rafmacalaba/data-use-mentions-extended huggingface dataset:
# 3.2 Market data

I consider 10 years of daily data for US Treasury zero rates (provided by Bloomberg):

|Term|Ticker|
|---|---|
|3M|I02503M Index|
|6M|I02506M Index|
|1Y|I0251Y Index|
|2Y|I0252Y Index|
|3Y|I0253Y Index|
|5Y|I0255Y Index|
|7Y|I0257Y Index|
|10Y|I02510Y Index|
|30Y|I02530Y Index|

Table 2: Zero rates Using these rates, I compute the daily returns on the corresponding zero-coupon bonds: These returns are then arranged in a [500 x 9] matrix _for every observation_ . In other words, every matrix contains the latest 500 observations of these returns.

The choice of the past 500 observations is motivated by the widely adopted convention in market risk of using the previous two years of returns for the estimation of the parameters used for ex ante risk calculations.

In addition to the return matrix, some model versions presented here augment the returns data with cross-sectional data. These models are referred to in this document as ’mixed’ models.

The basic data used in these cases are presented in table 3.

9
While this paper highlights the e¤ects of payment system reform for our sample countries, structural changes to the banking system also occurred along other dimensions. Figure 2 provides evidence that privatization resulted in a reduction of government ownership of banking assets in many of the sample countries. Moreover, it shows that the speed of privatization varies across countries. While the government no longer owned a sizable fraction of banking system assets at or soon after the start of the sample period in Estonia, Latvia and Hungary, a gradual process of privatization was still ongoing in some other countries during our sample period. Figure 2 also documents a rising proportion of banking system assets that are foreign-owned. Finally, countries have undertaken major reforms to the regulatory and supervisory framework that banks are subject to. Figure 3 charts the path of a banking system reform index constructed by the European Bank for Reconstruction and Development (EBRD). A higher banking reform index indicates that banking laws, regulations and prudential supervision are closer to the Bank of International Settlements (BIS) standards.<sup>8</sup> In addition to the paths for the share of government ownership, the share of foreign ownership and banking system reform, Figures 2 and 3 contain a line corresponding to the date of introduction of a modern interbank payment system. While in principle, other aspects of structural reforms may represent confounding in‡uences for our analysis, Figures 2 and 3 suggest that payment system reforms are not contemporaneous with other structural changes, but that instead there is independent variation in the timing of payment system reforms – potentially re‡ecting di¤erences in central bank preferences and resources dedicated to payment system reforms – which can be exploited in the regression analysis.

> 8The index takes the following values:

1 Little progress beyond establishment of a two-tier system.

2 Signi...cant liberalisation of interest rates and credit allocation; limited use of directed credit or interest rate ceilings.

3 Substantial progress in establishment of bank solvency and of a framework for prudential supervision and
**Table** 5. **Products with the Largest Change in Regional Orientation** _Toward_ **Mercosur Markets Over 1988 to 1994.**

||Exports (|$000)|Regional Ori|ent Index|RO Index<br>Change|1994 RCA<br>Index|
|---|---|---|---|---|---|---|
|Commodity (SITC)|1988|1994|1988|1994|||
|Non-Alcoholic Beverages (111)|349|26,238|2.35|48.47|46.12|0.05|
|Lead (685)|642|219|3.03|25.42|22.39|0.00|
|Prepared Dairy (022-024)|23,495|204,019|4.31|22.49|18.17|0.13|
|<br>Non-Wheat Meal or Flour (047)|4|954|0.05|17.26|17.21|0.04|
|Perfumes and Cosmetics (553)|4,766|86,282|5.22|13.37|8.16|0.14|
|Wheat Meal or Flour (046)|65|35,051|0.22|5.67|5.44|1.08|
|Cork Manufactures (633)|18|721|1.18|6.30|5.13|0.05|
|<br>Preserved Vegetables (055)|23,404|48,745|17.66|22.61|4.95|0.13|
|Articles of Paper (642)|15,763|72,249|2.16|7.10|4.93|0.20|
|Road Vehicles Non-Motor (
., those with a high degree of likelihood (90 percent) under a smaller program expansion (targeting the poorest 18 percent nationally), are noticeable across several demographic and economic indicators. Majority of the early referrals are likely to come from urban centers such as Mosul, Tikrit and Ramadi with large concentration of IDPs and rural areas that experienced the conflict most - Shirqat, Tel Afar, and Baiji. Significant number of these households are headed by a female member (30 percent) and appear to have larger household sizes (8 members on average). They are also characterized by significant prevalence of disability and chronic illness among the head of the household. High unemployment, low income and expenditures but relatively high level of debt are other recognized characteristics among this group.<sup>15</sup> This initial analysis, however, has been based on probabilistic estimates from two different sources of data. Moreover, just because a household is likely to be eligible does not necessarily mean they are willing to be referred. Households with perceived ISIS affiliation may mean some households do not want to enter the formal government social protection system.

To confirm the promising referral percentages from the desk analysis, but also determine the percentage of likely households who are also willing to be referred, a light field test is proposed. Under this test, the same households would be asked the full set of questions required to construct full MoLSA and VM/MCNA scores. This would provide stronger evidence of the apparent overlap between the two targeting systems. Moreover, willingness and perception questions could be asked to assess the combination of likely eligibility and willingness to be referred, which will ultimately determine how many potential MoLSA beneficiaries could come from the humanitarian database.

> 15 While the focus of the paper has been how close the PPMT scores are to the full PMT scores and how to overcome the difference, a more flexible ranking approach discussed in Annex D can be another alternative.

21
proxy for labor productivity, offering a practical measure of output per unit of labor input, especially when value-added data are unavailable. Bloom et al. (2010) emphasize its effectiveness in capturing labor efficiency and management practices, while Syverson (2011) notes that it may reflect other factors, such as market power or capital intensity, which could distort comparisons. Despite these limitations, Bartelsman et al. (2004) highlight its importance in developing economies, where sales data often provide the most accessible alternative for productivity analysis.

4 Sales and sales per worker are winsorized at the 5 and 95 percentiles.

5 5
||(145.575)|(132.229)|(68.952)|(39.759)|(21.273)|(19.503)|(12.786)|(12.308)|(5.566)|(6.426)|(2.891)|(1.583)|(4.410)|(2.906)|(4.848)|
|Observations|1,318|1,227|1,175|1,148|964|931|860|628|621|617|541|538|617|415|464|
|Number of country_code_|110|104|103|100|89|86|80|64|63|63|61|60|63|48|50|

Note: Annual information between 2005 and 2017 for 64 EMDE countries. _Public debt_ includes debt data for general government. In case this data is not available, debt data for the central government is used. _Policy rate_ is the Central Bank or Monetary Authority’s benchmark interest rate for monetary policy. In case that policy rate is not available, money market rate is used. Panel data regressions with first-order error structure to adjust for serial autocorrelation. Country-fixed effects included. Dummy variable capturing Global Financial Crisis period included. t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1 43
Second, ― _Who will be evaluated”?_ (A fresh class of graduates or those who have already a few years‘ experiences). Given the fact that many fresh engineers change jobs within a year or so, external effects such as in-house training should be removed as much as possible in order to assess learning outcomes at institutions. It was therefore decided that the target population would be the fresh graduates from technical and engineering institutions for whom this was their first job. Hence, employers were asked not to consider engineers for whom this was not their first job after graduation.

Third, ― _At what level should the employers evaluate?_ (At individual level, at institutional level such as Indian Institutes of Technology, National Institutes of Technology, institutions that participated in the Technical Education Quality Improvement Program, etc, or by overall average of all fresh hires)‖. It was decided to send one single questionnaire to each company due to the administrative burden to deal with multiple questionnaires per different graduate group. Employers then evaluated all first-time-employed engineers hired over the previous four years together as one group.

Fourth, ― _How will the questionnaires be distributed to companies?_ (Online survey, email invitation, or paper-based questionnaire via physical mail)‖. The on-line survey was finally selected as the survey method because it is easy to manage and organize the data collected from employers. Further, on-line survey can be easily used again in the next round of the employer satisfaction survey in two years. FICCI randomly selected employers who were provided with a username and password. After logging into the survey, employers were asked to complete the survey and provide company characteristics.

# **3.3 Survey Instrument**

The questionnaire design builds upon three sources: the expected learning outcomes used for accreditation by the National Board of Accreditation (NBA), previous employer surveys, and consultations with employers. The questionnaire is divided into three sections; (i) Overall satisfaction level, (ii) Importance and Satisfaction of 26 different
Figure 7: The increase in reported assets is greater for Argentina’s wealthiest 0.1% <!-- Start of picture text --> Reported assets<br>(2015 = 100) 2013-15 2016<br>300 Amnesty Amnesty<br>250 p99.95-p99.99<br>p99.9-p99.95<br>200<br>Top 0.01%<br>p99.5-p99.9<br>150<br>p99-p99.5<br>100<br>p98-p99<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text --> _Notes:_ this figure compares the assets reported yearly by the wealthiest 2% of adults (aged 20 and above) separately by bins of increasing assets relative to 2015. Individuals below the top 1% had moderate increases in their average assets after the amnesty. By contrast, the wealthiest 0.5% of taxpayers declared substantially more assets after the program. In particular, the rise was remarkable among the top 0.1% who, four years after the amnesty, reported to own two to three times as much assets as before the scheme. _Source:_ authors’ calculations using data from AFIP statistical yearbooks.

30
_LCi − LC_ ) so that _γ_ 2 measures the correlation between FDI and economic growth when local conditions are at their mean value (the main effect of these local conditions is included in matrix _Xi_ ). All other variables are defined as in Equation 1.

Columns 2-3 and 5-6 of Table 1 show that the relationship between FDI inflows and economic growth is stronger in countries with sufficiently high levels of education or sufficiently deep credit markets. Figure 1 illustrates this result by plotting the marginal effect of FDI on growth at different levels of financial depth (panels a and c) and education (panels b and d).

When we estimate the model using data for 1970-89, the direct link between FDI and growth is not statistically significant when we interact FDI with credit to the private sector over GDP (Column 2 of Table 1). This result indicates that there is no significant correlation between FDI and growth when financial depth is at its cross-country mean. However, the correlation between FDI and GDP growth becomes positive and statistically significant when financial depth is 25 percentage points above the cross-country mean (panel a of Figure 1). The point estimates imply that a one-standard deviation increase in FDI is associated with 1 percentage point increase in GDP growth when credit to the private sector is 25 percentage points above the sample mean. This is a large effect if one considers that average growth in the sample is 1.5%. The correlation

> 5These are the variables used in the baseline estimations of Borensztein et al. (1998) and Alfaro et al. (2004).

> 6The vintage of the World Development Indicators data that we use is different from that used in the papers that we replicate. The more recent vintages of the WDI contains updated FDI data. In these updated data some values are different from those used in earlier studies, even when they refer to the same country-year (we would like to thank Laura Alfaro for telling us about this). It is thus reassuring that the original results can be reproduced with
each country in the sample. A definition of a high-debt regime for country i at time t, _Dit_ , is available: _Dit_ = 1 if _Bi,t > B_<sup>_∗_</sup> , _Dit_ = 0 otherwise.

# **3 Testing the EFC hypothesis with new narrative data**

In this section, I test the EFC hypothesis empirically by using a new source of crosscountry quarterly narrative data of tax-based consolidations as a direct measure of fiscal austerity. The narrative data is briefly introduced and a quality check of the 7
**Table 5 Output growth and commodity prices**

||**Depen**<br>|**dent variabl**<br>|**e: GDP grow**<br>|**th**<br>|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|||EMDEs|||Full sample||
|Commodity export price<br>index|0.085***|0.063***|0.080***|0.024|0.018|0.026|
|Terms of trade||0.029|||0.026||
|GDP growth (t-1)|||0.367***|||0.366***|
|Commodity export price<br>index x EMDE||||0.062**|0.046*|0.054**|
|_F_-test||||***|***|***|
|Observations|381|364|370|533|436|518|
|Countries|11|11|11|15|15|15|

_Source_ : World Bank.

_Note:_ Panel least squares with country fixed effects. All variables are in log-differences. The estimates in columns (1)-(3) are based on the sample of emerging market and developing economies (EMDEs) only while those in columns (4)-(6) are based on the full sample that includes advanced-economy commodity exporters. _F_ -test in columns (4)-(6) evaluates the joint significance of commodity export price index and the interaction of the index with the
.............. 26**|
|A. Global Competitiveness Report............................................................................ 26|
|B.<br>World Bank Knowledge Economy Index (KEI)................................................... 29|
|C.<br>Porter and Stern: National Innovation Capacity.................................................. 32|
|**IV.** **Reforming Latvia’s National Innovation System ................................................ 35**|
|A. Enterprise Sector – Little Innovation and Less R&D........................................... 35|
|**V.** **Latvia’s NIS – Change without Reform .......
Second, the figures for public provincial banks include Buenos Aires, a case apart from the others. When Buenos Aires data are removed, the percentage of good credits in each category drops by about 5%. After removing Buenos Aires and correcting for potential overstatements, the percentages of good credits will almost certainly trail those for the earliest privatizers by an even greater amount. Therefore, it is fair to conclude that, as of 1 996, the earliest privatizers had much higher quality portfolios thani the remaining public provincial banks.

Whether the privatized banks can maintain quality is another important question. Indications are, however, positive. Of the early privatizers, nearly half have been private for at least two years -- the averages in Table 9, therefore, reflect some ability to maintain the portfolio quality inherited after purging residual assets and liabilities. In addition, the operating income and cost data presented above is encouraging. At the very least, beginning and recent privatizers can take heart that, in the cases to date, the provinces have been able to create a privatized entity whose initial portfolio quality approaches that of the top private banks.

It is more difficult to draw strong conclusions regarding portfolio composition. To some extent, the composition may depend upon the resource endowments of the province in which the bank resides. It is difficult, therefore, to argue that the industrial composition of the private bank portfolios is inherently superior to that of the other banks, and that we should expect privatized provincial. banks to create similar portfolios over time. We might, however, expect less credit to be allocated to the government once political incentives have been replaced by purely economic ones. The data indicate that the remaining public provincial banks have the highest share of their portfolios concentrated in government services (17.6%), followed by beginning privatizers (14.8%). Recent and early privatizers allocate a little more than 10% of their portfolios to government services, while private banks devote a little less than 5% of theirs
to spend a very minimum of two hours at a given water point to water their cattle and that cattle watering would occur during daylight hours. To allow for a complete listing, daylight hours were segmented into two‐hour time slots, during each of which enumeration team leaders completed a full listing of all nomadic households at the water point at that time. As not all persons present at water points were members of nomadic households, but may instead be from close‐by rural settlements, the listing form contained a number of questions identifying nomadic households. The form also asked for informed consent to be interviewed. Upon completing a two‐hour listing period, up to three households were randomly selected from the list of consenting nomadic households gathered during this time slot. Interviews were then scheduled with the selected households at a time and place convenient for the household respondent.<sup>4</sup> Based on this sampling design, sampling weights were calculated after the completion of data collection (see Appendix for the derivation Sampling weights).

# 3. Data collection

Wave 2 of the Somali High Frequency Survey was implemented using computer assisted personal interviewing (CAPI), whereby enumerators were equipped with tablet computers which contained the survey questionnaire (section 3.1) and would upload completed interviews to the project’s Survey 4 Additional rules applied in special cases: (i) If no nomadic households were found or arrived at a given water point, enumeration teams remained at the water point for three days. If no nomadic household had arrived, the water point was replaced. (ii) If nomadic households were present but arrived at very low frequencies, so that teams struggled to reach the required number of interviews, they would stay for a maximum of 12 days. Then teams would leave whether or not they have reached the required number of interviews. If 3 or fewer (but at least 1) nomadic households arrived during any two‐hour listing period, all listed households were interviewed during that period.

14
with Boko Haram fell dramatically after 2015.<sup>4</sup> However up until late 2018, the period studied in this paper, the group still maintained the capacity to engage in violent attacks, remaining a highly dangerous threat to the lives of thousands of civilians and the stability of the region (ACLED, 2019).

The Armed Conflict Location and Event Data Project (ACLED) lists 4,440 violent events for the period between January 2009 and July 2019. **Figure A1a** shows the temporal distribution of events between 2009 and 2018 (the year of the survey). The spatial spread of these events in **Figure A2a** illustrates that ‘high-intensity’ conflict – as proxied by the total number of violent events – took place in urban or peri-urban areas of Borno state in and near Maiduguri. In relative terms, rural areas were characterized by less intense conflict.

Many of Boko Haram’s operations target civilians and women and girls in particular. Compared to other terrorist organizations, such as Al-Shabaab, their attacks are even more lethal and include more abductions (Matfess, 2020). Extreme violence against women and girls increased in 2014 during a period of relative strength and includes many forms of violence, such as lethal attacks and forced abduction (Holpuch, 2018). The reasons for targeting civilians and women in particular are multifaceted and appear to vary, including both instrumental and ideological motives (Matfess, 2020).

**Displacement** . By the end of 2020, the conflict had displaced about 2.1 million individuals in Northeast Nigeria (UNHCR, 2021). <mark>An estimated 56% of the internally displaced population are children</mark> (IOM, 2020). The number of IDPs increased rapidly in 2014 and has remained at more or less the same level to the present day. **Figure A1b** displays reported dates of (first) displacement in our survey data, which is representative for the displaced population in
strengthen compliance.<sup>58</sup> Even in places where progress has been slower overall, research has captured important successes if political commitment has been present: for example, some localities have successfully introduced and expanded digitized property tax systems,<sup>59</sup> while other administrations have expanded the use of third-party data for income tax enforcement.<sup>60</sup> Qualitatively, many senior tax administrators themselves have become defenders, and promoters, of the modernized approaches to enforcement introduced in recent decades – though others have continued to attempt to preserve more informal ways of working.

Nevertheless, reform results have been disappointing in many areas. While VAT performance has been relatively strong, total revenue gains have struggled to compensate for decreases in trade tax

> 54 Schreiber, L. (2018). Broadening the base: Improving tax administration in Indonesia, 2006-2016. _Innovations for Successful Societies_ . New Jersey: Princeton University.

> 55 Khan, A., Khwaja, A., & Olken, B. (2016). Tax farming redux: Experimental evidence on performance pay for tax collectors. _Quarterly Journal of Economics_ , (1):219-271.

> 56 Iversen, V., Fjeldstad, O-H., Bahiigwa, G., Ellis, F., & James, R. (2006). Private tax collection - Remnant of the past or a way forward? Evidence from rural Uganda. _Public Administration and Development_ , 26 (2):317-328.

> 57 Ali, M., Shifa, A., Shimeles, A., & Woldeyes, F. (2015). Information technology and fiscal capacity in a developing country: Evidence from Ethiopia. _ICTD Working Paper 31._ Brighton: International Centre for Tax and Development.

> 58 Pomeranz, D. (2015). No taxation without information: Deterrence and self-enforcement in the value added tax. _American Economic Review_ , 105 (8):2539-2569.

> 59 Jibao, S.
_Table 7 The effect of other subjective indicators on the number of protests and strikes_

||(1)<br>Incl.<br>Freedom<br>of life|(2)<br>Incl.<br>Get<br>ahead|(3)<br>Incl. SoL|(4)<br>Incl. Sol<br>getting<br>better|(5)<br>Incl.<br>FS<br>index|(6)<br>Incl.<br>CB<br>index|(7)<br>Incl. all<br>variables|
|---|---|---|---|---|---|---|---|
|Freedom of life (%)|-0.018**<br>(0.006)||||||0.003<br>(0.005)|
|Get ahead (%)||-0.017*|||||-0.011|
|||(0.007)|||||(0.007)|
|Standard of living (SoL) (%)|||-0.016*<br>(0.007)||||-0.005<br>(0.008)|
|SoL getting better (%)||||-0.013*|||-0.000|
|||||(0.006)|||(0.006)|
|FS Index|||||0.008<br>(0.006)||0.018*<br>(0.008)|
|CB Index||||||-0.016<br>(0.010)