rafmacalaba/datause-encoder-data huggingface dataset:
elements. This enhances our confidence that such adjustments are not strictly necessary, but hope to include them as a robustness test in the future. For example, if we want a regional average we can weight countries to construct a reliable ‘regional average’ that is not distorted by artificially weighting each country equally since the true region consists of larger countries.

# **7. Conclusion**

To meaningfully compare learning across countries, we need a measure of learning that is comparable. The growth of international standardized achievement tests, which are carefully constructed, psychometrically tested, standardized assessments implemented globally is a huge step in this direction. However, the countries that participate in these tests are often high and middle-income countries. This limits our ability to track, compare, or understand education patterns in developing countries – the countries that often have the most to gain from education.

One option is to wait to make comparisons for low-income countries to participate in international assessments. Although this is a worthy aspiration and we hope it happens, it will take a long time. Moreover, this approach would render a rich array of retrospective data null, limiting longitudinal and panel data analysis. Alternatively, we can use a rigorous approach – albeit with caveats – to harmonize available learning data across different types of international and regional assessments. This is the approach we take in this paper, creating a _Harmonized Learning Outcomes_ database which builds on previous work.

Recently, UNESCO endorsed the harmonization of learning outcomes as a useful approach as part of the Global Alliance to Monitor Learning (GAML) (UIS, 2017). This database is part of a World Bank and UNESCO Institute for Statistics (UIS) partnership to advance this effort. In the long term, the ambition and ideal is to deploy a worldwide proficiency assessment for numeracy and literacy. Until then, and to enable rich longitudinal panel data analysis, harmonization of existing learning assessments provides the next best alternative to compare education quality on a global scale. Moreover, as more countries join international and regional assessments, and do so for longer, the accuracy and robustness of the harmonization exercise will improve.

The crux of our harmonization methodology hinges on construction of an index that enables us to include developing countries, and more countries overall. We use _doubloon_ countries that participate in both regional and international assessments as an anchor. This enables inclusion of regional assessments from Latin America and Sub-Saharan Africa. Because of this relatively 43
16 Initial inspection of Tables 1 and 2 as well as Fgj (allows easily to detect a positive link between aggregate inflation and relative price variability. Fig. depicts three different measures of standard deviation'<sup>8</sup> used in the analysis along with the aggregate inflation. Because of disproportionately big values of these statistics for 4 initial observations (see table 1 , they have been dropped from the sample.'<sup>9</sup> This enables to observe the relationship in a much more detailed scale.

Figure 2: INFLATION AND VARIOUS MEASURES OF VARIANCE <!-- Start of picture text --> 30A' T -p5<br>I ~~~ 9<br>Il, 1 g= inflation - left scale 4<br>-2-- sd - right scale 3.5<br>20% / o- wsd - right scale<br>15 n<br>S10<br>- CO 0') -r CO .- ~~ 02 - CO 02 ~~~~- - 02 CO<br>multiplied by 500 and sd I!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ by 15 to fit the tsdgureghtsscale .<br>COD - CO~ CO' - CO C CO -- CO 02 C02 cO'CO 02 2 C CO CO C\O CO CO, C\2O' r - C3 2 ~,c " rc<br>CO 0O OC OCOC OC CO CO CO C CO CO CO<br>source: author's calculations using GUS data<br><!-- End of picture text -->

> **18** Standard deviation was used instead of variance because of its bigger correlation with inflation (see Table 3). _Wsd_ has been multiplied by **500** and _sd by_ **15** to **fit** the figure's scale.

19For initial observations (1989-1990) inflation and variance move very closely together
**Table 6** : Spatial Analysis of FDI inflows

||(1)|(2)|(3)|
|---|---|---|---|
|Regional GDP|0.254<sup>*</sup>|0.252<sup>*</sup>|0.165<sup>*</sup>|
||(1.85)|(1.73)|(0.70)|
|Governance Index|0.559<sup>***</sup>|0.560<sup>***</sup>|0.561<sup>***</sup>|
||(4.40)|(4.32)|(4.33)|
|Unemployment||-0.001|-0.001|
|||(-0.03)|(-0.02)|
|Private Firms|||0.163|
||||(0.47)|
|**_Spatial Terms_**||||
|W.FDI|0.314<sup>***</sup>|0.314<sup>***</sup>|0.315<sup>***</sup>|
||(3.59)|(3.60)|(3.24)|
|Observations|240|240|240|
|Wald test|10.50|10.46|16.50|
|**Direct effects**||||
|Regional GDP|0.259<sup>*</sup>|0.257<sup>*</sup>|0.166|
||(1.86)|(1.74)|(0.47)|
|Governance Index|0.571<sup>***</sup>|0.571<sup>***</sup>|0.572<sup>***</sup>|
||(4.10)|(4.45)|(4.46)|
|Unemployment||-0.001|-0.001|
|||(-0.03)|(-0.0.2)|
|Private Firms|||0.167<br>(0.47)|
|**Indirect effects**||||
|Regional GDP|0.093|0.093|0.229|
||(1.65)|(1.58)|(0.70)|
|Governance Index|0.206<sup>***</sup>|0.206<sup>***</sup>|0.780<sup>***</sup>|
||(2.74)|(2.68)|(5.03)|
|Unemployment||-0.000|-0.001|
|||(-0.03)|(-0.02)|
|Private Firms|||0.227<br>(0.70)|

Notes: Standard errors are in parentheses. *, ** and *** denote significance at the 10%, 5% and 1%, respectively. Binary variables Port and Regional areas are dropped from this fixed effect model.

Overall, it is worth noting that the inclusion of the spatial term does not affect our initial results above. Notice that both binary variables Port and Regional Development areas were dropped from this fixed effect model. A general observation of note is that the direct effect appears to
world (Murty and El Sabh, 1992). The reasons for this disproportional large impact of storm surges on the coast of Bangladesh were reported<sup>16</sup> to be the following:

- The phenomenon of recurvature of tropical cyclones in the Bay of Bengal,

- Shallow continental shelf, especially in the eastern part of Bangladesh<sup>17</sup> ,

- High tidal range<sup>18</sup> ,

- Triangular shape at the head of the Bay of Bengal<sup>19</sup> ,

- Almost sea‐level geography of the Bangladesh coastal land,

- High density of population and coastal protection system.

The Meghna estuarine region is the area where most of the surge amplifications occur. UNDP has identified Bangladesh to be the most vulnerable country in the world to tropical cyclones (UNDP, 2004).

Inundation due to storm surges generated by severe cyclones pose a threat to lives and (their) properties in the coastal region. Historical (time series) records of storm surge height are scarce in Bangladesh. However, existing literature indicates a range of 1.5 to 9.0 meter high storm surges during various severe cyclones. Storm surge heights in excess of 10 m or even more are also not uncommon with an occasional reference in the literature. For example, the Bakerganj cyclone had the greatest reported surge height of 13.6 m in 1876 and another cyclone had the height of 10 m in 1970 (SMRC, 2000).

A surge can be even more devastating if it makes a landfall during high tide. In general, it has been observed that the frequency of a wave (surge plus tide) along Bangladesh coast with a height of about 10 m is approximately once in 20 years, and the frequency of a wave with a height of about 7 m is approximately once in 5 years (MCSP, 1993).

In addition to these exceptional surges, waves caused by wind also occur; the dimensions of which depend on wind speed and direction, water depth, and duration of wind blowing over

> 16 For example, see Ali, 1999 .

> 17 The coast line of Bangladesh is characterized by a wide continental shelf, especially off the eastern part of Bangladesh.  This wide shelf amplifies the storm surges as the tangential sea‐level wind‐ stress field associated with the tropical cyclone pushes the sea water from the deep water side onto the shelf.  Being pushed from the south by wind stress, the water has no place to go but upwards; which is the storm surge.

> 18 Records indicate 7‐8 m high tide in the Sandwip Channel.

19 The triangular shape at the head of the Bay of Bengal helps to funnel the sea water pushed by the wind towards the coast and causes further amplification of the surge.
# **Table 4: Japan: Top 5 Apparel Import Shifts: 1995, 2000, 2005, & 2007-2008**

[Value in $US Million; % Represents Country/Region’s % of Year’s World Value]

|**Country/ Region**|**1995**||**2000**||**2005**||**2007**|**2008**||
|---|---|---|---|---|---|---|---|---|---|
||**Value**|**%**|**Value**|**%**|**Value**|**%**|**Value**|**Value**|**%**|
|**World**|**18,758**||**19,709**||**22,541**||**23,999**|**25,866**||
|China|10,626|56.6|14,713|74.7|18,243|80.9|19,795|21,350|82.8|
|EU-15|2,398|12.8|1,476|7.5|1,556|6.9|1,515|1,457|5.6|
|Vietnam|--||591|3.0|610|2.7|717|865|3.4|
|Thailand|503|2.7|--||--||271|313|1.2|
|Korea|1,847|9.8|951|4.8|436|1.9|258|227|0.9|
|USA|1,096|5.8|468|2.4|296|1.3|--|--||
|**Top 5 Total & %**|**of World**|**Impo**|**rts**|||||||
||**16,469**|**87.8**|**18,200**|**92.3**|**21,141**|**93.8**|**22,555**|**24,213**|**93.9**|

--Indicates country not in Top 5 for the year Source: UN Comtrade, SITC 84, Rev. 3., Imports to Japan

# **C. Characteristics of Top Apparel Exporting Countries**

By the end of 2009, the economic recession that hit the apparel retail markets of all the advanced industrial countries had rippled throughout the supply chain in developing economies as well. A striking trend is that the largest low-cost apparel producers in the developing world, such as China, India, Bangladesh, and Vietnam, have actually managed to increase their export shares in major global markets (see Tables 2, 3 and 4 below). This may reflect a substitution effect of the economic recession, in which the lowest cost suppliers gain market share vis-à-vis more expensive rivals.

China is the clear winner by far in the global apparel export race during the past 15 years. Between 1995 and 2008, China more than doubled its share of global apparel exports from 15.2% to 33.2 %, and it had a fivefold increase in the value of its apparel exports, from $24 billion to $120 billion. Other than the EU-27, which includes intra-European Union trade, the next six apparel exporters combined (Turkey, Bangladesh, India, Vietnam, Indonesia, and Mexico) account for less than half (15.4%) of China’s export total in 2008 (see Table 5).

7
model shows how farmers may be prevented from engaging in exporting activities, and thus from earning higher income, when local marketing costs are too high. We then test this hypothesis using farming data from Uganda (the Uganda National Household Survey). We combine household-level information on income, poverty and exporting activities with village-level measures of availability of local agricultural export markets. These markets include a variety of outlets: standard district markets and road stalls; export intermediaries (truck services and farmgate buyers); and foreign direct investment in the form of coffee or tea plantations.

Our empirical analysis establishes a negative relationship between local availability of export markets and poverty: controlling for all the relevant household and district determinants and taking account of endogeneity issues, we find that the presence of export markets leads to lower poverty in rural Uganda. We explore the exports mechanism behind this link: we find that export markets acts as a facilitator of export agriculture cropping (households may be prevented from entering export cash cropping if trading costs are too high) and that poverty among producers of export crops is lower than poverty among subsistence farmers (major export crops have higher returns than food crops). Overall, we establish that lower export marketing costs induce export crop participation, which raises household income and decreases the likelihood of poverty. This indicates the presence of a poverty-reducing market access effect.

It is not surprising that domestic factors affect poverty and agricultural earnings. The merit of our investigation is the finding of complementarities between those domestic factors and the export opportunities: without domestic factors, the gains from trade may not be realizable but, without exports, some domestic factors may become less relevant for poverty alleviation. In addition, we identify one specific, and potentially important, factor among these complementarities, namely the marketing costs of export crops.<sup>4</sup> Hence, the Uganda case is an instance where the standard prescriptions of education and health policies could be combined with more specific measures to encourage the development of local export markets in a successful attempt towards poverty reduction.

> 4See Collier and Gunning (1999) for a thorough description of several major constraints to development in Africa.

2
The modelled flood zone takes a ring-like shape around the inner city of Kampala. In Inner Kampala, 12% of all jobs are directly located in flood zones with a return period of 50 years. Out of these jobs, a considerable fraction is affected by significant flood depths: In a 50 year flood, 10% of all jobs in Inner Kampala would be affected by flooding with a water depth of more than 15 centimeters. In Kampala, floods with 10-, 50-, or 100-year return periods have relatively similar effects on employment as shown in Figure 9. Generally, most jobs are only affected by relatively shallow depths as evident in the fast decline of the fraction flooded with increasing flood depth.

Figure 9: Fraction of all jobs in Kampala that are directly located in a flood zone and the associated flood depth <!-- Start of picture text --> 14%<br>10-year return period<br>12% 50-year return period<br>100-year return period<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>0 0.15 0.3 0.45 0.6 0.75 0.9 1.05<br>Flooding depth ( x meters or higher)<br>Fraction of jobs located in flood zone<br><!-- End of picture text --> The exposure of jobs to flooding varies significantly across cities. Figure 10 compares the fraction of jobs located in flood zones in Bamako, Dar es Salaam, Kampala, and Kigali. It should be noted that the point estimates for the percentage of jobs affected by a certain flood depend on the defined area of analysis: Assuming that cities are more likely to be located in flood zones close to rivers or the sea, extending the area of analysis could reduce the area of flooding and consequently the relative share of urban jobs located in flood zones. For the sake of consistency, the area of analysis corresponds to the city boundaries defined by OSM.

Valuable comparisons between cities can be drawn from the rate with which exposure increases as flood events become more extreme. In Kampala and Kigali, the share of flood exposed jobs stays relatively flat as flood events intensify. Most firms affected by a catastrophic event (e.g. with a 100-year return period) are also likely to be affected by less intense and more frequent events. In these it is more the flood depth rather than the flood extent that increases for higher intensity events. This could mean the residents and firms in flood zones are sensitized to the risk of flooding due to frequent exposure. On the contrary, in Bamako and Dar es Salaam the extent of flooding increases significantly for higher return periods. Especially in Bamako, an extreme event has a drastically wider reach than lower intensity events. This implies that a majority of people exposed to a low-probability event will not be exposed to more frequent smaller events. As a result, they may be less prepared and have a lower capacity to respond and recover. A likely explanation for these different dynamics lies in the topography of the respective city areas.

15
77 storage improvements to name a few. The precise corrective measures will require detailed analyses of African transport costs and systems at the country or regional level. This study's findings accent the importance of further analysis in this direction.

In the interim, several OECD policy initiatives could help alleviate Africa's international transport problems:

* Technical assistance. International shipping has undergone a major transformation in which procedures for cargo unitization, port operations, and related logistical functions have evolved into highly complex operations requiring a considerable degree of technical expertise. Since most African countries have limited access to such expertise, technical assistance programs (such as those provided by UNCTAD and the World Bank) should be expanded and also extended to related activities (such as customs clearance procedures) that impinge on the efficiency of international transport operations.

* Finance and development. Due to insufficient attention and funds, many African countries' fleets, ports, and connecting inland transport infrastructure have deteriorated, or have become technologically outmoded. OECD countries should sponsor a comprehensive survey and report on sub-Saharan Africa's transport problems along with policy proposals for their alleviation. Given the small size of many African countries, and its influence on cargo volumes and the utilization of efficient transport technologies, further attention should be given to the development of regional ports and required inland infra-structure.

- The situation of land-locked African countries needs special attention given the major problems these countries face in transitting their neighbor's territories. A major effort should attempt to identify the special trade problems of the land-locked African countries and formulate policy suggestions for their reduction.
**Table 5. Means and Percentages for Selected ECD Indicators by Survey Round and** **<u>Treatment Status, Philippines ECD Study</u>**

|||Round 1|||Round 3||
|---|---|---|---|---|---|---|
|Indicator/Region|Program|Non-|Total|Program|Non-|Total|
|||program|||program||
|**1. Cognitive, Social,**|**Motor and**|**Language De**|**velopment**||||
|Gross motor skills|–.197|–.296|–.223|.091|–.329*|–.021|
||[.043]|[.053]|[.034]|[.029]|[.081]|[.029]|
|Fine motor skills|–.237|–.134|–.217|–.491|–.629|–.526|
||[.041]|[.080]|[.034]|[.052]|[.052]|[.040]|
|Expressive|–.255|–.277|–.260|–.012|–.416*|–.116|
|language|||||||
||[.034]|[.055]|[.029]|[.024]|[.061]|[.024]|
|Receptive|–.209|–.133|–.192|–.046|–.444*|–.149|
|language|||||||
||[.040]|[.085]|[.031]|[.050]|[.103]|[.049]|
|Cognitive|–.480|–.322*|–.443|–.653|–.906*|–.720|
||[.031]|[.047]|[.025]|[.042]|[.064]|[.034]|
|Self-help|.003|–.135|–.027|.147|–.038*|.090|
||[.032]|[.112]|[.038]|[.038]|[.068]|[.033]|
|Social-emotional|.108|.034|.084|.087|–.237*|–.002|
||[.046]|[.099]|[.038]|[.051]|[.092]|[.048]|
|**2. Anthropometrics**|||||||
|Height-for-age Z|–1.57|–1.67|–1.59|–1.79|–1.88|–1.81|
||[.036]|[.045]|[.028]|[.037]|[.047]|[.030]|
|Weight-for-height|–0.638|–0.696|–0.647|–0.355|–.526*|–0.399|
|Z|||||||
||[.038]|[.056]|[.031]|[.044]|[.034]|[.033]|
|Percentage stunted|34.9|38.7|35.7|43.4|48.6|44.5|
||[.014]|[.021]|[.011]|[.016]|[.024]|[.013]|
|Percentage wasted|7.4|6.1|7.1|2.4|2.5|2.4|
||[.009]|[.012]|[.007]|[.003]|[.005]|[.003]|
|**3. Worms**|25.8|33.7*|27.5|29.6|33.9|30.4|
||[.018]|[.024]|[.014]|[.021]|[.034]|[.017]|
|**4. Diarrhea (in**<br>**past two weeks)**|10.1|9.9|10.1|2.8|4.3|3.1|
||[.013]|[.015]|[.010]|[.005]|[.009]|[.005]|
|**5. Hemoglobin**|10.9|10.8|10.9|11.3|11.6*|11.4|
|(gms/deciliter)|||||||
||[.055]|[.055]|[.042]|[.059]|[.066]|[.046]|
|Percentage anemic|45.7|48.5|46.3|41|31.0*|38.2|
||[.016]|[.022]|[.013]|[.019]|[.034]|[.015]|

Standard errors are in brackets. *Mean is significantly different between program and non-program children at the .05 level.

11
# Table 9: Financial development or institutions.

The dependent variable is the standard deviation of the rate of growth of real value added for each 4 digit ISIC industry in each country. All regressions included a country and industry specific effect. Liquidity needs is the ratio of total inventories over annual sales for U.S. corporate firms in the same industry between 1980 and 1989. Capital per worker is the average ratio of capital to total number of employees for each sector in the US between 1980 and 1997. Human capital is the measure of the share in total wage bill of workers with some college and college graduated in each industry in the U.S., obtained from Autor et. al. (1999). High technology is a measure of the use of computer technology in each industry in the U.S., also from Autor et. al.(1999). Constraints on the executive is a measure of the level of discretion of the executive power, and was obtained from the Polity IV database. Initial GDP per capita is the log of GDP per capita in PPP adjusted dollars. All coefficients were estimated by 2SLS. A country’s legal origin was used as instrument of its level of financial development. The level of constraints on the executive was instrumented using the degree of settler mortality for former colonies, therefore the sample used in columns (3) to (5) includes only countries that were colonized. Column (1) reproduces the coefficient obtained in the base case <u>(column (5),</u> Table 3) for comparison.

|Variables|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Interaction (liquidity needs|-1.206*|-0.728*|-2.562*|-2.382*|-2.825*|
|X private credit)|(0.227)|(0.332)|(0.898)|(0.868)|(1.127)|
|Interaction (liquidity needs||-0.021||||
|X initial GDP per capita)||(0.110)||||
|Interaction (technology use|||0.115|||
|X constraints on executive)|||(0.064)|||
|Interaction (human capital||||0.005||
|X constraints on executive)||||(0.061)||
|Interaction (capital intensity|||||-0.032|
|X constraints on executive)|||||(0.037)|
|Diferential in volatility||||||
|Percentage points|4.1|2.5|8.8|8.2|9.7|
|Fraction of IQR|59|36|127|118|140|
|Obs.|2301|2301|1316|1316|1332|

Note:*=Significant at 5% confidence. Robust standard errors in parenthesis.

40
difference for bond issuers is statistically significant. In particular, the growth in employees for the median firm that issued bonds was 7.7 percent per year, compared to only 0.06 percent for the median non-issuer.

Issuing and non-issuing firms seem to significantly differ with respect to their liability structure. First of all, issuing firms tend to be more leveraged than non-issuing firms, especially those that issue either bonds or loans. While the leverage of the median non-issuer firm stood at 35.9 percent during our sample period, a firm that issued equity, bonds, or syndicated loans had a leverage of 44.2 percent, 60.4 percent, and 50.3 percent, respectively. Issuers also tend to have a longer-maturity liability structure. The share of long-term debt in total liabilities for the median non-issuer stood at 9.5 percent, whereas equity, bond, and syndicated loan issuers had 14.3 percent, 39.4 percent, and 38.7 percent of long-term debt over liabilities, respectively.

Looking at profitability for issuing and non-issuing firms, the findings are more ambiguous. According to the firm’s ROE results, only bond and loan issuers seem to have significantly higher ratios than non-issuers, while the ROE for equity issuers is lower but insignificant. The ratio of retained earnings to assets show qualitatively similar results, although the estimates are barely significant. Comparisons of ROA, on the other hand, show that all type of issuers have lower returns on their assets than non-issuers.

# **5. How Does Firm Size Evolve for Issuing and Non-issuing Firms?**

In this section we look at how firm dynamics are related to their capital raising activity. In particular, we look at ex ante and ex post differences in the assets, turnover, and the number of employees of issuing firms, compared to a control group of non-issuers. We examine whether firms that issue equity, bonds, or syndicated loans grow faster than non-issuing firms. Moreover, we investigate how the relative performance of both issuers and non-issuers evolve over the whole FSD.

14