rafmacalaba/datause-encoder-data huggingface dataset:
areas and plateaus. The primary sampling units (PSU) are villages. A total of 380,305 rural households were interviewed during the month of June 1992. The survey questionnaire design follows closely that of the DHS. There are four basic survey instruments: (1) a household questionnaire, (2) a questionnaire for women, (3) a questionnaire for children, and (4) a village questionnaire (only for the rural sample).

All women who had given birth since 1976 are interviewed. All births between 1977-92 are recorded with the outcomes, dates, place, and type of birth assistance. For children who died before age 5 at the time of the survey, information on date, place and cause of death are collected. Studies of several DHS show evidence of downward bias in reporting child deaths; i.e., the longer the recall period, the more likely the respondents misreport the case. In order to minimize the possible recall bias on child deaths, we consider live births in the five years preceding the survey, which give a sample of 160,899 births.<sup>4</sup> The sample distribution by province is presented in Table 1 in the appendix.

Since the 1992 CHS followed closely on the heels of the 1990 census, it is worth comparing estimates of province-level child mortality rates across the two data sources. As summarized in Table 1, this comparison is encouraging; it shows that for most provinces (22 out of 29) the mortality estimates from the CHS are roughly in line with those from the census.<sup>5</sup> The CHS collects information on cause of child death based on the mother’s report. Responses are chosen from a set of causes and symptoms questionnaire,<sup>6</sup> rather than conducting a verbal autopsy.<sup>7</sup> Given that about 77% of the rural deaths occurred at home, and the difficulties in ascertaining cause of death even under the best of circumstances, we expect considerable noise in this variable. Nevertheless, ignoring

> 4 The number of child deaths in urban areas (555 over a 5-year period ) is too small to permit a separate survival analysis, hence we  focus on rural areas.

> 5 The seven provinces which have a considerably different estimate of IMR include Hanan, Jilin, Jiansu and Guanxi, Zhejian, Hubei, Guanqi.

> 6 Detailed survey methods are reported in “1992 National Health survey of children: proceedings on survey and research”, published by the China State Statistical Bureau.

> 7 Verbal autopsies (VA) are commonly used in the field of epidemiological research to collect information on causes of death in countries where child deaths often occurred at home without medical attention. It involves the design of a syndrome module and verification from medical personnel. Martha Anker (1997) provides an assessment of the reliability of  VA.

4
**The Textile-Clothing Value Chain in India and Bangladesh: How Appropriate Policies Can Promote (or Inhibit) Trade and Investment**<sup>**1**</sup>

Mahfuz Kabir Surendar Singh Michael J. Ferrantino<sup>2</sup> JEL Classifications: F14, L52, O14 Keywords; apparel, Bangladesh, India, textiles, trade policy, value chains

> 1 This paper is based on a detailed background study titled “ _Mapping Bilateral Value Chains between India and Bangladesh: A Case Study of Textile and Clothing Sector_ ” was sponsored by South Asian Regional Integration Unit and Trade Unit of Research Department of the World Bank. The study report can be made available upon request.

> 2 Mahfuz Kabir is Acting Research Director, Bangladesh Institute of International and Strategic Studies (BIISS), Dhaka, Bangladesh.  Surendar Singh is Fellow, Consumer Unity Trust Society (CUTS International), Jaipur, India. Michael J. Ferrantino is Lead Economist and Global Product Specialist for Trade Policy and Integration, The World Bank, Washington, DC, United States. The authors may be contacted at mahfuz@biiss.org; drsurendarsingh@gmail.com, and mferrantino@worldbank.org
financial condition gets riskier. A country’s capital account openness and domestic credit market development also influence the distance. Debt characteristics, including whether the bond issue is callable and its maturity, matter for both bounded and non-bounded bonds, although the bond securitization and collateralization mainly determines whether and how high the corporates are rated above the sovereign for non-bounded firms. Finally, a Heckman selection estimation confirms the robustness of our main results to sample selection bias in debt issuance.

As emerging markets liberalize their capital accounts, firms from emerging countries have been raising a substantial financing through bond issuances in international markets since 1990s (Gozzi et al., 2015). However, the number of rated firms with access to international markets is still limited for lack of information in assessing prospective debtors’ credit risk. Our results underscore the relative importance of bond-level and firm-level attributes in estimating their prospective bond ratings compared to country-level and global factors.

The paper is structured as follows. Section 2 describes the data and presents some descriptive statistics. Section 3 explains our empirical strategy, and sections 4 and 5 provide empirical results. Section 6 concludes.

# **2.  Data Description**

## **_2.1 Sovereign ratings and sub-sovereign foreign currency bond ratings_**

The sample includes 5,033 long-term foreign currency sub-sovereign bonds rated by the three major international rating agencies (S&P, Moody’s and Fitch Ratings) for 47 EMDEs (see Appendix table 1a for country coverage). 2,185 bonds were issued by non-financial issuers and 2,848 bonds by financial firms. The largest numbers of sub-sovereign bonds by non-financial firms are in the oil & gas, telecommunications, utility & energy and real estate & property sectors (see Appendix 1b).

Bond ratings by the three major international agencies for sub-sovereign foreign currency debt and other bond characteristics were obtained from Dealogic. Sovereign credit ratings were obtained from Bloomberg and Thomson Reuters. Debt in default was excluded from the sample. The conversion from letter to numeric ratings on a 1-21 scale, with higher values indicating 4
Central Africa. Insofar as international trips may face greater regulatory barriers or search frictions, domestic surveys may underestimate the overall rate.

Individual surveys may collect the share of trips which are empty, the share of miles traveled which are empty, or the probability of not finding a backhaul job. I discuss each type of measure below.

**Empty Miles** The most direct way to measure the amount of empty driving is in terms of vehicle-miles (vehicle-kilometers). This information may be recovered from firm surveys and records, either conducted by private industry associations as in Williams and Murray (2020) or by national governments as in Eurostat (2021). Site surveys can infer an estimate of empty vehicle-miles if they collect information about the distance of trips in addition to the loaded/empty status. Similarly, trip audits as used in McKinnon and Ge (2006) that collect distance information can provide empty mileage estimates. Detailed location data from apps may also allow researcher to reconstruct empty miles under some assumptions, as Heilmann (2020) does with Uber Freight data. I present studies of empty miles in Table 4.

**Empty Trips** The next measure of empty driving comes from studies that estimate the number of empty trips. These estimates are most often generated by site surveys, which randomly poll trucks passing through a site (often a checkpoint or a rest stop) and ask whether the truck is loaded or empty. Some trip audits, such as the Danish study used in Abate and Kveiborg (2013) and Abate (2014) or the Irish study used in Central Statistics Office (2019), also measure whether or not a trip was loaded, independent of the distance traveled. I present studies of empty trips in Table 5.

**Backhaul Probability** The third common measure of empty driving estimates the probability that a truck can find a load for a backhaul trip. Typically, these estimates are recovered by directly asking firms about how often they can find backhauls, as in Council (2017) or Osborne et al. (2014). In some cases, a study infers the backhaul direction of a trip based on knowledge of geographic imbalance, as in Wilson (1987). I present studies of backhaul probability in Table 6.

**Aggregation** Several assumptions are needed to convert the three study types into a common measure of empty miles. To convert backhaul estimates into empty miles estimates, I assume that all trips involve a fronthaul and backhaul leg, and the fronthaul is always loaded. Under this assumption, the share of empty trips is half the probability of not finding

6
**Table 4. Impacts of Improved Growth and Quality Exports in China and India, (relative to base, 2020)**

|||We|lfare||Ex|ports|Terms-of-|Trade Effects|
|---|---|---|---|---|---|---|---|---|
||Grow|th|Growth, V<br>& Qua|ariety<br>lity|Growth|Growth, Variety<br>& Quality|Growth|Growth, Variety<br>& Quality|
|Regions|2001 $m|%|2001 $m|%|%|%|2001 $m|2001 $m|
|Australia & N Zealand|2743|0.45|5568|0.91|-0.06|0.72|2652|5240|
|China|1145733|39.9|1253425|43.6|29.41|55.34|-48229|38159|
|Japan|6588|0.16|17276|0.42|2.44|4.80|9186|18946|
|Korea|829|0.11|7451|1.00|3.45|5.83|-957|4646|
|Hong Kong/Taiwan|3811|0.53|12749|1.78|1.94|3.78|4260|13307|
|Indonesia|791|0.27|1822|0.61|0.18|-0.10|723|1907|
|Malaysia|1555|0.87|3636|2.03|0.27|0.02|1570|3698|
|Philippines|-627|-0.57|-994|-0.89|-0.26|-3.19|-559|-583|
|Singapore|-2280|-1.68|-458|-0.34|4.92|6.50|-159|2019|
|Thailand|-639|-0.31|492|0.24|1.63|2.33|-857|312|
|Vietnam|-41|-0.07|166|0.29|-1.10|-2.33|63|468|
|Rest of S E Asia|424|0.41|603|0.58|-2.85|-2.11|382|541|
|India|361740|33.7|394490|36.7|28.89|47.05|-12379|10661|
|Rest of South Asia|-962|-0.35|-159|-0.06|1.60|2.98|-1110|-517|
|Canada|2767|0.32|5182|0.59|-0.91|-1.43|2634|4736|
|USA|124|0.00|20262|0.15|0.67|2.87|479|20671|
|Mexico|535|0.06|1000|0.11|-1.33|-2.37|175|489|
|Argentina and Brazil|1410|0.13|3134|0.28|-0.06|0.45|1072|2570|
|Rest of Latin America|3015|0.36|4703|0.56|-0.48|-0.26|2652|4251|
|EU 25 & EFTA|-4306|-0.04|16893|0.18|-0.14|-0.18|3013|22183|
|Former Soviet Union|9958|1.37|12914|1.77|1.34|2.34|9750|12039|
|M East & North Africa|23780|1.31|29108|1.60|-1.50|-1.50|22592|27568|
|Sub-Saharan Africa|4904|0.96|7676|1.50|-0.24|0.80|4004|6439|
|Rest of the World|-688|-0.34|-500|-0.24|1.46|2.37|-596|-282|
|_Low inc. ctries (LICs)_|_366065_|_17.9_|_402775_|_19.7_|_14.04_|_23.44_|_-9039_|17592|
|_Mid inc. ctries (MICs)_|_1184823_|_13.1_|_1308743_|_14.5_|_10.70_|_20.39_|_-11707_|90130|
|_High income countries_|_10275_|_0.03_|_84923_|_0.28_|_0.79_|_1.73_|_21109_|91749|
|World|**1561163**|**3.8**|**1796437**|**4.3**|**4.4 **|**8.5**|**363**|**199472 **|
|LICs (excl India)|_4325_|_0.46_|_8286_|_0.87_|_-0.07_|_0.77_|_3339_|_6931_|
|MICs (excl China)|_39091_|_0.61_|_55315_|_0.87_|_-0.18_|_-0.16_|_36522_|_51971_|

Source: Authors’ simulations with modified GTAP model; see details in text..

21
- 15 _-_

the secondary education level as a main factor of the low participation of female students in science and technology related fields at the tertiary level. Moreover, girls' tendency to have lower completion rates at the primary and secondary levels was noted as a major obstacle to women's access to higher education in projects in China, Malawi, Mali, and Niger. To broaden this pool, student places for girls in teacher training program and dormitories were reserved in Malawi and Niger, respectively.

4) Lack of Dorm Facilities: In general, higher education institutions in most developing countries are established in relatively well developed urban areas. Most people, especially from rural areas, have limited a;ess to higher education because of the distance to higher education institutions. The distance problem is a particularly critical obstacle for girls since parents often are concerned about their daughters' transportation to distant institutions and about their living alone. Provision of culturally appropriate boarding facilities is often used as an approach to reduce the distance problem to increase female participation in tertiary education, and lack of boarding facilities is identified as a barrier to female participation. However, only three projects identified the lack of culturally appropriate boarding facilities as a constraint on female participation in tertiary education. A Bank-supported project in Yemen provided culturally appropriate hostel facilities on the campuses of the Faculties of Education which increased female enrollment by about 50%. In India there is also evidence that the low women's demand for higher education is due partly to the lack of separate dorm facilities for women.

Interventions without Analysis: One interesting trend observable across all regions throughout both decades is the tendency to introduce gender-specific interventions without proper analysis of the gender barriers (see Appendix Table 1). For ir stance, when enrollment rates for women are relatively low, often it is believed that simple expansion of school places and/or provision of boarding facilities for female students will increase the enrollments of women in higher education. As a recent Bank study on secondary vocational education found, the results of such an assumption have been often disappointing because the low participation rate of women in higher education may be due to factors other than lack of space or accommodation. Failure to recognize these factors may prohibit the project from realizing the objectives set at appraisal
<!-- Start of picture text --> a) Correlation of RISE Renewable Energy and Perceived Grid Readiness from Survey Data<br><!-- End of picture text --> <!-- Start of picture text --> 90<br>o 80 @ Ghana South Africa<br>8<br>nw 7/0<br>> R? = 0.1268<br>><br>@ 60<br>Ww » , 8 eo. @ Kenya<br>o Cote d'Ivoire ‘Malawi 8 Uganda -_-"7 -<br>we}FA© 50 Cameroon; Ethiopia fwand\ Zambia - ~- -~-<br>5 Zimbabwe @ a <7”<br>a& 40 NigerDRCOO BiersTogo (sone«= _ Le -- Tanzania/<br>o 3 [Burundi » §— Fritrea @ Nigeria<br>ty Soc<br>MO r) Angola” CON8° \iozambique<br>a 20 liberia ChagudanGuinea @ Madagascar @ Mali<br>Mauritania<br>10 Central African Republic<br>@ South Sudan<br>0 @ Somalia<br>1.0 0.5 00 05 10 15 2.0 25 30 35 40 45 50 5.5 6.0<br><!-- End of picture text --> <!-- Start of picture text --> Perceived Grid Readiness (Generation, Distribution, Transmission) (Net Positive Score)<br><!-- End of picture text --> b) Correlation of RISE Off-Grid and Perceived Off-Grid Readiness from Survey Data <!-- Start of picture text --> 90<br>80 Ethiopia<br>South Africa Ne e<br>o $ ; @ Kenya @ Uganda R2 =0.11<br>fa Rwanda C<br>re) 70 ameroon » --<br>3 Céte d'Ivoire@ @ Ghana @ Togo Tanzania a ”<br>o ; @ Benin e oo -<br>ao 60 Zimbabwe @ ; e Zambia, -<br>o Burkina Faso =o<br>9 Niger —<br>o Sudan e~ ”<br><t 50 Angola “wo<br>a oe \ @ Senegal<br>° @ Malawie TM Guinea<br>ui 40 nn as a un Nigeria<br>s -” A DRC Mauritania @ Mali<br>o 30 Sierra Leone<br>N Eritrea<br>Ww 8 Mozambique<br>o=a 20 LiberiaCongo @“Mada gascar<br>e<br>10 Chad @<br>South Sudan Central African Republic<br>@ Somalia<br>0<br>1.0 0.5 00 05 10 15 20 25 30 35 40 45 50 5.5 6.0<br>Perceived Off- and Mini-grid Readiness (Net<br>Positive Score)<br><!-- End of picture text -->
# **Figure 6b. Estimated Shifts in Fuel and Hydrous Ethanol Demand**

<!-- Start of picture text --> Fuel<br>$/GEEL<br>P'F<br>c<br>PF<br>DF D'F<br>CF C'F GEELs of fuel<br>Hydrous ethanol<br>$/GEEL<br>P'<br>E100<br>d<br>P<br>E100<br>D'H<br>DH<br>C'H CH GEELs of hydrous<br>ethanol<br><!-- End of picture text --> 16
|**Table OA6. Characteristics of eligible households, matc**|**hed sample**|Lottery|||No Lotter|y||P-value|of t-test||
|---|---|---|---|---|---|---|---|---|---|---|
|||Beneficiar|<br>Non-benef||Beneficiar|Non-benef|||||
||Total|y<br>(B)|<br>.<br>(NB)|Total|y<br>(B)|.<br>(NB)|(2) = (3)|(5) = (6)|(2) = (5)|(3) = (6)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Head of household is male (0/1)|0.663|0.649|0.677|0.642|0.640|0.646|_0.686_|_0.945_|_0.888_|_0.657_|
|Age of household head|49.4|49.5|49.3|45.5|46.3|43.9|_0.916_|_0.271_|_0.287_|_0.019_|
|Head of household is married (0/1)|0.813|0.784|0.844|0.825|0.809|0.854|_0.262_|_0.454_|_0.715_|_0.869_|
|Head of household can read and write (0/1)|0.751|0.722|0.781|0.832|0.820|0.854|_0.433_|_0.752_|_0.145_|_0.441_|
|Raven's index (0/3)|0.917|0.887|0.948|1.06|1.09|1.000|_0.622_|_0.693_|_0.180_|_0.795_|
|Related to government officials or influentials (0/1)|0.155|0.175|0.135|0.146|0.124|0.188|_0.485_|_0.440_|_0.397_|_0.468_|
|Socializes with government officials or influentials (0/1)|0.155|0.175|0.135|0.146|0.124|0.188|_0.485_|_0.440_|_0.397_|_0.468_|
|House has concrete floor (0/1)|0.539|0.526|0.552|0.504|0.494|0.521|_0.741_|_0.774_|_0.729_|_0.776_|
|Food insecurity PCA index|-0.291|-0.115|-0.468|-0.298|-0.322|-0.254|_0.215_|_0.875_|_0.493_|_0.560_|
|Has cultivable land larger than 1 Ha (0/1)|0.389|0.392|0.385|0.445|0.427|0.479|_0.932_|_0.526_|_0.632_|_0.284_|
|Maize yield (Kgs/acre) in 2009/10|676.1|733.1|619.0|745.8|750.2|737.1|_0.254_|_0.932_|_0.899_|_0.345_|
|Used agricultural inputs in 2009/10 (0/1)|0.658|0.629|0.688|0.664|0.640|0.708|_0.353_|_0.494_|_0.903_|_0.834_|
|Number of Observations|193|97|96|137|89|48|193|137|186|144|
|P-value ofjoint F-test that all coefficients are 0|||||||_0.425_|_0.824_|_0.605_|_0.679_|

Note: This table shows descriptive statistics using individual level data from the baseline household survey. The sample is restricted to households in non-lottery who are similar in observable characteristics based on a nearest neighbor propensity score matching algorithm. In lottery villages we also restrict the sample to households who share the propensity score distribution support of households in no lottery villages. Column 1 reports averages in lottery villages. Columns 2 and 3 report averages for beneficiaries and non-beneficiaries of the NAIVS voucher program in 2010/11 in lottery villages. We report analogous figures for non-lottery villages in Columns 4-6. In Columns 7-10 we report the p-values of the mean comparison between different groups. P-value of joint F-test corresponds to the p-value of a joint F-test that all coefficients of a regression of beneficiary status (Columns 7 and 8) or lottery dummy (Column 9 and 10) against characteristics are zero. All p-values are computed using the t-asymptotic wild cluster bootstrap procedure described in Cameron et al. (2008). Clustering was done at the village level. Range of values for categorical variables are given in parenthesis. (0/1) indicates dummy variables, equal to one if the response is yes. See Appendix Table 1 for definition of variables.
<!-- Start of picture text --> Policy Research<br>| ~~~Trade Policy<br>WPS 1096<br><!-- End of picture text -->

This paper-a product of the Trade Policy Division, Policy Research Department-was prepared for the World Bank research project, Industrial Competition, Productive Efficiency, and Their Relations to Trade Regimes (RPO 674-46). Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Dawn Ballantyne, room N 10-023, extension 37947 (February 1993, 39 pages).

The economic literature now accepts theoretical arguments that liberal, outward-oriented trade policy is better than restrictive, inward-oriented policies. Traditionally such arguments for the gains from trade have rested on the concept of allocative efficiency. But a new argument for liberal trade has emerged: increased technical efficiency or productivity. The best-known attempts to link trade policy and productivity are b r ed on "X-efficiency," economies of scale, capacity use, increased competition, and technological catch-up.

the endogeneity of factor inputs or because managers have some knowledge about the noise in the production function.

Haddad then estimated the effect of various trade and market-structure variables on the level of TFP, as well as on the deviation of firm TFP from the efficiency frontier. The results are not very sensitive to the different measures of TFP and show that trade openness has a significant positive effect on firm productivity through: i Outward orientation from export promotion.

Haddad estimates total factor productivity (TFP) at the firm level using panel data from the Moroccan industrial census in a productionfunction framework during Morocco's period of trade liberalization (1984-89). Haddad corr.cted for several problems that usually bias the e.,mate of productivity. The use of panel data allowed Haddad to take into account the heterogeneity across firms. These firm-specific effects were tested for randomness. Differences between large firms and small firms were checked. She also corrected for errors in measuring capital stock, so common in data from developing countries, and for simultaneity bias because of

* Import liberalization.

- More direct foreign investment.

By splitting the sample into protected and unprotected sectors, Haddad showed lower productivity in protected sectors.

The results are clear. Trade liberalization in M3rocco improved productivity in manufacturing firns, so they could exploit their comparative advantage and compete better with foreign firms.

ThePolicy Research Working PaperSeries disseminates the fndings of work under way in the Bank. Anobjectiveof the series is to **get** these fndings out quickly, even if presentations are less than fully polished. The findings, interpretations. and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
<!-- Start of picture text --> 7*<br>Uniform is more efficient Pe*<br>e®e Proportional is more efficient rae<br>6} ‘ ri<br>pig“<br>87<br>Fa© 5 'i ¢ Pa<br>ba ' fe<br>S ' : <<br>j= ! a<br>_51' .a<br>°°|'d . 3 ‘wu’ ra<br>ray&ts» id<br>raoO fetely 1 } e@’<br>oe 2 ‘ ’ a °<br>=| tO mad<br>o al Pp?<br>5} LE%<br>[+e] ty =<br>eS ee ee<br>cia|<br>et<br>'<br>Pol # 1<br>00 1 2 3 4 5 6 7<br>Benefit-cost fatio of proportional transfers<br><!-- End of picture text -->