rafmacalaba/datause-encoder-data huggingface dataset:
of Mooers et al. (2008).<sup>9</sup> For species indicator construction, we normalize these probabilities so that a weight of 1.0 is assigned to species in the highest category (Critically Endangered). Table 7 tabulates conversions from Red List codes to normalized species weights, using four probability assignments. Three employ IUCN estimates to derive measures of extinction probability over the next 50, 100 and 500 years. The fourth draws on Isaac et al. (2007), who combine a direct extinction risk measure with a measure of each species' isolation on a phylogenetic tree.<sup>10</sup>

**Table 7:  Normalized species aggregation weights**<sup>a</sup>

|||Normal|ized Extin<br>IUC|ction Proba<br>N: Future Y|bilities<br>ears|
|---|---|---|---|---|---|
|IUCN||||||
|<br>Code|Status|Isaac<sup>b</sup>|50|100|500|
|CR|CriticallyEndangered|1.00000|1.00000|1.00000|1.00000|
|EN|Endangered|0.50000|0.43299|0.66770|0.99600|
|VU|Vulnerable|0.25000|0.05155|0.10010|0.39000|
|NT|Near Threatened|0.12500|0.00412|0.01000|0.02000|
|LC|Least Concern|0.06250|0.00005|0.00010|0.00050|
||Rounded Weight Ratios|||||
||CR:EN|2|2|1|1|
||CR:VU|4|19|10|3|
||CR:NT|8|243|100|50|
||CR:LC|16|20,000|10,000|2,000|

a Data source: Mooers et al. (2008).

b From calculations by Mooers et al., based on Isaac et al. (2007).

Table 7 shows that Isaac’s inclusion of the phylogenetic isolation factor changes the weight ratios substantially, particularly for species in the lowest threat category (Least Concern). We explore the implications for hypothetical areas A and B in Table 8. A is populated by only 2

> 9 The IUCN's current classification categories are Critically Endangered, Endangered, Vulnerable, Near Threatened and Least Concern.

> 10 A phylogenetic tree is a branching tree diagram that traces the evolutionary descent of different species from a common ancestor.  Species in sparse (isolated) branches of a phylogenetic tree are relatively unique, since they share common descent patterns with fewer other species.

40
season bound (cassava). Among staple grains, seasonality is highest for maize (33.1 percent on average) and lowest for rice (16.6 percent). These gaps are two and a half to three times higher than on the international reference markets, pointing to substantial excess seasonality. While excess seasonality is observed in virtually all the maize and rice markets studied, there is wide heterogeneity within and across countries. Seasonality is especially high in Malawi, where maize is also the main staple, causing a double seasonality burden for most households.

In what follows, section 2 sets the stage by reviewing general considerations on the data, seasonality metrics and the overall estimation approach. Section 3 looks at the commonly used methods for estimating the seasonal gap and shows that these can result in upwardly biased estimates when data samples are short. The performance of alternative and more parsimonious seasonality models is examined in section 4. Section 5 introduces the price data from the 13 commodities and seven African countries examined here and discusses the findings. Section 6 concludes.

# **2 Material, metrics and method – general considerations**

Many developing country governments publish monthly prices for staple food commodities for major locations in their territories. These prices are obtained by sending observers to markets in these locations, who record the prices at which the different commodities are transacted. It is unclear how much intra‐month averaging is undertaken, but at least for some countries (e.g. Uganda), the monthly prices derive from weekly observations. Much of this price information results from the FEWSNET initiative, supported by USAID, and the FAO’s GIEWSNET initiative.

Three features of these price data stand out. First, the price data collection initiatives are relatively recent, so that the time series available are usually short. Second, in many of the price series, the frequent occurrence of missing observations compounds the short duration of the series. Gaps may arise for example because the observers did not see transactions in the foods in question when they visited the markets. In some other instances, prices are missing for all locations in a particular month suggesting an administrative explanation. Finally, in most countries, only a small number of (mainly urban) locations (five to 15) are covered, although some governments (Malawi in our sample) attempt to be more comprehensive. These 4
- 25 -

# Tabe.2J Coa'd.: _Fuel_ SuM In lbeUl Powr G_entl In am O.ILC.D. Coutrie in 1976

||Coal d<br>Nohe<br>SoDdFuel|Oil|as|
|---|---|---|---|
|Puero Rico|0.0|100.0|0.0|
|Rwands<br>|0.0|100.0|0.0|
|St.He<br>|0.0|102.0|0.0|
|Saudi Aabia|0.0|71.0|29.0|
|Senegl|0.0|100.0|0.0|
|Seyhee|0.0|100.0|0.0|
|SIgao|0.0|100.0|0.0|
|SouthAfIca<br>|100.0<br>|0.0<br>|0.0<br>|
|SdLanka|0.0|100.0|0.0|
|Sudan|0.0|100.0|0.0|
|Thaa<br>|7.1|92.9|0.0|
|m1h5a|0.0|46.|_53._|
|U. Rep. of Camuon|0.0|100.0|0.0|
|Uruuy<br>|0.0|100.0|0.0|
|Venmijel|0.0|27.9|72.1|
|Za|0.0|100.0|0.0|

-o=: As for table 2.10.
**Table 2: Employment rates among 20- to 64-year-olds in 2008 and national targets for 2020**

||Employed as a percentage of people<br>between 20-64(2008)|National Targets(2020)|
|---|---|---|
|Bulgaria|70.5|76|
|Czech Rep|70.6|75|
|Estonia|76.4|76|
|Hungary|65.4|75|
|Latvia|74.5|73|
|Lithuania|73.5|73|
|Poland|65.7|71|
|Romania|68.5|70|
|Slovakia|71.9|72|
|Slovenia|65.6|75|

_Source:_ World Bank staff calculations using EU-SILC data.

Significant variation is observed in the _early school leaving rate_ . In 2008, Slovakia (2.5 percent), Poland (3.8 percent), and Slovenia (4.1 percent) had relatively low early school leaving rates, while Bulgaria (19.6 percent), Latvia (15.9 percent), and Romania (15.3 percent) had relatively high rates (Table 3). With an indicative European target of 10 percent, countries with high early school leaving rates have generally set themselves ambitious goals. For example, Bulgaria expects to reduce its early school leaving rate from 19.6 percent in 2008 to 11 percent by 2020, and Romania expects to reduce its rate from 15.3 percent to 11.3 percent.

**Table 3: Early school leaving rates in 2008 and national targets for 2020**

||Earlyschool leavers(percent)<br>Na|tional Targets: Early School<br>Leaving (percent)|
|---|---|---|
|Bulgaria|19.6|11|
|Czech Republic|6.3|6|
|Estonia|13.8|10|
|Hungary|12.0|10|
|Latvia|15.9|13|
|Lithuania|4.8|6|
|Poland|3.8|5|
|Romania|15.3|11|
|Slovakia|2.5|6|
|Slovenia|4.1|5|

_Source:_ World Bank staff calculations using EU-SILC data.

The _tertiary education completion rate_ varies between a high of 45.2 percent in Lithuania and a low of 15.9 percent in the Czech Republic (Table 4). Five NMSs have set targets that fall below the 40 percent region-wide target set for the EU-27 countries, given the challenges this target poses for them. To illustrate, consider that to reach the national targets by 2020, Bulgaria will have to increase its 2011 tertiary education completion rate by 50 percent, the Czech Republic by 30 percent, and Romania by 25
# **Examining the Economic Impact of COVID-19 in India through Daily Electricity Consumption and Nighttime Light Intensity**

**Robert C. M. Beyer**<sup>1</sup>

**Sebastian Franco-Bedoya**<sup>2</sup>

**Virgilio Galdo**<sup>3</sup>

**Keywords:** COVID-19, lockdown, electricity consumption, nighttime light intensity, India **JEL codes:** E01, E65, O53, Q43, R11

> 1World Bank, email: rcmbeyer@worldbank.org.

> 2World Bank, email: sfranco2@worldbank.org.

> 3World Bank, email: vgaldo@worldbank.org.

> This paper should not be reported as representing the views of the World Bank. We are grateful to Hans Timmer, Valerie Mercer-Blackman, Phil Hammons, Dhruv Sharma, Rangeet Gosh, and Rishabh Choudhary for excellent comments and suggestions.
# **Figure 6 Exports Produced in Technology-Driven Industries**

<!-- Start of picture text --> 1995 1996 1997 1998 1999<br>50<br>40<br>30<br>%<br>20<br>10<br>0<br>Bulgaria Czech Estonia Hungary Latvia Lithuania Poland Romania Slovak Slovenia<br>Rep. Rep.<br><!-- End of picture text --> _Source_ : WIIW, 2001

- Exports generated by labor-intensive industries account for a growing share of exports in Bulgaria, Romania and the Baltic states and for a declining proportion of exports in the Czech Republic and Hungary.  In Latvia, the share of exports produced by labor intensive industries almost doubled in 5 years, growing from 34% of exports in 1995 to 63% in 1999 and is the highest among all NMS’s.

**Figure 7 Exports Generated by Labor-Intensive Industries**

<!-- Start of picture text --> 1995 1996 1997 1998 1999<br>70<br>60<br>50<br>40<br>%<br>30<br>20<br>10<br>0<br>Bulgaria Czech Es to nia Hungary Latvia Lithuania P o land Ro mania Slo vak Slo venia<br>Rep. Rep.<br><!-- End of picture text --> _Source_ : WIIW, 2001

- Latvia’s cumulative trade deficit was approximately $3.9 billion from 1997-2001. This consisted of a growing surplus in labor-intensive sectors, especially wood products and textiles, and a large deficit in knowledge-intensive sectors: chemicals, machinery, electrical and optical equipment, and transport equipment.

16
- 12 -

of female trainees aged 25 years and under have found employment, as compared to 32 percent of the control group. For the sample of young females, these differences disappear over the course of the first year: after nine months the survival curves for trainees and controls are virtually identical. For the sample of older women, the trainee-control difference increases over the year so that 75 percent of trainees have left unemployment after 12 months, as compared to 47 percent of controls.

We also compared employment effects for women with different degrees of attachment to the labor force. If training is enhanced by initial skill or schooling endowments, we might expect training effectiveness to be diminished for women with low attachment to the labor force because of skill obsolescence. To explore this hypothesis, we distinguish between (1) the women that worked sometime in the six months prior to training, and (2) those who were without work for a longer period of time. Figure 2 shows the survival curves for these two groups of women. In both cases, the trainees fared better than the controls. However, trainees who recently left employment exit unemployment after training more quickly than women who were reentering the workforce after a long inactive spell.

Table 4 presents the results of estimating a cox proportional hazards model of unemployment duration for the pooled sample of women. They suggest that differences in exit rates between trainees and controls disappear once account is taken of individual characteristics. The coefficient on the training indicator variable is invariably close to zero; however, this result is due in large part to the unemployment effects of labor force attachment shown in Figure 2. When an interaction term between training and duration of prior unemployment is included in the cox model, the results suggest that women who entered training after a relatively short spell of unemployment exit more quickly than controls; those who entered training after a long spell out of the labor force exit more slowly.

# EmRloyment Probabilities Over Time

We also compared employment probabilities of trainees and controls over progressively longer intervals of time. The ENEU reports the labor market status of the control group at three, six, nine, and twelve months after the third quarter of 1990 (when we first observe them). For PROBECAT trainees, we use start and end dates from their retrospective histories to define labor market status variables for comparable intervals of time after the completion of training.
2 credit to banks) and (b) credit issued to nonfinancial private firms divided by GDP. Financial systems that primarily fund private firms probably provide more services than financial systems that simply funnel credit to the government or state enterprises. Although each financial indicator has shortcomings, using this array of indicators provides a richer picture of financial development than if we used only a single measure.

In the tradition of recent cross-country studies of growth, we study the relationship between financial development and long-run output growth. Furthermore, we undertake a preliminary exploration of the "channels" through which financial development is linked to growth by examining two sources of growth. First, we study the rate of physical capital accumulation, measured both as an estimate of the per capita growth rate of physical capital and the ratio of investment to GDP. Second, we study improvements in the efficiency with which society allocates capital, which we measure as a growth residual after controlling for physical capital accumulation. For short, we refer to per capita GDP growth, the rate of capital accumulation, and improvements in economic efficiency as "growth indicators." We report two sets of findings. The first set involves the strength of the contemporaneous relationship between financial development and the growth indicators; we study the strength of the partial correlation of the average level of financial development over the 1960-1989 period witih the average rate of real per capita GDP growth, the rate of physical capital accumulation, and the rate of improvement in economic efficiency over the same period. We find that higher levels of financial development are positively associated with faster rates of economic growth, physical capital accumulation, and economic efficiency improvements both before and after controlling for numerous country and policy characteristics.

The second set of findings focuses on the relationship between financial development and future rates of long-run growth, physical capital accumulation, and economic efficiency improvements. We find that the predetermined component of financial development is a good predictor of long-
these very specific gaps that incorporating employee characteristics will likely become a critical part of the analytical work.<sup>12</sup> The remainder of this document is as follows. In Section II, we discuss the construction of the data set and broad patterns in the data that illustrate the institutional context for our findings. Section III provides the key descriptive findings on salary gaps by gender and nationality in terms of each individual contributor— composition, entry salaries, salary growth and turnover. Section IV discusses the results of our dynamic accounting framework, which allows us to decompose the aggregate and career gaps into each of their sub-components using simulations. Section V concludes.

# II. Data

The World Bank Group's Human Resource Longitudinal Database was constructed in order to better understand salary dynamics and career differences across subgroups such as gender and nationality. The data set is structured in a panel from 1987 to 2015 with staff uniquely identified through a universal personnel identifier (UPI) that never changes for an individual, even across disjointed employment spells. The data are gathered from two human resource databases at the WBG—PeopleSoft/Business Intelligence and Talent Management—with data from each year taken as a snapshot on June 30.

PeopleSoft/Business Intelligence contains information on the staff’s universal personnel identified (UPI); compensation and benefits (e.g. salaries); personal backgrounds (e.g. gender, age); professional situation (e.g. professional grade); location (e.g. HQ or country-office based) and role and movements within the organization (e.g. promotions and lateral moves). In addition to these, information on the yearly performance rating (SRIs), which is available from 2000 onwards, is drawn from the Talent Management database.

Unfortunately, PeopleSoft does not contain reliable data going back to 1987 and multiple changes in the WBG ranging from types and grades of employees to corrections in the employment spells had to be carefully dealt with. A brief description of the types of employees and how they have changed over time is necessary to interpret the results; the accompanying data Appendix and codebook provides further details.

The most important broad distinction between types of employees at the WBG is between staff members and consultants. Our data contain all staff members. The data set has no information on Short-Term

> 12 We caution that the WBG’s data on pre-entry characteristics of staff are incomplete. Some of this is because we have staff members in our data who were hired as far back as 1955, but this is also because pre-entry data on staff are not standardized. They are based on individual CVs submitted by staff and the information contained in these CVs can vary dramatically. As one example, the variable that captures the highest educational degree is missing for 55 percent of staff in the data.

7
BNLA survey provides rich information on labor outcomes such as (current) employment status, work sectors, labor income, and receipt of government benefits. Following the existing literature (e.g., Beaman, 2012; LoPalo, 2019), we use two measures of labor market outcomes available from the BNLA: (i) employment status conditional on being in the labor force (i.e., this variable equals one if having a paid job in the last seven days and zero otherwise); and (ii) weekly labor income (as measured by total real income of all jobs in thousand Australian dollars). We also examine other outcomes such as refugees’ own labor market outcomes (including labor force participation (LFP), employment type, and employment skills levels and sector) and life satisfaction outcomes as well as their partners’ and children’s outcomes (including partner’s employment status, child mental health, and school performance).

Mental health of refugees is measured by the six-item Kessler Screening Scale for Psychological Distress (K6), developed by Kessler et al. (2002). The six items ask individuals how often, during the past four weeks preceding the survey, an individual felt: (1) nervous, (2) hopeless, (3) restless or fidgety, (4) so depressed that nothing could cheer them up, (5) everything was an effort, and (6) life is meaningless (worthless). The answers are subsequently scored on a five-point scale, with total scores ranging from 6 to 30, where a higher score indicates worse mental health. For easier interpretation, we follow the common practice in the literature by standardizing mental health scores, such that the total score has a mean of zero and a standard deviation of one (e.g., Frijters et al., 2014). Respondents in the BNLA survey were also asked whether they had experienced or witnessed any traumatic events before arriving in Australia. These include extreme living conditions (e.g., lack of food, water, shelter, or medicine), war/conflict, violence, kidnapping or imprisonment, political/religious persecution, natural disaster, and/or other events. We provide a more detailed description of the data and summary statistics of all the variables in Appendix B. We also supplement our 7
et al., 2025). We extend this line by demonstrating that periods of forced displacement in weak institutional settings can reinforce the power and resources of criminal organizations linked to autocratic regimes. In this regard, we also complement the work analyzing authoritarian regimes (Besley and Kudamatsu, 2008; Acemoglu and Robinson, 2009; Acemoglu et al., 2019; Hsieh et al., 2011; Egorov and Sonin, 2023; Gonz ́alez et al., 2023; Yang, 2024 ). However, rather than focus on the effects of the regimes themselves, we investigate how forced displacement may reinforce the power of such leaders.

Finally, we contribute to the literature by combining unique data sources to study the effects of forced displacement outflows on an origin country with weak institutional development, as is typically the case with emigration of this type. In such settings, data are rarely available (Martinez 2022; Chiovelli et al. 2023), since autocratic regimes prevent their collection and such countries are often so profoundly in crisis that data collection becomes extremely challenging. For this reason, we integrate data from multiple unique sources including labor force surveys, remote-sensing variables, population censuses, and surveys of Venezuelan migrants in Colombia, as well as web-scraped digital data on electoral results, private-firm expropriations, and international economic sanctions. We can thus comprehensively assess the complexity of forced displacement and its wide-ranging effects in origin locations.

# **2 Local Context: Venezuela’s Unraveling**

Venezuela, a historically unequal society highly reliant on oil, entered an economic recession in the late 1990s due to falling oil prices and a downturn in its non-oil sector, which fueled widespread discontent and led to the election of Hugo Ch ́avez as president in 1998 (Hausmann and Rodr ́ıguez 2014). Ch ́avez’s presidency brought sweeping changes, including constitutional reforms that concentrated power in the executive branch and weakened political accountability. His administration also expropriated private firms, spurring a sharp contraction of the private sector (Panel A, Figure A.1). Despite early political instability 10