rafmacalaba/datause-encoder-data huggingface dataset:
|_cons|20.388***|25.156***|28.127***|22.302***|21.423***|28.104***|35.492***|20.758***|11.662***|15.815***|16.095***|13.631***|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||19.95|20.3|17.46|13.09|14.22|14.71|9.99|5.5|4.88|6.2|5.65|4.7|
|R Adj sq.|0.251|0.272|0.273|0.285|0.345|0.365|0.367|0.393|0.047|0.069|0.07|0.076|
|N|10906|10906|10906|10906|4922|4922|4922|4922|5984|5984|5984|5984|

The regression also includes oblast dummies, dummies for different types of schools and evening schools. The full table can be found in excel tables which can be made available upon request. *** means significant at the 1% level, ** at the 5 % level and * at the 10 percent level.T statistics are based on robust standard errors for the OLS specifications.

**_Table 10b – Bottom Students, Ukrainian_**

|||To<br>|tal<br>|||Ur<br>|ban<br>|||R<br>|ural<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|# students|-0.013***|-0.013***|-0.018***|-0.015***|-0.013***|-0.013***|-0.028***|-0.018***|0.003|0.003|-0.014|-0.013|
||-16.1|-15.49|-6.83|-5.84|-15.31|-15.69|-9.56|-6.24|0.8|0.96|-1.45|-1.36|
|(# students)<sup>2</sup>|||0.000***|0.000*|||0.000***|0.000***|||0.000*|0.000*|
||||2.58|1.87|||6.02|3.5|||1.85|1.79|
|Student/Teacher|0.231**|0.231***|0.323|0.382|0.022|0.088|-0.203|-0.074|0.001|-0.001|0.427|0.454|
||2.48|2.58|1.33|1.57|0.19|0.81|-0.62|-0.24|0.01|-0.01|0.85|0.9|
|(Stud./Teacher)<sup>2</sup>|||-0.004|-0.007|||0.014|0.008|||-0.016|-0.017|
||||-0.49|-0.8|||1.31|0.77|||-0.63|-0.7|
|Class Size|0.155***|0.125***|0.673***|0.678***|0.008|-0.035|0.910***|0.862***|0.127**|0.130**|0.267|0.282|
||4.43|3.62|4.55|4.61|0.17|-0.75|3.25|3.2|2.36|2.5|1.22|1.29|
|(Class Size)<sup>2</sup>|||-0.015***|-0.015***|||-0.021***|-0.020***|||-0.004|-0.004|
||||-4.19|-4.31|||-3.5|-3.37|||-0.61|-0.7|
|Urban|-1.132**|-1.933***|-1.845***|-1.166**|||||||||
||-2.27|-3.93|-3.48|-2.18|||||||||
|Gender Comp.||0.190***|0.190***|0.199***||0.178***|0.177***|0.189***||0.197***|0.197***|0.199***|
|||14.56|14.6|15.17||7.42|7.49|8.37||12.71|12.71|12.68|
|Unemployment rate 2009||0.005|0.013|0.065||-0.095|-0.062|0.019||0.134|0.122|0.135|
|||0.04|0.1|0.5||-0.51|-0.33|0.1||0.78|0.71|0.78|
|Average Wage 2009||0.001*|0.001*|0.001**||0.002***|0.002***|0.002***||-0.001|-0.001|-0.001|
|||1.74|1.96|2.28||3|3|3.19||-0.73|-0.67|-0.6|

46
Postponing action is likely to result in diminished cost-effectiveness of future treatment using ACTs.

3. A global subsidy for two or more ACTs is likely to be far more effective in delaying the onset of resistance and saving lives than reliance on a single or even a limited number of combinations. Such an intervention is highly cost effective at averting deaths and disease burden compared to a policy of doing nothing and is always better than subsidizing a single drug combination. The global subsidy program should support locally appropriate combination and treatment strategies, while keeping in mind the importance of transboundary spillovers of actions/decisions undertaken by any single country.

The rationale for considering ACT effectiveness to be a global public good is that resistance may arise in any single country and that use of AMT or even of ACT may serve to speed up the rate at which the resistant strain proliferates worldwide. A worstcase scenario would be one in which poor malaria policymaking results in the emergence of artemisinin resistance in a single country or region. This would compromise treatment in all countries where ACTs are introduced. Therefore, there is need for investment in surveillance so that the global subsidy program can be nimble in detecting emergence of resistance and changing drug combinations.

# **References**

- Aron, J. L. (1988). “Mathematical modelling of immunity of malaria.” _Mathematical Biosciences_ **90** : 385-96.

- Arrow, K. J., C. B. Panosian, et al., Eds. (2004). _Saving lives, buying time: economics of malaria drugs in an age of resistance_ . Board on Global Health. Washington, D.C.: Institute of Medicine.

- Bonhoeffer, S., M. Lipsitch, et al. (1997). “Evaluating treatment protocols to prevent antibiotic resistance.” _Proc. Natl. Acad. Sci._ <u>, USA</u> **94** : 12106-11.

- Cornille-Brogger, R., H. M. Mathews, et al. (1978). “Changing patterns in the humoral immune response to malaria before, during, and after the application of control measures: a longitudinal study in the West African savanna.” _Bull World Health Organ_ **56** (4): 579-600.

14
- 32 -

A3.4 The description given of each barrier is followed by a reference identifying the surveys from which the information came. This reference is followed by the category that it belongs to. Next, barriers are rated by country in terms of their scope, severity, and importance.

# **Import-related barriers**

A3.5 **Customs Import Duty.** Unlike their competitor countries, the European Union in particular, the Maghreb countries have enacted customs tariffs that frequently penalize the use of modern means of transport and their maintenance with taxes on spare parts. (Category THC. Transport Chains: Cereals, MO; Professional Associations, MO; Textiles, MO; Iron/Steel and Machinery, TU).

A3.6 **Freight Conference** Agreements. Some of these agreements, which originated for historical reasons or in bilateral traffic arrangements, set North-South and South-North rate schedules for Algeria, Morocco, and Tunisia. These rates ensure consignors that general cargo and ro/ro services will regularly be available, but they eliminate competition based on rebates. Conference agreements, which group and protect signatory shipowners, prescribe the number and types of vessels in service, the freight rates for different categories of merchandise, and the rebates (prorated according to tonnage) to the consignor appearing on the bill of landing. Agreements of the 40-40-20 type (UNCTAD agreements) cover actual goods traffic, granting national registry vessels exclusive rights to 80% of freight volume and leaving the remaining 20% open to competition.

A3.7 **Handling Costs.** All Algerian and Moroccan ports, as well as the main ports of Tunisia, are operated by government-owned cargo handling companies. This results in: Elimination of the beneficial effects of competition on rate schedules. Low productivity as a consequence of poor company management. Poor company self-financing capacity as a consequence of insufficient budget allocations for maintenance and equipment purchases by zone. Rigidities on the part of dock workers.

A3.8 The excess cost attributable to these factors may be as high as 30% of the rates that would be quoted by a private independent operator. (Category, TDE1. Chains: Iron/Steel, AL; Electrical and Mechanical Equipment, AL; Port Authorities, AL; Transport/Maritime, AL; Cargo, Almonds and Industrial Machinery, MO.) A3.9 **Domestic Transport Regulations:** The most striking example is seen in Morocco in the case of the National Transportation Authority (ONT, _Office National des Transports),_ which regulates and coordinates all domestic shipments by vehicles weighing more than 8 tons ( in gross weight). The country is divided into independent administrative zones. No vehicle may travel without an ONT road permit _(feuille de route)_ which costs 5% of the value of the freight, or a receipt for payment of an additional 4.5 to 5.0% coordination tax (which is partially
<u>Table 2: Baseline GDP, $2014 billion</u>

||2014|2017|2020|2025|2030|
|---|---|---|---|---|---|
|World|78,226|87,588|97,935|115,447|133,052|
|BRI Area|24,152|29,238|35,076|45,361|55,733|
|Cambodia|17|21|26|36|47|
|China|10,351|13,267|16,639|22,342|27,642|
|Indonesia|890|1,091|1,335|1,826|2,380|
|Lao PDR|12|15|19|26|34|
|Malaysia|338|389|444|548|662|
|Philippines|285|327|376|469|581|
|Thailand|404|463|533|663|802|
|Vietnam|186|229|282|374|477|
|Rest of East Asia|527|607|697|846|997|
|Bangladesh|173|212|261|355|463|
|India|2,042|2,477|3,015|4,064|5,274|
|Nepal|20|22|25|32|41|
|Pakistan|243|274|313|398|510|
|Sri Lanka|80|96|114|147|182|
|Rest of South Asia|25|29|34|43|55|
|EU ECA|4,971|5,575|6,208|7,292|8,383|
|Russian Federation|2,031|2,281|2,533|2,965|3,397|
|Poland|545|596|652|733|805|
|Rest of Eastern Europe|229|258|286|339|393|
|Kazakhstan|227|273|325|436|534|
|Kyrgyz Republic|7|9|10|14|18|
|Rest of Central Asia|219|257|304|388|482|
|Iran,Islamic Rep.|425|450|495|584|675|
|Egypt,Arab Rep.|301|355|432|578|760|
|Turkey|799|919|1,031|1,219|1,419|
|Rest of MENA|2,752|3,207|3,667|4,512|5,451|
|Kenya|61|73|88|119|162|
|Tanzania|48|59|73|108|156|
|Non-BRI Area|54,074|58,350|62,859|70,086|77,319|
|Ethiopia|56|68|84|115|158|
|Rest of Sub-Saharan Africa|1,579|1,877|2,248|2,993|3,947|
|Latin America and Caribbean|6,405|7,185|8,042|9,518|11,057|
|United States|17,348|18,928|20,544|22,934|25,084|
|Rest of High-income|10,268|10,982|11,681|12,808|13,870|
|Rest of Western Europe|18,418|19,310|20,260|21,718|23,203|

- 23 -
<!-- Start of picture text --> the goods do not require any special phytosanitary, environmental, or safety standards beyond what<br>is required internationally. They disclose a considerable range of country experiences: these export<br>operations cost as little as $300-$400 in Tonga, China, Israel, Singapore, and UAE, whereas they<br>run at nearly ten times that level in Gabon and Tajikistan. On average, the cost is around $1278 per<br>container (excluding OECD and EU countries). To our knowledge, these data have not previously<br>been used in empirical work, although Djankov et al. (2006) use closely related Doing Business se-<br>ries on the amount of time and number of administrative procedures required to export and import.<br>As expected, they find that these factors impact negatively on bilateral trade.<br>To measure domestic market entry costs, we use the "Starting a Business" component of Do-<br>ing Business (see Djankov et al., 2002). This source includes indicators on the costs, time, and<br>number of procedures required for an entrepreneur to start-up and formally operate a local limited<br>liability company with general industrial or commercial activities. This includes legally required<br>pre-registration, registration, and post-registration activities. Only official costs are considered,<br>based on information gathered from the company law, commercial code, and specific regulation<br>and fee schedules. Together, we refer to these as the costs of domestic market entry ("Entry Cost").<br>As far as we are aware, this is the most comprehensive source of cross-country information on<br>business start-up costs, and has previously been used in the trade context by Helpman et al. (2007):<br>they find that higher entry costs are negatively associated with the probability that two countries<br>engage in trade.<br>2.3 Non-Parametric Evidence<br>Based on the theoretical approach that we develop in the next section, our main contentions in<br>this paper concern the policy determinants of export diversification in developing countries. Using<br>the sources just described, we will argue that lower costs of exporting, international transport, and<br>domestic market entry, as well as higher import costs, are all potentially associated with greater<br>diversification. The basic intuition for this is simple: existing domestic producers move into export<br>markets whenever the returns they expect are sufficiently high and the costs they face are suffi-<br>9<br><!-- End of picture text -->
|**Data Pro**|**perties**||||**M**|**odeling Approach**|
|---|---|---|---|---|---|---|
|**Sample**<br>**and**|**Freuenc**|||**Te**|||
|**Author (year)**<br> <br>**Source**|**qy**|**Variables**|**Integration**|**yp**|**Specification**|**Estimates**|
|Alami 2001<br>1980 I –<br>1993 IV<br>IMF -<br>IFS|Quarterly,<br>_DP_<br>represents a<br>pulse dummy<br>variable such<br>that_DP_= 1<br>at 1991:II<br>and zero<br>otherwise_,_<br>_DL_<br>represents a<br>level dummy<br>variable such<br>that_DL_= 1<br>for all t ><br>1991:II and<br>zero<br>otherwise,<br>and_DT_<br>represents a<br>slope dummy<br>variable such<br>that_DT_= 1,<br>2,...,9 (since<br>the number<br>of post break<br>observations<br>is 9) for all t<br>beginning<br>1991:II and<br>zero<br>otherwise if<br>there is an<br>increase in<br>the slope of<br>the trend, or<br>_DT_<br>would take a<br>decreasing<br>number|(F) Ratio of<br>foreign<br>currency<br>denominated<br>deposits to<br>total<br>liquidity, (X)<br>expected<br>change in<br>exchange<br>rate, (i*-i)<br>interest rate<br>differential,<br>(FCD) total<br>holdings of<br>foreign<br>currency<br>deposits in<br>local banks,<br>(FCDA) ~ in<br>banks abroad,<br>M2, (LE) log<br>of tertiary<br>exchange<br>rate,<br>(LESPT) ~<br>to official<br>exchange rate|F, LE,<br>LEPST are<br>I (1) all<br>others were<br>I(0)|ECM|10 models:<br>_1) F = LE +_<br>_LDIF_<br>_2) F = LE_<br>_3) F = DEP +_<br>_LDIF_<br>_4) F = DEP_<br>_5) F = LE SPT_<br>_+ LDIF_<br>_6) F = LESPT_<br>_7) F = DEP +_<br>_PBV_<br>_8) F = PBV_<br>_9) F = LESPT_<br>_+ PBV_<br>_10) F = LDIF_|In this study, quarterly data for Egypt<br>covering the period 1981:IV to 1993:IV is<br>used to analyze the current episode of<br>dollarization. As expected, given that<br>foreign currency deposits in Egypt earn a<br>competitive rate of return, using the<br>coefficient of the expected rate of depreciation<br>(_LE, DEP_or_LESPT_) as a measure<br>of currency substitution, the results of the<br>error correction models reported in<br>The study suggest that currency substitution is<br>essentially absent in the short-run in<br>estimates of Egyptian foreign currency<br>denominated deposits held in Egyptian<br>economy|

92
7...................7

# **TABLE OF CONTENTS**

|I. Introduction ..........<br>,<br>...<br>3|
|---|
|IH. Early Experiences with Privatizations ..............................<br>........5|
|II.1 The Formation of State-Owned Enterprises (SOEs)<br>_.5_|
|11.2 The Early Phase of Privatizations..............<br>7..|
|III. The Recent Wave of Privatizations, 1988-1992 .10|
|11.IThe Data ........<br>,,,,<br>10|
|III.2 Global Trends in Privatizations .11|
|111.2 Trer,ds in Developing Country Privatizations ............................................ 12|
|IV. Privatizations as a Source of Foreign Exchange .17|
|IV.1 Data ....<br>17|
|IV.2 Overall Trends ............<br>17|
|IV.3 Portfolio Equity Investments .19|
|IV4 Foreign Direct Investment ....................<br>21|
|V. Privatization Experiences at the Country Level .22|
|VI. Do Privatizations Attract Foreign Direct Investors<sup>.</sup><br>24|
|VII. Conclusion .....................<br>31|
|ANNEX: COUNTRY NOTES .33|
|A Latin America and the Caribbean .......................<br>, 33|
|B Europe and Central Asia .37|
|C East Asia and Pacific .39|
|D South Asia .40|
|E Sub-Saharan Africa .41|
|F North Africa and the Middle East .......................<br>42|

# APPENDIX

Table A-1: Privatization Transactions in the Developing World: 1988-1992 Table A-2: Privatization Transactions in the Industrialized World: 1988-1992 Table A-3: Foreign Direct Investment from Privatizations: 1988-1992 Table A-4: Privatizations as Share of Foreign Direct Investment: 1988-1992

# BIBLIOGRAPHY
Combined, Table 1 demonstrates that violence significantly affects displacement decisions. But despite the stronger increase in fatalities following displacement in origin districts, there are a number of ways in which the violence in origin districts of displaced households is similar to violence in the districts of residence of non-displaced households. First, Table 2 re-estimates the baseline specifications presented in Table 1, but uses the number of violent incidents as the dependent variable. The results demonstrate that there is little evidence for a similar pattern in the number of violent incidents. In the most complete specification in column (3), the increase in violent incidents in origin districts is indistinguishable from the increase for non-displaced households. Furthermore, we can rule out a relative increase in the number of violent incidents in origin districts of displaced households of larger than 0.44 monthly violent incidents.

Second, apart from the increase in violence over time, the average level of predisplacement violence in origin districts for displaced households is very similar to the average violence in the months before the most recent survey of non-displaced households. This is true of both fatalities and the number of violent incidents. Specifically, the coefficients on the displacement indicator in column (1) of Tables 1 and 2 are small in magnitude, vary in signs between specifications, and are indistinguishable from zero.<sup>16</sup> The same is true of migration districts in column (4) of Tables 1 and 2, as well.

Combined, these results demonstrate that living in close proximity to violence is a factor in displacement. But the results also raise questions about why the change in violence in origin districts of displaced households relative to the change in the district of residence of non-displaced households was not stronger in magnitude and more robust across measures of violence, particularly given the significant share of respondents reporting that violence was the primary reason for their displacement (e.g., TFPM 2016; OCHA 2018).

# **Section 5. Heterogeneity in How Violence Forces Displacement**

In this section, we empirically explore two potential sources of heterogeneity in how

> 16The displacement indicator is absorbed by the household fixed effects in the other specifications estimated in the table.

14
investigated that both open defecation and improved sanitation access are useful measures of sanitary environments to which a child ultimately is exposed. The greater the fraction of households that do not use a toilet, or that do defecate in the open, the greater the chance that a child comes into contact with germs or parasites. As children are exposed to disease, this may have implications for their cognitive abilities at early ages.

For the poorest households (first two quintiles), Figures 7, 8 and 9 show the association between average fitted values of communication, social and motor skills, respectively, and children’s age; with and without _TV_ asset present in the household. The fitted values and confidence intervals showed that there are higher averages for the poorest households with treatment status compared to control households. In most cases, as ages of children progress, there is even a flat or increasing curve of cognitive skills, with overall confidence intervals decreasing as age progresses. The importance of poverty status can give light into the importance of access to basic or improved sanitation in creating a disease environment that adversely affect nutritional status. Since the surveys also collect open defecation practices and improved sanitation access, it is important to show at which point the externalities of sanitation might be present. This is because when sanitation externalities are present there may be a higher induced behavior change than expected.

Figure 10 shows a negative relationship between improved sanitation and open defecation when the proportion of households in each of these indicators is aggregated by district/city level. The gap between the comparison groups shows that at the same level of open defecation, the treatment group may have higher access to improved sanitation, compared to the control group. After a district shows more than 40% open defecation the gap widens and it increases even more after each district reaches 65% open defecation rates. The increasing gap by treatment status may indicate the presence of externalities across households as those in the treatment group decide to have more access to improved sanitation compared to those without exposure to treatment.

Finally, Figure 11 shows a u-shaped relationship between handwashing and improved sanitation access, aggregated by district/city level. In this case externalities of 25
20 The second alternative scenario is based on similar higher national rates of growth, but this time thanks to faster rural development. This could be achieved by using foreign concessional loans to boost investments and productivity in agriculture, and by strengthening the links between the farm and nonfarm sectors. The rationale for promoting rural development comes of course from the fact that the elasticity of poverty to growth is higher in rural than in urban areas. This scenario assumes an annual rural GDP growth rate of 5 percent per year. The level of savings needed is the same than in the previous higher growth scenario. In 2008, the national headcount would be 3 points lower than in the base case scenario, which is not a very large gain. The proportionate gain (as compared to the base case scenario) is much larger for the poverty gap and the squared poverty gap, the later reaching 0.82 in year 2008, which should not be surprising since these measures better take into account inequality which increases less with rural growth than with urban growth. Note finally that an additional reason why there would be lower inequality in 2008 under the pro-rural scenario is because the between group component of inequality (with groups corresponding to households living in urban and rural areas) would be lower.

# **VI Conclusion**

Apart from a few exceptions (India and the United States), panel data techniques have not been used to analyze the relationships between growth, inequality, and poverty within single countries, apparently because two few observations are available to researchers. Yet, this constraint can be removed provided researchers are willing to conduct their analysis at the regional level. This was shown in this paper using data from Bangladesh. A regional panel was constructed for fourteen geographical areas, with data for five points in time between 1983 and 1996. This panel enabled us to estimate the impact of growth and inequality on poverty, as well as the impact of growth on inequality. Some of these results could not be obtained with standard methods of analysis relying on point estimates of the elasticity
Croke et al. (2016) Together, these factors imply that it will be important to use the most efficient estimators possible, for example, by including baseline values of weight when possible rather than just endline values, in order to improve statistical power. It will also require efforts to employ all the available studies and data.

# **3 Sample and data extraction procedures**

This section describes trial inclusion criteria, the search procedure for identifying studies, and the procedures for extracting data from included trials.

# **3.1 Trial inclusion criteria**

Our analysis includes randomized controlled trials of deworming MDA with multiple doses that include child body weight as an outcome. Following what TMSDG term their “main comparison” (Taylor-Robinson et al. 2015, p. 4), we consider only trials in which multiple doses of deworming treatment were administered, and include treatment effect estimates from the longest follow-up reported.<sup>16</sup> We focus on child weight gain because it is an important nutritional outcome which could potentially be improved over relatively short time horizons.<sup>17</sup> Moreover, there is substantial evidence for this outcome available in existing studies, which we expect to lead to relatively more adequate statistical power.<sup>18</sup> Weight is also highlighted as one of the three primary outcomes examined in

> 16 The median length of study in both our sample and the TMSDG sample is 12 months.

> 17 The studies we consider generally take place in low-income settings where child obesity is not considered a widespread issue.

> 18 For many of the other outcomes they examine, TMSDG include relatively few studies in their metaanalysis. Only weight (10 studies), height (7), and hemoglobin (7) have more than three studies that are aggregated in formal meta-analysis. As height deficiencies and stunting are generally conceived in the nutrition literature as the result of cumulative undernutrition over extended periods, we considered that height was unlikely to respond to deworming over the course of relatively short run trials (the median length is 12 months). Hemoglobin is an important outcome but it is most closely associated with

15