or turnkey contract).  This will then facilitate the grantor placing more strict obligations on the grantor to deliver the works to a fixed price and by a fixed date.

# **3.3 REFURBISH**

The _Refurbish_ obligation involves refurbishment or expansion of existing assets. These existing assets may have been recently built another contractor or may have been in place for some time. _Refurbish_ involves similar risks to the _Build_ obligations, while the presence of existing assets raises a number of additional risks associated with

- The condition of existing assets - their lifecycle, appropriateness for the intended purpose and for the refurbishment or expansion works planned. There may not be an up to date asset register or any other record of the condition of those assets, and therefore tis risk can be difficult to manage.

- Any latent defects that may exist in the existing assets– even where the assets have been well maintained, defects may exist from their manufacture or construction. With existing works, it is often difficult to identify such defects, or to confirm the quality of existing construction. Also, the construction contractor for those works is unlikely to make available warranties as to the quality of the works unless such warranties are obtained in advance of the work they perform.

- Design of the existing assets and interfaces with the refurbishment, since the new works must interface with the design of the existing works, the materials used, the technology adopted, etc.  The design of the existing works may limit the design options for the refurbishment of those works,

32
14,812 are aged 12 to 24 and are from developing countries. Of these, 6,922 came to Spain in 1999 or later. The occupational classification consists of 66 2-digit occupation categories.

_United Kingdom:_ Data are from the 3% sample of anonymised records (SARS) from the 2001 Census, provided by the Cathie Marsh Centre for Census and Surveys Research (University of Manchester). The dataset contains individual records for 134,800 individuals born outside of the United Kingdom, of whom 13,500 are aged 12 to 24 and from developing countries. Of these, 1442 came to the United Kingdom within the last year. Individuals with a country of birth classified as other in the SARS are not counted as developing country migrants here, since this category includes both developed countries (Australia, New Zealand) and some developing countries (South America). The occupational classication consists of only 22 categories, and so Herfindahl indices of occupational concentration are not calculated.

_United States_ : Data are from the 5% public use sample of the 2000 Census, obtained through the Integrated Public Use Microdata Series (IPUMS) website (Ruggles et al. 2004). The dataset contains individual records for 1,614,057 individuals born outside of the United States, of whom 238,132 are aged 12 to 24 and are from developing countries. Of these, 35,057 came to the United States in 1999 or later, defined here as recent migrants. The occupation classification consists of 475 occupation codes.

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It can then be shown that changes in the urbanization rate depend on the difference between the urban and rural rates of natural increase (see Web Theory Appendix 1). Industrial Europe and the four developing areas had on average similar rural rates of natural increase. But they widely differed in their urban rates of natural increase. While “mushroom villages” have always existed, “mushroom cities” are the novel feature of the 20th century. We show in a calibration exercise that faster changes in urbanization are indeed associated with higher urban rates of natural increase ceteris paribus (see Web Theory Appx Fig. 1).

The decompositions suggest that urban natural increase expands the urban population in both absolute terms (the urban growth rate) and relative terms (the change in the urbanization rate). For example, one urban (rural) newborn has an instantaneous effect of 1 on the urban (rural) population. However, in the long run, individuals choose their place of residence – urban vs. rural – depending on their utility in each location. The long-run equilibrium effect of urban natural increase thus depends on urban and rural utility levels, and, ultimately, the endogenous dynamic responses of migration and fertility to the increase in urban population. The long-term effects of urban natural increase could be smaller than its short-term effects. If urban newborns eventually crowd-out cities as adults, urban natural increase could have a dissuasive effect on future rural-to-urban migration and/or future urban fertility, further reducing urban growth. However, the long-term effects could also exceed the short-term effects if urban natural increase leads to higher urban fertility, wherein urban congestion and the corresponding low income level prevent any adjustment in fertility, due to the trade-off between child quantity and child quality (Galor, 2012).<sup>11</sup> To assess the extent to which these decomposition findings stand up to these dynamic adjustments over time as well as additional factors that may jointly affect urban growth and urbanization as well as natural increase, a series of multivariate (panel) specifications are explored. These additional exercises are not meant to establish causality, but rather to provide additional insight into whether the findings hold beyond the transition (i.e. in the longer run) and beyond an accounting sense.

# **3. MULTIVARIATE ROBUSTNESS ANALYSIS**

# **3.1 Absolute Urban Growth**

We use panel data for 35 countries that were still developing countries in 1960. We adapt equation (5) and run the following model for _t_ = [1960s, 1970s, 1980s, 1990s, 2000s]: where _△Upopc_ , _t/Upopc_ , _t_ is the annual urban growth rate (%) of country _c_ in decade _t_ . Our variable of interest is the urban rate of natural increase (per 100 people, or %) of country _c_ in

> 11Additionally, urban natural increase affects the age-sex composition of the cities while migration affects

future urban fertility through crowding-out and age-sex structure effects. 7