rafmacalaba/data-use-mentions-extended huggingface dataset:
composites combining processes, outputs and outcomes which make it hard to assess the effectiveness of government reforms. Fourth, many indicators which do not rely on expert judgement, instead track the existence of specific regulatory management tools and thus not adequate for assessing how each tool is used in practice and how they interact with their contexts such as changing economic circumstances.

While such measurement has been useful, it clearly has limitations in terms of accuracy and validity, calling for new metrics. The availability of large volumes of administrative data on laws and regulations open up new avenues for better gauging the determinants, extent, and effects of volatile laws and regulations. Such legislative predictability indicators should further contribute to a more encompassing understanding of legislative quality.

# **3.1 Legislative and Regulatory Data**

More and more data has been made publicly available in recent years by political institutions through dedicated websites and open-data portals. Yet, the scope and quality of legislative data that are made available still display a strong cross-country variation in terms of temporal scope and depth. Nonetheless, most countries tend to disclose the title and text of legislation, its date of publication and the ID number of previous versions of legislation. The data collection work underpinning the present analysis follows the data collection methods, database structure, and quality standards established by the research team (Fazekas et al, 2024). The wider data collection effort and process is described in Appendix B. Here, we outline the specific data collection approach in Jordan.

The data collection process consists of four stages: source identification, source annotation, web scraping and parsing, as well as data evaluation. First, source identification was conducted in consultation with Jordan’s Legislation and Opinion Bureau (LOB) which hosts the website and managed legislative data. Second, a detailed source annotation was created that precisely marks each variable in the dataset (table 1) on the LOB website. Such annotations are highly technical, offering guidance for the subsequent programming work. Third, the data collection process includes
measure for the balanced panel. Columns 2 and 3 then consider the 52 firms for which we have both the 2016 WMS and Anexo K measured during and after the intervention. We continue to see a statistically significant impact of the individual treatment on the Anexo K measure using this sub-sample both during and post-intervention, and a significant impact of the group treatment during the intervention, with the magnitude of the estimated effect only falling in a substantive way for the group treatment post-intervention, although with a wide confidence interval. In contrast, there is no significant impact on the WMS using this same sample. The foot of the table converts the estimated treatment effects into confidence intervals expressed in terms of standard deviation changes in the respective management practice. We see that not only are the WMS treatment effects statistically insignificant while those for the Anexo K outcome are statistically significant, but the 95 percent confidence interval for the effect of the individual treatment effect does not even overlap for the two outcomes. This suggests that the lack of impact on the WMS is not simply a matter of the sample composition or statistical power. Likewise, when we restrict to the same sample as the MOPS in columns 4 and 5, we find significant treatment impacts on the Anexo K, and no significant impact on the MOPS, although in this case the confidence intervals do overlap.

67
country. Severe natural disasters are those that generate damages equivalent to at least 1 percent of the GDP of the country affected that year (based on the total estimated damages reported by EM-DAT). Disasters include floods, earthquakes, droughts, storms, landslides, volcanic activities, extreme temperatures, and wildfires.

Appendix Table A8 shows that the relationship between SCI and forecast error remains robust, although the sample size shrinks. After accounting for the aforementioned additional factors, the quality of the data ecosystem seems to matter less for the forecast accuracy, as the magnitude of the SCI decreases and is less statistically significant across the World Bank, IMF, and private sector’s samples (columns 1 through 3), given the smaller sample size. The coefficient of the MENA dummy is also statistically insignificant, possibly suggesting that MENA has high absolute forecast errors due to these additional factors. However, data capacity reduces the forecast optimism bias (forecast errors) and remains statistically significant at the 5 percent level for both the World Bank and the IMF’s samples (columns 4 and 5). The coefficient of 21
|Secondary level|0.15|0.26|
|University level|0.04|0.07|
|Indigenous|0.38|0.41|
|Married|0.91|0.87|
|Number of children 0-3|1.30|1.17|
|Number of children 4-6|1.22|1.12|
|Number of children 7-12|1.77|1.47|
|Number of people > 12|3.29|3.28|
|Presence of grandmother|0.05|0.05|
|Household head|0.20|0.22|
|Spouse works|0.87|0.89|
|Income stability|0.52|0.56|
|Household Home & WASH index|0.00|0.04|
|Urban|0.50|0.57|
|Panel B: Municipality structure of male employment|||
|Share of male employment in primary activities|0.62|0.46|
|Share of male employment in construction|0.10|0.10|
|Share of male employment in manufacturing|0.08|0.08|
|Share of male employment in white collar services|0.03|0.11|
|Share of employment in other services|0.17|0.25|
|Observations|1,176,160|1,696,260|

Sources: 2002 and 2018 National Census of Guatemala.

Notes: Statistics in Panel A for women ages 25 to 49 who either have spouses that are household heads or that are household heads themselves and who live in municipalities appearing in both censuses (331 municipalities). Table A1 in the paper provides the definition of each variable.

> 17 We exclude 7 municipalities due to missing data.

42
# **References**

<mark>Ahluwalia, M., N. Carter, and H. Chenery (1979). “Growth and Poverty in Developing Countries,”</mark> _<mark>Journal of Development Economics</mark>_ <mark>, Vol. 6, No. 3, pp. 299-341.</mark> <mark>Aguiar, M., and G. Gopinath (2007). “Emerging Market Business Cycles: The Cycle is the Trend,”</mark> _<mark>Journal of Political Economy</mark>_ <mark>, Vol. 115, No. 1, pp. 69-102.</mark> <mark>Alvaredo, F., and L. Gasparini (2015). “Recent Trends in Inequality and Poverty in Developing Countries,” in A. Atkinson and F. Bourguignon, eds.,</mark> _<mark>Handbook of Income Distribution</mark>_ <mark>, pp. 697-805.</mark> Atamanov, A., J.P. Azevedo, R.A. Castañeda, S. Chen, P.A. Corral Rodas, R. Dewina, C. Diaz-Bonilla, D.M. Jolliffe, C. Lakner, K. Lee, D.G. Mahler, J. Montes, R. Mungai, M.C. Nguyen, E.B. Prydz, P. Sangraula, K. Scott, A.E. Wambile, J. Yang, and Q. Zhao (2018). _April 2018 PovcalNet Update: What’s New_ (Washington, DC: World Bank).

<mark>Bhalla, S. (2002).</mark> _<mark>Imagine There’s No Country: Poverty, Inequality and Growth in the Era of Globalization</mark>_ <mark>(Washington, DC: Institute for International Economics).</
Harmonized Learning Outcome); (2) the Enterprise Survey indicator on access to bank finance (Overdraft) and the Global Competitiveness indicator on Financial Development (or the _Doing Business_ on Getting Credit); or (3) the Enterprise Survey indicator on Land Access Obstacle and the _Doing Business_ score on Dealing with a Construction Permit.

The mild or nonexistent correlation of related indicators from various sources suggests that they likely differ in their explanatory power for firm performance. The lack of correlation between different sources could also indicate that these measures capture different aspects (such as formal schooling and on-the-job training) of a theme (such as skills).

# **4. Associations between firm performance and de jure and de facto measures of the business environment**

We estimate the association between the business environment and firm performance, as captured by firm employment growth and the labor productivity level. These two measures capture both dynamic and static performances of firms. We focus on firm employment growth for two reasons. First, firm growth is a key source of overall national economic growth, and a country in which firms are failing to grow is unlikely to develop. Firm growth is especially important for the path to prosperity for developing countries because the vast majority of firms are small , yet relatively larger firms in developing countries are significantly more productive (Hsieh and Olken 2014). Furthermore, there is evidence that cities featuring a sufficiently large share of relatively larger firms (those with more than 50 employees) tend to facilitate firm employment growth (Clarke, Li, and Xu 2016) and firm productivity growth in relatively lowincome countries (Reyes, Roberts, and Xu 2017). Because growth rates are heavily influenced by outlier issues,<sup>10</sup> we follow Davis, Haltiwanger and Schuh ( 1996 ) by calculating mid-point growth rates by dividing the change in employment (or labor productivity) between the survey year and three years earlier by the simple average of employment (or labor productivity) in the beginning and ending years.
- Torrisi, O. (2020). Armed conflict and the timing of childbearing in azerbaijan. _Population and Development Review_ 46(3):501–556.

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- Torrisi, O. (2023). Young-age exposure to armed conflict and women’s experiences of intimate partner violence. _Journal of Marriage and Family_ 85(1):7–32.

- Valente, C. (2011). What did the maoists ever do for us? education and marriage of women exposed to civil conflict in nepal. _Education and Marriage of Women Exposed to Civil Conflict in Nepal (July 1, 2011). World Bank Policy Research Working Paper_ (5741).

- van Noort, S. and Goyal, T. (2020). Is Ethnic Violence Self-Perpetuating? QuasiExperimental Evidence from Hindu-Muslim Riots in India. _Quasi-Experimental Evidence from Hindu-Muslim Riots in India (April 20, 2020)_ .

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- Varshney, A. (2008). _Ethnic Conflict and Civic Life_ . Yale University Press.

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- Blankespoor, B., T. Croft, T. Dontamsetti, B. Mayala, and S. Murray (2021). Spatial anonymization: Guidance note prepared for the Inter-Secretariat working group on household surveys. UN Inter-secretariat Working Group on Household Surveys Task Force on Spatial Anonymization in Public-Use Household Survey Datasets. https://unstats.un.org/iswghs/taskforces/documents/Spatial ~~A~~ nonymization ~~R~~ eport ~~s~~ ubmit01272021 ~~I~~ SWGHS.pdf.

- Bosilovich, M., R. Lucchesi, and M. Suarez (2016). MERRA-2: File specification. GMAO Office Note No. 9 (Version 1.1). `http://gmao.gsfc.nasa.gov/pubs/office_notes` .

- Br ̈uckner, M. and A. Ciccone (2011). Rain and the democratic window of opportunity. _Econometrica 79_ (3), 923–47.

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- Central Statistics Agency of Ethiopia (CSA) (2015). Ethiopia Socioeconomic Survey 20132014. Public Use Dataset. Ref: ETH ~~2~~ 013 ~~E~~ SS ~~v~~ 02 ~~M~~
section outlines the main features of the datasets.

## **3.1 U.S. Manufacturing firms Across Industries and Time**

The CM is conducted every five years and the empirical part of this paper makes use of CM information from 1992, 1997, and 2002.<sup>11</sup> The unit of observation for the Census is a manufacturing establishment, or plant, and it contains detail information on inputs and output of all establishments.<sup>12</sup> For 1992 and 1997, plants are classified at the four-digit Standard Industrial Classification level (SIC4). In 2002, industry classification changed to the 6-digit North American Industry Clas-

> ones for which differentiation and variety is important.

> 10See Bernard et al. (2009) for a complete description of the LFTTD and its construction. For an extensive discussion of the LBD see Jarmin and Miranda (2002).

> 11Though CM data are available for earlier periods, I cannot use them because export information on the LFTTD is not available.

> 12The CM imputes input usage data for small manufactures, referred to in the data as “administrative records”. As it is customary in the U.S. microdata research —see Bernard, Redding and Schott (2010)—, these observations are excluded from the analysis.

10
**<u>Observations</u>** <u>200 401</u>

# _Source_ : Own calculations using WBSCPS and administrative data.

_Notes_ : This table shows descriptive statistics for the 601 surveyed programs in Brazil. The unit of observation is a program. Columns (1) and (2) refer to the 200 programs that do not match to students in the individual-level dataset; columns (3) and (4) refer to the 401 programs that match (with 2+ students per program); and column (5) presents the p-value for the t-test of the difference in means between columns (1) and (3). P-value is in boldface when the difference is significant at the 1, 5 or 10% level. Panel A refers to program quality determinants, classified by category. Panel B refers to student body, program, and HEI characteristics, and Panel C to noise controls. In the case of dummy variables, only those with a mean between 0.10 and 0.9 are included in this table and in the estimation. Statistics are weighted by the WBSCPS sampling weights.

76
<!-- Start of picture text --> International tourist arrivals and employment in the travel and tourism<br>industry, 2017<br>Oo=} ° °<br>Z °°<br>a 0 8 ° °<br>sS)& ° °° ° °<br>=g2 oO ° © 9°“> 8elIRN3 ° ° Feta Q °<br>cya a Se© epz2 ° TUNogo0 @ARE°<br>&e+ ° oo6 8 6 Noor 5° °<br>fc °° oeoo ° 9 0 ° Bo egiebOo @BHR<br>° ° ° °<br>N °<br>°<br>4 6 8 10 12<br>Intl Tourist Arrivals ('000, logs)<br>Source: Data from WEF Travel and Tourism Competitiveness Report 2019.<br>International tourist arrivals and GDP in the travel and tourism<br>industry, 2017<br>°<br>N=<br>° °<br>Bo<br>et 8° oecyeTM ‘©<br>g oo o @IRN @IRN [AR<br>o ° So ye oo<br>a @& e@KWT o © oof Oa TUN °<br>a oe gSBHPO<br>BE ° oo ° ° ° °°<br>fs) ° @YE ° %, %, eMbT<br>o eo 6 8 g° °<br>° 2 °00 °adad 3©eo°o°° °<br>t °<br>4 6 8 10 12<br>Intl Tourist Arrivals Arrivals ('000, logs)<br>Source: Data from WEF WEF Travel and Tourism Competitiveness Report 2019.<br><!-- End of picture text --> <!-- Start of picture text --> Bo ° oecyeTM oecyeTM<br>
impact of the pandemic from the 2018/19 survey from Rodriguez and Atamanov (2021). We apply these growth rates to our distribution for the Islamic Republic of Iran for 2019. For <mark>Türkiye,</mark> we have percentile specific simulated per capita growth rates in income from 2019 to 2020 from Baez and Celik (2021). We collapse these to ventile specific growth rates to smoothen out some variance and apply them to our 2019 vector, noting that the 2019 vector is based on a consumption aggregate rather than an income aggregate. For Ethiopia, from Wieser et al. (2022) we have predicted consumption levels from a survey-to-survey imputation from the 2018/19 household survey to a high frequency phone survey carried out in 2020. We convert these predictions to per capita percentile growth rates from 2018/19 to 2020 and apply them to our 2019 vector for Ethiopia. For Tanzania, we use estimates of the growth in mean disposable income due to the pandemic by income quartile from Lastunen et al. (2021). The growth rates are relative to a counterfactual dataset created to reflect the situation right before the pandemic started. We apply these growth rates to our 2019 distribution, noting that the 2019 distribution is based on a consumption aggregate.

37
Knobel next estimates a similar gravity model on services trade data for all countries in the world for which data are available. His results also indicate that service imports into Russia are well below predicted levels for countries with similar characteristics. Knobel argues that it is highly likely that the lower level of services trade is due to existing barriers to services imports. Knobel estimates, at the sector level, the countries that import the most services after adjusting for their characteristics. He finds that the country most open to service imports overall is the U.S., but the result differs across sectors. Relative to their predicted levels of services imports, the countries that import the most, by sector, are the following: transportation services, communication, computer and information services, personal cultural and recreational services, other business services — USA; travel — Armenia; construction — Kuwait; insurance — United Arab Emirates; financial services — Luxembourg. Knobel calls these countries the world leaders in services imports and develops a measure of distance from the world leader in each sector. He finds that Russia is very far from the world leader in services imports in all sectors. Russia is closest to the world leader in transportation, travel and personal and cultural services, and furthest from the leader in insurance, computer and other business services. Knobel expects a substantial increase in imported services in Russia due to services liberalization, since Russia is so far from world leaders, and, as a consequence, Russia would benefit from a large increase in competition and an introduction of foreign best practices in the Russian market.

Finally, Knobel develops a model in which he estimates labor productivity as a function of services sector liberalization in Russia and a set of industry variables that might affect industry productivity. He uses the EBRD indices of services sector liberalization for the years 2000-2008. Like Fernandes (2007), he develops an index that takes into account that the impact of services liberalization in different sectors that depends on the intensity with which a sector 20
an _outcome_ of the program. Thus, the program, at its _maximum_ effect estimated with the results of Table 6, could increase the performance of the average student by one-half of the standard deviation of the percentage change in test scores for the respective sub-ample.

It should be noted that the variables measuring the characteristics of students, parents, school personnel and facilities are all numerical indices constructed by C.E.E analysts. Some indices aggregate answers to as many as a dozen questions in the original survey. The model in Table 6 is a simple, parsimonious representation. In particular, it could be argued that if the characteristics of the demand (family and community background, parental attitude towards and involvement in schooling, academic history, self-esteem, etc.) were adequately measured, the additional effect of the PARE program would be smaller, even insignificant. Alternatively, if the characteristics of the supply 4 Three observations may be pertinent. First, for students attending native schools it seems that selfesteem, measured at 5th grade and residence in a community with greater access to public services is _negatively_ correlated with performance. One plausible explanation is due to the conflictual character of native education: Students that are positively self-selected may have a greater resentment in attending special schools. Second, and for the same group, while the performance of teachers does not seem to alter significantly the perfornance of students, the performance of principals and supervisors does. This result may be due to the generally poor quality of teaching in native schools. Finally, it is puzzling to note that, in the nrral sub-sample, parental involvement _diminishes_ students' performance. One possible reason for this is the possibility that parental involvement increases as the quality of the school diminishes. Parents act only when the problems are large and apparent.

9
the incremental arrears

> 15 _IMF and Pakistan agree to a $5.3 Billion Bailout._ Declan Walsh and Salman Masood. New York Times, July 4, 2013.

> 16 Although government statistical sources mention circular debt, there does not appear to be a continuous series of data that would permit a simple extension of Table 1 to earlier and to more recent years. “Dynamics of Circular Debt in Pakistan and its Resolution”. S. Ali and S. Babar. 2010, _The Lahore Journal of Economics_ (15) pp.61-74.

17
output in 2010. Our dataset contains information on 152 firms (727 firm-year observations) for 18 subsectors of the machine building industry.<sup>5</sup> The employment growth rate for the full sample of firms (see Table 1) indicates that the average firm in the machine building industry reduced its employment by approximately 2.5% yearly over the period considered. Interestingly, however, a small fraction of outlying firms exhibited impressive growth rates, with a maximum amount of more than 200% employment growth. At a more disaggregated level, privately organized firms tended to outperform state-owned and mixed-owned firms in terms of employment growth. Accordingly, privately owned firms reduced their number of employees by 1.8%, on average, although the maximum (annual) increase in employment in this group of firms amounted to 24.3%. The average employment growth rates in the other two groups of firms are -2.6% and -2.5%, respectively. One should be cautious, however, when interpreting these results, given the relatively small number of privately-owned firms in this sample. Similarly, comparing firms directly reporting to the Ministry of Industry with non-ministry-reporting firms, we find that the former group performed significantly worse. The average ministry-reporting firm exhibits an annual growth rate of -4.1% while non-reporting firms, on average, keep their level of employment unchanged.<sup>6</sup> Focusing on different types of firms, such as head companies of vertical chains, affiliates of such chains and independent firms, the descriptive statistics with regard to employment growth do not reveal remarkable differences across these groups. However, these types of firms may differ in other respects that affect their growth pattern. In our econometric analysis we will use this classification in order to analyse the impact of vertical integration on the growth performance in the Belarusian machine building industries.

Disaggregating firm growth by sub-industry within the machine building sector reveals that

> 5These are the automotive subsector, bearings, chemical and