rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> Figure 6: Adults living in agricultural households use various sources<br>for credit<br>Adults living in a household where growing crops is the main source of household income<br>(%), 2017<br>Central African Rep. iz<br>Congo, Dem. Rep. sl<br><!-- End of picture text --> Source: Global Findex database. Note: The total length of the bar represents the share of adults living in a household where growing crops is the main source of income. People may borrow from multiple sources, but categories are constructed to be mutually exclusive. Borrowed from a bank or used credit for agricultural inputs includes all adults whose household purchased agricultural inputs using a loan or supplier credit in the past five years. Borrowed formally includes all adults who borrowed any money froma financial institution or through the use ofa credit card in the past year but did not borrow from a bank or used credit for agricultural inputs in the past five years. Borrowed semiformally, through family or friends, or from other sources includes all adults who borrowed any money semiformally (from a savings club), through family or friends, or from other sources in the past year but not from a bank or used credit for agricultural inputs and not formally. The survey on financial risk management asked respondents about their source for purchasing agricultural inputs in the past five years. The Global Findex database asked respondents about their method of borrowing in the past year. The figure above combines the two responses.
103|
|Program characteristics (PCA score)|0.02|1.25|-7.59|0.20|2.43|2,082|
|_Institution characteristics_|||||||
|HEI is public|0.30|0.46|0|0|1|2,103|
|HEI is a university|0.21|0.41|0|0|1|2,103|
|HEI is for profit|0.20|0.40|0|0|1|2,103|
|HEI age|37.84|30.80|1|32|481|2,094|
|Number of programs in the HEI|21.59|36.30|1|10|268|2,103|
|<br>HEI characteristics (PCA score)|0.08|1.16|-1.84|-0.21|9.57|2,094|
|**Panel C. Noise controls**|||||||
|Survey conducted during COVID|0.48|0.50|0|0|1|2,103|
|Number of attempts to complete the survey|8.36|3.16|1|9|17|2,103|
|Survey completed by phone|0.33|0.47|0|0|1|2,103|

_Sources_ : Own calculations using WBSCPS and administrative data.

_Notes_ : This table shows the descriptive statistics of the main variables used in the analysis. An observation corresponds to a program. Dummy variables included in the list are those with means between 0.1 and 0.9. Statistics are weighted by WBSCPS sampling weights. Panel A refers to quality determinants, presented by category. Panel B refers to characteristics of the student body, program, and higher education institution (HEI)
, with some turnaround in the first quarter of 2012** . Driven by more prudent risk management practices, European banks have been tightening lending standards since fall 2011 (figure 11). The ECB‘s lending surveys (ECB 2012a, 2012b) show that credit conditions worsened sharply across the Euro Area in the fourth quarter of 2011, but, while remaining tight, the net tightening of credit standards fell substantially in the first quarter of 2012, both for loans to nonfinancial corporations and to households. Key drivers of tightening conditions in 2011 were a deteriorating economic outlook, limited access to market financing, and tight liquidity conditions. The easing of tighter lending conditions in 2012:Q1 mainly reflects milder pressures from cost of funds and balance sheet constraints, in particular regarding banks‘ access to funding and their liquidity position. Banks report a sizeable fall in net demand for loans to corporations in 2012:Q1, but expect higher demand in 2012:Q2 and a further decline in the tightening of credit conditions.
respectively. It is important to point out that these food commodities constitute the bulk of the exempted food products. Other exempted foodstuffs are grouped in the “other foods” category; but the structure of the data set does provide us a means to further investigate the relative share of such goods. Annual per capita food expenditure varies between 116,877 and 507,085 FC. Aggregate food prices varies by commodity, with animal proteins being the most expensive, while cereals are the cheapest.

There are variations in food shares, aggregate prices, and expenditure levels by area of residence and poverty status. Less expensive food commodities tend to represent a relatively large 10
particular, if vulnerable communities depend on natural resources for income generation, or when the poor are largely concentrated in environmentally risky areas (Reckien et al., 2017).

In Paraguay, climate-related shocks may have contributed to stalled poverty levels in the last decade. While Paraguay was able to reduce poverty in half between 2003 and 2022, from 51.4% to 24.7%, the pace of poverty reduction has stalled since 2014. At the same time, Paraguay experienced an increase in the frequency of floods and droughts, as well as in temperatures. In 2019 alone, the country not only recorded the highest average temperature, with an annual temperature of 24<sup>_◦_</sup> C, but also experienced severe droughts, floods, and even heat waves (GRIDGenever European Comission, 2023). Furthermore, historical data denote that years in which the country experienced droughts and floods were associated with sharp reductions in Gross Domestic Product (GDP) per capita and with increases in poverty levels (Gayoso de Ervin & Rubiano, 2023), evidencing the possible link between climate change and socioeconomic indica-

> 1Defined as natural disasters (landslides, storms, droughts and floods) resulting in 100,000 or more people affected, or 1,000 or more deaths, or at least 2% of GDP in estimated economic damages (OECD, 2022).

1
increases the most over the sample period in areas with relatively strong access to finance.

Using a panel of Chinese manufacturing firms over the period 2001-2007, Chen and Guariglia (2011) find that, especially for illiquid firms, productivity is strongly constrained by the availability of internal finance. Furthermore, these authors find higher sensitivities of productivity to cash flow for private exporters, but lower sensitivities for foreign exporters.

Finally, Clarkea, Cullb and Kisunkoc (2012) study how country and firm characteristics affected firms‘ financial constraints and their likelihood of survival during the early phase of the recent global financial crisis in Eastern Europe and Central Asia (ECA). These authors find that financial constraints during the crisis were less severe in countries with well-established foreign banks, and that changes in the severity of financial constraints were more pronounced for large firms than others during the crisis. Controlling for other relevant characteristics, firms were more likely to survive the crisis if they had access to external credit.

> 11 The survival of uncompetitive firms with the help of government support might result in a decrease in market share and the profits of competitive firms. Thus, uncompetitive firms might replace competitive ones with overdraft facility policy.

8
prediction is most similar to the real-world application of cellular data modeling known as “spatial infill” or “spatial extrapolation,” while different-survey prediction is an example of “time extrapolation.”

# **_Pre-Processing_**

## **_Data Cleaning and Enrichment_**

Raw CDR data are invariably noisy and require pre-processing before they can be analyzed. The primary sources of data noise are: (i) gaps or inconsistencies caused by MNO network operation decisions, including technological changes that affect CDR comparability; and (ii) the presence of business lines, text recharge numbers or other connections that do not reflect communications between individual customers. Pre-processing begins by identifying inconsistent or irrelevant CDRs and dropping them from the dataset. Each customer in the CDR dataset is then assigned a home location, which is generally inferred based on the network cell in which that customer is most active after 6pm. However, if that cell has less than 30 percent more activity than the next-most-active cell during the same period, it is assumed that the user is itinerant and no home location is assigned.

## **_Spatial Harmonization_**

Ensuring that all datasets share a common spatial scale is an important step in data preparation. While the network cell is the natural spatial unit for CDR data, poverty rates are generally calculated using administrative boundaries, in this case the _municipio_ . These spatial structures must be reconciled in order to analyze the data.

In the model presented below, poverty data are mapped onto the network cells. Each cell is assigned an area-weighted average of the poverty rates in the _municipios_ it covers. Network cells located entirely within one _municipio_ are assigned that _municipio’s_ value. This process, illustrated in Figure 4, ensures that all datasets use the common geography of the network cell, which then becomes the unit of analysis. More complex approaches could also be adopted, such as weighting the distribution by population density.

**9**
, et al. (2016) present a methodology for estimating the volume of gas flared at each site with the accuracy of the flared gas volume estimates rated at ±9.5%. These data and methods have been used to identify global flaring locations and to estimate global gas flaring volumes (World Bank, 2020). A publicly available website: www.gasflaretracker.ng has the geographic coordinates of each flaring point in Nigeria as well as monthly estimates of the flare volume from each location.

Our child health data come from the 2013 and 2018 Nigerian Demographic Health Survey (DHS). These nationally representative cross-sectional surveys have demographic and health details for women aged (15-49) and for children aged (0-5). A stratified, two-stage cluster design is used, with the geocoordinates of each cluster reported with some displacement for confidentiality reasons.<sup>7</sup> The DHS collects information on women’s birth histories and child health characteristics including self-reported

> 7 The displacement is up to 2km for urban clusters and 5km for rural clusters, with 1 percent of rural clusters displaced up to 10km.

6
factors, with appropriate and accurate hydrological data they can be designed to mitigate or reduce the adverse effects on water points. Finally, the lack of hydrological data is a major impediment to understanding the water point failures especially during the first year after their installation.

It is evident that water points managed by village committees have a much higher likelihood of failure than those managed by other groups, confirming the evidence discussed in the Tanzania context. During the first years of the water points installation, hydrogeological factors play a major role in determining their functionality. However, the type of pump matters over time with evidence showing that gravity pumps have a much higher likelihood of failure over the long term. Overall, there is a need to improve the capacity at the village level to manage and maintain water pumps as suggested. This can be done through appropriate technical support and financial transfers from the LGAs to the villages. Finally, technology as well as hydrological characteristics which affect the water pumps should also be taken into consideration in constructing future water points.

20
Population Census 2010.<sup>14</sup> The three sets of indicators are defined as follows:

# _E-commerce penetration indexes:_

> 13 See Appendix 1 for a detailed description of the construction of the indicators.

> 14 Measures using different sources of population yield consistent results. We use census population information in our main analysis, rather than the population reported in the statistics year books. In addition, we apply the 2013 residential population at the county level collected from local statistical bureaus as a robust check.

·· 9
# **Figure 11 Relative profitability of SOEs**

(percentage points relative to private firms)

# **Figure 12 Return on assets of industrial firms, aggregate data**

(profit as a share of total assets, percent) <!-- Start of picture text --> ROA Leverage<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>1998 2000 2002 2004 2006 2008 2010 2012<br><!-- End of picture text --> <!-- Start of picture text --> State owned State holding<br>Private and foreign<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2001 2004 2007 2010 2013 2016<br><!-- End of picture text --> Source: Authors’ regression estimates based on NBS’s Source: NBS; authors’ calculations. Industrial Enterprise Survey. Note: The figure plots the coefficient on the ownership dummy by year.

Starting in 1979, SOEs were given more autonomy and non-SOEs were allowed to enter the market, creating competition. After 1992, China prioritized ownership reforms, including passing the 1992 Company Law and 1993 Competition Law and developing an institutional framework for a business sector with diversified ownership. The authorities also allowed SOEs to shed redundant workers in the process of ownership change. In 1995, the policy of “grasping the large, letting go of the small” deepened SOE reform. In 2003, the State-owned Assets Supervision and Administration Commission (SASAC) was created with the goal to preserve and increase state-owned assets through restructuring and consolidation.<sup>17</sup> In line with other studies (e.g., Brandt and Zhu, 2010; Hsieh
meanings of a good life.<sup>6</sup> There is also evidence supporting the assumption of _ordinal interpersonal comparability_ implicit in subjective well-being analysis, i.e. two individuals reporting similar answers to life satisfaction questions can be presumed to enjoy similar levels of well-being (van Praag 2007)<sup>7,8</sup> .

The literature thus supports the view that subjective well-being measures can be taken seriously, and that the insights that may be gained over and above conventional data on objective household welfare are informative. Yet, the concerns with respect to the interpretation of subjective well-being data remain. To what extent can subjective well-being measures provide reliable and consistent measures of welfare across individuals, countries and time? The objective of this paper is to understand how various subjective well-being measures are related with objective welfare measures and examine their consistency across different scales and within households.

The Life in Transition Survey (LiTS) provides a unique opportunity for analysis of the reliability and consistency of subjective well-being measures, not only across individuals, but also across time and countries. The survey was simultaneously administered in 34 countries of Europe and Central Asia in two different rounds (2006 and 2010). It includes (i) comparable data for the countries of Central and Eastern Europe and Central Asia, and, in 2010, for several Western European countries; (ii) objective measures of welfare based on household expenditures and assets; and (iii) different measures of subjective well-being (notably life satisfaction and subjective relative income questions).

By consistency and reliability we primarily mean the following: (i) in the case of life satisfaction, we take advantage of the alternative scales and examine whether the country-level and individual-level relationships between life satisfaction and important determinants of life satisfaction such as objective welfare measures and other socio-demographic characteristics are robust to the choice of the formulation of the life satisfaction question; and (ii) in the case of the subjective household welfare question (welfare ladder) we look for congruence in the accounts
# DATA LINKS/CITATIONS FOR MAPS

**_All maps were created by Dr. Andrew Jacobson, Duke University, unless otherwise noted._**

**Cities** : Reed, T. and S. Trubetskoy (2018) The Belt and Road Initiative and the Value of Urban Land. World Bank mimeo. **Ports** : Reed, T. and S. Trubetskoy (2018) The Belt and Road Initiative and the Value of Urban Land. World Bank mimeo.

**Roads & rails** : Reed, T. and S. Trubetskoy (2018) The Belt and Road Initiative and the Value of Urban Land. World Bank mimeo. **Administrative boundaries** : GADM v2.8 ‐ <u>https://gadm.org/index.html .</u>

**Forest change** : Global Forest Watch 2015 v1.3 ‐ https://www.globalforestwatch.org/ .

Hansen, Matthew C., et al. "High-resolution global maps of 21st-century forest cover change." _science_ 342.6160 (2013): 850-853.

**Cropland** 2015: ESA CCI land cover V2.0 ‐ <u>https://www.esa‐landcover‐cci.org/?q=node/175</u>

Cite – ESA Climate Change Initiative – Land Cover Project 2017 Copyright notice: © ESA Climate Change Initiative ‐ Land Cover project 2017 Should you write any scientific publication on the results of research activities that use one or several CCI‐LC products as input, you shall acknowledge the ESA CCI Land Cover project in the text of the publication and provide ‐ the project with an electronic copy of the publication (contact@esalandcover <u>cci.org).</u>

**Protected areas** : UNEP & IUCN. 2018. The World Database on Protected Areas (WDPA). Cambridge: UNEP‐ WCMC. <u>https:
## **Figure 8. Skills developed in training (among firms with training programs)**

<!-- Start of picture text --> Job-specific technical skills<br>Managerial and leadership skills<br>Numeracy/math skills<br>other<br>Problem solving/critical thinking skills<br>Foreign languages skills<br>Interpersonal and communication skills<br>0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%<br>Source: Enterprise Survey data, authors<br><!-- End of picture text -->

# _Shaping factors of firm capabilities_

The dynamics on firm capabilities are often shaped by underlying structural factors, especially in differences by industry, location and type of firm. Bloom and Van Reenen (2010) point to the importance of competition in shaping management practices. They hypothesize that in a competitive economy, firms with low management practices would be outcompeted by more productive firms with higher levels of firm capabilities. It is therefore expected that in sectors/locations with higher degrees of competition, higher levels of firm capabilities are found.

For Mozambique, our analysis does not find evidence for this relationship between competition and firm capabilities (see also McKenzie and Woodruff, 2017, for similar finding in developing countries). Table 5 shows regressions of management practices and skills on the HerfindahlHirschman Index (HHI)<sup>11</sup> , a measure of market concentration, calculated at the nationwide industry level, province-industry level and district-industry level. There is little evidence for a negative relationship between market concentration and management practices: most of the coefficients are positive and insignificant. This lack of significant relationship does not necessarily

> 11 The Herfindahl Hirschman Index (HHI) is a commonly used measure of market concentration, among others (Syverson 2019). It is calculated by taking the sum of squared market shares of a firm in a particular market (H 12
# Export Volumes

Columns 6-8 of Tables S1a and S1b describe the WTF data. These descriptives focus on the intensive-margin estimations that require exporter-importer-industry observations maintain the minimum threshold of trade volume. Columns 6 and 7 provide comparable statistics about the mean export levels and growth rates for included routes. Germany and Japan have the highest average volumes, and Nicaragua and El Salvador have the lowest average volumes. Export growth rates are strongest in Nicaragua, Congo, and Costa Rica, and they are lowest in Guatemala and Zimbabwe. From an industry perspective, trade volumes have the highest average values in industries 382-384 (Machinery and Transportation equipment), and the lowest average volumes are observed in industry 361 (Pottery, china, earthenware). Industry 383 (Machinery, electric) has the highest growth rate, while industries 353 (Petroleum refineries) and 371 (Iron and steel) have the lowest.

Column 8 of Table S1a documents the share of total WTF exports for countries that are included in this sample. The main reasons why exports are not included are lack of corresponding UNIDO labor productivity estimates or that the exports are going to the United States. This sample accounts for 78% of exports to destinations other than United States from these countries (about 63% if exports to the United States are included in the denominator). Column 8 of Table S1b provides comparable data for industries. The sample accounts for 69% of exports in these industries to destinations other than United States. This share is lower than 78% due to the inclusion of exporters not captured in Table S1a. Much of the decline on the industry side comes through limited representation of major petroleum producers.

Price deflators are not available for this sample (exports or labor productivity data). To the extent that exporter-industry-year deflators are comprised of exporter-year, industry-year, and exporter-industry components, the fixed effects and first differencing strategy will control for them automatically. Residual exporter-industry-year trends could bias OLS estimations. The IV estimations will overcome any such OLS biases due to deflators.

As a final
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Figure 2.4 . Cost of importing goods by road through alternative gateways**

a. In U.S. dollars <!-- Start of picture text --> 450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Djibouti-Addis Dar es Salaam- Dar es Salaam- Dar es Salaam- Kigali<br>Bujumbura Kampala<br>Customs Border Transport Ports<br>b. By step (% of total time)<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Djibouti- Mombassa- Dar es Mombasa- Dar es Mombasa- Dar es Mombasa-<br>Addis Bujumbura Salaam- Kampala Salaam- Kigali Salaam- Juba<br>Bujumbura Kampala Kigali<br>Ports Transport Border Customs<br>Share of costs for importing goods<br>importing goods<br>Costs (US$ per tonne) of<br><!-- End of picture text --> Source: Data collected from ―Trading Across Borders‖; Nathan Associates 2010; and AICD ports database.

In order to understand overall corridor performance, it is helpful to examine the national performance of the various modal components. The performance of the corridor can be only as good as the performance of the national transport systems that comprise it. To this end, the performance of the national road, rail, and ports sectors is briefly reviewed in the remainder of the section, with a view to identifying national weaknesses that may have serious repercussions at the regional level.

# Roads

Data from 2008 reveal that around 73 percent of roads in EAC are paved. A much lower 57 percent of the regional road network in East Africa is paved (table 2.3