rafmacalaba/datause-encoder-data huggingface dataset:
# **I. Introduction**

Almost 1.2 billion people in the world live without electricity. A billion more only have an unreliable and intermittent supply. Maintenance failures, transmission congestion, and inadequate generation all contribute to frequent and long hours of blackouts in the developing world. According to World Bank Enterprise Surveys, firms in a third of developing countries experience at least 20 hours of power outages in a typical month. The situation is even worse in South Asia, where businesses report almost one outage a day, lasting on average 5.3 hours each.

Power outages impose sizable costs on households, firms, and the environment. Lack of access to reliable electricity is a major barrier to economic advancement for underserved populations (Samad and Zhang, 2016 and 2017). Unreliable power supply also negatively affects firms’ productivity, resulting in lost output and revenue (Reinikka and Svensson 2002; Alby, Dethier et al. 2011; Allcott, CollardWexler et al. 2014, Andersen and Dalgaard, 2013, Grainger and Zhang, 2017a, b). Blackouts also lead to environmental damages when households have to rely on kerosene for lighting and businesses use diesel-based self-generators. Both kerosene lamps and diesel generators are major sources of black carbon, contributing to climate change and degraded air quality.

Although power outages have severe economic and environmental consequences, tracking when and where power outages occur is often difficult. Data on loadshedding are often not reported consistently and are always difficult to acquire. Household surveys based on a Multi-Tier Framework developed by the World Bank offer an alternative way to monitor quality of power supply. But household surveys are costly to implement, representative at a highly-aggregated level, and only reflect a snapshot in time due to limited frequency.

In this paper, we introduce a new method of using high-frequency satellite imagery of nightly lights data to detect near real-time power outages at a highly disaggregated level. This measure identifies outage-prone areas by detecting excess fluctuations in light outputs. To develop this measure, we process the complete historical archive of sub-orbital DMSP-OLS nighttime imagery captured over South Asia on every night since 1993, including 5 terabytes of data encompassing some 30,000 visible band images. Our approach is a departure from prior research. Most earlier analysis of nighttime lights data uses annual composite images which describe the average brightness of a locality over a calendar year. While annual composites provide a useful estimate of average brightness, they cannot be used to assess volatility of brightness which indicates disruptions to power supply. To the 2
Data for this market are available from the beginning of 2011 and the annual average price (Figure 12) has fallen steadily from a peak reached in 2013 of 8 pesos/kwh to the 2017 value of 3.4 pesos/kWh (US$ 0.068). These gains are passed on to consumers through the pricing formula determined by the ERC. Some of these gains are due to the large fall in oil prices from 2013 to 2016, but the declining importance of oil in the power sector indicates that other forces were involved in the reduction of the WESM price.

**Figure 11: Annual average wholesale electricity spot market price (pesos/kwh)**

<!-- Start of picture text --> 9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> _Source_ : Philippines Electricity Market Corporation: WWW.wesm.ph At a utility level, efficiency measures related to distribution and collection losses for MERALCO are shown in **Figure** **_13_** and for BENECO in **Figure** **_14_** . In both cases the total percentage loss of revenue due to collection losses and T&D losses fell over the period.<sup>13</sup> MERALCO experienced collection losses only during a three-year period with other years seeing a collection rate of at least 100 percent. Distribution losses declined to about 8 percent of production (equivalent to 0.8 percent of revenue). BENECO experienced larger losses, with collection rates dipping to 97 percent in 2011-2012, and distribution losses at 10 percent of output. Together these losses were equivalent to 2 percent of BENECO’s revenue. An index of average tariffs (relative to the 1992 value) in local currency and in US $ is included as a reference.

**Figure 12: Evolution of MERALCO’s operational inefficiencies and average tariffs, 1992-2015**

> 13 There were no data on collection losses of MERALCO prior to 2005, and no data for either loss for BENECO prior to 2009.

35
**Table 2.5 Private credit by banks and other financial institutions as a percentage of GDP**

||Private credit by<br>deposit money<br>banks as % GDP|Private credit by<br>deposit money<br>banks (2006)|Private credit by deposit<br>money banks and other<br>financial institutions as %<br>GDP|Private credit by deposit<br>money banks and other<br>financial institutions<br>(2006)|
|---|---|---|---|---|
|Country|end-2006|(US$ millions)|end-2006|(US$ millions)|
|Benin|16.1|834.8|16.1|834.8|
|Burkina Faso|19.2|1,083.6|19.2|1,083.6|
|Cameroon|9.2|1,724.6|9.2|1,724.6|
|Cape Verde|63.7|585.6|63.7|585.6|
|Chad|2.8|170.8|2.8|170.8|
|Congo, Dem. Rep.|2.1|149.4|2.1|149.4|
|Côte d’Ivoire|14.9|2,563.3|14.9|2,563.3|
|Ethiopia|23.3|3,090.6|26.9|3,576.1|
|Ghana|19.9|2,231.9|19.9|2,231.9|
|Kenya|25.1|5,868.6|25.9|6,064.3|
|Lesotho|7.5|125.8|7.5|125.8|
|Madagascar|10.9|591.5|10.9|591.5|
|Malawi|9.0|199.1|12.1|268.7|
|Mozambique|11.3|856.9|11.3|856.9|
|Namibia|61.5|3,936.4|61.5|3,936.4|
|Niger|9.0|319.4|9.0|319.4|
|Nigeria|12.6|15,012.6|12.6|15,012.6|
|Rwanda|13.9|296.6|13.9|296.6|
|Senegal|26.7|2,221.8|26.7|2,221.8|
|South Africa|76.5|194,296.7|144.8|367,986.3|
|Sudan|13.8|5,127.1|13.8|5,127.1|
|Tanzania|11.7|1,542.6|11.7|1,542.6|
|Uganda|8.1|734.4|8.1|734.4|
|Zambia|8.1|842.8|8.1|842.8|
|Chile|70.9|96265.9|86.5|117,489.1|
|Malaysia|117.6|174885.2|117.6|174,885.2|

Source: IMF IFS June 2007.

Note: Private credit by deposit money banks is calculated as claims on the private sector by deposit money banks.

The longest reported maturities for bank loans in the focus countries are still several years shorter than in Chile (see table 2.3), in which the longest terms for bank loans are 25 years (for road construction).<sup>18</sup> Financial sector officials in Ghana, Lesotho, Namibia, South Africa, Uganda, and Zambia reported maximum maturity terms of 20 years, the longest such maturities among the focus countries. Eight other countries reported maximum loan maturities of “10 years plus,” while maximum maturities in four countries were reported as five or more years. Even where 20-year terms are reportedly available, they may not be affordable for infrastructure purposes. In Ghana and Zambia, for example, average 18 According to La Superintendencia de Bancos e Instituciones Financieras (SBIF), Chile’s regulator of banks and other financial institutions.

16
Table 1a: Input counts in manufacturing <u>plants, 2000</u>

|Organized||Unorganized sect<br>|or<br>|
|---|---|---|---|
|sector|Total|Male owned|Female owned|
|A. Input counts among all plants||||
|Mean value<br>2.60†|2.35|2.44|1.91|
|Standard deviation<br>1.79|1.48|1.52|1.18|
|Median value<br>2.00|2.00|2.00|2.00|
|B. Input counts among plants in urban a|reas|||
|Mean value<br>2.78†|2.53|2.64|2.00|
|Standard deviation<br>1.81|1.53|1.56|1.25|
|Median value<br>3.00|2.00|2.00|2.00|
|C. Input counts among plants in rural ar|eas|||
|Mean value<br>2.32†*|2.09*|2.15*|1.77*|
|Standard deviation<br>1.74|1.35|1.40|1.06|
|Median value<br>2.00|2.00|2.00|2.00|

Notes: Descriptive statistics taken from Annual Survey of Industries (ASI) and National Sample Statistics (NSS). Input counts range from zero inputs to a maximum of five inputs. An asterisk * indicates rural-area mean is different from urban-area sample at 10% level of significance. The symbol † denotes organized sector mean is different from unorganized sample mean at 10% level of significance. Appendix Table 1 provides additional descriptive statistics for district-level conditions.

Table 1b: Table 1a using normalized input counts

|Organized||Unorganized sect<br>|or<br>|
|---|---|---|---|
|sector|Total|Male owned|Female owned|
|A. Input counts among all plants||||
|Mean value<br>-0.05†|0.01|0.10|-0.38|
|Standard deviation<br>1.67|1.32|1.35|1.11|
|Median value<br>-0.17|-0.21|-0.07|-0.49|
|B. Input counts among plants in urban|areas|||
|Mean value<br>0.10†|0.09|0.19|-0.37|
|Standard deviation<br>1.64|1.38|1.41|1.17|
|Median value<br>0.13|-0.06|0.09|-0.49|
|C. Input counts among plants in rural a|reas|||
|Mean value<br>-0.30†*|-0.10*|-0.04*|-0.40*|
|Standard deviation<br>1.68|1.21|1.24|1.01|
|Median value<br>-0.61|-0.39|-0.34|-0.49|

Notes: See Table 1a. Normalized input counts are residuals from a regression of input counts on industry fixed effects and plant size dummies.
# **Appendix: Supplementary Tables**

**Table A1:Means of household outcomes by** **<u>participation status and year, phase 1 districts only</u>**

||**20**<br>**Partic**|**04**<br>**ipant**|**20**<br>**Partic**|**06**<br>**ipant**|**20**<br>**Partic**|**08**<br>**ipant**|
|---|---|---|---|---|---|---|
||**No **|**Yes**|**No **|**Yes**|**No **|**Yes**|
|Phase 1 districts|||||||
|Consumption per capita (Rs/year)|7,401|6,576|9,972|8,549|13,125|12,533|
|Energy intake per capita (Kcal/day)|2,014|2,004|2,446|2,246|2,542|2,444|
|Protein intake per capita (g/day)|45|44|50|47|53|51|
|Total nonfinancial assets per capita<br>(Rs/year)|3,092|2,140|4,446|3,038|6,499|5,601|
|Number of households|1,383|1,008|1,344|998|1,410|1,017|
|Phase 2 and 3 districts|||||||
|Consumption per capita (Rs/year)|7,401|6,576|9,972|8,549|13,125|12,533|
|Energy intake per capita (Kcal/day)|2,014|2,004|2,446|2,246|2,542|2,444|
|Protein intake per capita (g/day)|45|44|50|47|53|51|
|Total nonfinancial assets per capita<br>(Rs/year)|3,092|2,140|4,446|3,038|6,499|5,601|
|Number of households|1,383|1,008|1,344|998|1,410|1,017|

Source: Authors’ computation from household survey and administrative data.

25
unclear. Given country-specific initial conditions, shock., and adjustment packages, country studies are the most effective framework for analyzing the adjustment ptocess.

xi. Further research and analysis that focuies on process monitori.ig is critical. For example, the database can be improved by monitoring key education indicators duning the adjustment process- Research should seek to complement existing comparative statics assessments with analyse3 of the dynamics of sector adjustment. Particular questions for further research that have important implications for the design and implementation of adjustment programs are given below. But even in the absence of precise answers, evidence suggests probable links between adjustment measures and the education sector. Progress is evident in the design of World Bank adjustment lending, but much more needs to be done to address education sector issues.

* How do macroeconomic policies, particularly changes in public expenditure levels, affect the supply of education? How do changes in supply in turn affect coverage, quality, and equity in the education sector?

* How is contraction in the education sector managed--what are the dynamics cf budget cuts among different subsectors of education? What political forces promote or impede adjustments? If resource transfers from higher to primary education are widely recommended, why are they not implemented? How, if at all, can adjustment lending affect institutional bottlenecks?

Is education protected or vulnerable relative to other sectors during periods ,, fiscal retrenchment, increasing debt, or civil service reform? Does education vulnerability vary by region or income level? If differences exist, can they be explained by differences in the adjustment process between country groups? What types of policies or conditions enable a country to protect education (i.e., deliver a reasonable level of service), and specifically basic education?

_e '0To_ what extent have local government, community, private, or household resources replaced declining government resources? How do decentralization and cost recovery in turn affect coverage, quality, and equity?

**_o_** How and why has demand for education changed during adjustment? How do changes in incomes (the household's or individual's ability to invest in human capital), the opportunity cost of attending school, private returns to investing in education, or health and nutrition affect coverage, quality, and equity?

iv
To construct this data set we standardized international assessments, such as PISA and TIMMS, across types of exams by linking them to the United States as a reference point, since the United States participates in all international assessments. We further standardized tests over time by linking our United States reference point to the National Assessment of Educational Progress (NAEP), which has been administered in the United States since 1969. Finally, we include developing countries that have participated in regional assessments such as LLECE, PASEC, and SACMEQ, by using scores from _doubloon countries_ that participated in both a regional and international assessment as an index.

While our database allows the comparison of many countries over time, the development of this database still requires improvements and extensions. For example, our database should continually be updated with results from the most recent international and regional achievement tests. Additionally, our anchoring methodology for developing countries, which makes use of _doubloon countries,_ could be made more accurate. As time goes on and more Latin American and African countries participate in PISA and TIMMS, this will become increasingly feasible.

Our ultimate goal is to use our extended and updated version of the Altinok and Murseli (2007) database to inform us about which causal inputs lead to better learning. To that end, we provide an application of our international database.

We are interested in governance indicators and macroeconomic variables and identify some insightful associations between our governance indicators and adjusted test score outcomes. Governance indicators involving Economic Freedom and Civil Rights show positive associations with test scores. For a sub-set of Latin American and African countries, however, only Economic Freedom and Democracy significantly affect student achievement. These results are robust to the inclusion of several governance and macroeconomic indicators as well as lagged variables. The use of country and time fixed effects supports a causal interpretation of our results.

Our paper marks only a starting point in benchmarking progress in human capital quality and educational institutions around the globe. We have created one of the first databases on student achievement that is comparable across tests, across countries and over time. We also include the first wide array of developing countries in such an international database. More research must be done in order to improve our approach. In particular, there should be a focus on credibly 33"
# **Table 6 – Trace Statistic for Soybeans**

|**19**|||||||||||||||**16.99**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**18**||||||||||||||**25.97**|**23.48 **||
|**17**|||||||||||||8.48|**18.79 **|**23.32 **||
|**16**||||||||||||**20.57**|**23.41**|**15.91 **|**18.50 **||
|**15**|||||||||||**17.16**|10.02|14.99|9.76|11.02||
|**14**||||||||||**15.51**|10.07|9.85|9.12|7.84|7.52||
|**13**|||||||||**17.43**|**20.73 **|**19.10**|15.33|15.36|7.53|8.71||
|**12**||||||||15.01|8.17|10.03|**20.70 **|12.73|9.70|15.28|12.92||
|**11**|||||||12.16|11.97|4.93|11.39|15.04|7.21|6.99|10.30|8.62|adings.|
|**10**||||||7.48|9.84|7.35|8.20|10.96|**15.41**|7.84|9.99|**17.16**|**17.97**|n row he|
|**9**|||||19.94|5.10|12.35|11.83|8.82|13.57|**16.73 **|7.40|10.48|**16.17 **|**16.61 **|ces as i|
|**8**||||10.04|10.78|9.81|13.21|15.39|12.20|15.10|13.80|11.30|12.73|7.85|7.37|e provin|
|**7**|||13.02|15.53|17.34|22.21|17.17|13.37|8.91|**19.06**|15.09|9.17|10.57|**22.04**|14.46|the sam|
|**6**||15.15|12.20|3.81|5.52|14.64|11.61|14.13|5.54|**19.01 **|**26.80**|11.49|8.43|11.51|18.85|pond to|
|**5**|11.10|**28.51**|9.59|8.19|10.97|8.81|6.76|11.98|6.81|14.52|13.27|6.75|8.42|**15.89**|11.21|**ld**<br>s corres|
|**4**|9.85<br> 12.14|13.46|8.34|**17.22**|**15.94**|6.41|8.95|8.51|7.31|11.70|**17.93**|11.05|**18.30**|**17.61 **|**17.31**|nts in**bo**<br>heading|
|**3**|6.05<br>7.83<br>12.97|10.50|9.90|4.86|5.32|9.20|8.92|11.03|7.19|9.78|**16.48 **|9.49|5.98|8.26|7.68|oefficie<br>column|
|**2**|4.22<br>**16.77**<br>5.38<br>6.26|5.60|11.30|10.45|10.70|4.05|8.20|11.81|9.28|**19.44**|**17.69 **|10.00|**17.97**|**18.68**|**18.00**|ificant c<br>bers in|
|**1**|6.69<br> 13.79<br>6.39<br>7.85<br>**19.47**|10.74|5.72|5.09|4.63|11.36|11.25|7.35|5.30|8.56|**18.08 **|**25.68**|7.65|6.12|9.01|(1) Sign<br>(2) Num|
|**ince**|**ral Java**<br>**ral Sulaw** <br>**Java**<br>**Kalim**<br>**h Sulaw**|**law**|**h Kalim**|**h Sulaw**|**Aceh**|**Bengk**|**Jambi**|<br>**Lamp**|<br>**Medan**|**Padang**|<br>**Palemb**|**Riau**|**Java**|**Nusat**|**akarta**|<br>**Notes:**|
|**rov**|**ali**<br>**ent**<br>**ent**<br>**ast**<br>**ast**<br>**ort**|**E Su**|**out**|**out**|**um**|**um**|**um**|<br>**um**|<br>**um**|**um**|<br>**um**|**um**|**est**|**est**|**ogy**||
|**P**|**1 B**<br>**2 C**<br>**3 C**<br>**4 E**<br>**5 E**<br>**6 N**|**7 S**|**8 S**|**9 S**|**10 S**|**11 S**|**12 S**|<br>**13 S**|<br>**14 S**|**15 S**|<br>**16 S**|**17 S**|**18 W**|**19 W**|**20 Y**||

29
measure (PCA 5), 57.52% is explained by the extensive/operational measures of informality (no license, no registration, no VAT registration number). The remaining 42.48% is explained by the two intensive proxies of informality (contributions to employees’ social security and bank account).

# **3.2.2 Types of Informal Firms: Necessity, Parasite, and De Soto**

Informal firms can either be born out of necessity with a survival motive; are parasitic such that they choose to remain informal to evade costs; or are high-potential but per the De Soto view remain informal due to burdensome regulations. The type of informality is correlated with the initial productivity of firms when they enter the market and is contingent on the human capital of the entrepreneurs (Ulyssea, 2018). We use the pre-pandemic productivity measure to analyze the distributions across formal and informal firms. To this end, we take the following steps: _First_ , we regress productivity on firm attributes such as age and other initial characteristics, including size, sector, whether the firm is female-led, and country fixed effects: Where _γs_ are sector fixed effects; _φc_ are country fixed effects; and _νj_ are size effects, where _j_ =small, medium, and large.

_Second_ , using the residual of this regression, that is, unexplained labor productivity, we calculate the 25<sup>_th_</sup> and the 75<sup>_th_</sup> percentiles of the distribution of this conditional productivity by country and sector for fully formal firms - on both extensive and intensive margins - as measured by PCA(5).

In the last step, we map the distribution of informal firms to one of the three groups: below 25<sup>_th_</sup> percentile; between 25<sup>_th_</sup> and 75<sup>_th_</sup> percentile, and; above 75<sup>_th_</sup> percentile. We expect that informal firms below the 25<sup>_th_</sup> percentile threshold should be more similar to necessity or survival firms as they have very low productivity compared with formal firms of similar characteristics.

For the other two types of informal firms, the distinction is more difficult with our dataset. For example, in contrast with Ulyssea (2018), we do not have the pre-entry productivity signal but labor productivity before the pandemic, implying that the firm has already internalized the cost-benefits trade-offs and decided to remain informal. Given that the costs relative to productivity benefits are lower for parasite firms (compared to De Soto), we expect firms between the bottom and top quartile of the productivity distribution would 8
Policy Research Working Paper 7965

# **Abstract**

This paper studies migration choices in the presence of liquidity constraints and varying costs of migration. This paper presents a simple theoretical framework that analyzes migration response to both push and pull factors in such settings. This framework implies that a shock to the push factors in the origin leads to differential observed response to migration to various destinations, as the shocks affect different parts of the wealth distribution. The implications of this framework are tested in the context of international migration from Nepal, using a panel of 452 villages observed at three periods in the 2000s. The analysis uses rainfall shocks and deaths due to conflict as “push” shocks and growth in manufacturing and construction in destination countries as “pull” shocks. The findings show that a rainfall shock that increases household income by US$ 100 increases migration to India by 54 percent but has no effect on migration elsewhere. An increase in conflict, which reduces consumption and amenity of the wealthier more, increases migration abroad, especially from urban areas. An increase in demand from the destination countries, especially the Gulf countries and Malaysia, has strong effects on migration to those destinations. These findings are consistent with the theoretical framework, and suggest the presence of large liquidity constraints. An increase in income can boost migration to India, whereas a reduction in the cost of migration might increase profitable migration elsewhere. The responsiveness to “pull” shocks suggests that households are willing to take advantage of these opportunities.

This paper is a product of the Social Protection and Labor Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mshrestha1@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