rafmacalaba/data-use-mentions-extended huggingface dataset:
# **5.2 Robustness**

There were several modeling choices made in the estimation of the baseline results. This section briefly explores the robustness of the results to these choices. The discussion above already showed that the use of a model in levels or differences does not importantly affect the results. In what follows we explore the role of the number of lags, the measure of disasters, the measure of output, and the order of the variables in the VAR.

The results of each of these exercises, reported in figures 3 to 7 show that the findings discussed above are not crucially driven by these modeling choices. Adding a third lag turns positive the point estimate for the GDP impact of climatic disasters, but as in the previous case, the impact is not statistically significant (Figure 3). The conclusions regarding deficits, expenditures, and revenues are largely unaffected.

We use two different indicators of the occurrence of disasters. First, a simple index that takes the value 1 if at least one disaster of each category took place in a given year (Figure 4, Panel A). Second, a more complex index that takes into account the month when the disaster occurs, thus reporting the impact of a disaster occurring January 1<sup>st</sup> (Figure 4, Panel B). In both cases, the output and fiscal impacts of disasters are similar to those reported in the baseline results. Similar results are also obtained when using the Penn World Tables’ measure of real per capita GDP that adjusts for purchasing power parity instead of the measure in constant dollars (Figure 5).

As explained in section 3, the baseline estimation in levels included the changes in the (log) interest rate instead of the level of this variable because in some cases its impulse responses suggested non-stationary behavior. While this choice makes a difference for the estimated responses of this variable, it does not importantly affect the estimated responses of output and the fiscal variables to disasters, as shown in
Korean firms in 1998 and 1999 to determine whether international transactions were concentrated in different industries from domestic transactions. Surprisingly, we find that domestic acquisitions in this period are very similar to foreign acquisitions by sector of both the target and the acquiring firm. This suggests that the incentives that drive international acquisitions are similar to those that drive domestic acquisitions. The main difference between domestic and foreign acquisitions is the size of the transactions. The median reported foreign transaction value was over 65 percent larger than the median reported domestic transaction. We also find that the industrial distribution of foreign acquisitions in Korea mirrors the industrial distribution of multinational operations, suggesting that certain industries may naturally be multinational. Overall, these results suggest that internationalization is a form of consolidation, which is not very different from domestic consolidation, but which occurs on a larger scale.

Second, using data on foreign ownership from 1998 and 1999 and firm characteristics from the _preceding_ year on nearly all firms listed on the Korea stock exchange (KSE), we examine which sectors and firms have attracted foreign investment. Any correlation we observe between firm characteristics and foreign ownership must be a result of foreign preferences because Korea only opened to foreign investment at the end of 1997. We find that foreign investment in Korea is focused on larger and more profitable firms, and on firms with relatively low debt ratios. Foreign investment is also more likely when a greater share of firm output is exported. The evidence suggests that foreign ownership gravitates towards the better firms.

While our analysis does not directly answer the question of how foreign ownership affects growth, it does imply that foreigners invest in and acquire the better firms in high growth industries in Korea. This means that internationalization in ownership is at least partly a result of firm and industry growth. In addition, it implies that studies which attempt to measure the affect of foreign 3
**|
||[0.00657]|[0.00638]|[0.00703]|[0.00461]|[0.00461]|
|**Bachelor's Degree or higher**|0.453***|0.476***|0.515***|0.389***|0.447***|
||[0.00636]|[0.00679]|[0.00734]|[0.00492]|[0.00484]|
|**Constant**|5.636***|5.683***|6.304***|5.809***|5.987***|
||[0.0815]|[0.129]|[0.134]|[0.0526]|[0.0917]|
|**Observations**<br>|41234|39445|35880|75851|78745|
|**Country fixed effects**|yes|yes|yes|yes|yes|
|**R-squared**|0.41|0.40|0.40|0.37|0.36|
|**b. EDUCATION INDEX 2**||**DECADE: 1970-19**|**79**|**DECADE**|**: 1980-1989**|
||**CENSUS 1980**<br>|**CENSUS 1990**<br>|**CENSUS 2000**<br>|**CENSUS 1990**<br>|**CENSUS 2000**<br>|
|**Age**|0.00140*|0.00141|-0.00202|0.00236***|0.00163***|
||[0
cost of providing real services will depend on scenario-specific input prices and efficiency in government service production.

For all the scenarios that it simulates, MAMS provides wage and employment by activity and labor category (with labor split between three educational levels) as well as non-labor incomes. In the microsimulation model, this information is used, in turn, to generate a full picture of the impact of changes in the labor market variables on the level and distribution of micro-level household incomes.

As the database for our microsimulation model, we use the Yemen 2005/06 HBS; it covers 98,941 individuals in 13,136 households. The information from the household survey is used to estimate the poverty and inequality impacts of different counterfactual scenarios, linking the microsimulation model to the results generated by MAMS. MAMS communicates with the microsimulation model in a ―top-down‖ fashion (i.e., without feedback) via the following aggregate linking variables: wages, sectoral structure of employment, unemployment, and non-labor income. That is to say, results from MAMS scenarios are taken and applied to the full distribution of income as given by the household survey and assume there are no further feedback effects that would affect MAMS. The microsimulation model follows the non-parametric methodology described in Vos and Sanchez (2010) but extended to consider changes in non-labor income. The labor-market structure is defined in terms of the following parameters: unemployment among different groups of the population at working age defined according to skill, sectoral structure of employment, relative and overall level of wages, and skill composition of the employed population. The effect of altering each of those six parameters on poverty and inequality is analyzed. In addition, we consider changes in non-labor income. Specifically, the (per capita) non-labor income is increased or decreased in order to match the change in per capita household income estimated using MAMS. The change in per capita non-labor income depends on the household initial endowment of non-labor income. As a consequence, the absolute change in non-labor income is larger for those
.67<sup>***</sup>|0.10|0.53|-0.43<sup>***</sup>|
|_MPI poor households_|||||||
|Years of schooling|0.71|0.78|-0.06|0.72|0.65|0.07|
|School attendance|0.39|0.42|-0.03|0.47|0.67|-0.20<sup>**</sup>|
|Unemployment|0.07|0.00|0.07|0.06|0.06|0.00|
|Legal identification|0.75|0.99|-0.24<sup>***</sup>|0.86|0.91|-0.05<sup>**</sup>|
|Early marriage|0.20|0.90|- 0.70<sup>***</sup>|0.19|0.66|-0.47<sup>***</sup>|

Source: Authors’ calculations using the South Sudan HFS (2017). Significance at 1% ***, 5% **, and 10%

* levels.

South Sudan sees the largest gender gap of all countries for displaced women, among the poor and population-wide (70- and 67-percentage points, respectively). Displaced women are also more deprived than host women, although the differences are larger among the wider population.

# _5.2.5 Sudan_

In Sudan (table 11), the gender gaps in education and financial security are significant only among the wider population. Deprivations are higher for male hosts in primary school completion and female hosts in legal identification, whereas females face higher deprivations in employment in both displaced and host communities
# **3 The Data**

The dataset consists of transaction-level records obtained from the Lao PDR Customs Department reporting all<sup>3</sup> legal export and import transactions covering the entire Lao exporter population. The following information is included: the checkpoint used; the form type (for special status, re-exports, tariff exemptions, etc.); the date (year, month, and day); HS 8-digit code of the product (a mix of revision 2002 and 2007 which has been harmonized for the analysis); the export license number and the firm name; the destination (using only a 2-letter code); the unit, amount and weight; and the value in US dollars (USD) and Lao kip (LAK). The analysis uses only the 6-digit HS code for international comparison purposes. Data coverage spans the period from October 2005 to September 2010 – the entire period of 60 months for which Customs have recorded data in this format. The 1138 firms, 668 HS-6 products and 88 destinations in the analysis sample combine to create 5053 FPDs after several manipulations for survival analysis.

Firm names have been discarded for anonymity purposes, and the firm identification is carried out based on export license numbers. This helps the analysis because firm names, recorded in Lao script, can have multiple transliterations and formatting (e.g. including or not the word “firm”, before or after the name) making individual identification by name unreliable. Very small firms or individuals are given a generic code. There are also shortcomings in using the export license number for the identification strategy. The crucial assumption is that licenses are owned by exporting producers and this can be invalidated when the license is in fact used by other entities which are irrelevant for export survival behavior (e.g. brokers, freight forwarders). Moreover, license numbers could be shared by multiple firms because of mergers, splits or for tax evasion purposes. Nevertheless, anecdotal evidence from personal interviews indicates that this is not
0.1|0.2|0.1|0.1***|
|State schools|0.3|0.7|-0.4***|0.6|0.9|-0.3***|
|High school|0.1|0.2|-0.1***|0.4|0.6|-0.2***|
|<br>% of classes with full-time education|0.0|0.0|0.0|0.0|0.0|0.0|
|Duration of school day|4.3|4.4|-0.1|4.3|4.4|-0.1**|
|N|611|84||555|127||

_Sources: Prova Brasil,_ School Census, and INEP. _Note:_ Table reports the average characteristics for the schools that joined _Mais Educação_ in 2008 and remained until 2011 (columns (1) and (4)) and for the schools that joined the program in 2008 but left the program over the period (columns (2) and (5)). Columns (3) and (6) report the mean differences of the variables between the two groups. _T_ tests were performed to detect the mean differences of the variables between the two groups. *significant at 10%; **significant at 5%; ***significant at 1%. Panel A reports these statistics for the schools from the fifth-grade sample. Panel B reports these statistics for schools from the ninth-grade sample. Our sample include only schools that have information for all variables over all the analyzed period.
. For ease of reading, countries with labels correspond to those used in our sample of six Latin American and 17 developed ones. The visual impression is striking: a majority of blue bars lie to the left of the ...gure and the majority of red bars lie to the right (indicating a larger share of HtM individuals in emerging countries). In fact, the average share of HtM individuals is twice as large in emerging countries as in developed countries (47.5 percent vs. 23.8 percent, with a statistically signi...cant di¤erence). For our sample of six Latin American countries, the average share of HtM individuals is even larger, reaching 60 percent.<sup>25</sup> While using a di¤erent period of coverage, measure, and identi...cation strategy, this evidence seems to match fairly well that of country-speci...c studies. For example, for Mexico, we ...nd that 72 percent of individuals are HtM and Cugat (2019) ...nds that for years 2016-2018 about 58 percent of Mexican households have no access to formal ...nancial markets. For the United States, we ...nd that 27 percent of individuals are HtM and Kaplan et al. (2014) ...nd that for the period 1989-2010 between 25 and 40 percent of American households are HtM, with their preferred estimate being one-third. For Korea, we ...nd that 20 percent of individuals are HtM and Song (2019) ...nds that

> 24 In the case of the United States, this 1/20 of GNI per capita in local currency for the year 2017 was equivalent to 2,380 dollars which is very similar to Lusardi et al. (2011).

> 25 It is worth noting that the Global Findex survey of 2014 would reach virtually identical results regarding the share of HtM individuals, �, with emerging (developed) countries
country-specific distribution of productivity at entry from the size distribution of entrants. This information, together with the idiosyncratic distortions of entering firms, allows us to back out a distribution of productivity from firms that are one years old. A more sophisticated approach would be to give entrants a choice of various initial distributions to draw from, at different costs. The biggest hurdle to both approaches is that most databases do not report the firm’s age, making it impossible to separately identify technology adoption at entry from the innovation decisions of firms over the life cycle. Since we do count with some cross-country data on the life cycle dynamics of firms, we 69
>6</sup> It is also important to emphasize that if a given location is covered by 3G, it is by definition covered by 2G, likewise 3G if covered by 4G. Therefore, in this paper, 3G and 4G coverage are hereafter referred to as 3G/4G: thus essentially focusing on mobile network coverage that supports broadband Internet (3G/4G) and those that only support voice and text messaging (2G). The data on 2G coverage used in the

> 4https://www.collinsbartholomew.com/mobile-coverage-maps/

> 5This is particularly important for countries without frequent updates from mobile operators

> 6Theoretically, the main differences between 3G and 4G is that the later is expected to be much faster than the former in theory, although in practice this is not always the case. See https://www.pcmag.com/news/ 3g-vs-4g-whats-the-difference

5
level. Also, there is a positive effect on high school mathematics achievement from certified teachers only when the certification is in mathematics. Finally, reports that the training institution attended by the teacher has a positive effect on student achievement, particularly at the secondary level, but points out that this can be a reflection of the cognitive ability of the teacher.

Harris and Sass (2011) carry out a survey of the researches developed up to 2010, highlighting the fragility of previous work to control the heterogeneity among students only by their characteristics observed (covariates of students). The authors cite evidence that students who present greater learning capacity and fewer disciplinary problems tend to be allocated in classes with more experienced and qualified teachers, so that the lack of appropriate controls for the unobservable characteristics tends to generate biased estimates of the effects associated with the training of teachers.

Regarding the studies that have addressed the problem of selection bias, Harris and Sass (2011) point out eight studies that used fixed effects at the student level to control for the heterogeneity not observed, and five that explored random assignment experiments between students and teachers or that use natural experiments. None of the studies finds positive and statistically significant effect of the nature of higher education on the future teacher performance and the majority finds no relationship between the selectivity of higher education institution and teacher performance. Finally, with the exception of two studies that have identified significant and positive effects of master’s degree education of teachers on students’ math scores, all other studies either indicate absence of statistically significant effect or even negative effect of holding a postgrad degree on math and language students’ performance.

Since unobservable characteristics of teachers (like their IQ) can influence their pre-service training and also affect their future performance in the classroom, Harris and Sass (2011) incorporate in their study the teachers grade in the college admission exam as a way of controlling the effect of higher education for the pre-college skills, thus avoiding a major source of bias.<sup>10</sup
18 estimate the forward-looking economic cost of the service without having to rely on detailed cost studies that otherwise would be necessary. This alternative methodology provides a non-discretionary framework within which regulators and firms can discuss with a significant degree of objectivity, and which could provide an independent check on the accuracy of firms' cost studies. However, lunch is not free: these alternative approaches require much more time and effort in both data collection and preparation as well as the time and effort spent on model design.

In general, reliable and detailed information (as required by costs models) is a scarce good in developing countries. In this paper we have established the minimum information requirements that a regulator needs to implement a cost proxy model approach, and we have shown that this 'data constraint' need not be that binding. In particular, the HCPM can run with the following inputs:

- **Census data.** Each unit surveyed in the Census (e.g. Census block, or Census block group) should be referenced to a system of coordinates (e.g. latitude and longitude; nevertheless, the mrodel can be modified to work with another system -even with an Excel worksheet- when latitude and longitude coordinates are not available). There should be information on the number of households in each Census unit, and on the number of units in a Census unit group.

- **Location of wire centers.** Each wire center in the study area should be referenced to a system of coordinates (e.g. latitude and longitude; nevertheless, the model can be modified to work with another system -even with an Excel worksheet- when latitude and longitude coordinates are not available). There should be information on the number of lines provided by each of the wire centers. The telephone companies could provide this information.

- All other inputs are provided by the model (i.e. factor prices, technologies, etc.), based on information for the US, but
69 Wells, Stephen. "Turnover Statstics and Trade Reporting," London Stock Exchange Ouarterly with Oualitv of Markets Review, Spring 1994, pp. 23-6.

Whited, Toni M. "Debt, Liquidity Constraints, and Corporate Investment: Evidence from Panel Data," Journal of Finance, September 1992, 41(4), pp. 1425-60.

Williamson, Stephen D. "Costly Monitoring, Financial Intermediation, and Equilibrium Credit Rationing," Journal of Monetary Economics September 1986, 1j(2), pp. 159-79.

Williamson, Stephen D. "Costly Monitoring, Loan Contracts, and Equilibrium Credit Rationing," Quarterly Journal of Economics, February 1987a, 102(1), pp. 135-45.

Williamson, Stephen D. "Financial Internediation, Business Failures, and Real Business Cycles," Journal of Political Economics, December 1987b, 25(6), pp. 1196-1216.

Williamson, Stephen D. and Wright, Randall. "Barter and Monetary Exchange under Private Information," American Economic Review March 1994, 4(1), pp. 104-23.

World Bank. World Development Report Washington, D.C.: Oxford University Press, 1989.
2045|
|Mauritius<br>2|168|2000|||||
|Nazambique<br>74|537|2040|Algeria|81|374|2025|
|Namibia<br>5|478|2035|Egypt, Arab Rep. of|132|273|2020|
|Riunion<br>1|176|2005|Libya|17|463|2025|
|Rwanda<br>40|658|2040|Morocco|59|270|2020|
|Seychelles<br>0.1|186|2010|Sudan|101|459|2035|
|Somalia<br>30|565|2040|Tunisia|18|256|2015|
|South Africa<br>90|283|2020|Other North Africa|1|483-|2035|
|Swaziland<br>3|495|2035|North Africa<br>total|410|328|2035|
|Tanzania<br>123|555|2035|||||
|Uganda<br>82|561|2040|America|1224|183|2030|
|Zaire<br>142|464|2035|Antigua and Barbuda|0.1|<br>170|<br>2030|
|Zafbis<br>37|545|2035|Argentina|52|169|2005|
|Zimbabwe<br>33|394|2025|Bahamas|0.5|209|2005|
|East Africa total<br>1125|487|1045|Barbados|0.3|119|2030|
||||Belize|1|319|2015|
|Angola<br>43|486|2040|Bolivia|24|372|2030|
|Benin<br>22|554|2035|Brazil|306|225|2015|
|Burkina Faso<br>42|532|2040|Chile<br>-|20|162|2000
gleaned from the World Bank‘s private participation in infrastructure database (http://ppi.worldbank.org/). One template should be completed for each country.

Template 4 contains a list of transactions in infrastructure services involving private participation. The source is the World Bank database on private participation in infrastructure (PPI), available online. The PPI database is undoubtedly the most consistent and comprehensive cross-country documentation of private transactions in infrastructure sector in developing countries. When analyzing the results of the exercise, the PPI data provide perspective on the relative importance of the public sector vis-à-vis the potential and current activities of the private sector. This is a great example of how tapping secondary sources in the pre-field stage leverages the SPI exercise.

**Template 3 Private participation in infrastructure**

|**Name of**<br>**project**<br>_BOT=Build Ow_|**Modality**<br>**(concession, BOT,**<br>**management**<br>**contract, and so on)**<br>_ned and Transfer_|**Sector**|**Subsector**|**Contract**<br>**period**|**Investment year**<br>**or financial**<br>**closure year**|**Value of**<br>**investment**<br>**commitment**<br>**(US$ million)**|
|---|---|---|---|---|---|---|

20
.0660**|.1771|.1106|.0664***|.2843|.1759|.1085***|
|Tsunami|0.1950|0.1522|.0427*|.3001|.2390|.0610**|.3525|.3200|.0326|
|Landslides|0.2164|0.1503|.0661***|.2595|.2038|.0557**|.3334|.2362|.0972***|
|Storm surges|0.1411|0.0975|.0436**|.1116|.0688|.0428**|.1522|.1220|.0302|
|Other|0.0337|0.0326|.0011|.0653|.0938|-.0285|.0864|.0337|.0527*|

Note: 1) Tests of significance with robust standard errors comparing means between groups are reported *** p<0.01, ** p<0.05, * p<0.1. 2) Bottom 40 percent refers to the bottom first two quintiles, while the Top 60 percent refers to the remaining upper three quintiles. 3) Results for 2021 are from the sub-sample of 1,487 households that were panel and for which consumption quintiles could be estimated. 4) Results for 2022 are from the 1,529 observations for which income quintiles could be assigned. 5) Results for 2023 are from entire sample of wave 4. See the methodological footnote above on the creation of consumption quintiles.

Rural households are more exposed to natural hazard than urban households, and this is exacerbated for rural households in the lowest quintiles. Urban households in the middle- and high-consumption households are in lees risk for natural hazards (Table 5, appendix A1). In this context, comparing the 5
_Source: Authors’ calculations based on the Central Bank of Egypt data._

**Table 1.d - External Position Variables**

|_Period average_|**1987-90**|**1991-98**|**99-2004**|**2005-08**|
|---|---|---|---|---|
||_Percent o_|_f GDP (unle_|_ss otherwi_|_se stated)_|
|Trade Balance|-7.8|-14.8|-10.5|-12.4|
|Suez Canal dues|na|3.5|2.4|3.3|
|Tourism|na|4.4|4.7|6.7|
|Private remittances|5.0|5.1|3.4|4.9|
|Private remittances (Bn US$)|3.7|4.1|3.0|6. 1|
|Current Account Balance|-1.3|2.5|0.7|1.8|
|Foreign Direct Investment (Bn US$)|1.0|1.0|0.7|8.6|
|Foreign Direct Investment|1.3|1.9|0.8|6.7|
|Capital flows|_Billion_|_US$ (unless_|_otherwise_|_stated)_|
|Net International Reserves|1.7|14.1|13.6|25.2|
|Not Foreign Assets, annual inflows|1.1|1.2|-0.9|7.5|
|External Debt|42.2|31.8|29.5|31.1|
|External Debt to Exports (%)|583|215.5|156|78.3|

_Source: Authors’ calculations based on the Central Bank of Egypt