rafmacalaba/datause-encoder-data huggingface dataset:
|Organization|Current Function|Major Deficiencies/Problems|
|---|---|---|
|R&H Department|*<br>Construction and<br>maintenance of primary<br>and secondary roads.|*<br>Road networks have<br>intervened with water<br>courses and affected<br>hydrological regime.<br>* Little or no coordination<br>with BWDB and other<br>water sector agencies.|
|PHE Department.|*<br>Rural and urban water<br>supply and sanitation.|*<br>Little or no coordination<br>with BWDB and other<br>water sector agencies.<br>.<br>Inadequate enforcement<br>of water regulations.|
|DAE|*Information<br>dissemination on<br>agricultural technology<br>including water and land-|*Little or no coordination<br>with BWDB and other<br>water sector agencies.|
||use.||
|BADC|*<br>Operation of low lift<br>pumps and tubewells.<br>* Harnessing of hill<br>streams.<br>* Salinity control.<br>Distribution of water for<br>irrigation.|*<br>Little or no coordination<br>with other water sector<br>agencies.<br>.<br>Inadequate responsibility<br>for enforcement of water<br>regulations.|
|BIWTA|*River conservancy work,<br>including river training<br>for navigational purpose.<br>* Disseminating<br>navigational and<br>meteorological<br>information, including<br>river charts.<br>* Hydrographic survey.<br>.<br>Programming for<br>dredging and revival of<br>dead or dying rivers,<br>channels, canals, etc.<br>.<br>Develop, maintain and<br>operate inland river ports.<br>Develop rural water<br>transport.|*<br>Inadequate coordination<br>with other water sector<br>agencies.<br>*<br>Inadequate responsibility<br>for enforcement of water<br>regulations.|

40
2004; Hummels et al., 2009; Lugovskyy and Skiba, 2015). In addition, the Maritime Transport Costs (MTC) database, prepared by OECD, combines the above-mentioned data on freight expenditures with the COMTRADE dataset and other data sources to provide a comprehensive transport costs database for 43 importing countries from 218 countries at 6-digit HS product level from 1991 to 2007 (Korinek, 2011).

Another useful dataset comes from the U.S. Army Corps of Engineers(ACE). It focuses on the U.S. waterborne transport and includes freight expenditures at HS4-product level and U.S. ports of loading and unloading. These data have been used to study the effect of policy on maritime transport costs (Fink et al., 2002) and to estimate port efficiency and its impact on trade flows (Blonigen and Wilson, 2008, 2018; Clark et al., 2004).

More recently, researchers can obtain customs data from some countries with freight expenditures at the transaction level. For example, Panjiva, a part of S&P Global, provides these data for Chilean and Colombian imports from 2009 onward. These data also contain trade values, quantities, mode of transportation, port of loading and unloading, shipment weight and volume, the identity of the importing firm, information on whether the importing firm or exporting firm is responsible for arranging transportation, and the identity of the carrier or logistic firm arranging transportation. The data on freight expenditures collected from custom declarations or bills of ladings is a direct measurement of transport cost. Therefore, these datasets represent the best sources of data to study the size and nature of maritime transport costs. However, they are available only for a small number of countries.

Panjiva also provides the US import bills of lading. These data scope out a large portion of a shipment’s journey; a typical observation includes where a shipment originated, ports of lading and unlading, vessel-specific IMO Number, where the shipment is ultimately sent within the US (shipment destination), and some product characteristics (e.g., shipment HS8 code, TEU volume, and weight). Unfortunately, Panjiva’s US Imports lack _direct_ measures of freight cost, and the value of the shipments are reported for a striking minority of observations.

Another useful data supplier is Clarksons Research—the world’s leading shipbroker. Its Shipping 19
|48|Paper and paperboard|90.7|144.2|9.3|-11.9|11.5|3.2|10.6|2.4|
|---|---|---|---|---|---|---|---|---|---|
|49|<br>Printed books and newspapers|75.6|138.3|23.3|13.7|8.2|4.3|12.5|8.0|
|50|<br>Silk and waste silk|169.8|0.0|2.2|-1.7|.|.|2.2|-1.3|
|52|Fabrics of cotton|3,492.4|9,790.9|25.3|15.2|7.5|2.1|12.2|6.6|
|54|Flax and ramie|7.7|261.7|6.1|-4.8|4.2|0.3|4.2|-0.9|
|_55_|Cotton|73.9|329.1|33.3|27.8|5.2|1.2|10.4|5.8|
|56|Discontinuous man-made fibers|1.1|2,686.6|10.1|-58.3|19.2|10.1|18.5|9.3|
|57|Other textile materials|62.6|22.2|25.5|11.2|3.0|-1.2|17.1|9.8|
|<br>61|<br>Articles of apparel and clothing|3,926.9|78,761.1|16.3|5.2|5.7|2.1|6.2|0.6|
|<br>62|<br>Other textile articles|<br>32,214.4|<br>154,885.3|19.8|4.7|5.0|1.3|7.5|1.8|
|<br>63|Old clothing and other textiles|<br>161.8|<br>1,375.8|46.9|30.9|4.7|0.5|9.1|4.0|
|<br>64|<br>Footwear|263.3|<br>2,834.1|15.9|0.0|3.2|-0.7|4.3|-0.6|
|65|Headgear and parts|440.5|<br>369.5|10.7|-2.0|6.8|_-0.5_|8.8|-1.3|
|<br>66|<br>Accessories like umbrellas|0.0|55.2|.||7.1|-0.7|7.1|_-0.5_|
|67|Skins of birds for wigs or beards|27.6|982.1|10.7|2.8|4.1|-2.1|4.3|-1.8|
|<br>68|<br>Articles of stone or plaster|247.1|1,679.6|45.8|29.9|13.4|_0.5_|17.5|_5.1_|
|69|<br>Ceramic products|64.2|<br>571.8|28.6|15.3|16.3|7.4|17.1|8.1|
|<br>70|<br>Glass and glassware|37.7|2,568.8|34.6|27.6|7.4|1.9|7.7|1.5|
|71|<br>Pearls and precious stones|219,152.2|<br>648.2|0.5|0.3|6.3|0.2|0.5|0.3|
|72|<br>Ferrous products|0.0|23,137.7|.|.|4.5|-5.8|4.5|-5.8|
|<br>73|<br>Iron and steel|20.7|<br>548.8|11.5|-10.7|7.2|0.5|7.3|-0.2|
|74|Copper and articles|128.9|30,000.9|14.2|2.7|3.9|1.3|4.0|1.0|
|<br>75|<br>Nickel and articles|0.0|<br>8,489.4|.|.|3.1|1.1|2.9|0.9|
|<br>76|Aluminum and articles|0.0|<br>791.5|.|.|4.7|0.2|5.3|_0.5_|
|78|Lead and articles|0.0|447.6|.|.|8.1|2.4|8.1|-0.3|
|79|Zinc and articles|0.0|988.1|.|.|0.2|_-5.1_|0.2|-4.6|
|81|Other base metal products|71.4|45,864.5|14.0|11.0|0.8|0.1|0.6|-0.1|
|<br>82|<br>Tools and cutlery|25.7|<br>166.6|4.2|-2.6|4.6|0.5|4.5|-0.1|
|<br>83|<br>Articlesofbasemetal|192.1|285.1|26.5|12.6|13.1|6.4|17.4|11.0|
|<br>84|Boilers and machinery appliances|3,348.8|1,029.9|2.5|-0.2|3.2|0.3|2.4|0.1|
|85|<br>Electrical mnachinery|<br>4,490.2|3,104.2|3.2|0.5|2.2|0.2|2.4|-0.7|
|87|<br>Vehicles other than railways|3.6|1,060.8|14.1|-8.5|1.9|-2.1|1.8|-2.7|
|90|<br>Scientific instruments|5877.8|<br>437.8|6.0|-0.2|1.1|-1.1|5.6|1.8|
|<br>91|<br>Clocks and watches|,<br>85.3|7.1|4.2|-5.2|23.7|15.4|6.6|3.0|
|<br>92|<br>Musical instruments|25.6|151.5|42.1|26.1|17.4|12.9|20.2|15.1|
|<br>94|<br>Fumiture and parts|_50.5_|_2,555.0_|87.4|70.9|8.5|-1.2|10.8|1.0|
|95|<br>Toys and games|520.1|<br>912.9|11.0|-14.3|8.4|1.1|9.3|1.7|
|<br>96|<br>Miscellaneous manufactures|302.4|921.2|18.2|6.3|3.4|-0.6|7.0|1.6|
||||||||||63|
|97|Works of art|2,737.3|129.3|8.3|7.1|5.3|3.7|7.7|.|
|<br>|<br>|<br>161198|59090|37|26|85|53|38|24|
|98|Special classification|,.|,.|.|.|.|.|.|.|

'.0
There are perhaps too many issues regarding the sustainability of anti-drug policies in producer countries to be discussed in this chapter. However it is worth mentioning a few. On one hand, there is the question of who should bear the costs of alternative livelihood programs and eradication activities? Producer or consumer countries? While governments in the producer countries face internal pressure from farmers who claim, perhaps sincerely, that their only way of subsistence is the cultivation of coca<sup>41</sup> , they also face external pressure from the consumer countries to fight against the production of illegal drugs (in the form, for instance, of the threat of being labeled a “narco-state” by the international community, or of not being “certified” by the United States’ State Department each year). But, besides being an illegal activity, cocaine production and trafficking have had direct links with terrorist and insurgent activities, especially in the case of Colombia. Most probably, if producer countries stop fighting the drug trade and consumer countries do not penalize them for doing this, the price of cocaine would drop dramatically and the drug business would no longer be a source of financial resources for illegal armed groups in producer countries. There is enough evidence of the involvement of guerrilla and paramilitary groups in illegal drug production and trafficking to finance their war against each other and against the Colombian state.<sup>42</sup> The funds to fight against the production of illegal drugs are limited and many times the governments in producer countries have to sacrifice other, perhaps more productive, investments in order to finance the war against drugs. Also, funds provided by consumer countries (mostly developed economies) are often earmarked to be spent on predetermined activities, which leave little or no room for governments to

> 41 See CIP (2004).

> 42 See, among others, Rangel (2000), Rabasa and Chalk (2001), Thoumi (2003), Bottía (2003), and Diaz and Sanchez (2004).

37
# _<u>6.2. Survey-to-survey imputation</u>_

We test the sensitivity of the survey-to-survey imputations to different specifications of the regression model used to impute consumption into the different rounds of the GHS. These additional checks aim to address possible issues with seasonality in the non-monetary indicators used in the consumption model, such as (1) the share of household heads employed in non-farm activities and in wage-employment and (2) consumption dummies for imported rice, beef, and fresh fish. Employment and consumption of specific items might vary significantly during the year. Since the GHS is only collected during the postplanting and post-harvest seasons, the value of these indicators might be different from the average value of the same indicators collected over a 12-month period (as in the 2018/19 NLSS). For example, if a shock on imports were to hit the country during months not covered by the GHS, this could impact the comparability of indicators measuring consumption of imported rice.

One way to check if this is the case is to compare the summary statistics of the non-monetary indicators in the post-planting and post-harvesting visit of the GHS for the same survey year. Table _13_ shows the average value of each indicator for each visit of the 2018/19 GHS. We find that, while many of the variables included in the regression model show little seasonal difference, others show significant differences between the two periods. The largest differences are registered for variables measuring household head’s type of employment – such as employment in non-farming activities decreases between the post-planting and post-harvesting visit – and the consumption frequency dummies.

Fully testing the sensitivity of the survey-to-survey imputation approach therefore relies on excluding these potentially-seasonal variables from the estimation of the consumption model and seeing how the results change. The alternative models exclude wage-employment (Model 1); wage-employment and a dummy variable for there being no toilet facility in the household (Model 2); wage-employment, no toilet facility, and the imported rice consumption dummy (Model 3); non-farm employment (Model 4); and all employment and food consumption dummy variables (Model 5). The estimates change very little when applying these different models. Table _14_ shows the results for each wave of the GHS. The imputed estimates for each wave remain stable when removing different sets of variables and estimates remain within 2 percentage points of the main results.

Lastly, we test the robustness of our results to different distributional assumptions for the household consumption vector. The survey-to-survey imputations presented so far assume that household consumption is lognormally distributed. To test whether this is a valid assumption, we apply zeroskewedness and Box-Cox transformations to the original 2018/19 NLSS consumption vector and to the imputations into the 2018/19 GHS. To test whether this improves the imputations – that is, if the imputed 20
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**Figure 8. Growth drove poverty changes in 1999-2004, lowering its contribution in the second period (poverty line of $2.5/day)**

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**Therefore, on the same note, the incidence of economic growth was pro-poor during the period.** Consumption growth was high in Moldova, particularly in the first half of the 2000s, and benefitted the poor and less well-off. In both periods under study, consumption for those at the bottom of the consumption distribution was significantly higher than the mean. The negative slope of the annual growth incidence curves for both periods confirms that the poor benefited disproportionately more from economic growth (Figure 9). Similar patterns are observed for urban and rural areas, although with slightly higher growth in urban areas for those at the bottom of the distribution (see Annex Figure A1).

**The incidence of growth between 2006 and 2011 contributed not only to poverty reduction, but also to boosting shared prosperity and falling inequality** . Between 2006 and 2011, consumption growth of the bottom 40 percent in Moldova was high at 5.7 percent, and higher than mean consumption growth at 2.9 percent (Figure 10). Inequality, measured by the Gini coefficient, declined in both periods, and in both urban and rural areas (Figure 11). The Gini coefficient fell from 0.42 in 1999 to 0.33 in 2004, and from 0.33 to 0.28 between 2006 and 2011. Other measures of consumption inequality, including consumption ratios at various percentiles of the distribution, are consistent with a story of falling inequality.

9
possible to do more sophisticated modeling of fiscal outcomes based on modified versions of the World Bank’s existing macroeconomic models for the six countries.

**The simple analysis suggests that in some countries, minimal compliance with the overalldeficit rule will not bring debt down to a prudent level by 2025.** With the simplifying assumption that all changes in debt go through the deficit, we can estimate the constant overall balance needed to achieve a given debt level if we specify the future growth rate of nominal GDP.<sup>11</sup> Table 5 shows estimates of the balances that would be needed to achieve debt levels of 30, 45, and 60 percent of GDP by the year 2025 with nominal growth of 5 percent a year. It should be noted that this simplified and mechanical exercise is only for illustrative purposes, and the results should be treated bearing in mind this caveat. Some insights are worth presenting:

- Albania would need to run a surplus of 0.8 percent of GDP every year to achieve its debt target of 45 percent of GDP by 2025—much more than is required by the deficit rule.

- Serbia, which starts with somewhat less debt, could reach debt of 45 percent of GDP, and thus compliance with its debt rule, by running a constant deficit of 0.9 percent of GDP— a slightly tighter target than the average deficit its formula-based rule aims for.

- Montenegro would need to run overall deficits that are much smaller than the 3 percent of GDP permitted by its rule to comply with its 60 percent debt rule by 2025.

- Kosovo and Bosnia and Herzegovina are in a much better position because of their lower starting levels of debt. And while the table suggests that these two countries might appear to have room to grow their deficits, they should carefully consider the fiscal sustainability implications of higher debt levels.

**Table 5. Overall Balances Needed to Achieve Selected Debt Levels by 2025** Percent of GDP

||**30%**|**45%**|**60%**|
|---|---|---|---|
|Albania|3.3|0.8|-1.6|
|Bosnia and Herzegovina|-0.7|-3.1|-5.6|
|Kosovo|-2.9|-5.3|-7.7|
|North Macedonia|1.3|-1.2|-3.6|
|Montenegro|3.9|1.5|-1.0|
|Serbia|1.5|-0.9|-3.4|

11 It is more common to analyze the relationship between debt and the primary balance. We have analyzed the relationship between debt and the overall balance because, as Table 1 shows, overall-balance rules are more common than primary-balance rules in the region, as indeed they are elsewhere. Specifically, we apply equation 19 in Escolano (2010), which, rearranged and with a slight change in notation to avoid confusion with the symbols used in the current paper, states that the average overall balance, , needed to bring debt from its current level, 0, to a given level, , in years with a nominal growth rate of GDP of is given by bb<sup>�</sup> DD NN DD NN =<sup>1</sup> 0γγ ~~�~~<sup>−γγ</sup> � NN where is the average debt over bb<sup>�</sup> , which we approximate by assuming that debt declines linearly. NNDD<sup>�</sup> + DD −DD NN 1 + γγ DD<sup>�</sup> tt= 0, . . . NN−1 21
payment dates, the implementing agency collected attendance records for all the conditional students in the program to make sure that they were complying with the program requirements and attending school.

The cash transfers took place monthly and at each meeting some basic information was collected for each sample respondent, such as who was picking up the money (girl, guardian, or proxy), how far they had to travel, etc. As part of the transfer program, monthly school attendance of all the conditional cash transfer recipients was checked and payment for the following month was withheld for any student whose attendance was below 75% of the number of days school was in session for the previous month. However, no one was ever kicked out of the program, i.e. cash transfer payments were independent of each other across months.

# _3.3. Household surveys_

The annual household survey consists of a multi-topic questionnaire administered to the households in which the selected sample respondents reside. The survey consists of two parts: one that is administered to the head of the household and another that is administered to the core respondent, i.e. the sampled girl from our target population. The former collects information on the household roster, dwelling characteristics, household assets and durables, shocks and consumption. The core respondent survey provides information about her family background, her education and labor market participation, her health, her dating patterns, sexual behavior, marital expectations, knowledge of HIV/AIDS, her social networks, as well as her own consumption of girl-specific goods (such as soaps, mobile phone airtime, clothing, braids, sodas and alcoholic drinks, etc.). Community characteristics are also collected in a separate short community questionnaire. This paper utilizes data from the baseline survey (October 2007February 2008) and follow-up data (October 2008-February 2009) to analyze the one-year impact of the program on self-reported school enrollment and literacy.

13
castes”).<sup>5</sup> The high castes had high ritual status, basic freedoms, and the power to extract forced labor from low castes. The low castes were denied any basic social rights, could not sell their labor and goods in markets, were barred from schools, temples, and courthouses and were relentlessly stigmatized through the practice of Untouchability (Shah _et al._ 2006). The low castes provided forced labor to high caste individuals on demand, and the demand for cheap labor was a factor in the persistence of Untouchability (Bayley 1999).

_2. Heritability of caste and rigidities of caste ranking at the extreme ends of the caste hierarchy._ An individual’s specific caste is determined by the accident of birth, and individual mobility across castes is basically not possible in an individual’s lifetime. In rural India, castes are endogamous. There are strong norms against cross-caste marriages. Marriages between high and low caste persons are particularly harshly punished and sometimes lead to “public lynching of couples or their relatives, murder (of the bride, groom or their relatives), rape, public beatings and other sanctions” (NYU 2007, p. 11). Although caste boundaries and caste rank can change over long time periods, the status of the _specific_ high-ranked castes (Brahmin and Thakur) and low-ranked castes (Chamar and Pasi) from which we draw our experimental subjects goes back millennia (Gupta 2000). The absence of across-caste mobility for these groups rules out selection bias and enables us to study the impact of caste status on individuals’ willingness to punish norm violations. The high castes in our sample thus constitute a meaningful control group for the low castes in our sample.

_3. Overlapping wealth distributions._ Despite the vast difference in social status between high and low castes, there is today considerable individual variation in wealth, consumption, and education within the high and low castes.<sup>6</sup> We have not only poor low-caste subjects, but also many poor high-caste subjects in our sample. By controlling for individual differences in wealth and education, we can rule out that differences in punishment behavior across castes are caused by those individual differences.

> 5 Once called Untouchables, low caste individuals are today called Dalits, a non-pejorative term that connotes oppressed or ground down. They made up 16 percent of the Indian population in the 2001 Census. They are not ethnically distinct from other castes (Gupta 2000).

> 6 With the abolition of the Zamindari (landlord) system in Uttar Pradesh in 1952, many Dalit agricultural laborers acquired ownership rights of the lands that they had been cultivating.

3
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