rafmacalaba/data-use-mentions-extended huggingface dataset:
Although it is rare to have data that both contain detailed information on firms and the workers they employ and are as comprehensive in coverage over the span of a crisis period, some limitations of the SI data should be kept in mind. First, the census only includes firms with 20 or more employees and is consequently not representative of the entire manufacturing sector. Moreover, ̳entry‘ is defined as entry into the survey, which happens when a new establishment with 20 or more employees is started or when a pre-existing establishment passes this threshold. Similarly, ̳exit‘ could be due to firm closure or dropping below the threshold. Any temporary lapses in reporting are not counted as exits.<sup>13</sup> Second, only net changes in employment at the firm-level are observed. This could understate the total amount of churning that takes place. Moreover, we do not know whether a reduction in firm size is accompanied by forced layoffs or achieved by natural attrition, that is, whether workers leave voluntary or involuntary. A related concern is that the definition of the employment variable changed slightly in 2001, with unpaid workers no longer being reported separately. However, the number of such workers had not been large, and the results are robust to excluding the period 2001-2004.<sup>14</sup> Moreover, our occupational categorization of workers in to blue-and white collar categories is crude and we may consequently fail to document subtler gender differences occurring at finer levels of aggregation.

In addition, the data do not contain information on individuals. For example, we have no information on the number of hours they work, their age, their tenure and experience. Consequently, changes on the intensive margin cannot be identified and it is not possible to control for workers‘ observed and unobserved personal characteristics.

Appendix B presents supporting evidence on the evolution of employment and wages in the broader manufacturing sector based on repeated representative cross-sectional labor force surveys, the SAKERNAS data. This analysis corroborates the results presented below and
>1718</sup> We also investigate whether those who were more idle prior to being employed benefit more from employment. We find no impact along this margin, suggesting that the elimination of boredom _per se_ is not the driving force behind the psychosocial value of employment (Appendix Table A10).

> 17Most respondents do not track their day by time, making collection of reliable time use data challenging (though recent literature documents the broader unreliability of such data). We piloted a variety of strategies, and settled on asking respondents how much time they spent on a set of activities in the previous day. Methods such as calling respondents at various times of day to document their activities were not feasible in the camps given the lack of mobile phones.

> 18Given that individuals in the employment arm report spending an average of 2.5 hours per day engaged in the work assignment, the absence of time use effects is perhaps surprising. Given the distribution of work time over the course of the day (40 minute intervals), we suspect that rather than supplanting any single activity during a worker’s day, the task instead shed a few minutes off of many activities. Both recollection and detection of these small margins of substitution are less likely. As such, while we cannot rule out that the effects of the employment arm may arise from small changes in how time is spent, our results suggest that substantial differences in time use are unlikely to drive the non-pecuniary impacts we document.

22
and electricity consumption. We find that electricity consumption increased by 0.95 percent for each percent additional economic activity. For India, electricity increased slightly above 1 percent. Similarly, data on nighttime lights has also shown to be able to track economic activity. Henderson et al. (2012) develop a statistical framework to use satellite data on night lights to augment official income growth measures. They show that for countries with poor national income accounts, the optimal estimate of growth is a composite with roughly equal weights on conventionally measured growth and growth predicted from lights.

In this section, we explore the usefulness of electricity consumption and satellite night light as proxies for economic activity in India. To do so, we estimate the following quarterly (t) model: where the log of GVA is regressed on the log of electricity consumption and the log of nighttime light intensity. In addition, we include a trend and quarter fixed effects, _qt_ , to control of the intra-year seasonality of the data. We aggregate the electricity consumption and nighttime light data to quarterly frequency to match the frequency of the GVA and estimate the model for the period from the second quarter of 2013 to the first quarter of 2020.<sup>12</sup> Table 1: GVA, electricity and nighttime lights

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||log GVA|log GVA|log GVA|log GVA|log GVA|log GVA|log GVA|log electricity|log electricity|
|log electricity|1.296<sup>_∗∗∗_</sup>|0.350<sup>_∗∗∗_
date for the data was August 31, 2014.

Fielded between November 2015 and April 2016, phase II surveyed 24,400 establishments (of an original intended sample of 31,152 establishments drawn from the set of establishments surveyed under phase I).<sup>7</sup> Data collected under phase II included workforce, wages and salaries, stocks, value of fixed assets, quantity and cost of inputs purchased, other

> 6 Ghana’s statistical authorities have administered four previous business establishment censuses focused exclusively on industrial establishments.

> 7 With establishments surveyed under phase I serving as the frame, the intended phase-II sample is comprised of all establishments with workforces of 50 or more workers and a representative sample of establishments with workforces of fewer than 50 workers.

8
data, hidden debt revelations are particularly sizeable during sovereign default episodes and IMF programs, when the sovereign’s books come under close scrutiny.<sup>2</sup> Third, we use our new data to shed light on the role of hidden debts during sovereign default episodes and the debt resolution process. One key concern among bondholders during sovereign restructurings is that hidden debt can dilute the recovery value of their own marketable claims. During recent debt restructuring episodes, for example in Zambia, such concerns amplified coordination issues and led to substantial delays in debt resolution, with potentially severe costs for debtors and creditors alike.<sup>3</sup> To systematically analyze the role of hidden debt in default episodes we combine our data on debt under-reporting with data on the outcomes of all sovereign restructurings with private creditors since 1970. We find that higher hidden debt at the onset of a restructuring is associated with both longer restructuring episodes and larger creditor losses, which suggests that high hidden debts do indeed dilute the recovery value of market investors.

Motivated by these findings, we develop a quantitative sovereign debt and default model (in the tradition of Eaton and Gersovitz, 1981, Arellano, 2008, and Aguiar and Gopinath, 2006) with hidden debt, asymmetric information and an endogenous information acquisition problem that generates hidden debt revelations. The sovereign debtor in the model faces an exogenous hidden debt accumulation process that is not observed by investors in the country’s market debt. Each period, however, the investors need to decide whether to monitor the sovereign’s books. In line with our empirical evidence, the model features a recovery rate for market debt that is diluted by undisclosed hidden debts of the sovereign. This aspect gives bond investors further incentives to monitor the sovereign, therefore triggering revelations of hidden debt, particularly during bad times when sovereign default risk is high, just as we observe in the data.

We calibrate this model with our new data and analyze the effects of both hidden debt and its revelation
a panel. Note, however, that market entrance and exit can only be approximated; a firm may no longer be observed if its sales have fallen below the selection threshold or if it has actually left the market. Similarly, market size can only be approximated because the panel is drawn from a selection from the universe of firms, rather than from the whole market. We look specifically at the ICT sector, which we separate into hardware, software, telecommunications, and infrastructure subsectors.<sup>4</sup> The survey collected 2,955 observations from ICT firms over the 11-year period. Of these firms, about 34% were active in markets where competition was enforced. In other words, of 940 distinct firms from all ICT sectors, 57% were subject to competition enforcement in their sub-sector. The largest number of firms produced hardware, which accounted for 48% of total ICT sales in the 2007–17 period; telecommunications accounted for 22% of sales, software for 17%, and infrastructure for 14%. Apart from Hardware, the variations in markup were fairly constant over the sample period (Figure 11).

Most firms surveyed were established in the 2000s because of the inclusion criterion of large firms. Except for telecommunications, ICT employs a large share of labor in valueadded. Telecommunications and infrastructure have the highest shares in concentration (proxied by the four-firm ratio) and markups, calculated from variable profits. 5 This is to be expected given the large entry costs, economies of scale and scope.

ICT regulation and enforcement data are drawn from the Peruvian regulatory authorities: the Ministry of Transport and Communications (MTC), Osiptel for telecommunications and infrastructure, and Indecopi for other regulations pertaining to software and hardware.

The MTC is the technical authority for defining and enforcing policy; one of its goals is to connect economic agents nationwide. It has multiple functions, but we focus on its role in the design, coordination, and evaluation of regulations for infrastructure and telecommunications; the evaluation and
motor skills, psychosocial skills, and language acquisition. These impacts are concentrated among children ages 37 months and older—for the younger children, ages 6-36 months, the estimated program effects are generally insignificant, and are as likely to be positive as negative. When the results are disaggregated by the length of exposure, effects are most clearly observed among children who have been exposed to the PIDI program for more than a year. Behrman, Cheng, and Todd also calculate cost-benefit ratios for the PIDI program. This is an important concern: At the time of the evaluation, the cost of the PIDI program was approximately $43 per month, in a country with per capita annual GDP of $800 in exchange-rate converted pesos, and $2,540 in PPP terms. The PIDI program is estimated to have an impact on height, cognitive development, and schooling of participating children. Behrman, Cheng, and Todd heroically combine these estimated program impacts with data on wages from Bolivia and a number of other countries to argue that there are positive cost-benefit ratios to PIDI under a variety of plausible assumptions and discount rates.

17
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

- v. Begin detailed search of national sources e.g. ministries of energy, industry, and regulatory agencies,

- vi. Agree on required data after looking at all published sources,

- vii. Draft questionnaire for survey of energy ministries and regulators,

- ix. Collate final results,

- x. Post data base on the Internet when finalised,

- xi. Solicit voluntary contributions and updates from authorities, organisations, and companies, and

- xii. Review, maintain, and update periodically.

29
- Corbacho, A., D.W. Gingerich, V. Oliveros,  and M. Ruiz-Vega. 2016. “Corruption as a SelfFulfilling Prophecy: Evidence from a Survey Experiment in Costa Rica. _American Journal of Political Science_ 60 (4): 1077–92.

- Cortes, P. 2015. “The Feminization of International Migration and Its Effects on the Children Left Behind: Evidence from the Philippines.” _World Development_ 65: 62–78.

- Docquier, F., and H. Rapoport. 2012. “Globalization, Brain Drain, and Development.” _Journal of Economic Literature_ : 681–730.

- Dong, B., U. Dulleck, and B. Torgler. 2012. “Conditional Corruption.” Journal of Economic Psychology 33 (3): 609–27.

- Emran, M. S., F. Shilpi, and A. Islam. 2013. “Admission Is Free Only If Your Dad Is Rich!: Distributional Effects of Corruption in Schools in Developing Countries.” World Bank Policy Research Working Paper 6671, World Bank.

- ESP/NEPC. 2010. Drawing the Line: Parental Informal Payments for Education across Eurasia. Education Support Program (ESP) of the Open Society Institute, Budapest.

- Fehr, E., and S. Gächter. 2000. “Cooperation and Punishment in Public Goods Experiments.” _The American Economic Review_ 90 (4): 980–94.

- Finlay, K., and L.M. Magnusson. 2009. “Implementing Weak-Instrument Robust Tests for a General Class of Instrumental-Variables Models.” _Stata Journal_ 9 (3): 1–26.

- Henderson, J.V., A. Storeygard, and D.N. Weil. 2012. “Measuring Economic Growth from Outer Space.” _American Economic Review_ 102 (2): 994–1028.

- Heyneman, S.P., K.H. Anderson, and N. Nuraliyeva. 2008. “The Cost
vere --7<br>2 a ee<br>Ss ee<br>> an<br>6<br>1 2 3 4 5<br>Intensive GBF Tech Index<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>9 9 oe<br>=-o oa “7ae<br>aaBsa o d oe “7-a<br>< a<br>g ue<br>sg et a<br>reOo 7 cee eo aoe enna eeeeee----<br>mathfoD “7 wer ueo7 eoocrt<br>12 3 4 5<br>Extensive SSBF Tech Index<br><!-- End of picture text --> <!-- Start of picture text --> 1<br>S oan<br>=-o ues 7 ped<br>oa3 o d 9g Le --o7 ce<br>< oa<br>3 8 a<br>sg ee<br>rero) were --<br>mathOoD Ty Pea eeetee _uu------------------<br>1 2 3 4 5<br>Intensive SSBF Tech Index<br><!-- End of picture text -->
the demographics of the poor, who have a higher dependency ratio (2 working adults with mean household size of 5.8), the wage rate would need to be Rs 42.<sup>11</sup> The second approach is to base the calculations on the existing (State-level) minimum wage rates. This has been advocated by supporters of the EGS in India. At the time of writing, a wage rate has not been set for the proposed national EGS, though supporters have proposed a wage rate (in 2005) of Rs 60 per day, being an average of the State-level statutory minimum wage rates. Adjusting only for inflation (using the Consumer Price Index for Agricultural Laborers) this is equivalent to Rs 55 in 1999-2000. However, this is a deceptive comparison. While the statutory minimum wage rate refers to a full working day, and might be taken to reflect a reasonably high level of effort, the wage rates calculated from surveys average across workers with a range of effort levels and lengths of the working day.<sup>12</sup> An alternative source of wage data is the series Agricultural Wages in India collected by the Ministry of Agriculture, based on their monitoring of male agricultural wages for a full working day, at district level. Aggregating up the state level estimates for 1999-2000 (weighting by the shares of agricultural workers by state), the mean male agricultural wage from this source is Rs 51 per day, which is 13% higher than the male wage rate of Rs 45 from the NSS. Deflating the Rs 55 wage rate by the same factor gives a wage of Rs 49. Rounding off, we will use an EGS wage rate in 19992000 of Rs 50 per day as the comparator to the Rs 60 for 2005.

So one can defend at least two possible reference “living wage” for an EGS in 1999-00, namely around Rs 40 per day (which happens to also be the average wage rate) and Rs 50. We include both
Y0|37,232|Computed<br>based<br>on<br>the<br>FiLoSoFi<br>database<br>(INSEE‐DGFiP‐Cnaf‐Cnav‐CCMSA2015).|

# 2.2 FLOODS AND FLOOD MANAGEMENT POLICIES

Our study of the impact of floods on this idealized city focuses on capital destruction. The model does not account for other consequences of floods, assuming, for example, that it is possible to avoid loss of life and injury through early warning systems. We consider that the agglomeration has two zones: a

> 2 CLAMEUR (https://clameur.fr/) is an association of several public and private organizations that studies the evolution of rents in France.

> 3 <u>https://www.igedd.developpement-durable.gouv.fr/rubrique.php3?id_rubrique=138, accessed on January 13, 2023.</u>

6
of appliances (%)**

|Technology | year and scenario|2035<br>baseline|2035<br>mitigation|Annual rate at which existing<br>appliances are replaced with<br>efficient appliance|
|---|---|---|---|
|Publiclights|5%|60%|2.50%|
|Lightbulbs|1%|87%|3.00%|
|Plastic double-glazed windows|80%|100%|5.00%|
|Roof insulation|0%|89%|2.00%|
|Wall Insulation|0%|89%|1.00%|

The emission factor for natural gas is 1.875 kg CO2/cub.m (National Inventory Report, 2015). For electricity, we used average grid emission factors. Grid emission factors will change overtime as electricity generation mix changes. Based on MARKAL-Georgia (MARKAL-Georgia, April 2015), the grid emission factors are as presented in table A.4.

33
maybe children whose parents are disabled should also receive educational supports or their families should receive supports and the creation of a more inclusive environment that eases their participation in society. Presumably, that would make education a more attractive choice for their children. Second, the Vietnam Household Living Standards Survey, which was conducted in 2006, has a special module on disability. Detailed data on disability allow for the analysis of the relation between disability and education. Third, Vietnam has a similar education system and economic development as many Asian countries, such as Thailand, Philippines, Indonesia, Laos, and Cambodia. Findings from this study might be relevant to not only Vietnam but also to other Asian developing countries.

The remainder of this paper is organized as follows. Section 2 introduces the data sources used in this study. Section 3 provides some descriptive data on disability and child education in Vietnam. Section 4 presents estimation of the impact of parental disability on child education. Finally, Section 5 concludes.

# **2. Data source**

Data used in this study are from the Vietnam Living Standards Survey (VHLSS) in 2006. The 2006 VHLSS was conducted by the General Statistics Office of Vietnam (GSO) with technical support from World Bank. The survey contains detailed information on the characteristics of individuals, households and communes.

Individual and household data include basic demographics, employment and labor force participation, education, health, income, expenditure, housing, fixed assets and durable goods, and the participation of households in socioeconomic programs. VHLSSs have been collected every two years since 2002, meaning there are four available VHLSSs from 2002 to 2010. However, unlike other VHLSSs, the 2006 VHLSS contains two special modules on education and disability. Information on education of individuals includes educational attainment and educational records in the schooling year 2005-2006 in primary, secondary and high schools. Information on disability includes types and reasons of different disabilities such as difficulty in hearing, seeing, walking, etc.

4
iii) If _F< F*,_ then (11) As in B(ii) above, the declining food share implies that py - _PF > 0._ The direction of change in the FGT indicator is ambiguous. Fewer individuals are considered poor but the measured depth of poverty increases.

We summarize the results from this section in Table 1.

_Table 1. Direction of Change in Measured Poverty As Consumption Becomes More Comprehensive_

||_'Traditional"_<br>_Poverty Line_|_'Austere"_<br>_Povertv Line_|
|---|---|---|
|Headcount|no change|decrease|
|FGT**_(__**_Ž_**1)**|increase|ambiguous|

Two conclusions can be drawn from this section. First, the method used to set the poverty line has a bearing on how measured poverty will change simply as a result of variation in the consumption definition. Second, different indicators of poverty do not all move in the same direction. Under the "traditional" poverty line, the headcount is unchanged as the consumption aggregate expands, but indicators of poverty in the FGT class which are sensitive to depth show an unambiguous increase as the consumption definition widens. Under the "austere" poverty line, the number in poverty falls so the headcount unambiguously declines. However, this is offset to some extent by a greater relative distance to the poverty line for some still in poverty. As a result, the movement of the FGT indicators is ambiguous as the consumption definition expands.

The exposition in this section has been in terms of just two definitions of consumption expenditures: food and a composite of both food and non-food expenditures. If we are interested in comparing two different definitions of consumption, both of which include non-food items, the foregoing analysis of the direction of biases is relevant as long as there is a behavioral regularity analogous to Engel's Law regarding the share of the more broadly defined expenditure
GHANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE financing major track rehabilitation, with the limited investments made being confined to purchases of rolling stock. The basic reason for this is that the traffic volumes on almost all African railways are well below the 1 to 1.5 million net tonnes of traffic needed per year needed to generate sufficient cash flow for investment finance, particularly given that rail tariffs are constrained by intense intermodal competition from the trucking sector. As a result, track upgrades have ultimately been funded by government borrowing from international financial institutions. In the case of Ghana, traffic is not much more than 0.2 million tonnes, making it improbable that track improvements could be funded by the private sector.

**Table 3. Railway indicators for Ghana and selected other countries, 2000–05**

||**GRC (Ghana)**|OCBN (Benin)|SITARAIL (Cote d’Ivoire –<br>Burkina Faso)|NRC (Nigeria)|TRANSRAIL (Senegal - Mali)|CAMRAIL (Cameroon)|SPOORNET (South Africa)|
|---|---|---|---|---|---|---|---|
|Concessioned (1)/ state-run (0)|**1**|0|1|1|1|1|0|
|Traffic density, freight, 1,000 tonne-km/km|**242**|148|494|15|318|1,091|5,319|
|**EFFICIENCY:**||||||||
|Staff: 1000 UT per staff|**84**|40|481|37||603|3,037|
|Coaches: 1000 passenger-km per coach|**416**|900|862|737||4,738|596|
|Cars: 1000 ton-km per wagon|**458*