rafmacalaba/data-use-mentions-extended huggingface dataset:
|400|404|48,534|8|
|Miscellaneous manufacturing|339|4,388|5,143|4,766|83,103|57|
|<br>Medical equipment and supplies|3391|3,343|4,374|3,859|56,661|68|
|Other miscellaneous manufacturing|other 339|1,045|769|907|26,442|34|
||||All R&D||||
|Industry and company size|NAICS codes|2004|2005|2004-2005 average|||
||||$millions||||
|Nonmanufacturing industries|21–23, 42, 44–81|61,013|67,969|64,491|2,120,877|30|
|Mining, extraction, and support activities|21|D|D|0|33,665|0|
|Utilities|22|202|210|206|223,395|1|
|Construction|23|1,481|D|741|57,187|13|
|Wholesale trade|42|D|D|0|107,485|0|
|Retail trade|44, 45|1,596|D|798|232,150|3|
|Transportation and warehousing*|48, 49|D|D|0|79,436|See Note|
|<br>Information|<br>51|22,593|23,836|23,215|445,489|52|
|Finance insurance and real estate|52 53|1,708|3,030|2,369|580,380|4|
|, ,<br>Professional scientific and technical services|,<br>54|28,709|32,021|30,365|261,500|116|
|, ,<br>Architectural, engineering
3 First, it is worth emphasizing that smaller (poorer) economies see lower mail volumes (letters per capita). Countries with a GDP per capita (PPP) of below $1,000 see mail volumes of below 1 per person per year—compared with closer to 100 per person in countries with a GDP per capita of above $5,000 (Table 1).

# **<u>Table 1:  Postal Structure and Development</u>**

|**Indicator**|**Average fo**<br>_<$1,000_|**r GDP Per Capita (PP**<br>_$1,000-_<br>|**P) Band**<br>_>$5,000_|
|---|---|---|---|
|||_$5,000_||
|Letters/Capita|0.9|4.6|98.4|
|Total letter|10m|634m|2,468m|
|volume||||
|KM2 per post<br>office|4,702|1,738|458|

_Source: Calculated from UPU and World Bank data, 2001_ The _average_ consumer in poorer countries sends perhaps one letter per year—and we know that the great majority will neither send nor receive a single letter (at least not through the network captured by Universal Postal Union (UPU) statistics). In particular, there is little demand from businesses (which account for as much as 80 percent of letters sent) to deliver mail to the poorest in developing countries. This is because most do not have services that require billing (telephony or electricity, as it might be), and they are hardly an attractive target for companies in terms of marketing purposes. Demand from individuals is also likely to be lower –the majority of people in developing countries do not use the postal
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 1. Infrastructure has contributed much to economic growth—but could contribute much more**

<!-- Start of picture text --> a. Infrastructure’s contribution to annual per capita economic b. Potential contributions of infrastructure to annual per capita<br>growth in West African countries, in percentage points, 2001-05 economic growth in West African countries, in percentage points<br>5<br>5<br>4<br>4<br>3 3<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>Roads Electricity Telecom Roads Electricity Telecom<br>Percentage Points Percentage Points<br>n a go r e i us<br>Beni Cote d'Ivoire Mauritius Guinea Burkina Faso Senegal Ghan Mauritania T o Nigeria South Africa Gambia Nig Sierra Leone Guinea-Bissau Niger Togo Nigeria Burkina Faso Guinea-Bissau Sierra Leone Benin Mauritania Gambia Senegal Ghana Guinea Cote d'Ivoire South Africa Maurit<br><!-- End of picture text --> a. Infrastructure’s contribution to annual per capita economic growth in West African countries, in percentage points, 2001-05 _Source_ : Calderon 2009.

Evidence from enterprise surveys suggests that infrastructure constraints were responsible for about 60 percent of the productivity handicap faced by Beninese firms (figure 2a), with the remainder traceable to poor governance, red tape, and financing constraints. Of the countries of the region, infrastructure constraints were highest in Benin. Customs clearance before export was the constraint that weighed most heavily on the country’s firms. The time waiting for phone connections was the second-most-important constraint for increasing productivity (figure 2b). Transport is also a major issue hindering firms’ productivity. The average output loss owing to electricity supply problems is about 7.5 percent, a number higher than in most countries in Sub Saharan Africa (World Bank 2005a).

*
|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 and International Finance Statistics._ The current account, which represents the balance of saving of the economy with the rest of the world, shows a clear structural change in its level in the early 1990s. From being a capital importer from the rest of the world (negative current account), the country became a savings exporter in the early 1990s (positive current account). The structural shift coincides with the adoption of ERSAP. In 2009, the current account balance turned negative again, indicating a need for external savings in a period of crisis when the world financial system came to almost a complete halt.

The current account also shows an oscillating pattern around a given level. While that level was negative up to the late 1980s, it became positive since then. To examine the determinants of the current account behavior in Egypt we use a simple version of the Obstfeld and Rogoff (1996) intertemporal consumption smoothing model according to which the current account is determined by transitory deviations of income from its permanent level, by fluctuations of investment around their trend, and by transitory deviations of public spending. A simple regression shows that income and investment are statistically significant explanatory variables of the current account behavior in Egypt (see Annex 1). Figure 5 shows how investment is the main driver of the current account oscillation. In particular, the fall of investment up to the mid 1990s is associated with the change in the current account. The fall in public investment implicit in the economic reform program explains the fall in overall investment. The predicted values would be those that the consumption smoothing 8
allocation of a number of critical nutrients. While calorie has been the focal point in previous literature, it neither addresses the growing concern of micronutrient malnutrition, nor provides adequate understanding of gender-role in alleviating malnutrition. Although, some studies (Behrman, 1988; Behrman and Deolalikar, 1990) focused on nutrients other than calorie, they applied a unitary framework, which could be misleading if the household decision-making process is affected by the bargaining power of different members. My second contribution is the demonstration of intrahousehold bargaining (in addition to typical income and price effects) on nutrient allocations. Third, previous studies have demonstrated intrahousehold bargaining on various outcomes other than individual nutrient intakes. I add to this literature by focusing on nutrient intakes using an innovative panel dataset of rural Bangladesh. Fourth, with the exception of a few natural experiments, previous studies have used bargaining measures that

> zinc (IFRPI-BIDS-INFS, 1998).

> 4To the best of my knowledge, this is one of the first exercises that applies a bargaining framework to analyse intrahousehold nutrient allocation based on actual individual dietary intakes.

> 5I use household head or husband and his wife or spouse interchangeably.

3
0.206]|
|Sole proprietorship|0.0229|0.0820|
||[0.181]|[0.205]|
|Partnership|0.0328|0.0878|
||[0.181]|[0.206]|
|Ltd Partnership|0.0284<br>[0.182]|0.0884<br>[0.206]|
|Model|Heckman|Heckman|
|Time FE|yes|yes|
|Geographic area FE|yes|yes|
|Additional controls|yes|yes|
|Observations|10386|10444|
|Selected|1911|1969|
|Not selected<br>|8475|8475|
|Wald _χ_<sup>2</sup>|296.31|498.86|
|Inverse Mill’s ratio|-0.0309*|-0.0308|

_Notes:_ Heckman selection model. In this table, we explicitly model the probability of being included in our analysis in a two-step Heckman-type selection model (Heckman, 1976; Lewis, 1974; Gronau, 1974). The selection equation models a firms’ probability of belonging to the panel (i.e., being interviewed in two consecutive waves of the WBES survey) depending on firms’ age, size, and belonging stratum (the intersection of sector and country, excluded in the main specification). The inverse Mill’s ratio is included as an additional regressor in the original specification (reported in the bottom panel). All regressors are timed consistently with previous analyses. Unreported controls follow the specification in Table 4. Measures are defined in Table B4. Robust standard errors in brackets. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

24
global agenda (e.g. Treasury). In the last column of Table A1 we display the economic sectors that are directly related with the purview of a committee using the NAICS 2007 classification.

> 19 Detailed data on all the bills received in the Senate for the years of our study was downloaded from the website of the Legislative Information System of the Mexican Ministry of Governance. The information for each bill includes its date, type of bill, topic, person/institution/political party that presented it, and the committee the bill was channeled to.

8
to 2.46 for Ethiopia and to 4 for Nigeria. Comparing with full costs at face value, infrastructure explains about 24-30% of these countries’ cost gap with the U.S. While this is not a trivial magnitude itself, it is likely to be a lower bound for the impact of infrastructure since omitted factors such as the cost and quality of transportation equipment, and fuel costs, are likely to interact with road quality.

Although the countries under study differ, it is noteworthy to compare the estimates from Atkin and Donaldson (2015) with the direct evidence from trucker surveys by Teravaninthorn and Raballand (2009), as the authors also do. The latter is reported in terms of per km cost for one truckload shipment, but in comparison, both the estimates and survey-based actual costs are expressed relative to the U.S. Estimated marginal cost of shipping relative to the U.S. mentioned above (2.56 and 4 in Ethiopia and Nigeria, respectively) are in the ballpark with the results from Teravaninthorn and Raballand 8 Origins are either identifiable production plants within each country or the main international port for imports. Asturias et al. (2019) follow a similar strategy and use prices charged by monopolists in different destinations to estimate transport costs in India from price differentials. Reduced price gaps after the construction of the Golden Quadrilateral highway are informative about the pro-competitive effect of reduced transport costs. Similarly, Donaldson (2018) uses price gaps in salt, a commodity produced in unique locations, to infer transport cost reducing effect of railroad construction in 19th century India.

17
firms that complain less about financing obstacles. Countries with larger specialized lenders also have more firms with overdraft facilities or loans. Many of the firm characteristics are also correlated with each other. Countries with more small firms, for instance, have fewer listed and younger firms. Our access indicators are also significantly correlated with our industry indicator of external dependence, with firms in industries more reliant on external finance reporting lower financing obstacles and a higher probability of having an account, a loan or an overdraft.

9
Gidey K, Gidey MT, Hailu BY, Gebreamlak ZB, Niriayo YL (2023). Clinical and economic burden of healthcare-associated infections: A prospective cohort study. PLoS One. 18(2): e0282141.

Glick P (2009). How reliable are surveys of client satisfaction with healthcare services? Evidence from matched facility and household data in Madagascar. Soc Sci Med 68: 368-79.

Gosling R, Mbatia R, Savage A, Mulligan JA, Reyburn H (2003). Prevalence of hospitalacquired infections in a tertiary referral hospital in northern Tanzania. Ann Trop Med Parasitol. 97(1): 69-73.

Greco D, Magombe I (2011). Hospital acquired infections in a large north Ugandan hospital. J Prev Med Hyg. 52(2): 55–8.

Gupta A, Ananthakrishnan AN (2021). Economic burden and cost-effectiveness of therapies for Clostridiodes difficile infection: a narrative review. Therap Adv Gastroenterol. 14: 1-13.

Harant A (2022). Assessing transparency and accountability of national action plans on antimicrobial resistance in 15 African countries. Antimicrob Resist Infect Control. 11(1): 15.

Horan TC, Andrus M, Dudeck MA (2008). CDC/NHSN surveillance definition of health careassociated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control. 36(5): 309–32.

Huynh BT, Kermorvant-Duchemin E, Chheang R, Randrianirina F et al (2021). Severe bacterial neonatal infections in Madagascar, Senegal, and Cambodia: a multicentric community-based cohort study. PLoS Med. 18(9): e1003681.

IHME (2014). Health Service Provision in Uganda: Assessing Facility Capacity and Costs of Care. Seattle, WA: Institute for Health Metrics and Evaluation (IHME).

IHME (2015). Health Service Provision in Kenya: Assessing Facility Capacity and Costs of Care. Seattle, WA: Institute for Health Metrics and Evaluation (IHME).

Irek EO, Amupitan AA, Aboderin AO
and/or birth cohort fixed effects _δa_ , and _γb_ since the marriage cohort matches were selected by their age relative to the reference woman. However, when many marriage cohorts are stacked within a given survey round, then age fixed effects can be included in the regression, since age of marriage varies across marriage cohorts. That is, within a given age there is still variation across marriage cohorts in the location in event-time.

However, age and birth cohort remain collinear for all marriage cohorts in a given survey round (country-year). However, when multiple survey rounds per country are stacked, birth cohort fixed effects _γb_<sup>_g_canbeincludedaswellastheageeffects.Alternatively,totakeinto</sup> account multiple surveys across countries, country and survey year fixed effects could also be included. Note, however, that in cross-country pooled sample, country-year, age, and birth cohort fixed effects are collinear, and so including any two of these three yields equivalent estimates.<sup>2</sup> The sample size in each country varies, so the analysis is weighted by marriage cohort size so that it is representative at the individual level, and provides identical coefficient estimates to estimation on the microdata in a “stacked” model. Standard errors are clustered at the marriage cohort level.

We also consider several heterogeneity analyses to investigate the mechanisms underlying the marriage penalty. To explore the role of urban residence, the same analysis as in equation 1 is conducted on the urban and rural samples separately. To explore the role of education, the analysis includes an interaction term between women’s higher education and the postmarriage indicator. A separate regression is run to analyze the role of husband’s education by including an interaction term between husband’s higher education and the post-marriage indicator. Higher education (or husband’s higher education) takes the value one if women (or their husbands) have more than secondary education. Finally, we explore the role of social norms similarly, interacting the post-marriage indicator with a marriage cohort-averaged gender
guaranteed credit and what determines the equilibrium outcome. Although we find adverse selection, as the existing literature does, we show that its contribution to aggregate risk can be contained.

# **3 Data**

We use three administrative data sets from various sources in Chile, depending on what is appropriate for the type of analysis we perform at different points. These data sets cover the entire formal private sector in Chile in rich detail. Below we describe the data sources, sample selection, and key variables.

First, we use granular confidential bank-to-firm information compiled by the Financial Market Commission (the financial supervisory agency) for all firms using the entire banking system. We have information on stocks and flows of credit. For stocks, we have data on the amount of debt each firm has with each bank in the system every month. We also know the number of days each loan in the system is past due. For flows, we have transaction-level data 6
-0.30*|-0.04|0.31*|-0.27*|-0.20*|0.42*|
|PES|-0.04|-0.07|-0.40*|0.28*|0.37*|-0.38*|0.29*|0.41*|
|Vacancy database|-0.09|0.03|-0.20*|0.11|0.19*|-0.28*|0.03|0.22*|
|Job placement|-0.10|0.01|-0.18*|0.11|0.16*|-0.22*|0.11|0.27*|
|Counseling|-0.03|-0.08|-0.12|0.02|0.13|-0.21*|0.17*|0.27*|
|Training|-0.15*|-0.02|-0.14|0.12|0.11|-0.20*|0.14|0.21*|
|Mobility support|-0.07|-0.11|-0.19*|0.20*|0.14|-0.28*|0.25*|0.30*|
|Internship/apprenticeship|0.16*|-0.07|0.06|0.07|-0.10|0.02|0.28*|0.00|
|National skills fund|-0.06|0.04|-0.24*|0.06|0.27*|-0.17*|0.04|0.21*|
|Training incentives|-0
Skill deficits reflect a mix of demand and supply factors. To operationalize the measurement of skill deficits, TESS elicited both qualitative and quantitative information on employers’ perceptions and assessments of skill deficits, and potential responses to mitigate these skill gaps, as described below.

# <u>Skill gap indicators</u>

- _Assessments of current and needed skills_ .  Employers assessed the severity of educational deficiencies as an obstacle to the firm’s operations and rated how well current competencies of their workforce matched the skills needed by the enterprise, in technical and job-specific skills, in soft skills such as problem solving, communication, language, as well as the work ethic of employees.

- _Operational problems resulting from inadequate skills._ Employers rated the extent to which skill deficits posed problems for several specific operational areas, including maintaining production levels, quality control, use of new technology, innovation, and sales and marketing.

# <u>Potential employer responses</u>

- _Company-sponsored training_ . Firms reported on in-service training to their current employees, whether from programs run in-house or by external providers. In-service training programs are designed to remedy deficits in pre-employment skills, upgrade existing skills, or develop new skills as requirements change.

- _Hiring new workers._ Employers reported on whether they had any job vacancies over the past two years and, if so, how many new workers were hired for each occupation. New hires represent one potential employer response to specific occupational skill shortages.

- _Use of foreign workers by occupation_ . As a measure of domestic availability of specific occupational skills, employers were asked about their use of expatriate workers (and the share of foreign workers) in different occupations.

- _Outsourcing of specialized services_ . Firms often outsource accounting, engineering, legal and architectural services. Such skills may not be available in-house, and there is interest in knowing whether they are sourced domestically or abroad.

A unique feature of the survey is the questions on the firm’s skills supply chain. Employers were asked about the education
789***|
|<br>stage treatment variables|(6,511)|(31,925)|(13,388)|(0.143)|(0.30)|(0.075)|(4,463)|
|P‐values|0.469|0.953|0.445|0.957|0.687|0.091|0.000|
|Sharpened two‐stageq‐values<sup>μ</sup><br>Note : Panel data from midline and endline<br>dummies (dummies for each triplet). Stand<br>truncated at the 99th percentile. Β: control<br>in Anderson (2008) using P‐values in table|0.755<br>surveys<br>ard error<br>ing for ba<br>6 and 7.|1<br>in 2015 an<br>s (in paren<br>seline val<br>***, **, * i|0.755<br>d 2016.<br>theses) a<br>ue. μ: Sha<br>ndicate s|1<br>All regress<br>re cluster<br>rpened tw<br>tatistical|1<br>ions are c<br>ed at the f<br>o‐stage q‐<br>significan|0.251<br>ontrolling<br>irm level.<br>values as<br>ce at 1, 5 a|0.001<br>for strata<br>α:<br>described<br>nd 10%|

39
Our dataset represents an unbalanced panel of annual observations from 1982-2008. We choose 1982 as the starting year in part because the ICRG measures, which comprise our _Institutional Capacity_ index, begin only in that year. It is also the year the Board adopted a comprehensive version of the current graduation policy.

The panel is unbalanced for several reasons. First, some countries were not IBRD members until well into the 1982-2008 period. This applies not only to new countries such as those emerging from the former Soviet Union and Yugoslavia, but also to many other countries including Switzerland. Second, because countries are not considered for IBRD graduation until they meet the income threshold, we restrict the sample to country-year observations in which the country’s income is above the threshold. Sixty-six member countries exceeded the threshold at some point during our sample period, but many did not do so in every year in the sample period. Third, several nations are missing data for certain variables in different years scattered throughout the sample time period. We are left with complete data for up to 906 total country-year observations, from 55 nations. Of these 55 countries, 21 are high-income countries that graduated from the Bank before the graduation policy was initially formulated in 1973 (or never borrowed at all). Arguably these countries were at very low risk of de-graduating throughout the 1982-2008 period, and their experience may have little relevance to those at higher risk. We therefore check the robustness of our results by dropping these 21 countries from the sample, leaving up to 437 observations in this second, smaller sample.

Descriptive statistics for all variables in both samples are presented in Table 2. The “graduate” variable shows that in 83 percent of the (country-year) observations in which the country exceeds the IBRD threshold, the country had graduated. However, that full sample (Sample 1) includes the high-income countries that never borrowed; without them (Sample 2), the ratio falls to 65%. Similarly, in Sample 1 borrowing commitments occur in 9.1
The GDB offers an overall country score, a pillar score, and a module score. The country scores are built on four pillar scores for governance, capability, availability, and use and impact. The module scores are available for the following thematic topics: (private) company information, land, political integrity, public finance, public procurement, climate action, health, and COVID19. The GDB assigns different weights to each of its indicators, depending on whether it is a primary indicator (more weight) or secondary indicator (less weight) or which pillar it belongs to (i.e., governance, capability, and availability pillars have more weight).

# _Other Tools_

The Ibrahim Index of African Governance Statistical Capacity (IIAG) is a regional assessment tool aiming at “measuring African governance performance”, where governance is defined as “the provision of the political, social, economic and environmental goods and services that every citizen has the right to expect from their state, and that a state has the responsibility to deliver to its citizens” (Mo Ibrahim Foundation, 2023). The IIAG consists of four main pillars of governance: Security and Rule of Law, Participation, Rights and Inclusion, Foundations for Economic Opportunity, and Human Development. These categories are comprised of 16 sub-categories and make up the Overall Governance score.

Besides the global and regional tools, there are several detailed self-assessment tools.<sup>1</sup> These include the European Snapshot Tool, the UN NQAF (National Quality Assurance Framework) self-check tool, and the Paris21 NSDS (National Strategies for the Development of Statistics) selfassessment tool. Unlike the tools mentioned above, these self-assessment tools do not offer explicit performance scores.

In summary, our brief review above suggests several key qualitative differences across the different tools. As their names suggest, the SPI (and SCI) aims to measure country statistical capacity, the other indexes focus more on aspects of statistical capacity such as data availability and open data standards (the ODIN