rafmacalaba/datause-encoder-data huggingface dataset:
The results suggest that these countries should be closely scrutinized and action plans to mitigate a debt disaster should be set.

**Figure 5:** Country indices

<!-- Start of picture text --> ALB DZA AGO ARG ARM AZE BGD BLR BLZ<br>−5 05<br>BOL BIH BWA BRA BGR CMR CHN COL COG<br>−5 05<br>CRI DJI DMA DOM ECU EGY SLV GAB GEO<br>−5 05<br>GHA GTM GUY HND IND IDN IRN JAM JOR<br>−5 05<br>KAZ KEN LBN LSO MRT MUS MEX MDA MNE<br>−5 05<br>MAR MMR MYS NIC NGA PAN PAK PRY PER<br>−5 05<br>PHL ROU RUS RWA SEN SRB SLB ZAF LCA<br>−5 05<br>SDN SWZ TJK TZA THA TUN TUR UKR UZB<br>−5 05<br>2002 2011 2002 2011 2002 2011 2002 2011 2002 2011 2002 2011<br>VUT VNM ZMB<br>−5 05<br>2002 2011 2002 2011 2002 2011<br>Exposure to public debt risk<br><!-- End of picture text --> The results can also be summarized for a cross-section (i.e., a given year). The vulnerability and the resilience indices jointly determine the ”Red-Square” measure (see Figure 6). The horizontal line depicts the vulnerability indices (centered on zero) for public and external debt, while the vertical line represents the resilience index.

A country that has rising vulnerabilities and a reduction in resilience falls into the red square. Countries with a reduction in vulnerabilities and an increase in resilience find themselves in the green square and are possibly less vulnerable to default risk. The two pink squares still indicate risk, but less than the red-square measure. The red square highlights countries such as Argentina, Lebanon, and Pakistan with heightened public debt exposure. For a country like Turkey, the predominant debt risk is related to the private sector and with falling reserves related to attempts to defend the currency.

# **Sources of vulnerability**

The methodology, with its estimated weights, allows decomposing the index into its components (see Appendix A for the relative weights). These weights summarize the importance of each of the variables in the construction of the indices in terms of total explained variation. The sum of the weights add up to 1. All the indicators, as mentioned above, are constructed such that an increase in any one of them should lead to an increase in vulnerability. This is done to avoid counteracting effects. As an example, for Argentina, 1 percent of the variation in the Fundamental Index is due to larger short-term debt, while 10
24 companies include rent to their managers, which may increase this component in the budget, relative to ATP 1.

The financial performance of the five ATP2 companies is presented in Table 10. The calculated average gross margin is nearly 25%, which indicates a sustainable level of profit. However, one company has a very low level of performance (8%) that is mainly explained by a low level of producer fee collection.

Based on the data in Table 10 and Table 12, the per producer cost of extension provision by ATP2 varies between $53 and $77 per year. This range is not too far apart from the estimated cost (between $30 and $66) of extension provision by ATP1, that was calculated earlier.

Another aspect of the financial status of the private companies that provide extension services to producers under the ATP2 program can be found in Tables 1 1 and 12 below.

Comparison of the private firms data in Table 11 suggest major differences in resource allocation for visits of farmers. While firm 1 allocates 24 working days per month for each extensionist, of which 83% (_20 days) are spent on producers' visits, firn 5 allocates 22 working days per month for each extensionist, of which 77% (_ 17 days) are spent on visits. Other interesting results are the big variation between the firms in both technician training and office work. If data on producer performance is available at the extension provider level, It could be used to estimate more accurate efficiency differences among the firms.

Table 10: Financial performance of five private companies in the period 8/95-6/96

|Item|Company I|Company 2|Company 3|Company4|Company 5|
|---|---|---|---|---|---|
|Incomefrom INTA|646583.97|613707.98|713207.76|626676.56|621872.28|
|Income from Producers|127957.48|<br>153466.99|170061.60|180000.00|142162.12|
|Income from Other Org.|9045.86|0.00|N/A|N/A|N/A|
|Other Income|1229.62|0.00|1378.24|N/A|N/A|
|Total Income|784816.93|767174.97|884647.60|806676.56|764034.4|
|Total Operational Expenses|721893.73|<br>543592.82|697144.33|525279.62|572100.85|
|GrossMargin (share)|0.08|0.29|0.21|_0.35_|<br>0.25|
|Cost of extension per producer (Cd)|499.57|<br>677.79|489.91|<br>467.33|<br>595.93|
|Cost of extension per producer ($)|56.76|<br>77.02|55.67|1<br>53.10|<br>67.71|

Note: Based on an exchange rate of 8.8 Cd per l$US in June 1996 N/A means not applicable
<!-- Start of picture text --> Employment, labor supply, and unemployment<br>0.5<br>0<br>-0.5<br>Employment Change<br>Labor Supply Change<br>Unemployment<br>-1<br>2020 2025 2030 2035<br> Year<br> Percentage<br><!-- End of picture text -->

**Figure 2:** Paths of relevant variables for the U.S. on aggregate. The cumulative percentage change in employment since 2019 is in blue, the cumulative percentage change in labor supply since 2019 is in green, and the level of unemployment (in percent) is in red. The years in the _x_ -axis go from 2019 until 2035.

is still 0.2% lower than pre-shock. In the model, the costs of switching between market sectors and entering/exiting the labor force are calibrated based on pre-pandemic mobility patterns. These patterns suggest that there are substantial costs associated with reallocating across sectors, which explains why agents return to the labor force gradually. Although the pandemic may have altered the costs of switching between sectors, the model does not account for such changes.<sup>10</sup>

> 10Another limitation of our model is that it does not feature an income effect on labor supply, because the agents do not have a level of wealth or assets that enters their labor decision. Agents simply compare their real consumption of participating in home production (which is constant) against the real consumption they obtain from participating in a given market sector (which is given by the real wage in that sector adjusted for unemployment). Moreover, the agents cannot choose their level of employment (or their “hours”); they simply choose their sector and supply all their units of labor inelastically to that sector.

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used (U.S.Dept. of Commerce). After deducting personal income taxes from the U.S. figures, medical services were compared, as shown in Table 6:

# Table 6: BILATERAL COMPARISON OF MEDICAL SERVICES

|Category|Price<br>(China's Yuan)|Price<br>_(U.S. $)_|Parity|<br>China<br>Weight (%)|U.S.<br>Weight(%)|
|---|---|---|---|---|---|
|Service of Physician|<br>3,335.19|103,771.84|0.0321|<br>70.41|23.39|
|Service of Dentists|3,335.19|125,518.99|0.0265|||
|Service of Nurses|<br>2,507.31|23,593.22|0.1062|||

Note: The weights in both countries refer to the weights for service of physicians, dentists, and nurses and related professional and semi-professional personnel (05.300) in medical care and health expenses (05.000).

# 3.3. Education and Government Comarison

47. Since an "output" of qualified students is very difficult to quantify, the approach in 'bis sector was similar to that in medical services. Following UNESCO's defintions, teachers can be classified in three levels: prmary school teachers, secondary school teachers, and college and universr.y professors. Because primary and secondary school teachers educated at the same level receive similar salaries, the education comparisons of ICP are divided into two categories: earnings of teachers in prnmazy and secondary school, and eanings of college and university professors. The bilateral comparison is shown in Table 7: Table 7: BILATERAL COMPARISON OF EDUCATION

|Category|Price<br>(China sYuan)|Price<br>(U.S.S)|Parity|<br>Chna<br>Weight (%/6|U.S.<br>)Weight_1%)_|
|---|---|---|---|---|---|
|.Earnings of Primary and||||||
|Secondary<br>School Techers|2,654.83|18,169.18|0.1461|<br>30.08|34.7|
|Earnings of College and<br> <br>||||||
|University<br>Professors|3.129.96|21.080.77|0.1485||.|
|Source:<br>from the_Sta_|_tistical Abstract o_|_f the United St_|_ates, 1988._|||

Note: The weights in both counries refer to the weight for education (07.400) in the category of recreation, enetainments educaton, and cultaral services (07.000).

21
differentials conditioned on worker characteristics.<sup>17</sup> We confirm Goldberg and Pavcnik’s (2003) findings that the vast majority (88%) of the change in informality takes place within sectors and hence seek identification off the variation in the impact of reforms across sectors.

All the dependent variables are computed yearly for each of the 18 industries<sup>18</sup> from 1983-2002, based on the PME and are defined above. The yearly destruction and creation rates are pooled instantaneous transitions computed using monthly data following the procedure of Geweke et. al. (1986) outlined in Annex 2. The formal sector size and the industry differentials corresponds to annual averages computed using the monthly inputs from the PME . The latter were obtained following Goldberg and Pavcnik (2003).<sup>19</sup> Our core specification is where _Yjt_ represents one of four dependent variables, α _j_ and α _t_ represents the industry and year fixed effects respectively. _TRADE jt_ is a vector containing both effective tariffs 17 Industry differentials come from the following model: _Fijt_ = _Hijt_ β _Ht_ + _Iijt INDDIF_ Fjt_ + ε _ijt_ where _Fijt_ is an indicator for whether a worker i employed in industry j at time t works in the formal sector, _H ijt_ is a vector of worker characteristics: gender, age, age2, education indicators (primary, secondary, superior) with associated coefficients β _Ht_ and _Iijt_ is a set of industry indicators (determining worker i’s industry affiliation) with associated coefficients _INDDIF_ Fjt_ (industry formality differentials) 18 Non Metallic, Metallic, Mechanical, Electrical, Transport, Furniture, Paper, Rubber, Leather, Chemicals, Petroleum, Personal Care, Plastic, Textile, Apparel, Food, Beverage, Tobacco (see Table 2).

19 Following Goldberg and Pavcnik, we first use a linear probability model to regress the informal dummy indicator on a vector of worker characteristics and on a set of industry indicators representing the workers’ industry affiliation. The coefficients on industry indicators can be considered “industry informality differentials” stripped of worker characteristics which are then pooled over time and regressed on trade related industry characteristics using industry fixed effects and in first differences specifications. Their results are based on Brazilian PME and on Colombian National Household Survey. In the first case they suggest that there is no statistical relationship between industry’s exposure to trade and probability of working in the informal sector, in the second, they report that tariffs’ declines are associated with an increase in informal employment prior to labor market reforms and suggest that compared to labor market rigidities, trade policy is of secondary importance in determining the incidence of informality.

18
|PPML<br>(5)<br>0.087<br>(0.041)<br>∗∗<br>0.658<br>(0.087)<br>∗∗∗|||g 316,317<br>g-in lasso|errors are<br>BT2 refers||
|---|---|---|---|---|---|
|approach<br>years)<br>PPML<br>(4)<br>−0.008<br>(0.062)<br>0.347<br>(0.119)<br>∗∗∗|0.118<br>(0.078)<br>0.182<br>(0.144)<br>0.034<br>(0.080)<br>0.124<br>0.099|0.116<br>(0.064)<br>∗<br>0.095<br>(0.053)<br>∗|PPML usin<br>ed by a plu|st standard<br> TBT29: T|s.|
|s for plug-in<br>16, every 4 <br>Post-lasso<br>(3)<br>0.349<br>(0.117)<br>∗∗∗|0.118<br>(0.077)<br>0.184<br>(0.142)<br>0.032<br>(0.078)<br>0.123<br>(0.099)|0.113<br>(0.061)<br>∗<br>0.089<br>(0.032)<br>∗∗∗|, estimated by<br>variables select|L. Cluster-robu<br> collinear with|on of standard|
|L result<br>(1964-20<br>Lasso<br>(2)<br>0.329|0.002<br>0.142<br>0.016<br>0.028|0.109<br>0.000|porter FE<br>s for all|ing PPM<br> perfectly|recogniti|
|ost-lasso PPM<br> Trade Flows <br>PPML<br>(1)<br>0.131<br>(0.044)<br>∗∗∗|tion||and exporter-im<br>PML coef cient|r experiments us<br>< .01. †TBT2 is|refers to mutual|
|PPML-lasso, and p<br>variable: Bilateral <br>l Injury|ansparency / Coordina<br>nition†<br>ernational Standards<br> Mutual Recognition|l Standards<br>in|-time, importer-time, <br> “Post-lasso” report P|r columns report furthe<br> .10 , ** p < .05 , *** p|al regulations; TBT29|
|ML, <br>ent <br>teria|—Tr<br>ecog<br>e Int<br>ent:|giona<br> Orig|orter<br>elled|othe<br>p <0|chnic|
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|PT<br>EU<br>A|CP<br>T<br>T<br>T|T<br>TF|Gr<br>ob|me<br>re|to|

22
to a treated household and the number of CFLs that household has at follow-up.

# **7.2 Checks of aggregate reliability effects**

To ensure that in Section 6.2 we are indeed measuring a local aggregate reliability effect, we perform two checks.

First, we calculate the intra-cluster correlation in the number of reported outages at followup. We have argued that intense saturation of treatment households within a transformer is causing a reduction in outages at the transformer-level. Then, household reported outages should be correlated with other household responses within the transformer. Our calculation indicates a intra-class correlation of 0.56, which means that responses within transformers are indeed highly correlated.<sup>60</sup> Second, we consider the estimated reduction in outages in conjunction with the difference between the expected and the estimated reductions in household electricity consumption to confirm that our series of results tell a consistent story. We make this comparison for the winter months, as that is the season for which we collected outage data.<sup>61</sup> In Section 5, we expected a reduction in households’ winter electricity consumption of approximately 42 kWh per month (Appendix Table 3). However, what we actually estimated was a winter reduction in electricity consumption of approximately 26 kWh per month (Appendix Table 4). This is a difference of 16 kWh per month, which is equivalent to approximately 14.4 hours of additional electricity consumption. In Section 6, we found the aggregate impacts of these electricity savings to be two fewer days without electricity per month due to unplanned outages. In conjunction with the previous result, this would mean two outages of approximately seven hours in duration each. This is very reasonable and assures us of the consistency across results.<sup>62</sup>

> 60Responses won’t be perfectly correlated with one another, as households in a transformer were surveyed on different dates and therefore the reference point of “past month” differs across households within a transformer.

> 61Winter is when outages are most prevalent and therefore when we would expect to see an aggregate reliability impact, if one occurs.

> 62Anecdotal evidence from both consumers and the utility indicate that outages can last between a few hours to a few days, depending on the repair required following the transformer overload.

25
Table 6.5: Access to Basic Infrastructure by Income Quintile in Dispersed Rural Areas in Argentina, 2003 <u>(percent)</u>

|Quintile|Electricity|Water|Trash<br>Collection|Paved<br>Road|Public<br>lighting|
|---|---|---|---|---|---|
|5 (richest)|67.6|20.0|6.3|15.9|22.5|
|4|70.8|19.3|8.2|15.9|27.9|
|3|67.7|28.3|10.5|17.4|17.4|
|2|71.8|23.2|6.1|20.5|16.1|
|1(poorest)|76.6|12.9|1.5|10.9|11.4|
|Source: Own calcu|lation based on RHS|2003.||||

Figure 6.1: Access to Publicly and Privately Provided Electricity <u>by Income Quintile in Dispersed Rural Areas in Argentina, 2003</u> <!-- Start of picture text --> Mendoza<br>Santiago<br>Chaco<br>Santa Fe<br>100<br>80<br>60<br>40<br>20<br>0<br>1st 2nd 3rd 4th 5th<br>Percent of households with access (poorest) (richest)<br><!-- End of picture text --> _Source_ : Own calculation based on RHS 2003.

The incidence of water access varies among rich and poor and provinces. Overall, two-thirds of rural drinking water service is provided by neighborhood groups and cooperatives and one-third by official provincial and municipal agencies (World Bank 2004). As Figure 6.2 shows, access to water varies across income groups. The trend is increasing for successively higher income quintiles in Mendoza. The first and second quintiles receive around 31 percent of the water services while the fourth and fifth receive more than 50 percent each.<sup>18</sup> The benefit incidence of water in Santiago del Estero is concentrated in the fourth quintile. In Chaco, the first quintile receives 20.3 percent of the service while the fourth only 15.3 percent; thus, the trend appears more progressive.

> 18 Although households in Mendoza have access to water, in some places it is contaminated and unsuitable for drinking.

35
19 implementation. Of the 10 sampled programs for each country/state, the full ITTSI was administered to the two largest programs targeting primary school teachers and the largest program that targeted secondary school teachers. The brief version of the instrument, the BITTSI, was administered in the remaining seven programs in the country/state. In total 48 at-scale programs completed the ITTSI and 91 at-scale programs completed the BITTSI across 14 countries.

# _Data Collection_

We applied the ITTSI survey through a combination of phone interviews with and online surveys of PD program coordinators. In a few instances (in The Gambia, El Salvador, and Mexico), depending on the preferences of the program coordinator and their primary language, program coordinators were given the option of completing the ITTSI questionnaire online. For the majority of programs, however, we held phone interviews with program coordinators, in which we asked them the questions included in the ITTSI survey items directly and filled out the instrument ourselves with their responses.

The ITTSI survey applied to the sample of at-scale programs consists of 70 indicators. We were able to collect information for an average of 66 of the 70 (94%) indicators of interest for the 48 at-scale teacher PD programs to which the full ITTSI survey was applied, and for 26.5 of the 27 (97%) indicators for the 91 programs to which the BITTSI was applied.

# _Analytical Strategy_

For the sample of at-scale PD programs, we compare the average of observed characteristics of at-scale teacher PD programs with the average for evaluated PD programs that resulted in the largest improvements in student learning (“top performers”), as measured by student
**Table 1. Macroeconomic and Welfare Impacts of Fuel Economy Standards of US CAFE Standards**

|**Study**|**Approach**|**Estimated Impacts**|
|---|---|---|
|Dacy et al.<br>(1980)|INFORUM<br>input–output<br>model|A net increase in employment of 140,000 jobs by 1985 due<br>to CAFÉ standards; job losses in steel, petroleum and gas,<br>and wholesale and retail trade sector are offset by new jobs<br>created in various service industries, plastics, metal<br>stampings, and other sectors.|
|Motor Vehicles<br>Manufacturers<br>Association<br>(1990)||The loss of between 159,000 and 315,000 jobs in<br>the motor vehicle industry|
|Geller et al.<br>(1992)|Input–output<br>model|Fuel savings of $54 billion (1990 dollar)<br>Increasing the fuel efficiency of passenger cars from 28 mpg<br>in 1990 to 40mpg in 2000 and 50 mpg in 2010 would create<br>244,000 by 2010|
|Goldberg<br>(1998)||Reduced fuel consumption by 19 million gallons per year;<br>the gasoline tax would have to increase by 780 percent, or<br>80 cents per gallon, to achieve the same fuel savings as the<br>CAFE standards.|

Source: Bezdek and Wendling (2005) Goldberg (1998) and Parry et al. (2004) argued that welfare gains depend upon myriad factors such as ability of the CAFE to function as a set of internal taxes on fuel inefficient vehicles, subsidies on fuel-efficient vehicles, local pollution, nationwide congestion, traffic accidents, and how consumers value fuel economy technologies and their opportunity costs. CAFE, according to Goldberg (1998), may not fare that badly from a welfare point of view because of its ability to function as a set of internal taxes (on fuel inefficient) and subsidies (on fuel-efficient vehicles) within each firm. Based on the estimates of CAFE’s impact on local pollution, nationwide congestion, and traffic accidents, Parry et al.(2004) found that, contingent upon how consumers value fuel economy technologies and their opportunity costs, higher fuel economy standards can produce anything from significant welfare gains, to very little or no effect, to significant welfare losses. Using marginal oil dependency and carbon externalities value of $0.16 and $0.12 per gallon respectively, they demonstrated that the reduction in fuel demand induced by improved fuel economy is welfare improving only when the marginal external 14
epidemic, during which face-to-face data collection came to a standstill, we used 2019 as benchmark year, implying that any censuses done after 2009 and surveys done after 2014 were considered up to date.

Our findings are threefold. First price data are typically collected (often at a monthly basis), but census and survey data are often out of date. Only 14 out of 20 countries are current on their population census; 9 out of 20 are up to date on their economic census. Only 5 out of the 20 countries carried out an establishment survey recently and about half the countries are up to date with respect to their health, labor force and consumption surveys (having been completed in 9, 11, and 14 countries respectively). The implication is that in almost half the cases, no or outdated microdata are used to produce core statistics including National Accounts and SDG reporting.

Our second finding is that only in few instances where microdata has been collected, they are made publicly accessible. Of the 140 potential microdata sets we looked for (7 data categories in 20 countries) 81 had been collected and as few as 25 were accessible. Remarkably, of these 25 about a third are not accessible through the website of the NSO; they can only be downloaded from international microdata repositories. Our third finding is that recent microdata is scarce in MENA. Summary statistics are generally available –as evidenced by the Open Data Inventory (ODIN). However, many of these statistics are necessarily based on outdated microdata and decision makers relying on such information would need to consider them with care.

Hence, we present a new indicator to complement the ODIN. The new indicator assesses the degree of openness of recent microdata as well as the ease with which microdata users can access this data on NSOs’ websites. Together with the ODIN, it gives a robust picture of data openness in MENA. It is cost effective, takes less time to collect and can be updated periodically as it only requires visits by the research team to the websites of all NSOs and international microdata libraries to determine the scores of its various elements with no input from the NSOs. The scores of MENA countries on the new microdata access indicator show that there is a strong case to be made to invest in the collection and release of microdata sets in the MENA region. The availability of recent microdata would make avoidable the situation where decision makers are informed by summary statistics that no longer reflect their economic and social realities. If such microdata were also made publicly available, it would further improve statistical transparency while also soliciting researchers to contribute their knowledge to help answer the pressing development questions of our time.

31