rafmacalaba/datause-encoder-data huggingface dataset:
|S/N|**Table A3 continued**<br>Data dimension|<br>Data category|Representative indicators|Categorical disaggregation|Coverage element 1 scoring guidelines*|
|---|---|---|---|---|---|
|12|Economic statistics|Price Indexes|(12.1) Consumer price index<br>(CPI);<br>(12.2) Producers price index<br>(PPI)|(12.1, 12.2) None|To receive full point: Must have all indicators. Any data in the<br>most recent 5 years (2013 onward) must be presented on at<br>least quarterly basis to receive a full point.<br>To receive half point:Must have at least one indicator. Data can<br>bepresented on a monthly, quarterly,or annual basis.|
|13||Government Finance|(13.1) Actual revenues;<br>(13.2) Actual expenditures.|(13.1) Revenue source;<br>(13.2) Administrative<br>classification; economic<br>classification; functional<br>classification.|To receive full point:Must have all indicators with one<br>disaggregation each.<br>To receive half point: Must have one indicator with one<br>disaggregation.|
|14||Money and Banking|(14.1) Money supply;<br>(14.2) Interest rates.|(14.1) M1; M2; M3;<br>(14.2) Rate type.|To receive full point: Must have all indicators with one<br>disaggregation. Must have at least three rates.<br>To receive half point:Must have one disaggregated indicator. If<br>indicator is(14.2),onlyone rate is needed.|
|15||International Trade|(15.1) Merchandise exports;<br>(15.2) Merchandise imports.|(15.1,<br>15.2)<br>Major<br>product<br>categories<br>(agricultural<br>products,<br>fuels,<br>mining,<br>manufactures, etc.).|To receive full point: Must have (15.1) and (15.2) imports by<br>major product categories. Any data in the most recent 5 years<br>(2013 onward) must be presented on at least quarterly basis to<br>receive a full point.|
||||||To receive half point: Must have one indicator with one<br>disaggregation. Data can be presented on a monthly, quarterly,<br>or annual basis.<br> **Table A3 continued**|

38
**_Energy demand models for policy_** **_<u>formulation</u>_**

POP(t+1) = c.POP(t), (2) Where c is a constant and POP is the population, t is the time period.

If the value of c is obtained by asking the boss, the forecast is based on the judgmental approach. If c is obtained through small-scale studies of controlled population, the model can be called an engineering model. If c is obtained by analyzing the time series of historical population, the model can be called an econometric model or a model estimated using the econometric approach.<sup>5</sup> Lipinsky (1990) suggested a three dimensional categorization of demand forecasting models based on complexity (simple-complex), dynamics (static-dynamic) and uncertainty (deterministic – probabilistic). In this study we have retained a simple classification of two broad categories: simple approaches and sophisticated approaches. In between there are direct surveys, which are also used for demand forecasting purposes.<sup>6</sup>

# **3.2.1 Simple approaches**

The simple approaches are easy to use indicators that can provide a quick understanding. Such techniques are relatively less common in academic literature although practitioners rely on them in many cases. Four such simple indicators commonly used for forecasting are: growth rates, elasticities (especially income elasticity), specific or unit consumption and energy intensity (See Box 5 for details). In addition, trend analysis that finds the growth trend by fitting a time trend line is also commonly used. All of these approaches rely on a single indicator and the forecast is informed by the assumed changes in the indicator during the forecast period. Clearly these methods lack explanatory power and

> 5 See Werbos (1990) for a highly readable, non-technical introduction to econometric approach of demand forecasting. See also Munasinghe and Meier (1993), Siddayao (1986), Codoni et al (1985), Donnelly (1987) for further details.

> 6 See Codoni et al (1985), Siddayao (1986), Munasinghe and Meier (1992), Craig et al (2002) for further discussions on some of these models.

25
6 and activities where no such networks exist; six of fte subsectors selected fit into the former category, and seven into the latter. For technical support systems, a crucial distinction is between simple, craft-based and complex, engineering and science based technologies.

1.2 omne Backgound Characteristics of Subsectors. Entrereneurs and Firms

Here we highligh **t** hree sets of characteristics of the firn samples: the rates of growth of the firms surveyed; the education and experience of entrepreneurs; and the relation between the fimns surveyed and pattems of subcontracting in their respective subsectors.

Not surDrisingly, given our focus on success, the Indonesian, Colombian and Korean firms sampled all have enjoyed very substantdal expansion since stal-up.1l/ As Table 2 shows, the start-up size of the Colombian firms sampled was very nmaii, with 76 percent of them having 20 or fewer employees, and only 10 percent 50 or more employees. By the time of the survey, however, these proportions had virtually reversed, with 54 percent in excess of 50 employees, and only 14 percent with fewer than 20. The averge start-up size of firms sampled in Indonesia was larger; even so, 48 percent of them commenced with fewer than 50 employees, and only 23 percent with more than 250. But by survey time only 9 percent still had fewer than 50 employees, while 56 percent had 250 or more. As for Korea, Table 2 documents substantial firm growth, though less than in the other countries. However, for Korea in particular these employment data subsanmially underestimate the grorith of firms, since Korean wages have risen very rapidly and firns have responded by substituing capital for labor, while achieving very rapid increases in viue added.

As Table- 3 shows, the levels of education of these successful SMEs are stiingly high. In both Korea and Colombia, over 80 percent of the entrepreneurs sampled bad completed at least some university; and, at 75 percent, the proportion was almost as high for Indonesia's garment and rattan entrepreneurs. Half of the Korean sample had undertaken their university studies in the field of science or engineering. And prior to startng their firms, over 90 percent .11,1 The Japanese firims are substantially older, and we do not have comparable data.
# **1 Introduction**

As of this writing, the COVID-19 pandemic continues to grow around the world. While epidemiological models suggest that rates of infection are eventually likely to reach a high level in much of the world, the eventual health impact remains uncertain. The pandemic remains in early stages in most developing countries.

The likely health impact of the virus in developing countries is a topic of urgent concern. Developing countries generally have younger populations, which should be an advantage in facing a disease which has the harshest impact for the elderly. On the other hand, developing countries have lower quality health care and less access to safe water and sanitation (World Bank 2020, June). An analysis based on age structure and comorbidity prevalence found that 16 percent of the population in Sub-Saharan Africa is at increased risk of severe COVID-19, as compared to 22 percent globally (Clark, Jit et al. 2020). Another predicts infection fatality rates (IFRs) for countries, taking into account age structure, comorbidities, and health system capacity, finding slightly lower IFRs for less developed countries than for more developed countries (Ghisolfi et al. 2020). A third study suggests that the share of COVID-19 deaths in developing countries may rise sharply as the pandemic matures (Schellekens and Sourrouille 2020).

A central question relevant to predicting the effects of the virus as the pandemic spreads is the impact on mortality by age. As the pandemic grows in developing countries, will it affect primarily the very old as it has in wealthy nations? Studies of case fatality rates (CFR) show much higher rates of death among the aged, particularly the very old (Jordan, Adab et al. 2020, Li, Xu et al. 2020, Zheng, Peng et al. 2020, Zhou, Yu et al. 2020). CFRs, however, have the disadvantage that cases are not defined consistently across countries. An alternative approach is to measure the agespecific mortality rate, using deaths attributed to COVID-19 by age group divided by the underlying population in the age group (Goldstein and Lee 2020). The analysis in this paper builds on the Goldstein and Lee analysis of 5 countries and employs data for 26 countries, including 9 middle-income countries and one low-income country.<sup>2</sup>

> 2 Following the World Bank country classifications, the high-income countries are Australia, Canada, Chile, Denmark, France, Germany, Hungary, Italy, Japan, Netherlands, Norway, Portugal, Republic of Korea, Spain, Sweden, Switzerland, the middle-income countries are Argentina, Brazil, Colombia, Malaysia, Mexico, Pakistan, Peru, Philippines, South Africa, and the low-income country is Afghanistan.

2
Because it is not clear where in the distribution of scores a lender would set the acceptance threshold, we trace outcomes along the full range of thresholds. Figure 1 shows how the default rate varies with the fraction of borrowers accepted (where borrowers with lowest predicted default are accepted first).

The receiver operating characteristic curve (ROC) plots the true positive rate of a classifier against the false positive rate, tracing out performance as the acceptance rate is varied. Following the credit scoring literature, we report the area under this curve (AUC) to summarize performance across the range of possible acceptance thresholds.<sup>12</sup> A naïve classifier would generate an AUC of 0.5 and a perfect classifier would generate an AUC of 1.0. Figure 2 illustrates the ROC for the best benchmark model (stepwise logistic) and the most conservative model using indicators derived from phone data (random forest weekly ensemble).

Results for a variety of specifications are presented in Table 3, measuring performance with AUC. (Performance is assessed with alternate metrics in Table S3). Results are presented for the entire sample, and then for the subsamples that do, and do not, have credit bureau records. In this population with thin files, credit bureau information does not perform especially well in predicting repayment (AUC 0.51-0.57). For bureau indicators, the logistic model outperforms the random forest, suggesting that the underlying relationship between those indicators and repayment is relatively linear. In contrast, standard models built on phone indicators (CDR) are predictive, reaching AUCs of 0.71-0.77 when trained and tested on the same time period. Our more conservative CDR-W models achieve lower performance when trained and tested in the same time period (AUCs 0.62-0.63), but also outperform credit bureau models. The performance of our models is also in the range of a sample of published within-time AUC estimates for traditional credit scoring on traditional loans in developed settings (0.50-0.79, shown in Table S4). Our method’s performance is similar overall and within each quartile of mobile phone usage, suggesting it picks up nuances in usage rather than overall usage (See Figure S2 and Björkegren and Grissen (2018)). Combining our indicators

> 12 Credit approval decisions tend to approve applicants with scores above a threshold. AUC has two useful properties for these types of decisions: they consider only the relative ranking of observations, and they trace through the range of potential thresholds.

15
Looking across social groups, the FLFP rate is highest among the ST communities, both in rural and urban areas, throughout the period between 1993-94 and 2011-12. This is followed by the participation rates of the SC, Other Backward Classes (OBCs) and others, in that order, in both rural and urban areas. In terms of change, the rural FLFP rate declined during the period of 199394 to 2011-12 for all social groups (Table 11). Even the urban FLFP rate declined during this period for all social groups, but the decline was far less pronounced than in rural areas.

**<u>Table 11: FLFP Rate by Social Group</u>**

|||Rural|FLFPR|||Urban|FLFPR||
|---|---|---|---|---|---|---|---|---|
||ST|SC|OBC|UC|ST|SC|OBC|UC|
|1993-94|66.0|51.5|--|41.3|36.6|31.7|--|21.7|
|1999-00|62.7|48.8|43.6|32.2|34.5|27.2|24.4|16.0|
|2004-05|62.5|49.0|46.4|38.1|37.5|30.7|28.8|20.1|
|2007-08|54.6|44.1|40.7|30.7|33.1|23.5|21.0|15.7|
|2009-10|46.4|38.0|35.6|28.1|31.7|25.8|20.9|15.4|
|2011-12|47.4|37.3|33.4|28.9|31.1|24.7|21.8|17.8|

Source: Authors’ own estimate based on NSSO data. Note: OBC category was created after 1993-94.

Although the FLFP rate was highest among the ST, they experienced the largest drop in FLFP rate in rural areas, a drop of 18.6 percentage points during 1993-94 to 2011-12. Of this 18.6 point drop, 15 percentage points of the decline occurred during 2004-05 to 2011-12. Upper caste females experienced the lowest drop (12.4 percentage points). The following figure suggests that the drop which occurred between 2004-05 and 2011-12 was almost three times that of the period between 1993-94 and 2004-05. On the contrary, the largest drop in urban areas was for the SC, by 7 percentage points. For the most part, the drop that was recorded among urban social groups was also concentrated in the period of 2004-05 to 2011-12.

21
# **Table 2**

# **Mexico-Social Network Effects Among Indigenous Adults 20-65 years of age**

|**Dependent variable**||**Rural areas**|**Semi-Urban**<br>**areas**|**Urban areas**|
|---|---|---|---|---|
||||**MALES**||
|**Internal migrant?**||**0.141**|0.017|**0.065**|
||t-value|**(6.77)*****|(0.32)|**(1.70)***|
||Adjusted R-squared|0.5698|0.6344|0.6869|
||Number of observations|145,464|41,119|25,792|
|**Receiving PROCAMPO/ O**|**PORTUNIDADES?**|0.010|0.009|0.001|
||t-value|(0.43)|(0.27)|(0.14)|
||Adjusted R-squared|0.2839|0.2421|0.0619|
||Number of observations|142,962|40,150|25,177|
||||**FEMALES**||
|**Internal migrant?**||**0.108**|0.0271|0.015|
||t-value|**(4.87)*****|(0.50)|(0.59)|
||Adjusted R-squared|0.5104|0.5995|0.7198|
||Number of observations|155,249|44,501|26,543|
|**Receiving PROCAMPO/ O**|**PORTUNIDADES?**|0.004|**-0.108**|0.004|
||t-value|(0.15)|**(-3.26)****|(0.53)|
||Adjusted R-squared|0.1761|0.2239|0.0340|
||Numberofobservations|152,512|43,224|25,702|

Source: Authors’ estimates of the social network effect based on the 2000 Population and Housing Census (10% sample) Notes:

- t-values based on robust standard errors corrected unknown forms of heteroskedasticity

- * p-value <0.10

- ** p-value<0.05

- *** p-value<0.01

13
# **Alternative Distance Measures**

Alternative distance measures allow to test whether the results remain robust when the formal firm exhibits different characteristics. The first alternative measure includes the distance of informal businesses to larger formal firms that participated in the World Bank Enterprise Survey (ES). Given that larger firms tend to be more technologically advanced, the use of this alternative distance informs us whether the distance effect is driven by the larger firms in the sample. Meanwhile, the second measure includes all formal firms that were enumerated (i.e., their GPS locations are recorded) regardless of whether they surveyed. Hence, it captures the fact that an informal business might be closer to another formal firm than suggested by the baseline measure. Lastly, the results using the distance to formal firms (micro and large) that have a website are presented to confirm whether the findings are driven by technologically more advanced formal firms.

As shown in Table 8, the alternative distance measures generate results that are in line with the baseline. Throughout the different specifications of the model, the direction and magnitude of the estimates are robust, with statistical significance dropping for the adoption of personal computers when using the model of all enumerated formal firms or the specification with formal firms with websites.<sup>18</sup>

**Table 8: Alternative Distance Measures**

||(1)<br>PC|(2)<br>Tablet|(3)<br>Phone|(4)<br>MM|(5)<br>Website|
|---|---|---|---|---|---|
|Distance of Informal to Closest Formal Firm (Baseline)|-0.596*|-0.741***|-0.180***|-0.202***|-0.197|
||(0.349)|(0.130)|(0.028)|(0.073)|(0.414)|
|Distance of Informal to Closest ES Firm|-0.604***|-0.417*|-0.216***|-0.196**|-0.378|
||(0.206)|(0.235)|(0.048)|(0.085)|(0.433)|
|Distance of Informal to Closest Formal Firm (Enumerated)|-0.431|-1.600|-0.271***|-0.342***|-0.224|
||(0.504)|(1.281)|(0.024)|(0.095)|(0.633)|
|Distance of Informal to Closest Formal Firm With Website|-0.399|-0.659***|-0.152***|-0.154***|-0.235|
||(0.329)|(0.221)|(0.036)|(0.042)|(0.243)|
|Number of Observations|901|901|901|891|678|

Notes: MM denotes mobile money. Each line represents a separate set of estimations with different distance measures included one at a time. The same set of control variables used in the baseline model are include in each set of the regressions here with results

> 18 These results remain even when using non-micro firms with websites. Results available upon request.

15
brand name and word of mouth, relate to established products that are unavailable to these firms.

I look at three of the signaling tools available to firms: obtaining a quality certification from the International Standards Organization (ISO), buying a technical license from a foreign firm, and having an external auditor review financial statements. The existing literature has done little to explain the effect of technical licenses and external financial auditors on the exporting performance of a firm. However, there is a growing marketing and business management literature that looks at the effect of ISO certification. For example, King and Terlaak (2006) use an 11-year panel of U.S. manufacturing facilities to analyze whether or not ISO certification is a good signal for quality.<sup>4</sup> They hypothesize that ISO certification may provide a way of communicating about unobservable firm attributes, thereby generating a growth effect for ISO certified organizations. They find that ISO certified facilities grow faster and that this advantage does not result from changes in quality performance, inventory management, within-firm production allocation, or pre-certification growth differences but rather from a growth in demand. They also find that certification is particularly beneficial for organizations that operate in large and advertising intensive industries, industries where information search costs may be higher. Their study confirms that ISO certification could be used as an efficient signal of quality.

Besides signaling tools I also analyze how a firm’s use of email and/or a website affects the firm’s export behavior. These technological marketing tools reduce search costs for potential customers in foreign markets. Many studies that look in to the effect of

> 4 Also see Heras et. al. (2002), Hallak and Sivadasan (2009), and Verhoogen (2008).

6
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Figure 2: Agriculture’s Contribution to Sudanese Economy <!-- Start of picture text --> Agriculture Value added (% of GDP) Agriculture Labour Share<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text --> _Source:_ ‘Agriculture Value Added’ from World Development Indicators (WDI 2018).<sup>2</sup> ‘Agriculture Labor Share’ from ILO (International Labour Organization) (ILOSTAT 2018).<sup>3</sup> Since the early 1990s, the relative rate of assistance (RRA)<sup>4</sup> (a measure of pro-agricultural policies), has been increasing for Sudan (Kym and Signe 2013). This measure has increased from being negative (indicating an anti-agricultural bias) to being slightly positive (indicating a pro-agricultural bias). Policies that restrict the export of agricultural commodities and promote the industrial sector by taxing the agricultural sector contribute to negative values for the RRA, as was observed in the early 1990s. As Sudan has reversed these policies, much like other African and developing countries, this indicator has steadily increased. However, since the 2007–08 world food price crisis, the increasing trend in this measure has reversed slightly. The increase in the RRA since the early 1990s, indicating an improving policy environment for the agricultural sector, may be a reason for the significant declines in rural poverty (as shown in Section II).

> 2 <u>https://data.worldbank.org/indicator/SL.AGR.EMPL.ZS?locations=SD.</u>

> 3 <u>https://www.ilo.org/ilostat/.</u>

> 4 The relative rate of assistance (RRA) is calculated using the nominal rate of assistance (NRA) for the agricultural sector and the NRA for the nonagricultural sector, by calculating - RRA= 100 × [ 100+NRA.ag −1] . The nominal 100+NRA.nonag rate of assistance for each sector is computed as the percentage by which government policies have raised gross returns for farmers above what they would be without the government’s intervention (or lowered them if NRA < 0). The RRA being below (above) 0, “provides a useful internationally comparable indication of the extent to which a country’s sectoral policy regime has an anti- (pro-) agricultural bias (Kym 2010).”

3