rafmacalaba/datause-encoder-data huggingface dataset:
- 10 -

Comparative Container Freight Rates<sup>9</sup>

||Through-Transport Rates in US$ Destination<br>Detroit<br>Nuremberg|
|---|---|
|**New Delhi**|**3,700**<br>**2,705**|
|**Bombay**|**3,300**<br>**2,305**|
|Bandung|3,120<br>2,075|
|Bangkok|2,620<br>2,075|
|Guangzhou|2,745<br>2,350|
|Nanjing|2,880<br>2,465|
|Petaling Jaya|2,520<br>1,925|
|Seoul|2,502<br>2,182|
|Singapore|2,520<br>1,825<br> <br>|
|Taipei|2,770<br>1,975|

(Note: Rates valid on May 15, 1989, per 20 foot container, including precarriage, non-conference sea transport and oncarriage costs. Ocean freight rates were quoted in US Dollars, local transport and freight handling rates were converted into US Dollars at the<sup>official exchange</sup> rates.)

# **Source: Lloyd's Intermodal Asia, June 1989.**

17. For the 8th Five-year Plan, the Government of India has established a target of 70 percent container penetration<sup>in the general</sup> cargo trades. A total of 25 million tons of 'containerizable'<sup>export and</sup> import cargoes is projected for 1995, the end of the Plan period.<sup>If 70</sup> percent of that volume would be containerized, there would be<sup>a need to</sup> move about 1.5 million containers<sup>1 0</sup> through the national trade logistics system. Is this target realistic? Trade growth has already exposed inherent constraints in the national transport network in the<sup>shape of</sup> ports, road, rail and shipping systems, that as yet have<sup>had little</sup> chance of dealing with the numbers of containers that have started to come their way. Clearly, the intermodal sector has a lot of catching<sup>up</sup> to do. But trying to push the transport industry to change quickly<sup>takes</sup>

- **9/** The **sizeable cost differential between cargo from Indian origins and**<sup>**cargo from other Asian**</sup> **sources is largely due to comparatively high costs of container**<sup>**handling and transport,**</sup> **i.e. pre-ocean carriage costs,** in **India. Reflecting inefficiencies**<sup>in</sup><sup>**local ports, the**</sup> **ocean freight rates out of India are also comparatively high.**<sup>**Except for Indonesia, there**</sup> **is only limited variation among ocean freight rates applicable to the**<sup>**other countries.**</sup>

- **lo/ Assuming 11.7 tons of cargo, on average, per** TEU **(Twenty-Foot-Equivalent-Unit)** in **1995. The revenue cargo per** TEU **is presently averaging just** under ,e **tons in** India, **reflecting**<sup>**a**</sup> **still limited efficiency in container stuffing operations.**<sup>**But as experience buiIds**up,</sup> **container loads can be expected to increase.**
statistically significant, but loses its economic significance. For Colombia the size of the coefficient on export intensity is nearly unchanged while the size of the coefficient on export intensity squared is almost halved in absolute value terms.

Where the ex-ante exporter productivity premium was estimated to be positive and significant in the sample with 20 or more employees, this is confirmed by larger and more precise estimates for nearly all cases in the samples with 10 or more employees depicted in Appendix III Table 6. An exception here is Sweden; in the sample with 20 or more employees none of the ex-ante exporter productivity premia are significant. In turn, when the units with 10 or more employees are added the ex-ante exporter productivity premia are negative for all years and nearly all of them are significant at the 5% level.

Finally, the results from Appendix III Table 8 do not provide any more evidence of ex-post exporter productivity premia than in the samples with 20 or more employees apart from the occasional cohort. This is despite the fact that the number of export starters and controls is somewhat larger in this more comprehensive set of units with 10 and more employees.

# **7.   Concluding Remarks**

The overall results from our study that uses comparable micro level panel data for 14 countries to look at the relationships between exports and productivity using identically specified empirical models are in line with the big picture that is by now familiar from the literature: Exporters are more productive than non-exporters when observed and unobserved heterogeneity is controlled for, and these exporter productivity premia tend to increase with the share of exports in total sales. Furthermore, we find strong evidence in favour of the selfselection hypothesis for the less developed countries in our sample, and for all EU-countries with data sets including a large enough number of export starters to investigate this issue but 21
Li' Table 9: Indices of Trade Compatibility In Europe, 1990

||||||||||Imports|||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||BLX|DNK|ESP|FIN|FRA|GER|GRC|ITA|NLD|NOR|PRT|SWE|U.K.|USA|AVG|Xcc|
||BLX|0.72|0.61|0.56|0.58|0.63|0.62|0.61|0.63|0.59|0.53|0.57|0.57|0.60|0.55|0.59|0.17|
||DNK|0.55|0.61|0.53|0.56|0.54|0.53|0.56|0.51|0.55|0.55|0.48|0.56|0.53|0.45|0.53|0.13|
||ESP|0.61|0.62|0.62|0.62|0.65|0.64|0.60|0.60|0.60|0.57|0.61|0.63|0.63|0.61|0.61|0.17|
||FIN|0.48|0.53|0.49|0.49|0.48|0.47|0.49|0.46|0.49|0.48|0.46|0.50|0.48|0.44|0.48|0.28|
|Exports|FRA|0.69|0.67|0.71|0.69|0.74|0.72|0.68|0.69|0.69|0.64|0.69|0.70|0.72|0.62|0.68|0.12|
||GER|0.67|0.68|0.71|0.72|0.73|0.69|0.65|0.67|0.67|0.64|0.68|0.74|0.69|0.62|0.68|0.14|
||GRC|0.40|0.36|0.30|0.34|0.35|0.40|0.35|0.36|0.35|0.34|0.33|0.35|0.34|0.33|0.35|0.18|
||ITA|0.61|0.64|0.60|0.67|0.67|0.67|0.61|0.56|0.63|0.62|0.63|0.69|0.63|0.58|0.63|0.12|
||NLD|0.65|0.66|0.59|0.60|0.66|0.65|0.59|0.63|0.67|0.58|0.55|0.61|0.62|0.54|0.61|0.13|
||NOR|0.36|0.39|0.40|0.38|0.39|0.37|0.37|0.38|0.38|0.36|0.37|0.37|0.36|0.38|0.38|0.38|
||PRT|_OA5_|0.46|OA3|0.46|0.47|_OA_|0.44|0.43|_OA6_|0.45|_OA3_|0.46|0.46|_OA6_|0.45|0.16|
||SWE|0.58|0.61|0.65|0.67|0.63|0.61|0.57|0.59|0.60|0.57|0.61|0.66|0.61|0.59|0.61|0.16|
||U.K.|0.65|0.68|0.72|0.71|0.73|0.70|0.66|0.69|0.71|0.65|0.66|0.74|0.76|0.66|0.69|0.12|
||USA|0.61|0.61|0.68|0.63|0.64|0.64|0.54|0.63|0.65|0.59|0.61|0.64|0.68|0.60|0.63|0.14|

Source: Estimated from data in Comtrade, U.N. Statistical Office
variables with the best predictive power, i.e. the lowest value of the prediction loss function, in each group are highlighted (ethnic inequality, natural resource rents, Freedom House, Political Terror Scale, real GDP growth, and neighbors in conflict).

In the next two columns of Table 3, we report the percentile of the distribution of each variable at which the optimal threshold is set, as well as the actual value of the variable at that percentile. In order to interpret the figures in this column, it is important to recall that variables that are negatively correlated with conflict in our sample have been re-oriented to be positively correlated with conflict. These variables (religious fractionalization, the CPIA, per capita income, real GDP growth, and the income effect of changes in the terms of trade) are indicated with a “*” in Table 3. For these variables, values _below_ the reported threshold (in percentile and value terms) correspond to predictions of conflict, while for all other variables, values _above_ the reported threshold correspond to predictions of conflict. To take a specific example, the threshold for predicting conflict based on the CPIA is set at the 22<sup>nd</sup> percentile of the distribution of re-oriented CPIA scores. This means that observations in the _bottom_ 22 percent of the CPIA distribution would have conflict predicted based on this variable. Interestingly, the value of this threshold of 3.2 is identical to the one used by the World Bank for many years to determine whether a country should be classified as “fragile”<sup>18</sup> .

In Table 4 we turn to the more interesting question of assessing predictions of conflict based on multiple explanatory variables. When we consider the joint predictive power of groups of correlates of conflict, the different classification rules combine the information from multiple explanatory variables in different ways, with important implications for overall predictive power. We illustrate the differences between classification rules by considering predictions based on three sets of variables. In Model 1 we examine predictions based on a parsimonious set of six variables, selected as the best two predictors of conflict within the three categories of explanatory variables, i.e. the highlighted rows in Table 3. In Model 2, we consider predictions based on the set of nine explanatory variables that individually are significantly correlated with conflict at the 95% significance level (based on the z-statistics in the last column of Table 3). Finally, in Model 3, we generate predictions based on the full set of all 16 explanatory variables. Naturally, the selection of variables in these three models is somewhat arbitrary,

> <u>�(���)</u>

> = (1 −�) .   Along the same lines, the Type 1 error rate is � , and with a weight on Type 1 errors of �= �<sup>�</sup> ��(���) 0.5 , this implies a prediction loss function of 0.5 .

> 18 Details on this rule for identifying fragile states can be found at:

> <u>http://pubdocs.worldbank.org/en/154851467143896227/FY17HLFS-Final-6272016.pdf</u>

23
**TABLE A6: Holding duration regressions are robust when restricting to the sample of realized investments.** Specifications are identical to those in Table 4 in the main text, but use the sample only of realized investments. Constant, R-squared, and number of observations not reported for brevity. Standard errors are clustered at the country-year. *** p<0.01, ** p<0.05, * p<0.1 Standard errors are robust to heteroskedasticity. *** p<0.01, ** p<0.05, * p<0.1

||(1)|(2)|(3)<br>Qua|(4)<br>ntile Regr|(5)<br>ession|(6)|
|---|---|---|---|---|---|---|
||OLS|0.1|0.25|0.5|0.75|0.9|
||PME|PME|PME|PME|PME|PME|
|Panel A of Table 4: Direct investment|s, vintage|pre-2010|||||
|Duration X 1(Duration < 10)|0.49***|0.00|0.04***|0.07***|0.09***|0.24***|
||(0.17)|(0.00)|(0.01)|(0.02)|(0.03)|(0.07)|
|Duration X 1(10 Duration < 20)|0.16*|0.01**|0.02***|0.03**|0.06***|0.17***|
||(0.09)|(0.00)|(0.00)|(0.01)|(0.02)|(0.06)|
|Duration X 1(20 Duration)|0.10|0.00|0.01**|0.01**|0.01|0.09*|
||(0.07)|(0.00)|(0.00)|(0.01)|(0.02)|(0.05)|
|Panel B of Table 4: Fund investments,|vintage|pre-2010|||||
|Duration X 1(Duration < 10)|-0.52***|-0.04|-0.05*|-0.04|-0.01|-0.07|
||(0.16)|(0.02)|(0.02)|(0.03)|(0.04)|(0.15)|
|Duration X 1(10 Duration < 20)|-0.31***|-0.01|-0.01|-0.01|0.00|-0.03|
||(0.09)|(0.01)|(0.01)|(0.01)|(0.02)|(0.08)|
|Duration X 1(20 Duration)|-0.22**|-0.00|-0.01|-0.03**|-0.03***|-0.09*|
||(0.10)|(0.01)|(0.01)|(0.01)|(0.01)|(0.05)|
|Country fxed efects|No|No|No|No|No|No|

51
capacity to offer these services. Many may be at risk of violence or trafficking. These are very important aspects from the perspective of welfare economists interested in measuring well‐being but these measurements are complex and not usually included in multidimensional indicators of deprivation or poverty. The dimensions of deprivations to consider are more numerous and more complex to measure. Again, there is very little research in welfare economics dedicated to the special needs of these populations.

**Risks and vulnerabilities** . The analysis of risk and vulnerability is also much more complex in the context of the forcibly displaced. Welfare economics has only approached these topics recently, in the past decade or so. Essentially, the idea is to measure the risk of being poor or falling poor in the future using cross‐ section or panel data studying spells of poverty over time. This is work that requires accurate and complex data sets that would be rarely available in a refugee or IDP context. More importantly, the nature of the problem changes. Refugees and IDPs are by definition more at risk and more vulnerable than regular populations and these vulnerabilities are not only linked to skills and efforts but to legal status, discrimination, limited mobility and other factors that are unique or much more acute with refugees and IDPs. How to take these factors into account with risks and vulnerability studies is a question that has not really been addressed yet by the economics profession.

# 5. An illustration: The welfare of Syrian Refugees

In this section, we review a study on the welfare of Syrian refugees in Jordan and Lebanon jointly conducted by the World Bank and the UNHCR in 2014. The study relied entirely on existing data collected by the UNHCR and provides an example of what welfare economics can do for the forcibly displaced from an analytical and policy perspective.

At the height of the crisis in 2014, Syria had an estimated 6.5 m IDPs, 4.4 m. refugees, and 1.5 m. additional migrants, which amounted to about half of the Syrian pre‐conflict population. Despite these numbers, relatively little was known about the wellbeing of these populations aside from anecdotal information and some data collected for the purpose of assistance of refugees. The UNHCR had the data to conduct such studies but not the time or the expertise and was understandably concerned about data protection whereas specialized organizations such as the World Bank did not have access to data and did not see refugees as part of their core mandate of reducing poverty worldwide. This changed in 2014 when the two organizations decided to join forces to prepare a welfare assessment of refugees in Jordan and 20
In order to implement this simulation in a computationally efficient manner, while respecting both the PISA sample and test design we estimate Lorenz curves of the learning distribution.<sup>57</sup> This procedure relies on simple summary statistics of the country level PISA data (15 equally spaced bins with the average test score in reading), computed using sample weights, replication weights, and the assessment’s plausible values. These data are then used to estimate the Lorenz parameters.

The basic building blocks of this methodology are the following two functions: _Lorenz curve Lc_ = _L_ ( _Pc_ ; B _c_ ) _Proficiency measure_ : _Pc_ = _P_ (mu _c_ / _z_ , B _c_ ) where _L_ is the share of the bottom _p_ percent of the student according to learning scores for a specific country _c_ ; B is a vector of (estimable) parameters of the Lorenz curve for a specific country _c_ , _P_ is a proficiency measure written as a function of the ratio of the mean learning score _mu (_ for a specific country _c)_ to the proficiency threshold _z_ and the parameters of the Lorenz curve of country _c_ .

The Lorenz curve captures all the information on the pattern of _relative learning inequalities_ in the student population. It is independent of any considerations of the absolute learning level. The share of students below a proficiency level captures an absolute standard of the student population. To calculate the parameters of the Lorenz curve, we test two functional forms – the Beta Lorenz<sup>58</sup> curve and the General Quadratic (GQ)<sup>59</sup> Lorenz curve. For the purpose of this exercise the General Quadratic (GQ) Lorenz curve was preferred, as it provided belter results both in terms of internal and external validation.<sup>60</sup> In the context of this exercise, we compute the share of learners below the PISA minimum proficiency level (MPL), the average learning gap in respect to the MPL, and the average learning gap severity also in respect to the same MPL. The main advantage of the learning gap and learning gap severity is the greater sensitivity of the measure to the inequality among those students below the MPL.

> 57 For a detailed discussion of common errors made by economists while using large-scale international assessments see Jerrim et al (2017)

> 58 Kakwani (1980)

> 59 Villasenor and Arnold (1989)

> 60 Lorenz parameters were estimated using the user written Stata ADO function GROUPDATA, available at <u>https://github.com/jpazvd/groupdata</u>

19
relative to the typical BLP problem. See Appendix E.3 for computational details.

# **5.5 Instruments**

For the identification of the demand-side and proficiency rate parameters, the main concern is the endogeneity of peer characteristics in (1) and (17). Thus, we instrument for _D jt_ using local demographics of the school’s neighborhood as of year 2000. To the extent that these are correlated with demand shocks _ξ jgt_ , this correlation is absorbed by the campus fixed effect _ξ j_ , and we expect ∆ _ξ jgt_ to be mean-independent of local demographics. We instrument using the following local neighborhood characteristics: percent of school-age children of each race and poverty status, average family income, average house value, percent of owner-occupied housing units, average number of children per family, number of public, private and charter schools, percent of families in each income bracket, ward indicators, and interactions between some of these variables with school type and grade level. Additional instruments include campus, grade and year dummies.

The instruments for the sampling error in school-year student demographics include school type, focus, and interactions of school type with ward. The instruments for sampling error in neighborhood-level variables are neighborhood-level number of public and charter schools, average family income, racial composition of school-age children, age distribution of school-age children, and ward dummies. Finally, the instruments used for proficiency rate estimation contains local demographics for the schools’ neighborhoods, similar to ones for ∆ _ξ jgt_ . They also include campus and year dummies.

# **5.6**

On the demand side, variation in school type, focus, grade level, location, tuition and peer characteristics vis-a-vis variation in household demographics and location helps identify preference parameters. A sufficient condition for identification is that the matrix of derivatives of sample moments with respect to the parameters have full column rank. Evaluated at our parameter estimates, this matrix indeed has full column rank. Although most parameters affect the predicted value of multiple moments, parameters ( _α_ , _β_ , _φ_ ) ̃ mainly affect predicted enrollment shares, and parameters _ω_ , _α_ , _β_<sup> ̃</sup> , _γ_ mainly affect � � predicted demographic and neighborhood moments. Proficiency rate parameters in (17) are identified by variation in school type, focus, grade level and peer characteristics.

On the supply side, conditional on the calibrated _b_ , _V_ and _F_ , parameters’ main effects on model predictions are as follows. Increasing _ζ_ lowers overall predicted entry, and increasing _σν_ changes the predicted distribution of entry across entry points, making it less sensitive to predicted enrollment and costs. Increasing _α_ ̆ raises predicted relocations’ frequency, and increasing _β_<sup> ̆</sup> lowers average predicted distance, respectively. Increasing _a_<sup>_q_</sup> , _b_<sup>_q_</sup> and _c_<sup>_q_</sup> raises the number of predicted closings, their sensitivity to proficiency rates and their predicted probability after the first five years, respectively.

26
31 Annex Table 1. Product composition of environnmentally sensitive goods, in 1970 and 1990

|SINc<br>||1970|1980|1990|
|---|---|---|---|---|
|251|Pulp**and**paper|4.3|2.9|3.2|
|332|Petroleum products|11.1|23.7|15.3|
|512|Organic chemicals|7.4|9.2|12.5|
|513|Inorganic chemicals|2.8|2.3|2.7|
|514|Other inorganic chemicals|_1.5_|1.5|_1.5_|
|515|Radioactive materials|0.3|1.5|0.9|
|521|Coal|0.2|_0.5_|_0.5_|
|561|Pertilizers|2.2|2.7|2.3|
|599|Other chemicals|4.4|4,3|5.3|
|631|Veneers, plywood|1.9|1.7|1.9|
|632|Wood manufactures|0.7|0.9|1.2|
|641|Paper**nd**paperboard|7.3|5.3 S|S'|
|642|Articles in paper|1.3|1.4|2.1|
|661|Cement, etc.|1.0|_1.5_|1.3|
|67|Iron and sel|23.9|18.3|17.4|
|63|Non**ferrous**metals|15.6|10.6|3.2|
|69|Metal nanufactures|14.3|10.3|14.3|
|||100.0|100.0|100.0|

Source: UN COMTRADE data base.

Annex Table 2. Share in total world exports and in manufactures, selected industnal countries, 1970-90

||Total||Manufa|ctures|
|---|---|---|---|---|
|Regions/countries|1970|1990|1970|1990|
||--|<br>~|~(%)-||
|Industrial|743|72.7|91.3|813|
|Austria|1.0|1.3|1.3|1.6|
|**Finland**|0.8|0.8|0.9|0.9|
|Norway|0.8|1.0|0.8|_0.5_|
|Sweden|2.3|1.8|2.9|2.0|
|Gemmany|11.7|12.2|17.2|15.2|
|Japan|6.6|8.8|10.2|11.3|
|United States|14.5|11.4|16.9|12.3|

Source: United nations COMTRADE data base.
# **Figure 4. Households by Tenure in Some Market and Transition Economies**

<!-- Start of picture text --> Denmark<br>Luxembourg<br>France<br>Poland<br>Belgium<br>Finland<br>Lithuania<br>Netherland<br>UK<br>Ireland<br>Serbia<br>Russia<br>Armenia<br>Romania<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Private Public Owner-occupied Free or other<br><!-- End of picture text --> <!-- Start of picture text --> 40%<br><!-- End of picture text --> _Source:_ See Table 1 (ECA countries) and Sak and Raponi (2002) _Unclear Responsibility for Common Property and Shadow Public Rental Sector_ . The tenant privatization wave of the 1990s left a large number of apartment buildings in the region with unclear ownership and responsibility with regard to common areas and surrounding land. In many of these M-F buildings, especially those in which public rental units remain, local governments or public utility companies continue to perform the full range of housing management and maintenance services. As a rule, because of a combination of historical bias against maintenance / repairs user fees during transition and the incentive problems of public providers, these services are delivered at insufficient payment levels. At the same time, the public providers cannot claim the benefits that legal ownership of the common areas or surrounding grounds would convey, including the option to evict defaulting users.

Persistent under-pricing of maintenance and repair services, lack of competition among providers, and the common investment disincentives for privatized apartment owners created by often unclear ownership of common areas and surrounding grounds, have all resulted in continued deferred maintenance and increasing physical deterioration and capital depreciation of much of the M-F apartment buildings in the region.<sup>26</sup> The ownership structure of both individual apartment units and common areas and land in the apartment sector seems to be as follows (Table 4).

> 26  See World Bank (2004a).

19