rafmacalaba/datause-encoder-data huggingface dataset:
Only 12 of the 54 eligible economics articles (22%) pass all of the reporting criteria for selection bias, while 40 of the 54 eligible medical articles do so. Performance varies across the three issues in this bias. Thirty-four of the 54 economics papers report adequately on their randomization procedure, but five of these use clearly deterministic methods to assign treatment. An alphabetic rule is used in one case and sorting by date of employment commencement is used in another. Less than half of the economics studies provide adequate information about the flow of potential participants in the trial. In the majority of economics articles, information on the number of participants at three important stages - screening for eligibility and exclusion from the study before and after eligibility was assessed - is not given, raising concerns about potential undocumented discrepancies between the declared population of interest and the sample studied (Manski 2013; Frijters, Kong, and Liu 2015). All but six of the 54 economics papers provide a table showing whether there was balance on observables at the time of randomization, suggesting that randomization was usually successful. Two papers that give this information show evidence suggesting that the randomization did not achieve the desired balance.

The largest issue related to attrition bias was failure to report how many participants progressed through the trial from enrolment to inclusion in the final analysis. More than two thirds of the economics RCTs we assessed have striking inconsistencies between the number of participants they enrolled and the number of observations included in the final analyses which were not discussed in the body of the paper or in the appendixes. The number of observations varies among final analyses in many of these papers, in some cases by more than 30 percent, often with no explanation for the 28
8 "long-run trends" in output as<sup>opposed to (a) cyclical variation in production</sup> or (b) transitional movements toward a steady-state Separating the "trend" component<sup>of production from the "cyclical"</sup> component requires that we have a good<sup>idea of the frequency of cycles.</sup> 7 In practice, taking averages over twenty<sup>year periods does not lo gross injustice</sup> **to** our **notions of** what **is a business cycle and** what is **not. Howeve-, when we** perform pooled cross-section, time-series investigations using five year averages, it is less clear that we<sup>have eliminated cyclical components.'</sup> A second time-aggregation problem involves distinguishirng steady-state growth from transitions toward the steady<sup>state growth path. Unfortunately,</sup> empirically distinguishing "long-run" trends from transitions toward<sup>these</sup> trends is less amenable to reasonable<sup>empirical approximations</sup> than expunging cyclical variation from GDP. Our<sup>"new" growth models predict that structural</sup> changes such as tax changes, government<sup>expenditure changes, and alterations</sup> in the legal system can alter steady-state growth rates. If such changes occur frequently, the economy will<sup>be continually adjusting towards a series</sup> of ever changing steady-state paths. This would imply that we can<sup>never</sup> emptrically capture the notion of<sup>steady-state</sup> GDP movements contained in our models - and our minds - because we<sup>never observe them. Some suggestive</sup> 7 This problem is confounded in cross-country studies because countries may not have the same cyclical frequencies.<sup>Furthermore, if we define cycles as</sup> periodic, self-generating movements<sup>in out--ut, the profession has not yet</sup> concluded that cycles exist. However,<sup>as lot.,</sup> as the steady-state path is fixed, aggregation in the absence of cycles<sup>will still capture steady-state trends.</sup> On the other hand, cycles may be<sup>importantly related to steady-state</sup> growth. Thus, "expunging" the<sup>"cyclical" component of output may remove</sup> important information about the long-run<sup>growth process.</sup> I This problem is further complicated<sup>because business cycle frequencies</sup> may be different across countries and<sup>across variables within the same country.</sup>
food subsidy, Philippines), to $493 (face-to-face nutrition education, Dominican Republic). The same cost for a mass-media nutrition education scheme ranged from $5-12 (costs in current prices). Costs per death averted would therefore generally be well above the $1500 figure, except for mass media projects (and the impact data on the latter are not very good). Data from the Narangwal study suggests that nuwrition interventions are most cost-effective for prenatal supplementation ($187 per death averted, 1988 prices), and become progressively more costly as children get older.

Financing data are also scanty. Nutrition expenditures seem to account for about 10% of health expenditures, both for donors and for individual countries (Chile is an outlier with 35%). For the 5 countries for which data were available, the share of nutrition expenditures in GDP ranged from 0.06 to 0.41% (three of the **five** were in the range 0.16-0.18%). About 90% of expenditures were financed by the central government, and 10% by local government. The poorer the country, the larger the share of central government financing from foreign sources.

More work is needed to improve data on costs, impacts and financing.

One priority area for future work includes better cost breakdowns for all programs (the food/non-food distinction is not very helpful: programs with high non-food costs for reasons of inefficiency cannot be distinguished from those with high non-food costs due to complementary service provision). One particular omission in the data is that there appear to be no data for the food/non-food cost breakdown for untargeted food subsidies. Another priority would be to obtain project cost data for nutrition education interventions and breastfeeding promotion (there are currently no data reported from any breastfeeding promortion projects): these data could be obtained for existing projects with limited work. More analysis of feeding programs could be undertaken using existing standardized project reports **(USAID PVO Child** Survival data, USAID Outreach data, WFP project proposals), to increase information about effects of project design. Another fruitful avenue for exploring the effect of project scale on costs would be to analyze existing feeding program data at the lowest service delivery unit level (e.g. anghanwadi level data for the Intensive Child Development Services
importance of different programs and services, also taking into account how the public views the likelihood that additional investment would indeed improve service performance. Additional information would be necessary to understand household priority rankings independent of current delivery systems. In future applications of city surveys, it would be advisable to include such questions in the investigation.

**_Table 7:  Municipal Programs: Priorities for Expansion of Programs, by income quintile, Cali, September 1999 (percent)_**

|--------------------------------------------|**_1_**<br>-------------|**_2_**<br>---------|**_3_**<br>----------|**_4_**<br>----------|**_5_**<br>---------|**_average_**<br>---------------|
|---|---|---|---|---|---|---|
|<br>education|<br>31.3|<br>30.9|<br>29.2|<br>32.3|<br>34.8|<br>**31.7**|
|health|19.5|19.9|30.2|23.6|23.9|**23.4**|
|employment & income prog.|18.9|22.2|18.6|18.7|19.8|**19.7**|
|nutrition programs|8.8|4.4|5.6|5.6|1.2|**5.1**|
|social housing|10.4|11.7|8.4|5.9|5.5|**4.8**|
|police|3.2|3.1|2.6|7.2|7.8|**4.5**|
|water|2.1|1.5|1.5|1.9|1.4|**1.7**|
|electric lighting<br>|1.8<br>|1.8<br>|0.8<br>|0.5<br>|1.5<br>|**1.3**<br>|
|communal households (ICBF)|1.0|2.6|0.6|1.5|0.6|**1.2**|
|public transport and roads|1.4|0.8|0.9|0.8|2.0|**1.2**|
|sports arenas|1.3|0.9|1.3|0.7|0.8|**1.0**|
|sewerage|0.5|0.1|0.2|0.8|0.2|**0.4**|
|garbage collection|0.0|0.0|0.1|0.7|0.5|**0.3**|

|------------------------------------------------------------------------------------------------------------|
|---|

Source: Own calculations based on EPSOC (1999)

**_Table 8:  Municipal Programs: Priorities for Cut-Back of Programs, by income quintile, Cali, September 1999 (percent)_**

|----------------------------------------------|**_1_**<br>----------|**_2_**<br>---------|**_3_**<br>---------|**_4_**<br>---------|**_5_**<br>--------|**_average_**<br>---------------|
|---|---|---|---|---|---|---|
|<br>health|<br>0.9|<br>0.2|<br>0.4|<br>0.8|<br>0.5|<br>0.6|
|education|0.8|0.6|1.1|0.7|1.1|0.9|
|water|2.8|1.0|1.1|0.7|0.3|1.2|
|nutrition programs|0.9|2.5|0.8|3.3|1.8|1.9|
|employment & income programs|0.5|1.7|3.1|1.9|3.6|2.2|
|garbage collection|0.7|3.8|1.9|1.6|2.6|2.2|
|sewerage|1.1|3.3|4.2|3.2|2.1|2.8|
|social housing|3.3|1.1|5.5|5.5|3.7|3.8|
|communal households (ICBF)|10.5|9.5|5.5|5.9|8.0|7.9|
|electric lighting|7.6|14.1|12.5|10.1|9.4|10.8|
|public transport<br>.|18.2|12.9|12.9|17.5|18.0|15.9|
|police|18.6|17.2|16.6|15.1|12.9|16.1|
|sports arenas|33.9|32.1|34.3|33.6|35.9|33.9|

------------------------------------------------------------------------------------------------------------

Source: Own calculations based on EPSOC (1999)

> 23 Additionally, the questions on evaluating education quality were only posed to those attending schools.  As we had shown before, a large number of secondary school age children do currently not attend school.  For such families, increasing education supply is obviously a high priority.

23
would reduce the need to borrow in order to meet the fiscal shortfall.<sup>3</sup> The traditional ex post funding approach usually precludes funds from being disbursed promptly in the aftermath of a natural disaster.

# 3.  Ex Post Funding Approach: What Is Wrong?

Historically, most governments have not taken much interest in ex ante management of disasters because of low perceived vulnerability levels and the fact that most severe hazards manifest themselves very infrequently (Kaplow 1991, Kunreuther 1996). In addition to this cognitive failure, there has been a willingness on the part of the international community to provide post disaster funding for vulnerable countries exposed to catastrophic events. The World Bank alone has disbursed US$38.3 billion of emergency and reconstruction loans (ERL) over the last twenty years, as shown on Figure 2. Almost 23 percent of these ERL were provided to Latin American and Caribbean countries and more than 18 percent to African countries.

**Figure 2. World Bank emergency lending, 1980-2003**

<!-- Start of picture text --> Middle East and North Africa 2056<br>Europe and Central Asia 3974<br>Africa 7054<br>South Asia 8178<br>East Asia and Pacific 8273<br>Latin America and Caribbean 8804<br>0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000<br>US$ millions<br><!-- End of picture text --> Source: World Bank (2003).

The availability of free or inexpensive post-disaster donor funding discourages proactive ex ante risk management on the part of disaster prone countries, such as looking into market driven risk transfer solutions, including reinsurance. Indeed, given the cost of risk financing solutions offered by the private markets, it is rational for the developing countries to rely largely on inexpensive ex post aid and development banks’ postemergency lending. This poses a “Samaritan’s dilemma” (Coate 1995).

As a consequence of underdeveloped domestic insurance markets and the lack of risk awareness or economic incentives to engage in ex ante risk management, governments generally adopt reactive response approaches to natural disasters, relying on domestic

> 3 Post-loss funding should not be used to replace lost assets, but to invest in post-loss projects that create the highest social and economic value (Clarke and Doherty 2004).

7
constructing a survey with a contextualized, project-specific set of variables to explain selection into the program, thereby minimizing unobserved heterogeneity. They then match on observable differences to examine whether the Jamaica Social Investment Fund increased the ability to engage in collective action and build trust. The answer is yes, but the effect was greater for more educated, better networked members of the community. Gugerty and Kremer (2000), in their random assignment study, also find that bringing in outside assistance may change the composition of beneficiary groups. The formation and training of village groups attracted wealthier and more educated men and women into leadership positions within the group because of the outside funding.

Does participation increase project sustainability? Here again the evidence is limited but instructive. Khwaja’s (2001) study suggests that projects managed by communities are more sustainable than projects managed by local governments because of better maintenance. Katz and Sara (1997) and Isham and Kahkohnen (1999a,b) also find strong associations between participation and sustainability, although they do not establish causal direction. In an in-depth anthropological study of the sustainability of participatory projects, Kleemeier (2000) examines a Malawi rural piped water project and finds that half the schemes are performing poorly, with the newest ones performing best. The weak sustainability, she argues, stems largely from the lack of institutional support from external agencies—echoing the conclusions of Katz and Sara (1997) and Newman and others (2002).

For water projects in Sub-Saharan Africa, Cleaver (1999) finds that even if communities are initially successful in creating the project, they may lack the material 32
25

# **8.  Effects of Tariffs and Transport Costs on Export Variety**

Table 4 presents least squares (LS) estimation linking export variety to all instruments and exogenous variables of the nonlinear 3SLS system presented in Table 2. This is similar but not identical to the first-stage estimation of the nonlinear system, which involves regressing the derivatives of each equation with respect to the parameters of the system on all the instruments and exogenous variables. For example, differentiating (21b) with respect to 1/(1 − σ n ) we obtain

> <u>12</u> ( scnt + s*nt ) ln λ cnt* , which is the export variety index for country c and sector n, times the average share of that industry.  In comparison, the regressions we present in Table 4 just use the export variety index ln λ cnt* as a dependent variable, which allows us to see the partial relationships between export variety and the trade cost variables.

The top part of Table 4 shows the effects of a one percentage point increase in the U.S. tariff on the export variety of the industry in the columns. Tariffs are constructed from detailed U.S. custom data by taking the ratio of duties paid over imports. They vary by industries, countries and years. We expect industry export variety to decrease with the own tariff of the industry, while there may exist some positive effects due to reallocation of resources among industries when there is a tariff increase in _other_ industries.

All industry export variety indexes are negatively correlated with own tariffs except for the textiles & garments and the electronics industry. For textiles & garments, MFA quotas are known to be more restrictive and binding than tariffs, which may explain the insignificant effect of tariffs on export variety. For the electronics industry, it could be the case that non-tariff barriers, transport costs and skilled labor endowments are more important in explaining expansion in export variety than tariffs. For the rest of the industries, the own tariff effects are all negative and statistically significant. A one percentage point increase in U.S. tariffs lowers
wait, and if yes how long? Finally, what would be the appropriate policy to be implemented today?

There appears to be much more uncertainty about the benefits of global warming abatemen **t** han about costs in the literature (as reflected in TAbles 2 and 3). The major source of uncertainty regarding the benefits lies in the uncertainties and impondemble impacts of climate change (scientific uncertainty), but major doubts also remain on the magnitude of the damages, and their conversion in monetary values. The uncertainty about the costs of limiting global warming principally lies in the choice of instruments to be implemented (e.g. carbon tax, regulations).<sup>4</sup> To illustrate the degree of uncertainty, Tables 2 and 3 report the estimates of recent studies on the expected costs and benefits. The estimated costs of a 40 percent abatement policy from baseline in 2025 range from only 0.26 percent of World GDP for Edmonds-Bums to 3.52 percent for Whally-Wigle, while the estimatod benefits (of 1 5 eliminating the damages of CO2 doubling) vary between 0 and 5.5 percent of GDP. 1<sup>6</sup> The overall uncertainty of costs and benefits in 2025 is 74 percent and 107 percent respectively, equivalent to a standard deviation of about 3.2 percent and 3.8 percent annually.

The correlation coefficient between discounted benefits and costs of global warming also influences greatly the optimal timing of intervention --the critical ratio 14. See Nordhaus (1993). for a good summary of the uncertainties of limifing global warming.

15. Which is expected to occur as early as 2025 (IPCC, 1990).

16. Although Nordhaus (1993) reports that most studies give quite similar results and find damages rnging between _1.0-1.5%_ US 1988 GDP for a CO2 doubling and a survey of scientific and economic experts by Nordhaus (1993) shows that a 3 C increase of average temperature in 2090 would cost on average 1.8% of GDP, an order of value close to the preceding ones, the great dispersion of the answers, ranging from 0 to 5.5% of GDP, illuswrates the uncertainty surrounding these estimations.

15
especially private informal, sector than their less educated counterparts. Married men are far more likely to stay put in the public sector, though being married at the baseline does little to help an informally employed man to secure a public sector job.

**Table 2: Determinants of formality-sector transitions between 2012 and 2018**

|Initial status in 2012||M<br>Public Forma|en<br>l|P|rivate Informa|l|
|---|---|---|---|---|---|---|
|Destination status in 2018|Public<br>Formal|Private<br>Formal|Private<br>Informal|Public<br>Formal|Private<br>Formal|Private<br>Informal|
|Age|-0.002|0.002|-0.008|0.007*|0.004|-0.005|
||(0.010)|(0.017)|(0.008)|(0.004)|(0.003)|(0.007)|
|Age^2|0.003|-0.004|0.002|-0.004*|0.001|0.001|
||(0.004)|(0.003)|(0.003)|(0.002)|(0.001)|(0.003)|
|Primary school|0.075|-0.002|-0.066*|0.004|0.065***|-0.074***|
||(0.039)|(0.033)|(0.025)|(0.006)|(0.010)|(0.017)|
|Secondary school|0.117**|-0.036|-0.082**|0.043***|0.073***|-0.103***|
||(0.042)|(0.032)|(0.034)|(0.012)|(0.014)|(0.012)|
|Post-Secondary|0.190***|-0.058|-0.133***|0.109***|0.118***|-0.235***|
||(0.036)|(0.036)|(0.028)|(0.008)|(0.020)|(0.018)|
|Married|0.150***|-0.082**|-0.078**|-0.002|0.005|-0.009|
||(0.024)|(0.034)|(0.031)|(0.005)|(0.008)|(0.015)|
|Household size|0.009|-0.006*|0.003|0.002|-0.000|0.004|
||(0.007)|(0.003)|(0.002)|(0.003)|(0.001)|(0.003)|
|Region dummies|✓|✓|✓|✓|✓|✓|
|Occupation dummies|✓|✓|✓|✓|✓|✓|
|Observations|1,713|1,713|1,713|4,305|4,305|4,305|

_Notes_ . *** p<0.01, **<0.05, *<0.1 _._ A linear probability model’s coefficient estimates and standard errors are reported. This table uses panel data from the Egypt Labor Market Panel Survey in 2012 and 2018. The sample is restricted to those aged at least 20 years old in 2012 and at most 59 years old in 2018. All control variables refer to 2012. Panel weights are used.

# **6. Concluding Remarks**

The persistence in and the rigidity of labor market states were always key characteristics of the Egyptian labor market. This paper revisited these questions relying on transition matrices to examine the dynamics of labor market transitions post-Arab Spring. The analysis relies on the two most recent rounds of the Egypt Labor Market Panel Surveys (ELMPS) and exploits the panel structure of the data to track individual employment status and job trajectories between the two rounds.

21

**Official Use**
**Table E1**

**Value Composition of Cotton in the US, 1970‐2009**

||**_Nominal pri_**|**_ces (US$/kg)_**|**_Value (U_**|**_S$/ton of se_**|**_ed cotton)_**|**_Contrib_**|**_ution (%)_**|
|---|---|---|---|---|---|---|---|
|**_Year_**|**_Lint_**|**_Seed_**|**_Lint_**|**_Seed_**|**_Total_**|**_Lint_**|**_Seed_**|
|1970|0.63|0.06|221|37|258|85.8|14.2|
|1971|0.74|0.06|259|37|296|87.5|12.5|
|1972|0.79|0.05|278|32|310|89.6|10.4|
|1973|1.36|0.10|474|65|539|87.9|12.1|
|1974|1.42|0.14|495|88|583|84.9|15.1|
|1975|1.16|0.10|406|63|469|86.6|13.4|
|1976|1.69|0.10|592|67|659|89.8|10.2|
|1977|1.55|0.07|544|46|590|92.2|7.8|
|1978|1.57|0.11|550|74|624|88.1|11.9|
|1979|1.69|0.12|591|79|670|88.3|11.7|
|1980|2.05|0.13|716|84|800|89.5|10.5|
|1981|1.85|0.09|646|56|702|92.0|8.0|
|1982|1.60|0.08|559|50|609|91.8|8.2|
|1983|1.85|0.17|649|108|757|85.7|14.3|
|1984|1.79|0.10|625|65|689|90.6|9.4|
|1985|1.32|0.07|461|43|504|91.5|8.5|
|1986|1.06|0.08|370|52|422|87.7|12.3|
|1987|1.65|0.08|577|54|631|91.5|8.5|
|1988|1.40|0.12|490|77|566|86.5|13.5|
|1989|1.67|0.11|586|68|654|89.6|10.4|
|1990|1.82|0.12|637|79|715|89.0|11.0|
|1991|1.68|0.07|587|46|633|92.7|7.3|
|1992|1.28|0.10|447|63|511|87.6|12.4|
|1993|1.28|0.11|448|73|522|85.9|14.1|
|1994|1.76|0.10|617|66|683|90.4|9.6|
|1995|2.13|0.11|745|69|814|91.5|8.5|
|1996|1.77|0.13|621|82|703|88.3|11.7|
|1997|1.75|0.12|612|79|690|88.6|11.4|
|1998|1.44|0.13|506|84|590|85.8|14.2|
|1999|1.17|0.09|410|58|468|87.6|12.4|
|2000|1.30|0.11|456|68|524|87.0|13.0|
|2001|1.06|0.09|370|59|429|86.3|13.7|
|2002|1.02|0.10|357|66|422|84.5|15.5|
|2003|1.40|0.12|490|76|566|86.6|13.4|
|2004|1.37|0.11|478|70|548|87.3|12.7|
|2005|1.22|0.10|426|62|488|87.2|12.8|
|2006|1.27|0.11|443|72|515|86.0|14.0|
|2007|1.40|0.16|488|105|594|82.3|17.7|
|2008|1.57|0.22|551|145|696|79.2|20.8|
|2009|1.38|0.16|484|103|587|82.4|17.6|

**Notes:** It is assumed that one ton of seed cotton gives 350 kgs of lint and 650 kgs of cottonseed. **Source:** Author’s calculations from _US Department of Agriculture_ data.

**_Page 59_**