rafmacalaba/datause-encoder-data huggingface dataset:
# **3 Data and descriptive statistics**

We built a country-level panel dataset covering 150 countries for which price data was available over the following period: 2008-2021 for fixed broadband, and 2012-2021 for mobile broadband. Our dataset includes data on price, SMCs, competition, and regulation, as well as several variables used as controls in the econometric modeling. Definitions and sources of variables are described in Table 1. Summary statistics are reported in Table 2, while correlation matrices are presented in Tables 3 and 4 in the Appendix.

## **3.1 Price data**

Telecom markets host a variety of packages with different levels of usage allowance and attributes, and this can make the measurement of price challenging in this sector. Several approaches are considered in the literature to address this challenge. Studies like Nicolle et al. (2018) use a hedonic price model, whereby price is estimated as a function of the attributes of packages. As such, a focus can be set on the price of a particular attribute, for instance, data allowance or quality, and be used in the analysis. Such modeling requires comprehensive data on all packages offered by operators and the results can be dependent on the functional form of the model. Other studies like Genakos et al. (2018) rely on the price of a particular basket of usage. As part of this approach, baskets of usage are defined, and the price of the least expensive offer is tracked for each basket. In this paper, we use the basket approach which does not require the availability of comprehensive data on packages – a task that would have been challenging to complete for a study that involves more than 150 countries/markets.

Interestingly, the ITU has defined several baskets of broadband packages and tracked their price since 2008.<sup>11</sup> We focused on two baskets that are the most representative of recent trends in data usage: (i) a basket of fixed broadband packages enabling at least 5 Gigabytes of data usage per month; and (ii) a basket of mobile broadband packages enabling at least 2 Gigabytes of data usage per month.<sup>12</sup> Figure 3 below reports the trends in fixed and mobile broadband prices in our dataset, with a faster drop in prices in countries starting from a high level.

> 11https://www.itu.int/en/ITU-D/Statistics/Dashboards/Pages/IPB.aspx.

> 12Data from Telegeography suggests an average monthly data usage per capita in developing countries rising from 9 GB in 2017 to 23 GB in 2020 on fixed broadband; and from 1.4 GB in 2017 to 4.2 GB in 2020 on mobile broadband. Note that ITU’s price basket has evolved over time to reflect growing data usage by the average consumer. Two changes occurred during the period we considered in this study: one in 2017 when the allowance of the fixed broadband basket increased from 1 GB to 5 GB, and the allowance of the mobile broadband basket increased from 1 GB to 1.5 GB; and in 2020 when the allowance of the mobile broadband basket increased to 2 GB.

9
An analysis of the road conditions in 2001 indicated that 23 percent of the network was in good condition, 34 percent in fair condition (requiring seals or thin overlays), 19 percent in poor condition (requiring overlays) and 24 percent in very poor condition (requiring thick overlays or reconstruction). By 2004, the successful implementation of the program resulted in roads in good and fair condition accounting for 75 percent of the total.

The sensitivity analysis shows that the first year rehabilitation and maintenance program IRR is 114 percent and the MIRR is 36 percent. Assuming there is an increase of 20 percent in agency costs, the IRR decreases to 97 percent and the MIRR to 33 percent. A 20 percent decrease in traffic decreases the IRR to 91 percent and the MIRR to 32 percent. A combination of increase agency costs by 20 percent and a decrease in traffic by 20 percent decreases to IRR to 77 and the MIRR to 30 percent.

## **TABLE 13: MEXICO: SENSITIVITY ANALYSIS**

|||0 %<br>|Increasein A<br><br>|gency Costs<br>20 <br>|%<br>|
|---|---|---|---|---|---|
|||IRR(%)|MIRR(%)|IRR(%)|MIRR(%)|
|Decrease in|0 %|114|36|97|33|
|Traffic|- 20 %|91|32|77|30|
|_Source: World Bank_|_, 20_<br>_o, Fe_<br>_00: Mexic_|_deral Hi_<br>_y Maint_<br>_ghwa_|_enance Project_|||

To have a net present value equal to zero, agency costs have to be 560 percent higher than the estimated costs or traffic levels reduced to only 18 percent of the estimated traffic. These events have a very low probability of occurrence indicating that the project’s economic viability was very robust.

# **<u>Recommendation 2: Latin America needs to bundle infrastructure investments with the adoption of policies aimed at improving the development and efficiency of logistics service providers.</u>**

Previous sections showed firms in Latin America need to have high levels of inventories. This responds to the lack of good quality infrastructure and unreliable service providers. For instance, it has been documented that the trucking industry in Colombia has very low standards of quality, imposing high costs to export oriented firms<sup>17</sup> .

The policies required to reduce high inventory levels are clear: improvements in transport infrastructure have to be accompanied with appropriate and effective regulation, for instance a good framework for the development of multimodal operators. Likewise the deregulation of associated markets can also have a significant impact on inventory levels and then reducing the costs of doing business. The ultimate goal of better regulation is to foster private sector investment to provide a menu of efficient logistics services, including among others dry ports and multimodal distribution centers.

> 17 World Bank “Infraestructura Logística y de Calidad para la Competitividad de Colombia” Report 35061, 2006

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services affiliates. The left panel shows the breakdown based on the number of affiliates and the right panel shows it by share of revenue. This relationship holds for both exporters and non‐exporters. Examples of sectors with high shares of post‐manufacturing services and low productivity include wearing apparel, leather, and manufacturing nec. No relationship is found for establishment services, while back‐office services are positively correlated with productivity. The back‐office finding indicates that sectors that produce more in‐house back‐office services such as chemicals, pharmaceuticals, and coke and petroleum, tend to have higher levels of productivity.

**Figure 3.8: Productivity and Post‐manufacturing affiliates and revenue (2014)**

<!-- Start of picture text --> Productivity and Post-manufacturing Affiliates Productivity and Post-manufacturing Revenue<br>Manufacturing firms Manufacturing firms<br>Basic metals Basic metals<br>TobaccoCoke, petroleum Coke, petroleum Tobacco<br>Chemicals Chemicals<br>ICT & electronics ICT & electronics<br>Paper Paper<br>Motor vehiclesElectircal machineryBeverages Motor vehiclesElectircal machinery Beverages<br>Rubber and plasticsTextiles Rubber and plastics Textiles<br>Pharmaceuticals Pharmaceuticals<br>Fabricated metalsmachinery Wood Leather Fabricated metalsmachineryWood Leather<br>Non-metallic minerals AgribusinessManufacturing nec Non-metallic minerals Agribusiness Manufacturing nec<br>Wearing apparel Wearing apparel<br>Printing, publish Furniture Printing, publishFurniture<br>Other transport eq Other transport eq<br>20 40 60 80 100 0 20 40 60 80<br>Share affiliates from Post-manufacturing Share revenue from Post-manufacturing<br>Source: EIS data from Turkey; author’s calculations.<br>8.5 8.5<br>8 8<br>7.5 7.5<br>TFP ACF TFP ACF<br>7 7<br>6.5 6.5<br><!-- End of picture text -->

**Benchmark regression analysis yields similar results: manufacturing firms with establishment, post‐ manufacturing, and post‐sales affiliates are less productive than their non‐servicified peers.** This holds for both non‐exporters and exporters. The results come from benchmark regressions of productivity as outlined in Box 3.2, where non‐servicified firms are the omitted category (i.e. the benchmark). The left‐ hand panel of Figure 3.9 shows the results for all non‐exporting goods firms as the omitted category, while exporters only are shown in the right‐hand panel. Only coefficients with a significant effect are displayed. A full account of the benchmark results, including the control variables, is shown in Table A3a and b in Annex 1. Both panels show that firms with establishment services are around 20 percent less productive than non‐servicified firms. Similarly, manufacturing firms that also produce post‐manufacturing services are on average 13.8 percent and 18.7 percent less productive in the case of exporters and non‐exporters respectively compared to their servicified peers.

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## **4.3 Heterogeneity of effects**

This section explores heterogeneity in the adoption of green technologies in response to the 2022 energy crisis. We first explore the results by technology, as shown in Figure 4(a). This repeats the specification in Equation 1 but keeps job postings from specific technology groups only as the outcome variable. The results show that the differential increase in total green technology postings by more-exposed establishments after February 2022 was driven by generation technologies. This includes those related to energy generation, including renewable energy and applications of renewable energy, energy storage, heatpumps, biofuels, and decentralized energy systems.

We next explore the results by country and job skill group among the generation technologies driving our overall results. Figure 4(b) shows results from regressions run separately for each country in our sample, exploiting only the variation in firm energy intensity within countries. The effects with the greatest order of magnitude were for the Netherlands, Switzerland, Italy, Germany and the UK. In contrast, other countries and notably the United States, did not see significant effects for more energy-intensive establishments, likely reflecting their lower reliance on imported fossil fuels and therefore exposure to the energy price shock. Finally, in a similar specification as in Equation 1, we explore results by skill level. Figure 4(c) plots coefficient estimates when the dependent variable is the IHS-transformed number of generation technologyrelated job openings for occupations at each skill level. The rise in labor demand for these technologies after February 2022 was concentrated almost solely in high-skilled jobs, while lowand medium- skilled jobs saw no statistically significant effect.

# **5. Conclusion**

This paper constructs new measures of the diffusion of a comprehensive range of green technologies using online job postings data from 16 advanced economies from 2014 to 2022. It shows that growth in these job openings rapidly accelerated in 2022, and explores the role of the 2022 global energy crisis in driving this accelerated diffusion. It shows that establishments with greater exposure to the crisis, as measured by their pre-crisis energy intensity and location in countries with greater dependence on natural gas imports, saw a relative increase in openings related to green technologies after February 2022. This increased hiring was concentrated in generation
would have otherwise led to a negative impact on child nutritional status in the absence of the PMT program. Our findings also suggest that the program helped children with severe malnutrition problems, but again the impacts were limited to those who were 1224 months of age at the time of the 2000 survey round.

# **References**

- Alderman, H., J. Behrman, V. Lavy and R. Menon. 2001. “Child Health and School Enrollment: A Longitudinal Analysis,” _Journal of Human Resources_ , 36/1: 185-205.

- Alderman, H., J. Behrman and J. Hoddinott. 2005. “Nutrition, Malnutrition, and Economic Growth,” _Health and Economic Growth: Findings and Policy Implications_ , Cambridge and London: MIT Press (2005): 169-94.

- Alderman, H., J. Hoddinot, and B. Kinsey. 2006. “Long Term Consequences of Early Childhood Malnutrition,” _Oxford Economic Papers_ , 58, 450-474.

- Armecin, G., J. Behrman,  P. Duazo,  S. Ghuman, S. Gultiano, E. King, and N. Lee. 2006. **“** Early Childhood Development Through an Integrated Program: Evidence from the Philippines **,”** World Bank Policy Research Working Paper _No. 3922_ (May).

- Baird, Sarah, Jed Friedman and Norbert Schady. 2007. “Aggregate Income Shocks and Infant Mortality in the Developing World,” The World Bank, Policy Research Working Paper 4346, and _Review of Economics and Statistics_ (in press).

- Behrman, J., Y. Cheng, and P.E. Todd. 2004. “Evaluating Preschool Programs When Length of Exposure to the Program Varies: A Nonparametric Approach,” _Review of Economics and Statistics_ , 86(1): 108-132.

- Block, S.A., L. Kiess, P. Webb, S. Kosen, R. Moench-Pfanner, M.W. Bloem and C.P. Timmer. 2004. “Macro shocks and micro outcomes: child nutrition during Indonesia’s crisis **,”** _Economics & Human Biology,_ 2(1): 21-44.

- Chomitz, Kenneth, Gunawan Setiadi, Azrul Azwar, Nusye Ismail and Widiyarti. 1998. “What do Doctors Want? Developing Incentives for Doctors to Serve in Indonesia's Rural and Remote Areas,” The World Bank, Policy Research Working Paper 1888.

- Duflo, E.  2001. “Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment,” _American Economic Review_ , 91(4): 795-813.

- Frankenberg, Elizabeth. 1995. “The Effects of Access to Health Care on Infant Mortality in Indonesia,” _Health Transition Review_ , 5, 143-163.

- Frankenberg, E. and D. Thomas. 2001. “Women’s Health and Pregnancy Outcomes: Do

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set, a large cross-sectional survey of adolescent students in Indonesia that is the baseline of an impact evaluation of a school-based program, and the measures of the outcomes and the main variables of interest. Section 4 describes our methodological approach and the limitations of our data and analysis. Section 5 presents the results for our three research questions. We then conclude with the possible policy implications of our findings.

# **2. What are aspirations and expectations?**

Aspirations are desires for the future that motivate. They can be about any desirable goals: completing a given education level, working in a dream job, getting married, or reaching a high social status (Ray 2006). They depend on awareness about possible opportunities, for example knowing about various educational pathways or types of jobs. They also depend on preferences, which are heavily influenced by the social groups one belongs to (Ray 2006; Genicot and Ray 2020). Once set, aspirations drive choices and effort towards them (Dalton, Ghosal, and Mani 2016). In this paper, we study educational aspirations, conceptualized as preferences for the ideal future completed level in a hypothetical scenario in which one had no constraints.

Expectations are the beliefs of the most likely outcome given one’s perceived current situation and constraints (Bernard and Taffesse 2014; La Ferrara 2019). They may coincide with aspirations, and their formations are undoubtedly related. However, expectations capture a different concept: one’s perceived likeliness to achieve one’s goals. Note that constraints are _perceived_ , not necessarily _actually_ impossible to overcome. For example, students can perceive the actual constraint of the amount of money they and their family can allocate to their schooling. However, they can also perceive economic constraints, such as schooling costs, which may or may not be actually binding, for example if the schooling cost can be overcome with a scholarship.

A handful of studies with measures of both aspirations and expectations suggest that they are different concepts. Given that aspirations are ideal outcomes and expectations are most likely ones, expectations are on average lower or at maximum equal to aspirations. In rural Ethiopia, a sample of adults had on average only slightly lower expectations for their children’s education than aspirations — 12.4 and 12.9 years, respectively — but had much lower expectations in terms of income, wealth, and social status, than the level they aspired to (Bernard et al. 2014).<sup>2</sup> Likewise, in a sample of poor households in Colombia, 14 percent of parents expected to complete university themselves while 33

> 2 Measures of expectations in the four dimensions were based on a survey question with a time horizon of ten years, while the aspirations question were based on wished achieved level in life, which may have influenced differential aspired and expected levels.

6
Policy Research Working Paper 5023

# **Abstract**

The human consequences of the current global financial crisis for the developing world are presumed to be severe yet few studies have quantified such impact. The authors estimate the additional number of infant deaths in sub-Saharan Africa likely due to the crisis and discuss possible mitigation strategies. They pool birth-level data as reported in female adult retrospective birth histories from all Demographic and Health Surveys collected in sub-Saharan Africa nations. This results in a data set of 639,000 births to 264,000 women in 30 countries. The authors use regression models with flexible controls for temporal trends to assess an infant’s likelihood of death as a function of fluctuations in national income. They then apply this estimated likelihood to expected growth shortfalls as a result of the crisis. At current growth projections, their estimates suggest there will be 30,000– 50,000 excess infant deaths in sub-Saharan Africa. Most of these additional deaths are likely to be poorer children (born to women in rural areas and lower education levels) and are overwhelmingly female. If the crisis continues to worsen the number of deaths may grow much larger, especially those to girls. Policies that protect the income of poor households and that maintain critical health services during times of economic contraction should be considered. Interventions targeted at female infants and young girls may be particularly beneficial.

This paper—a product of the Poverty and Inequality Team and Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to explore the economic and human consequences of economic crises. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jfriedman@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
migration rate is an extremely strong determinant of whether a household has a migrant member, with the F-statistic on the 1924 migration rate above 30. Therefore our estimation does not appear to be subject to weak instrument concerns.

# **3.1 Threats to Instrument Validity**

Our identifying assumption is that the historic community migration rates, reflecting the pattern of early development of the railroad system in Mexico, do not affect child health outcomes over 70 years later apart from their influence through current migration. Our instrumental variables estimation relies on this exogeneity assumption, and therefore it is important to consider possible threats reasons to its validity. We consider two such threats. The first is that different states in Mexico may have different disease environments which have persisted through time. Then, for example, households in the 1920s may have migrated to escape from states with poor health conditions, and households today may continue to do the same. If this were true, we would find households in high migration states having worse health outcomes, not as a result of migration, but as a consequence of the persistent poor health conditions in those states. We examine this possibility in Table 3 by testing for independence between the infant mortality rates in 1930 taken from INEGI (2001), the earliest available at the state level, and the 1924 state level migration rates. The Spearman rank correlation coefficient of 0.23 is insignificantly different from zero, so we cannot reject the null of independence. That is, there is no significant relationship between historic infant mortality and historic migration. Nevertheless, we will include the 1930 state infant mortality as an additional control in our analysis.

A second potential threat is that in addition to spurring migration, the pattern and timing of the historical development of the railroads led to more economic development and, in particular, was accompanied by greater development of health infrastructure. As such, the historic migration rate in a state could be positively correlated with the current level of health infrastructure in that state. Table 3 examines the relationship between the 1924 migration rate and four measures of statelevel health care provision in 1996 taken from Secretaría de Salud (1997): doctors, hospital beds, nurses, and hospitals per 100,000 of population. High migration states have significantly more hospital beds per capita, but no more doctors, nurses, or hospital buildings. As a final check on overall economic development, we see a marginally significant positive correlation between

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**MONGOLIA'S INFORMAL SECTOR -**

**Appendix B: Methodologies and Descriptive Statistics 40**

|8.|Which times of day do you usually drive a taxi?<br>in the morning<br>during the day<br>during the evening<br>at night|Y<br>Y<br>Y<br>Y|N<br>N<br>N<br>N|f241<br>**_[25]_**<br>[26]<br>[27]|
|---|---|---|---|---|
|9.|Who owns this automobile?<br>(D I own it.<br>T An economic entity owns it.'<br>|||[28]|
||' A relative owns it.<br>) A budget organization owns it.<sup>9</sup>'<br>0 A private person who is not a relative owns it.||||
|10.|When you drive a taxi for a full day (8 hours), how much money do you usually earn after<br>subtracting the cost of gasoline, repairs, maintenance and garage? (Do not subtract wages.)|||[29]|
|11.|What year was this car produced?|||[30]|
|12.|Which grade of fuel do you use? ((T=76, ©=93, *=diesel)|(x||[31]|
|13.|How much do you estimate this car is worth on today's market (dollars or th.tog)<sup>92</sup>?|||[32]|
|14.|How much do you pay each night for garage expenses?<br>(If you own your garage, then estimate how much you could earn per night by renting it.)|||[33]|
|15.|Do you keep written records of your transactions (purchases, revenues, profits, etc.)?|Y|N|f34,|
|16.|Do you currently make Social Insurance or Health Insurance contributions from your taxi income?|Y|N|[35]|
|17.|Do you or any of your employers currently make contributions to the Health Insurance Fund?|Y|N|E3,3|
|18.|Do you or any of your employers currently make contributions to the other Social<br>Insurance Funds?|Y|N|[371|
|19.|Does your spouse (if married) work for an employer who makes contributions to any of the<br>Health or Social Insurance Funds?|Y|N|[38]|