rafmacalaba/data-use-mentions-extended huggingface dataset:
ternet development.

Broadly speaking, we include any study with a specification broadly of the form: where _i_ is the aggregation level of the study, which we discuss below, _t_ is the time unit, on the right-hand side the independent variable _Xit_ is an indicator of infrastructure in one of the sectors defined above, and θ _i_ and θ _t_ are observation-level, and time fixed effects respectively. Note that not all studies always include the full set of fixed effects. Some are cross-sectional analyses and do not display within-unit time variation, others cover several time periods but do not report between-unit variation.

On the left-hand side the dependent variable _Yit_ is an economic or social outcome pertaining to one of the following categories:

- Output, which includes indicators of production, income, expenditures, or productivity, in levels or growth rates. We categorize it as output micro when observations are at the level of firms or households, and output macro when it is at a higher level of aggregation (district, regions, or country).

- Labor market, including indicators of employment and wages.

- Inequality and poverty, such as Gini or poverty share indicators.

- Trade, including exports and imports, as well as extensive margin indicators of access to external markets for example.

- Human capital, covering all indicators related to educational and health outcomes.

- Population, including levels as well as migration flows.

- Environmental variables, such as pollution, greenhouse gases (GHG) emissions, etc.

In order to be able to perform a quantitative meta-analysis on a comparable set of estimates, we restrict the sample to studies that report elasticities (where both the dependent 6
statements of affected firms to determine their cash outflows and inflows, as in Gatchev, Pulvino, and Tarhan, (2010) and Dasgupta, Noe, and Wang (2011). We find that the number of potential miracles is further reduced. Of the 6 potential miracles identified in the macro data, only in the case of Argentina, Malaysia, and Thailand, do we find a recovery in corporate sales before a recovery in short-term borrowing. In Indonesia, we find both sales and short-term credit recover at the same time where as in Mexico and Russia we find that while credit begins to recovers, sales still hasn’t recovered, suggesting that these 3 countries are not Phoenix Miracles. Of the 3 non-miracles identified in the macro data, the 2 crisis episodes in Turkey remain non-miracles at the micro level, with corporate sales and short-term credit not only moving together, but also not having recovered. However, Korea appears as a Phoenix miracle in the micro data with sales recovering before a recovery in credit markets.

Third, we find no evidence that the US is a Phoenix Miracle. At the macro level, we find that GDP and Bank Credit (defined as the aggregate amount of assets held by banks excluding vault cash) decline in 2008 but begin to recover simultaneously in 2009. At the firm level, we find that both Sales and Debt in Current Liabilities (aggregated across publicly traded firms in Compustat) recover simultaneously, suggesting that output recovery was not credit-less.

We then focus our analysis at the firm-level to understand the microeconomic foundations of what drives the recovery process in the emerging markets and the US. While the mechanism outlined in Calvo et al. (2006b) suggests that recovery is driven by restoring the stock of short term assets out of self-generated cash flows and discontinuing investments, we find that the recovery is not credit-less and that on average firms substitute short-term credit with long-term external finance either through long-term borrowing or capital issuance. Thus, these potential miracles are miracles in a very restricted sense, if
While the combination of projects with IP or DO rated moderately unsatisfactory or lower and those with three out of six flags may be a reasonable predictor for well-established projects, application of these two rules alone would not select recently approved projects, which are seldom rated moderately unsatisfactory or lower for any indicator. Hence, to complete the prediction model, and to potentially increase the prediction rate, it seems appropriate to include a third prediction module for those young projects that will turn out unsuccessful at exit, most likely as a result of poor quality at entry. Lacking a quality at entry indicator, the most reasonable proxy for such an indicator are lags from approval to signing of greater than three months and from approval to effectiveness of greater than six months. However, the correlation of the outcome rating with those delays is not been well established and appears rather weak, so this third rule or module would not be permanent, meaning that after the projects have completed the first year, the rule should no longer apply to them.

The country breakdown of the three watch list sets is in Table 1 of Annex 2.

In view of the well-established correlation between the outcome and the CPIA and recent country record, the percentages of project failure implied by the latest candor gap figures (see Table 1 of Annex 2) were used to calculate a predicted MU- rate for each country. This was then compared to the watch list generated by the model. The two lists have a few significant differences (China and Vietnam in particular), but overall there is a good consistency with 106 of projects identified at the regional level compared with 112 corresponding to 38 percent of expected MU- outcome based on the candor gap figure, as of July 1, 2014.

The composition of the list, broken down by module components, is summarized in Table 6.

**Table 6. FY15 Watch List Projects for EAP**

|**Age group**|**Three out of 6**<br>**flags
|||capital||||||||
|Malaysia|8%|8%|Not<br>liibl|180|No|50%|100<br>%|15%|No restrictions||
||||ege||||||||
|Philippines|10%|10%|<br>Not<br>eligible|180|25%|50%|100<br>%|15%|No more than 15%<br>of equity|25% of capital, plus<br>15% provided the loan is<br>adequately secured by<br>real estate mortgage or<br>the assignment of<br>readily marketable<br>bonds or other high<br>grade securities|
|Thailand|8.5%|4.25|4.25%|90|20%|50%|100<br>%|6%|Net long 20% of tier<br>I capital. Net short<br>15% of tier I capital|<br>25% of tier I capital|

Source: World Bank Data.
between 2005 and 2009 (but not between 2001 and 2005), we code migration according to step (iii) above.

Notes: the MxFLS website does not provide a specific module for migrants to the US in the second follow-up.

# **B.6 Nigeria Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA)**

Notes: the survey collects information on occupation of migrants in section 1 of each wave (Household Roster). However, it does not specify whether the migrant is selfemployed or a wage worker.

Baseline – Post Planting Wave 1 (August - October 2010):

- status: we record an individual as not if she answers no

- Employment working to question 4 (“During the past 7 days, have you worked for someone who is not a member of your household, for example, an enterprise, company, the government or any other individual?”), question 5 (“During the past 7 days, have you worked on a farm owned or rented by a member of your household, either in cultivating crops or in other farming tasks, or have you cared for livestock belonging to yourself or a member of your household?”), and question 6 (“During the past 7 days, have you worked on your own account or in a business enterprise belonging to you or someone in your household, for example, as a trader, shop `[U+2010]` keeper, barber, dressmaker, carpenter or taxi driver?”) of Section 3 (Labour).

- Self-employed: individuals who report being self-employed in question 15 (“Who is the employer in this job?”). We remove people self-employed in agricultural activities using the answer to question 13 (“What was your primary activity in your main job?”) and question 14 (“In what sector is this main activity?”).

- Wage worker: individuals who report not being self-employed in question 15, and that are not unpaid family workers or
)|(0.0576)|(0.0565)|(0.0574)|(0.0573)|
|US 10-year Treasury Bond Yield|-0.386***|-0.273***|-0.254***|-0.294***|-0.201**|-0.196**|
||(0.0824)|(0.0789)|(0.0786)|(0.0832)|(0.0792)|(0.0796)|
|ICRG Index|-1.933***|-2.062***|-1.945***|9.165**|-1.398***|-1.506***|
||(0.391)|(0.297)|(0.297)|(4.589)|(0.373)|(0.316)|
|Transparency Index (WB)|0.226|||9.737**|||
||(0.215)|||(4.326)|||
|Transparency Dummy (IMF) Subscription||-0.352***|||2.892**||
|||(0.0679)|||(1.386)||
|Transparency Dummy (IMF) Standard compliance|||-0.311***|||4.141***|
||||(0.0594)|||(1.359)|
|Transparency (WB) x ICRG||||-2.550**<br>|||
|||||(1.052)|||
|Transparency (IMF) Subscription x ICRG|||||-
Policy Research Working Paper 10772

# **Abstract**

The World Bank has published the Logistics Performance Index since 2007. The Logistics Performance Index used to be based exclusively on perception ratings from a global survey of logistics professionals. In 2023, it was augmented with key performance indicators derived from massive global international shipment tracking data (data on container shipping, air cargo, and postal logistics). The new set of indicators measure the speed and connectivity of international supply chains. This paper presents the data sources, rationale, and production of the indicators. It does not discuss the findings from the new indicators, nor does it introduce additional empirical work. The paper complements the 2023 issue of Connecting to Compete, the companion report to the Logistics Performance Index.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jarvis1@worldbank.org, cwiederer@worldbank.org, and dulybina@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
38 objective and disaggregated), there also differences in the types of variables included. Our database has no information on “collective executives”, such as military juntas; nor information on the scope of government action or regulation in the polity; nor on the creation, destruction or nature of regime change in polities. On the other hand, the DPI permits users to track more frequent and micro-level changes in regimes, such as the composition of the legislature, changes in executive, or changes in the electoral: competitiveness indices. It also features several dummy variables representing details of the electoral system and much more detail on parties in the system.

Political constraints. Henisz (1997) presents another valuable source of information on political institutions, in particular the data necessary to create a measure of checks and balances. His series addresses some key issues, such as the number of chambers in a legislature, the degree of federalism, Supreme Court turnover, factionalization of the legislature, and the relative influence of different parties among the executive, legislature, and judicial branches. From this information, he formulates a measure of political constraints that is analagous to, but different in construction from, the CHECKS variables reported here. The Henisz data has a longer time-series, but substantially fewer variables (e.g., with regard to government or opposition parties, party orientation, electoral rules, the different federalism indicators, etc.). Keefer and Stasavage (1999) report empirical results using CHECKS1, from the DPI, political constraints from Henisz, and Executive Constraints, a subjective variable from Polity _ If. The key conclusions are generally robust to which variable is used (central bank independence matters more in the presence of checks and balances). However, these variables are not so closely correlated that one can assume robustness.
However, whether these models calibrated on data for a certain year are better able to make accurate predictions on data for another year remains largely untested.<sup>1</sup> The issue of model stability remains.

This paper explores the potential of a different approach, without the need for prediction and model parameters. Instead of falling back

> 1. An interesting exception is Bansal (2020) who demonstrates temporal transferability of a simple machine learning model applied to satellite data to estimate the evolution of a district level development index (akin to the Human Development Index) using temporal satellite data as input.
/sup> j=18<sup>−1s(j) −s ̃(a)undertheassumptionthatallthose</sup> who have not yet served when exempted will not do so in the future. That is, we assume that until age a − 1 (or, alternatively, 1980) youths from exempt towns served in the army as youths from non exempt towns and then, because of the exemption, all were freed except those who were already in service in 1981. The lower panel of figure (6) reports for each cohort the estimated proportion of men from exempt towns who did not serve in the army. All youths exempt before formal eligibility were freed from service, while only about a half of the cohorts born between 1959 and 1961, exempt after

> 18This proportion is remarkably consistent with what can be estimated from the Indagine Longitudinale sulle Famiglie Italiane (ILFI; http://www.soc.unitn.it/ilfi/eng.index.html). The survey was started in 1998 with a full set of retrospective information on a representative sample of about 5,000 Italian households. Individuals were also asked whether they were ever drafted for compulsory military service (see Pisati and Schizzerotto (2004) for details). The nationwide proportion of adult males reporting never to have served in compulsory military service is between 0.25 and 0.3 and fairly stable across cohorts (data available upon request).
Table 1: Types of contracts, in percentage, by year

|||In acreage term|s|
|---|---|---|---|
|year|1992|2002|2012|
|sharecropping|45|43|31|
|fxed terms|45|50|58|
|fxed amount of money|25|30|41|
|fxed amount of crop output|20|20|17|
|from relatives|6|4|8|
|duration more than two years|42|41|65|
|written contract|18|10|15|
|||In contract ter|ms|
|year|1992|2002|2012|
|sharecropping|49|51|38|
|fxed terms|35|41|49|
|fxed amount of money|15|22|30|
|fxed amount of crop output|19|20|20|
|from relatives|10|3|9|
|duration more than two years|50|40|66|
|written contract|18|10|11|

Note: Kharif (rainy) season. India NSS Land and Livestock Survey Rounds 48, 59 and 70. The top panel presents the percentages in acreage terms (as a percentage of the total acreage rented in). The bottom panel presents the percentages in contract terms (as a percentage of the total number of rented in fields (plots)). Statistics are generated using NSS population weights to generate nationally representative statistics.

48
and short-run and long-run (the elasticity of demand is higher in the long-run). Consistent with economic theory, firms’ labor demand responses are more limited in the short run than in the intermediate and long run. Moreover, firms' demand for low-skilled labor is generally more sensitive to wage rate adjustments than the demand for medium- or high-skilled workers, given the ease with which low-skilled tasks can be automated or outsourced to countries with lower income levels.

**Table 4. Wage elasticity labor demand**

||Short-term|Long-term|
|---|---|---|
|Skilled|-0.5|-0.8|
|Unskilled|-0.7|-1.0|

_<mark>Source: Lichter, Peichl and Siegloch (2013).</mark>_

> 24 Higher wages, constituting increased costs, may lead to adverse employment effects, particularly in the long term. Some of these effects might arise from businesses shutting down instead of laying off employees. However, comprehensively assessing the shift in the distribution of businesses proves challenging due to the absence of detailed firm-level data. A more in-depth examination at the firm level is necessary to properly consider and understand this phenomenon.

33
taxes collected at the border between 5‐10%) with little variability; very few and limited special import regimes; moderate use of anti‐dumping regulations (between 2 and 5 new measures in the year); few restrictions on service trade (e.g., Services Trade Restrictiveness Index between 10 and 25); trade data beyond regular statistics is made available for policy analysis.

“b. Clear and consistent trade facilitation strategies at national and agency levels. Formal processes for consultation and cooperation with stakeholders. Performance standards established and monitored and data on clearance times publicly available. Information on trade‐ clearing procedures and formalities is published and accessible via the Internet. The hierarchy of regulatory framework and the links are clear. Appeal mechanisms are established and accessible. Few instances of corruption in border management agencies. Risk management used extensively (e.g., < 20 % Customs physical inspection). Speedy and predictable processing of goods (e.g., some facilitated procedures for traders with good compliance records). IT system used by Customs and other key border management agencies, with some interconnectivity. Largely open access to foreign transport service providers (e.g., transit transport services are fully liberalized within an economic grouping and transit routes are not specified on the permit).” The 1999 and 2015 criteria differ in that (a) the 2015 criteria are more detailed and longer, (b) the 2015 criteria have been modernized by, for instance, frequent references to the internet which was not a factor in 1999, and (c) the 2015 criteria refer to indexes which were unavailable in 1999, such as the Services Trade Restrictiveness Index.

On the whole, however, it appears that the overall direction of the criteria was rather similar in 2015 to what it was in 1999. For instance, in 1999 to get a ‘2’, the customs authorities “make discriminatory or <u>ad hoc</u> exemptions and valuations”, while in 2015 to get
Hence, frequent lightning strikes in a location negatively affect the electric-energy supply and household access to the electricity by damaging electricity grids. Following Andersen and Pablo (2012); Andersen et al. (2011); Andersen and Dalgaard (2013); Agrawal (2021), we use lightning density as an exogenous determinant of power disturbances and disruption to access to electricity that can affect the individuals’ location ”proximity”to the electricity grids. Note that while some of these papers have used lightning intensity as an instrument for the penetration of the Internet (Agrawal, 2021), the main argument in those papers is that lightning strikes damage electrical infrastructure useful to the functioning of the Internet.

In addition, bearing in mind that topography can be viewed as a geographic obstacle that makes infrastructure construction more expensive (Ostrom, 1990; Durante, 2009; Amorim et al., 2018; Dinkelman, 2011), we add terrain elevation to the exogenous variables that we consider as a determinant of the proximity to electrification grids. Therefore, in our empirical model, the proximity to the electrification grids is partly explained by the topography of the land on which the households are located, and the lightning intensity in that area defined by the mean of lightning strikes per square km each year.

We use satellite data generated by the National Aeronautics and Space Administration (NASA) on lightning strike intensity and aggregated by Manacorda and Tesei (2020); Cecil et al. (2014). They compute average lightning strike intensity between 1995 and 2010 in 0.5<sup>_◦_</sup> _×_ 0 _._ 5<sup>_◦_</sup> for Africa. In the DHS, we matched the closest location mean lightning density for a specific year with an individual’s location. As reported in Manacorda and Tesei (2020), with an average of 17.3 lightning strikes per square km per year, Africa has the highest lightning density on earth. The world
Figure 5: Estimated Semi-parametric Curve for UNIDO 2-digit Employment Data <!-- Start of picture text --> Estimated Non-Parametric Curve For Gini - UNIDO 2-digit Employment Data<br>rs) pret<br>fers "ly<br>‘Jpra” I<br>hd4 “ ry h<br>ws rwal vr _— ~~ é!<br>© rye<br>a<br>oe eel<br>5 , fase<br>iff<br>‘o NN WAff?<br>9 it!<br>aN. el!<br>Sse"<br>9<br>SF& SF& SCis] SC SF& EF& SFBS LCis] LF<br>Income midpoint<br><!-- End of picture text --> Source: Authors’ calculation <!-- Start of picture text --> Regression with the semi-<br><!-- End of picture text --> Figure 6: Slope coefficients and their significance in the Fixed-effect Regression with the semiparametric Method and Updated Data <!-- Start of picture text --> GDP Slope Estimates and Number of Observations o<br>8<br>3 +<br>S Nt 85: ip)<br>2° \ =<br>aW5<br>iSa<br>fe)<br>aOo —<br>D al | a<br>Te) os<br>8 Ss<br>3 FS)<br>er Ff& FF& SF& SFS SF& EC LS EF& &<br>Income Midpoints<br><!-- End of picture text -->

Source: Authors’ calculations 20
.g., Swindale and Billinsky 2006; WFP 2009); and (2) measuring food coping strategies often associated with consuming too little or consuming a poor quality diet, such as the Coping Strategies Index and the Reduced Coping Strategies Index (e.g., Maxwell and Caldwell 2008).

However, others have argued that additional psychological aspects related to food access should be captured in standard metrics (e.g., Webb et al. 2006; etc.). An additional set of experiential measures of food access, such as the Latin America and Caribbean Food Security Scale and the Household Food Insecurity Access Scale, extend food access 7