rafmacalaba/data-use-mentions-extended huggingface dataset:
_For my friends, anything; for my enemies, the law._ _Oscar Benavides, Former President of Peru_

# **I. Introduction**

Over the last decade there have been two major initiatives to measure the ―investment climate‖ that private sector firms face in developing countries, both with the goal of discovering and promoting those government policies and actions that could accelerate (broad based) economic growth.

Far and away the most famous of these has been the Doing Business (DB) indicators supported by the World Bank. DB assesses 10 elements of a country‘s policies that relate to private firms (e.g. starting a business, hiring workers, trading across borders, dealing with construction permits). DB provides ̳league tables‘ on each of these 10 regulatory areas separately, as well as providing a widely cited overall ―Ease of Doing Business‖ ranking (e.g. in the 2010 ranking, Singapore as ranked number one and Central African Republic at 183).<sup>1</sup> The DB exercise assesses the country‘s ―investment climate‖ with experts that examine the relevant laws and procedures to assess the time and cost of compliance with the variety of governmental regulations, as well as the ease with which a variety of contracts between private parties can be entered into and enforced.<sup>2</sup> A parallel effort, also carried out by the World Bank, has surveyed firms in developing countries. This questionnaire includes modules that ask about the firm and its operations, what the firms perceive as obstacles to their own business and about their interactions with

> 1 A Google search on ―ease of doing business‖ and ―world bank‖ produces half a million hits.

> 2 The Doing Business indicators were developed from collaborations with academics (e.g. Djankov, La Porta, Lopez-De-Silanes and Shleifer, 2002; Djankov, La Porta, Lopez-de-Silanes and Shleifer, 2003; Botero,  Djankov, La Porta, Lopez de Silanes and
measurement errors, but they are affected by how respondents are selected (Figure 3 and Appendix Table A3).

We find little evidence that errors of measurement bias the phone survey estimates. Interviewing respondents in person rather than on the phone does not lead to survey estimates that are aligned with administrative records. Similarly, reported vaccine uptake does not differ significantly between first-time and previous respondents of the phone surveys suggesting that panel conditioning effects do not bias estimates. Contrary to Wolter et al. (2022) who use a list experiment in an online sample in Germany to find experimenter demand biasing reported vaccine uptake, we do not find evidence of such effects in our sample. While we find proxy reports to have generally good accuracy, we do find that they miss out on some vaccinations obtained by other household members.

As for sample selection, we find no statistically significant difference in estimated vaccination rates between the phone survey sample of households and the full nationally representative sample of households. However, respondent selection matters. In line with the results of Bradley et al. (2021) from online surveys in the United States, estimated vaccine uptake is significantly lower when taking into account respondent selection effects at the individual level (Figure 3). Moving 28
of natural shocks, and historical data only offer limited guidance on the future. So, when taking long-term investment and planning decisions, selecting options that offer robust performance under a variety of scenarios is crucial (Hallegatte et al. 2019).

This study considers a 1-in-100 year return flood intensity to reflect a relatively rare and intense disaster. However, the concept of return periods is easily misunderstood and the probability underestimated. A 1- in-100 year flood has, on average, a 1% probability of occurrence in any given year. But this translates to or 10% probability in a decade, or 50% in a lifetime (68 years). These are significant probabilities that lie well within reasonable planning horizons of governments. For comparison, the Dutch flood protection system protects against events up to 1-in-10,000 years. In addition, it should be noted that these probabilities apply independently to a given river basin or microclimate. For the purpose of this study, we consider hundreds of thousands of such locations. This means that, globally, hundreds of 1-in-100 year flood events happen every year.

**Figure 1. The extent of 1-in-25 and 1-in-100-year floods in Vietnam’s North Central Coast Region**

_Source: Braese et al. 2020._

# **3.2. Population density**

This study estimates the location of people using the Global Human Settlement Layer (GHSL), produced by the EC JRC. It offers global coverage and is available for the years 1975, 1990, 2000, 2015, and 2020. While GHSL provides several layers (including those specifying built-up areas, population, and settlements), this study uses the population density map (GHSL-POP 2020). In a raster format, this data set provides the number of inhabitants per cell, with a resolution of 3 arc seconds (approximately 90 by 90 meters), thus specifying the distribution of population. This information is based on administrative or census-based population data, which is then disaggregated to grid cells based on distribution and
urban and rural) and the estate sectors in each district of the country were the selection domains. This makes our results representative at the district level too, which is essential for the results of the CGE-GIDD at the regional level.

> 16 <u>http://www.statistics.gov.lk/industry/ASI_2016_Report.pdf.</u>

> 17 Sri Lanka HIES 2016 is utilized for the descriptive analysis of workers section, despite that wage information is more reliable in the Sri Lanka Labor Force Survey (LFS 2015).

> 18 Analysis presented in this section relies on the 1-digit ISIC industry classification (10 sectors).

8
Students’ CPFs were collected on three occasions during the short-term impact evaluation. First, at parent workshops that were organized in some treatment schools. Second, on lists of enrolled students that classroom teachers submitted before the final follow-up survey, which made them eligible to enter a lottery with prizes. Third, during the final follow-up survey. This resulted in CPFs for 10,846 students. From these, 8,093 are valid numbers.

In Brazil it is common for young individuals to use the CPF of one of their parents when they do not have one for themselves, implying that some of the valid CPFs may be from parents instead of the students. For this reason, CPFs collected during the short-term impact evaluation may be wrong. Since they are also missing for 77 percent of the sample, we implemented an algorithm to find the CPFs based on the names and ages students provided on the surveys for the short-term impact evaluation.

We match students’ names with the registry of names from the Federal Government Revenue Service (SRF), the Brazilian agency in charge of federal tax collection. This registry contains all CPFs that ever existed in Brazil, along with name, gender, mother ́s name, date of birth and last updated location. Most adults in Brazil hold a CPF and are therefore present in the SRF registry. The number is necessary to open a bank account, have a formal job, pay taxes, etc. Even relatively poor individuals commonly have a CPF if they are 18 years or older.<sup>4</sup> Two main caveats arise from attempting to obtain a CPF based on collected names. The first is the possibility of misspelling of the reported names, and the second is the potential existence of homonyms. To address misspelling, we take advantage of the recurrent collection of names during the experiment. Students or their parents provided student names on nine occasions. We consider all different spellings for each name as possibly right in the matching exercise and compare all of them with the
economists that in the presence of close substitutes, import demand elasticities should be high and certainly higher than the typical estimates obtained by empiricists in the literature.

# 4. **Conclusions**

In this paper, we have offered a detailed analysis of MFA exports from Bangladesh to the United States. We have focused on estimating the United States' demand for MFA imports from Bangladesh. Our analysis differs from the existing studies on the subject in four important ways. First, we use a new methodology which exploits the fact that MFA exports are subject to binding quotas. Second, there is a tight connection between out theoretical model and econometric estimation. Third, we take explicit account of competitors of Bangladesh. Finally, we use highly disaggregated data which makes unit values a more reliable measure of prices than when aggregate data are used.

The results of our estimation are relatively robust to the inclusion of commodity- and time-fixed effects. The most surprising finding is the consistently high value of the own-price elasticity. Though this high value accords with trade theorists' prejudice that small countries can essentially behave as price takers, it is in conflict with the consensus view that demand elasticities rarely exceed 3 and are usually less than 2 in the literature.

An exception to the consensus view is Riedel (1988, 1989) who finds that the elasticity of demand for Hong Kong's exports of manufactures is infinity. But Riedel reaches this conclusion by estimating an equation with price on the left-hand side and quantity on the right-hand side and finding that the coefficient of the quantity is not statistically different from 0. We have argued that this is not persuasive evidence. Moreover, like other researchers, 26
for cohorts that are likely to have been in their formative years (in school) around the time the Nepali economy liberalized creating newer opportunities in the labor market. Looking across different social groups, Newars, followed closely by hill Brhamins and Chhettris have the best odds of surpassing their father’s education and occupation levels. On the other hand, Nepalis in Muslim and other minority caste categories followed by Terai Brahmin Middle Caste, and Dalit categories have the lowest odds of doing so. Finally, the odds of upward mobility are significantly better for households who have managed to also be mobile spatially. That is, individuals born in rural Nepal but currently living in urban areas have significantly better odds of doing better than their fathers than individuals who were born in rural areas and continued to stay in rural areas.

We also introduced and used synthetic panel methodology to show the extent of directional income movement within a generation. In particular, we used this technique to unpack net poverty movements and disentangle the proportion of Nepali population falling into and out of poverty. Our results show that for every two Nepalis moving out of poverty in the last couple of poverty reduction episodes, there is one Nepali falling back into poverty. Alongside these movements in and out of poverty, our results also suggest a sizeable persistence in poverty status: based on data from the three rounds of NLSS, we conclude that 60 percent of those that are found to be poor in any given year actually suffer from chronic poverty.

Generalizing, the notion of the risk of falling into poverty, we propose a distinctly economic definition of vulnerability that we argue is an improvement over heuristic definitions such as the level of clustering above 18
<!-- Start of picture text --> Cross-border transactions<br>(mode 1)<br>27.67<br>Consumption<br>2) Presence abroad<br>Senor (made 2) 2)<br>(mode 4} 4} 10.5%<br>3.0%<br>Commercial prasance prasance<br>(mode 3)<br><!-- End of picture text --> <!-- Start of picture text --> Cross-border transactions transactions<br>{inode 1) 1)<br>fb<br>Consumption<br>Presence abroad<br>of natural natural [mode 2)<br>persone 10.5%<br>{modevotemodevotevote 4hh<br>Coe relal<br>presence<br>{rode 5}<br><!-- End of picture text --> <!-- Start of picture text --> Cross-border transactions transactions Cross-border transactions<br>{inode 1) 1) (mode 1)<br>fb 27.67<br>Consumption<br>Presence abroad Consumption<br>of natural natural [mode 2) Presence abroad<br>persone 10.5% Senor (made 2) 2)<br>{modevotemodevotevote 4hh (mode 4} 4} 10.5%<br>3.0%<br>Coe relal<br>presence Commercial prasance prasance<br>{rode 5} (mode 3)<br>mTM2005 TM 2017<br>10.0<br>9.0<br>7.8<br>8.0<br>Pa 7.0<br>S 6.0<br>[-)<br>GS 5.0 4:3<br>5 3.7<br>2 4.0 *<br>= 3.
.91<br>(_0.864-3.60)_|0.503<br>(_0.227-0.930)_|174<br>_(78-351)_|12.2<br>_(5.43-24.8)_|
|Europe, LMI|71|1.32<br>_(0.596-2.48)_|0.346<br>_(0,157-0.641)_|120<br>_(54-242)_|8.38<br>_(3.75-17.1)_|
|South East Asia|409|7.60<br>_(3.43-14.3)_|2.00<br>_(0.902-3.69)_|691<br>_(309-1,390)_|48.3<br>_(21.6-98.6)_|
|Western<br>Pacific,LMI|179|3.33<br>_(1.5-6.26)_|0.874<br>_(0.395-1.62)_|302<br>(_135-609)_|21.1<br>_(9.44-43.2)_|
|Global|925|17.2<br>_(7.76-32.3)_|4.52<br>_(2.04-8.36)_|1,560<br>(_699-3,150)_|109<br>_(48.8-223)_|

_Source: Bivins et al. (2017). *Campylobacter, Cryptosporidium, and Rotavirus ** Dataset assembled by projecting prevalence of IWS found in IBNET onto JMP measures of access to piped-on-premise water supplies. *** LMI = Low-middle income. **** 95% confidence intervals within parenthesis._

**Figure 6: Impact of Intermittent
in the processing of the exporter-level

> 26 Given our understanding about which transactions are included in the raw files, for this comparison we consider gross export figures in COMTRADE (which include re-exports) for Albania, Kenya, Mauritius, Tanzania and Senegal and net export figures (gross exports minus re-exports) for Jordan, Uganda and Yemen. For all other countries, it does not matter which COMTRADE figure we choose as they report either insignificant or no re-exports.

> 27 For Mauritius we examined the discrepancy further at the country-HS 2-digit-year level and found that the average match percentage benefited significantly from the exclusion of observations belonging to Chapter 49. Similarly, for Cambodia and Macedonia the average match percentage benefits significantly from the exclusion of observations belonging, respectively to Chapter 49 and Chapter 62.

13
# **ANNEX 1: Variables Description: Bulgarian Microdata, period 1993 - 1995**

Firms with missing observations were first deleted. Then we averaged all variables for the penod 93/95. The only variables which we first calculate as the first difference and then averaged were investment, **I,** which is obtained as a first difference of tangible fixed assets, and the first difference of sales, _DSAL._ All the variables are expressed in USD million.

Data available for Bulgaria in AMADEUS:

||**Balance sheet**|
|---|---|
|**Assets**|**Liabilities**|
|Tangible fixed assets|Shareholders funds|
|Fixed assets|Long term debt|
|Stocks|Non-current liabilities|
|Debtors|Current liabilities|
|Cash & cash equivalent|Loans|
|Total assets|Creditors|
|Working capital||
|Number of employees||
||**Profit and Loss account**|
|Sales||
|Gross profit||
|<br>P/L before tax||
|Taxation||
|Costs of employees||
|Depreciation||
|Interest paid||
|<br>Cash flow||
|1.<br>**The following variables hav**|**e been averaged over the period of observation:**|
|K<br>tangible assets,||
|**LTDEBT**long terrn debt;||
|LOAN short term loans;||
|**CREDIT**trade credits;||
|**WCAP**working capital;||
|<br>**NEMIP**number of employees;||
|<br>**PLBTAX**profit and loss before tax;||
|**TAX**<br>taxation;||
|**DEP**<br>depreciation;||
|**IPAY
to tax, their ownership of significant assets that can serve as collateral, and their capacity to pool the resources needed to execute large-scale projects (Martinez et al. 2023).

# **Figure 1. Public investment in EMDEs**

<!-- Start of picture text --> A Public investment, percent of GDP B. Public investment, percent of total investment<br><!-- End of picture text --> <!-- Start of picture text --> Percent of GDP Percent of total investment<br>12 50<br>10 40<br>8<br>30<br>6<br>20<br>4<br>10<br>2<br>0 0<br>EMDEs AEs LICs Com. Com. EMDEs AEs LICs Com. Com.<br>Exp. Imp. Exp. Imp.<br>C. Public investment growth D. Public investment growth by EMDE group<br>Percent Percent<br>1990-2022 average 1990-2022 average<br>20<br>12<br>16<br>8 12<br>8<br>4<br>4<br>0 0<br>EMDEs EMDEs excl. World LICs Commodity Commodity<br>China exporters importers<br>1990-99 2000-09 2010-22 1990-99 2000-09 2010-22 1990-99 2000-09 2010-22 1990-99 2000-09 2010-22 1990-99 2000-09 2010-22 1990-99 2000-09 2010-22<br><!-- End of picture text --> _Sources:_ Haver Analytics; IMF Investment and Capital Stock Dataset (IMF 2021a); WDI (database); World Bank. _Note:_ AEs = advanced economies; EMDEs = emerging market and developing economies; Com. Exp. = commodityexporting EMDEs; Com. Imp. = commodity-importing EMDEs; LICs = low-income countries. A.B. Bars show means and whiskers show interquartile ranges for 2010-22 by group. Sample includes up to 36 advanced economies and 126 EMDEs.

C.D.
Based on a two‐stage propensity score–weighted fixed‐effects model, the analysis finds that electrification is associated with a broad range of social and economic benefits in Pakistan, including higher income and expenditures, better health and education outcomes for children, and increased women’s labor force participation and decision‐making power. Lack of reliable access to electricity is conservatively estimated to cost the economy $5.8 billion—2.6 percent of GDP—a year.

The rest of the paper is organized as follows. Section II reviews the status of electricity access in Pakistan and identifies factors contributing to the low quality of electricity supply in the country. Section III presents the data and the descriptive analysis. Section IV discusses the empirical strategy. Section V describes estimation results and findings. Section VI summarizes the paper’s main conclusions.

# **II. Electricity Access in Pakistan**

Pakistan has made remarkable progress in connecting its towns and villages to the electric grid over the past few decades. The _Global Tracking Framework_ report issued jointly by the World Bank and the International Energy Agency ranked Pakistan fourth in the world in terms of the number of people who gained access to electricity between 1990 and 2010 (after India, China, and Indonesia) (SEforAll 2014). Over this period, roughly 91 million people in Pakistan received electrical services for the first time.

The actual access rate to electricity in Pakistan is very much up to debate, however—and by any measure a large share of the population continues to live without electricity 24/7. Pakistan’s latest official household survey reported that 97.5 percent of the population had access to electricity in 2016 (99.7 percent in urban areas and 95.6 percent in rural areas). Estimates based on census data and the number of connections reported by utilities suggest that access to grid electricity was only about 74 percent in 2016 (90 percent in urban areas and 63 percent in rural areas in 2016) (IEA 2017)
employment needs as the basis for decisions on enrollments by specialty in a given school. Both formal (labor surveys) and informal (conferences) methods are used to establish manpower needs. These decisions are referred to the provincial education bureau, which consolidates and forwards them to the State Education Commission.

The two main streams of enrollment information -- through the Ministry of Labor and Personnel and the State Education Commission -- are in turn directed to the State Planning Commission. At this level, only total enrollment by type of schools is considered. Approval is said to be more or less routine, and is transmitted back to the schools via the channels through which information arrived.

Both STS and SVS can increase quotas for a given specialty, if requested to do so by an enterprise which is willing to pay the costs of additional training. This requires the approval of the provincial Labor and Personnel Bureau or Education Bureau, respectively. Above-quota training of this sort occurs regularly, and provides an important element of flexibility in meeting local manpower needs outside the formal planning process. SWS also train on contract for other enterprises, but the process seems much more integrated into annual enrollment planning and quotas.

A principal reason for the differences between the SWS and STS on the one hand, and the SVS on the other, lies in the labor assignment process. Students in the SWS and the STS are guaranteed placement by the owning enterprise if they graduate. Since 991 do, admission to either of these types of schools is a decision for employment. In the SVS, however, there is no formal assignment process although, as will be discussed, informal mechanisms in the better schools appear to lead to very high placement rates.

The systems for testing and admission to schools also differ. For the SWS, a nationwide entrance examination is prepared by the Ministry of Labor and Personnel Bureaus (except in Beijing, Shanghai, and Tientsin, which create and administer their own examinations). Students select the specialty in which they wish to enroll before
logarithm of city size for each country. Here again, it is least visible in Kenya and Sri Lanka. In Morocco, as we have seen, the negative relationship is driven by markedly lower poverty in Casablanca. With the exception of Kenya and Mexico, metropolitan poverty incidence is less than half of the average urban poverty in every country in our sample that has at least one metropolitan area.

To investigate the robustness of the inverse poverty-city size relationship with respect to variations in the poverty line, we plotted the cumulative distribution function

> 11 These regression coefficients are presented as illustrative of correlations only. City size is clearly endogenous, and there are evidently many omitted variables, so no inference of causality is possible. Some countries do not display the full set of regressions for lack of data (the Kenyan census for instance being very short, no information is available on infrastructure access).