rafmacalaba/data-use-mentions-extended huggingface dataset:
frequently have exclusive relationships to resolve information problems and/or avoid duplicative costs of multiple relationships.

Regarding the second motive, there is evidence that large U.S. firms not only engage in multiple banking relationships in order to offset the hold up problem, but they also reduce the control of their main banks and lending costs by issuing public debt at higher costs (Houston and James 1996 and Santos and Winton 2005, respectively).<sup>9</sup> A cross-country study finds that firms maintain a higher number of banking relationships in countries with unconcentrated banking sectors, contrary to the theoretical predictions (Ongena and Smith 2000).<sup>10</sup> Regarding the third motive, there is information on the effects of bank fragility and the costs to firms of losing their main banking relationship, but some of the evidence is also conflicting. One study using German data finds that borrower quality – which should be a proxy for the ability of the firm to recover in the event of the cutoff of bank funds – has no impact on the number of banking relationships (Machauer and Weber 2000).<sup>11</sup> However, a study using Italian data finds that the number of banking relationships is higher for firms with more leverage, suggesting lower quality, consistent with the third motive (Detragiache, Garella, and Guiso 2000). This study also finds that those borrowing from banks that are prone to financial distress – measured in terms of the bank’s size, volatility of liquidity, and nonperforming loans – are likely to maintain a larger number of banking relationships, consistent with the motive for multiple banking as insurance against the premature termination of services. A study of Argentine banks similarly finds that bank fragility has a positive impact on the likelihood that a firm will borrow from multiple banks (Berger, Klapper, and Udell 2001). However, Ongena and Smith (2000) find mixed support in this regard. They find that firms maintain a higher number of
secondary education induced by the program may have helped to correct for preexisting biases in beliefs and preferences of households and communities—or in their decisionmaking processes—in relation to girls’ secondary education. Relatedly, consistent with a multiple-equilibria characterization of the setting, program benefits may have functioned as an initial catalytic agent, triggering communities to ratchet up from a low girls’ secondary education equilibrium to a higher one (a shift in social norms). Unfortunately, we do not have appropriate data on parental beliefs regarding girls’ education to empirically test our conjectures.

We see two relatively straightforward directions for further evaluative research on the program. The first is to examine the effects on girls’ enrollment of the higher benefit amount (along with the new method for delivering benefits) introduced in 2017. Relative to average household consumption spending per adult equivalent in the province, the benefit amount increased more than fourfold, from 4 to 18 percent. Does the restored—and enhanced— economic incentive induce greater gains in girls’ secondary school enrollment? In due course, enough rounds of government Annual School Censuses should be available to examine the early effects of the program modification on girls’ secondary school enrollment.

The second direction is to examine whether the program improves girls’ academic achievement as measured by test scores, evidence that has been lacking for this program.<sup>26</sup> Under the management of the Punjab Examination Commission, the provincial and district education departments have been administering standardized exams to students in grades 5 and 8 annually. Validity and reliability appear to be a concern for the existing rounds of exams. But with enough improvement in the design and administration of the exams, future

> 26 While scarce, the available rigorous international evidence generally suggests that the effects of CCT programs on student academic achievement are small at best (Baird et al. 2014).

23
**Table 7. Drivers of the skill content of jobs: Demography and Technology. Developing countries sample.**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Non‐routine<br>analytical|Non‐routine<br>interpersonal|Routine cognitive|Routine Manual|Non‐routine<br>manual|
|Working age population (% of population), change|0.0665|0.331|0.161|‐0.112|‐0.123|
||(0.423)|(0.397)|(0.214)|(0.356)|(0.330)|
|Internet users (% of population), change|0.0326|0.0400|‐0.0460|‐0.0701|0.0264|
||(0.0653)|(0.0614)|(0.0331)|(0.0550)|(0.0509)|
|Working age population x Internet, change|0.0393*|0.0433**|‐0.0158|0.00526|0.0161|
||(0.0213)|(0.0200)|(0.0108)|(0.0180)|(0.0166)|
|Observations|355|355|355|355|355|

Note: OLS coefficients from a cross-country regression using the skill content of jobs (in first differences) as the dependent variable (multiplied by 100).

# _Robustness check: The role of the agricultural sector_

One of the limitations of the STEP surveys is that they cover urban areas only, which leads to an under-representation of the agricultural sector. This could introduce a bias to our estimates
From the previous analysis we derived the (shortest) transport time between each pair of cities. As our trade and production data are at the country level, a consistent aggregation approach is required. We address this by considering three essential parameters: location-specific friction, trade elasticity, and trade elasticity with respect to transport time. We aggregate transport time between cities at the country pair level using a theoretically robust iterative method that integrates population weights and a calibrated friction elasticity, as illustrated in Hinz (2017).

The theoretically consistent aggregation for the frictions at the level of the city pairs is as follows: When aggregating at the level of a pair of countries _ij_ , with exporter country _i_ and destination country _j_ , we take into account both the relative population of cities _k_ in _i_ and _l_ in _j_ , as well as the transport time elasticity, denoted as _δθ_ .<sup>12</sup> The transport time elasticity is calibrated using a simple iterative procedure as follows. We start with an arbitrary initial value of the elasticity (i.e. _δθ_ = _−_ 1). Then, we estimate the elasticity in a cross-section gravity equation for the year 2018 with origin and destination fixed effects and controlling for PTAs, language proximity, and shared colonial history (border dummies are not included because border crossing time is already included in transport time).<sup>13</sup> We retrieve the transportation time coefficient and then use it as a parameter in the aggregation equation ( _δθ_ = _β_<sup>_transportation_</sup> ). In the next iteration, this new aggregation parameter is used.<sup>14</sup> Using a network analysis to quantify shipping times and the impact of new and improved transportation infrastructure has advantages and disadvantages. On the plus side, a network analysis can be a useful tool to support modeling of international trade flows. It can enhance the understanding of the impact of changes in infrastructure or other factors on an interconnected system allowing to
|Students’ and peers' informal comments are very important in|-0.320||
|---|---|---|
|faculty evaluation|(0.705)<br>||
|Peer evaluation is very important in the faculty evaluation|-1.463*<br>(0.788)||
|Practical experience is very important when hiring faculty||-3.417<br>(2.234)|
|Almost all or all faculty participated in professional training last year||2.663*<br>(1.467)<br>|
|Faculty are evaluated more than once per year<br>||-4.393*<br>(2.305)|
|_Other Practices_|||
|Update or review admin data more than once per year|-0.274<br>(0.725)||
|Student body, program, and HEI characteristics (PCA scores)|✓|✓|
|Noise controls, country-and field-fixed effects|✓|✓|
|Observations|1,525|1,692|
|Mean of dependent variable|14.03|18.58|
|R-squared|0.085|0.107|
|Adj. R squared|0.0613|0.0890|

# _Source_ : Own estimations using WBSCPS data.

_Notes_ : This table shows coefficients from the (second stage) OLS regressions of dropout rate and extra time to graduation (ETG) on the determinants selected by LASSO in the first stage. The unit of observation is a program. Regressions are weighted using WBSCPS sampling weights. See definitions of outcomes and PCA scores in Appendix 1. Specifications control for PCA scores of characteristics of the student body, program, and HEI as well as for survey noise controls, country fixed effects, and field fixed effects. Standard errors clustered at HEI level are in parenthesis. Significance levels:
multicollinearity problems because the components are highly correlated. Therefore, we construct our own International Logistics Index (ILI) based on components 1-4 using the relative weights listed. A regression of the LPI on the ILI has an R<sup>2</sup> of more than 0.99, so the international logistics index explains a large proportion of the overall index. Therefore, while the ILI is conceptually more appropriate, using it makes little practical di¤erence.<sup>7</sup> Table 1 presents summary statistics for the full LPI and for our new International Logistics Index (ILI). High income countries have measures of quality that are of the order of three standard deviations higher. Approximately 70% of developing countries have an ILI of between 2 and 3, but there is still considerable variation. Appendix Table 1 lists the values for all countries. The worst performing country in our sample is Rwanda with an ILI value of 1.90 (although there is evidence of subsequent improvement from various anecdotal and data sources). Singapore has the best international logistics with an ILI of 4.28. South Africa (3.66) and Malaysia (3.63) have the highest ILI values out of the developing countries.

Our interest is exports from developing countries. Based on the availability of logistics data and possible instruments and controls, our sample consists of 88 low- and middle-income exporters. We have 116 importers regardless of income classi...cation.<sup>8</sup> We use merchandise exports data for 2005, using the IMF Direction of Trade Statistics. We observe 7,246 positive cross-border trade ‡ows, 2,548 zeros and 826 instances of missing values. We therefore need to account for potential sample selection issues in our model and econometric methodology.

We use 2005 GDP measured in constant (2000) US Dollars from the World Development Indicators. As will become clear, each country’s GDP share is an important component of our analysis
Capital Controls, Institutions, and Interactions" _Journal of Development Economics_ 81(1): 163-192.

Chinn, M. D. and H. Ito (2008) "A New Measure of Financial Openness." _Journal of Comparative Policy Analysis_ 10(3): 309-22.

Clare, A. D. and R. Courtenay (2001) “What Can We Learn About Monetary Policy Transparency from Financial Market Data?” _Bundesbank Series 1 Discussion Paper_ No. 2001,06.

De Gregorio (2008) “Transparency and Communications in Modern Central Banking.” At the 10<sup>th</sup> Annual General Meeting of the Interamerican Economics and Finance Journalist Association (July).

Dincer, N, N., and B. Eichengreen (2007) “Central Bank Transparency: Where, Why, and with What Effects?” _NBER Working Paper_ 13003.

Edwards, S. (1998) “The Mexican Peso Crisis: How Much Did We Know? When Did We Know It?” _The World Economy_ 1-30.

16
Harding, 2020, Akhtaruzzaman et al. 2017, Bøe et al. 2019).<sup>27</sup> There is less data to conduct similar analyses for mining, although one would expect a similar outcome. The only study of mining and political risks we are aware of looked at grassroots mine exploration budgets in Chile (Castillo, 2021). It shows that a tax reform that increased but stabilized tax levels – a signal of reduced political risk at the project level, caused firms benefitting from these agreements to increase exploration budgets by 32%, even though the stabilization agreement was accompanied by the higher tax rate. Firms that did not receive such agreements either reduced their exploration budgets or kept them the same. When less exploration is undertaken, fewer projects go forward, with less resource rent for the government to capture.

The Fraser Institute conducts an annual survey of international mining company executives to gain their opinions on which jurisdictions have less political risk and which have more. Its Policy Perception Index includes factors concerning the administration of current regulations, environmental regulations, regulatory duplication, the legal system and taxation regime, uncertainty concerning protected areas and disputed land claims, infrastructure, socioeconomic and community development conditions, trade barriers, political stability, labor regulations, quality of the geological database, security, and labor and skills availability. Of the 84 jurisdictions evaluated in 2021 (some are provinces or states rather than countries), African countries tend to rank in the lower half (Yunis and Aliakbari, 2022).

Africa’s most favorable country is Morocco, ranked second globally. Only three other African countries rank in the global top half – Namibia (29), Senegal (30), and Botswana (31). The Democratic Republic of the Congo (DRC) and Zimbabwe rank 78<sup>th</sup> and 79<sup>th</sup> respectively. From this, it is more likely that a mineral deposit in Morocco would generate more rents and more tax revenue
Intervention group 1, the invitation may serve as a cue to act and increase the individual’s sense of efficacy by clarifying how and where to screen.

**_Box 2: Personal invitation with peer group information (Intervention group 2)_**

Dear [Name, Surname], We cordially invite you to visit [Name of the medical facility] of [Name of the community] at the following address [Address] to be screened for diabetes and hypertension. According to official records in your personal medical card, in past 12 months you have not been screened for diabetes and hypertension at a medical facility. In terms of prevention and control of diseases, your visit to the medical facility is extremely important to ensure your personal healthcare.

<!-- Start of picture text --> We would like to inform you that based on official statistics for your community among your peers, X men and Y<br>women have already taken this screening, and what about you?<br>X Y<br>Please, prior to the visit make a call to your doctor for a proper appointment, using the telephone number presented<br>in the bottom of this invitation.<br><!-- End of picture text -->

**_Note:_** for a credible measurement of diabetes you must visit the doctor fasting since midnight, which means you should not eat or drink anything except water. The screening is free-of-charge, painless and not time-consuming. We highly recommend that you visit the medical facility soonest possible and to use the free-of-charge medical services of the primary healthcare facility.

|Looking forward to seeing you,|
|---|
|**DATE**|
|**DOCTOR**|
|**SIGNATURE**|

**Personal invitations with a labeled pharmacy voucher (Intervention group 3)** were delivered in person by study field workers and consisted of a verbal message and printed letters, signed by a
, and reaction shocks as well as other control variables; μ captures unobserved rr,tt−1 country heterogeneity; and ε are the residuals. The baseline specification is a panel logit model XX rr with random effects, as the Hausmann test suggests that the random effects model is appropriate rr,tt for debt and banking crises. For robustness, the appendix presents results for a probit model with random effects (table A6) and logit model with fixed effects (table A7).

# **3.** **<u>Data</u>**

# **3.1 Identification of U.S interest rate shocks**

The data included in the VAR model used to decompose U.S. interest rate shocks are provided in table A1. The table indicates the transformation used in the model and the data source. The model includes four variables and is estimated on monthly data from February 1982 to September 2022. The variables include 2-year and 10-year yields on U.S. Treasury bonds at a constant maturity (figure 1A). Due to the secular decline in U.S. interest rates over the past four decades, the data are transformed to be stationary using first differences, as the focus is on shocks over the business cycle. Equity prices are measured by the S&P 500 composite index and transformed to percent 7
.

Air transport connectivity for countries within East Africa is rather mixed. All countries except Burundi and Rwanda have relatively good international connectivity. Burundi is connected through five international city pairs, and Rwanda through seven. The other countries have a much higher number of international city pairs and are well connected with the regional hub in Kenya. It is because of its hub status— both in East Africa and beyond—that Kenya has exceptionally high international connectivity. Domestic connectivity within Ethiopia is the highest in the region. Kenya, on the other hand, has low domestic connectivity, which reflects the limited purchasing power of its domestic market. Burundi and Rwanda have no domestic connectivity (table 2.9).

**Table 2.9 . International and domestic connectivity for 2007 (number of city pairs served)**

|Domestic city<br>pairs|International city<br>pairs|
|---|---|
|Burundi<br>0|5|
|Ethiopia<br>45|52|
|Kenya<br>15|77|
|Rwanda<br>0|7|
|Sudan<br>13|32|
|Tanzania<br>19|38|
|Uganda<br>4|19|
|Source: Derived from Bofinger 2009.||

Not all countries within EAC and the broader East Africa region are connected with one another. They do, however, have direct flights to either Kenya or Ethiopia, which then have frequent direct flights to all of the EAC and East African countries (table 2.10). Most countries in the region have at least seven flights per week to one of the main regional hubs. The route between Tanzania and Kenya is the most frequently travelled, followed by the route between Kenya and Uganda.

29
Rice yields are generally variable across Sierra Leone and depend on the agro-ecological zone. Yields are highest (>2.5 tons per ha) in mangrove zones, then riverine (about 2.5 tons per ha), then inland valley swamps – IVS (> 1.5 tons per ha but less than 2.0 tons per ha), then boliland and upland (about 1 ton per ha on average). The yield assumptions for the five ecologies in the model are calculated from acreage and production information provided by the Ministry of Agriculture for 2016. It is assumed that there has been no significant change in yields between 2016 and 2018. This is not a restrictive assumption as the impact of the variation in yields on consumption, exports and imports can be simulated.

**Table 7: Sierra Leone, Cultivation, Production and Yield of Rice in All Ecologies (2016)**

|**District**|**Upla**|**nd Rice**|**IVS**|**Rice**|**Bolila**|**nd Rice**|**Rive**|**rain Rice**|**Mang**|**rove Rice**|
|---|---|---|---|---|---|---|---|---|---|---|
||**Area**<br>**(ha)**|**Prod (Mt)**<br>**Paddy**|**Area (ha)**|**Prod (Mt)**<br>**Paddy**|**Area**<br>**(ha)**|**Prod**<br>**(Mt)**<br>*
Table 2 presents the summary results of the consumption survey experiment. It reports the difference in the log per capita consumption measure of each design relative to the benchmark individual diary.<sup>7</sup> The estimates in table 2 derive from regressions of the natural logarithm of food, nonfood, and total consumption on binary indicators for module assignment (whereby the benchmark personal diary is the left out category). Because the survey experiment was randomized, the regressions include no covariate controls except for the survey cluster (the village or urban area sampling unit within which households were randomized to the various survey designs). The regressions in table 2 show that, with the exception of 7-day recall with the long list, the modules record between 8 percent and 33 percent less food consumption compared with the personal diary (column 3). The impact on total consumption is at a similar magnitude (column 2). In the diary approach to food consumption, the use of only one respondent to complete the diary for an entire household is associated with significantly lower food consumption, by 13–20 percent, most likely because some share of unobservable personal consumption of the other household members is omitted (not captured) by the respondent maintaining the diary. Differences in frequent nonfood consumption are also observed, especially in the diaries, again suggesting the importance of accurately recording personal consumption.<sup>8</sup> Regarding the recall survey approach, all mean food expenditures are lower than the benchmark. The mean of the 7-day long list lies nearest to the benchmark value, while modules with longer recall periods (14 days or the usual month) or more aggregated consumption categories (the collapsed list) record food consumption that is 17 percent to 33 percent lower. Even though the 7-day long list comes closest to the mean benchmark food consumption value in this experiment, it is difficult to extrapolate definitively that the 7-day long list will be the most accurate of the recall designs if it is applied in different settings. Because the
**Figure 2: Histogram of the number of direct connections from each node, 2005.**

<!-- Start of picture text --> 0 20 40 60 80 100<br>No. of Direct Connections<br>.05<br>.04<br>.03<br>.02<br>.01<br>0<br><!-- End of picture text --> These findings suggest that it is extremely important to account for hub and spoke interactions when measuring connectivity in the air transport network. Of course, this accords closely with thinking in the industry.<sup>8</sup> In the next section, we develop a measure of connectivity that does just that.

Graphical software developed for network analysis is also a popular tool for grasping the structure of a network, including hub and spoke structure and the centrality of nodes. However, any two-dimensional graphical illustration of the air transport network must be arbitrary, at least in part. Figure 3—prepared using the Pajek<sup>9</sup> freeware program—uses the air transport adjacency matrix to plot the most central nodes in the middle of the figure, based on closeness centrality (see Appendix 3 for a discussion of that concept). Nodes with less than 20 connections have been dropped. Unsurprisingly, this approach

> 8 It is outside the scope of this paper to discuss the factors that have given rise to the emergence of a hub-andspoke system in the air transport market. See, for example, Dempsey (1990) for a discussion of the role of policy and deregulation.

> 9 http://pajek.imfm.si/doku.php

10