rafmacalaba/data-use-mentions-extended huggingface dataset:
loan and were rejected or that were discouraged from applying for a loan.<sup>9</sup> The data also include extensive information on the sources of firms’ investments in fixed assets. These sources can be external (formal or informal) or internal.<sup>10</sup> Confirming the findings of Cabral and Mata (2003), figure 2 shows that the firm size distribution is skewed to the right and that the skewness tends to diminish with age. The size distribution of older firms is more symmetric than that of young firms.

As shown in figure 3, the firm size distribution is skewed more to the right in the manufacturing and services sectors, where micro and small firms make up about two-thirds of the sample. Figure 4 suggests that Africa has the largest share of micro and small firms while other regions have more medium-size and large firms. This again provides evidence of the “missing middle” in Africa.

Using the indicator of credit constrained created for this analysis, we split the sample into two groups, credit-constrained and non-credit-constrained firms. As figure 5 shows, the firm size distribution is skewed to the right for credit-constrained firms. This result is in line with the finding that being credit constrained has a negative effect on firm growth—and, especially, that this effect is largest for small firms. Taking this analysis further, we investigate the firm size distribution using the survey data on firms’ perceptions of access to finance, splitting the sample between those perceiving it as a major or very severe obstacle and those viewing it as a minor obstacle or no obstacle. Figure 6 shows that the size distribution of firms perceiving access to finance as a major or very severe obstacle is skewed to the right. This result is confirmed by the data for our sample showing that most of the firms regarding access to finance as a major or very severe obstacle are micro or small. The size distribution for firms perceiving access to finance as a minor obstacle or no obstacle is more symmetric
for 10 manufacturing industries in Germany, U.K.<br>and U.S.A.|
|Roy. A.D. (1982)|Used reweighted Kravis, Heston and Summers (1978) expenditure<br>PPPs to derive sector PPPs.|
|Klodt (1984)|Applied Kravis, Heston and Summers (1978) PPPs to 16 branches<br>of manufacturing for Germany, Japan and U.S.A., 1960, 1970 and<br>1978.|
|Guinchard (1984)|Uses Kravis, Heston and Summers (1982) expenditure PPPs (with<br>adjustment for taxes and trade margins) to derive PPPs for<br>some branches of manufacturing. For intermediate products he<br>used the exchange rate.|
|Jorgenson, Kuroda<br>and Nishimizu (1J36)|Applied "remapped" Kravis et al. (1975, 1978) PPPs to estimate<br>productivity differentials in Japan and USA (1960-79).|
|Roy,D.J. (1987)|Used reweighted expenditure PPPs from ICP IV, derived from a<br>tape provided by UNSO, for 60 countries for 1980.|
|SHORTC|UTS USING LIMITED INDUSTRY OF ORIGIN INFORMATION|
|Shinohara (1966)|Used 53 "representative" commodities for 89 countries in 1968<br>from UN Statistics of the kind now published in the Yearbook<br>of Industrial Statistics with value added weights from the<br>Japanese, UK, and US census of manufactures.|
|Maddison (1970)|Used a trade adjusted version of Shinohara's estimates at US<br>prices for 29 countries for 1965.|
|Blades (1982)|Used 54 commodities to compare USA and USSR in 1970, 1975 and<br>1978.|
Washington, D.C.

Mattoo, Aaditya (2005), Services in a Development Round: Three Goals and Three Proposals, Journal of World Trade, vol. 39, 1223-1238.

Mattoo, Aaditya, and Deepak Mishra (2009), Foreign Professionals in the United States: Regulatory Impediments to Trade, with Deepak Mishra, Journal of International Economic Law, vol. 12, 435-456.

Mattoo, Aaditya, and Joshua P. Meltzer (2018), International Data Flows and Privacy: The Conflict and Its Resolution, World Bank Policy Research Working Paper 8431.

NASSCOM‐DSCI (National Association of Software and Services Companies ‐ Data Security Council of India) (2013.), Survey of the Impact of EU Privacy Regulation on India’s Services Exporters.

30
Table 4: Heterogeneity Analysis

||(1)<br>_Outcom_|(2)<br>_e: Survey c_|(3)<br>_ompleted_|
|---|---|---|---|
|_i._Phone survey|0.348***|0.420***|0.456***|
||(0.057)|(0.045)|(0.045)|
|_ii._Female|-0.030|||
||(0.055)|||
|_iii._Female x Phone Survey|0.153**|||
||(0.077)|||
|_iv._Shares phone||-0.009||
|||(0.065)||
|_v._Shares phone x Phone survey||0.032||
|||(0.091)||
|_vi._Has Tigo money|||0.130**|
||||(0.062)|
|_vii._Has Tigo money x Phone survey|||-0.101|
||||(0.085)|
|Total effect on women ([_i_] + [_iii_])|0.501***|||
|Total effect on people who share phone ([_i_] + [_v_])||0.451***||
|Total effect on people that have Tigo money ([_i_] + [_vii_])|||0.356***|
|Mean of Dep.Var (WhatsApp)|0.299|0.299|0.299|
|R-squared|0.217|0.212|0.219|
|Observations|597|597|597|
|Controls (Age, Sex)|Yes|Yes|Yes
- 10 -

In order to assess the practical importance of the choice of base year, we proceeded to carry out 12 partial rebasings, recalculating CDP series at constant prices on the basis of value added series at constant prices, broken to n into 12 sectors, derived from the World Tables 1984. For tha* rpose we established a deflator for each sector, the prices of a~, year serving in turn for calculation of a global product series whose base was the correspondi-g year. 2/ In this way we were able to calculate for the period 1970-81 and for 73 countries, by regression, average annual growth rates on the basis of each of the years (i.e. 12 average growth rates per country). In more than half the cases (39 countries) tne gap between the maximum and minimum rates exceeds 0.5%. For 22 countries it exceeds 1%. In some cases, most of them oil-exporting countries, it is substantial (Nigeria 7.2%, Iran 4.2%, Congo 2.9%, etc.).

The illustrative model was estimated using the growth rates calculated for various base years for a sample of 40 countries. It was found first of all that the coefficient of determination varies between 0.54 and 0.62, the best estimates according to this criterion being obtained, curiously, by using as bases the years following the two oil shocks (1975, 1976, 1980, 1981). We next note that the value of the coefficients estimated hardly changes with the base year (with the exception of the constant) but that the "Student's" value for t changes appreciably. For example, the share of public expenditure in GDP, which is practically never significant, becomes so (at the 10% threshold) in base 1980. Thus, if the choice of the base year does not seem to entail any bias in estimating the coefficients, it can skew the selection of independent variables.

In brief,
demand; (ii) modeling electricity production; (iii) modeling electricity imports and (iv) modeling capacity constraints. Electricity demand is the sum of final and intermediate demand. The previous section described the general sectoral setup, which allows us to also model each sector’s demand for electricity. Final consumption is split between electricity and other types of consumption using a CES nest – i.e., electricity demand for final consumption depends on the elasticity of substitution, while intermediate electricity demand depends on fixed shares derived from an IO table. Electricity production is the sum of electricity value-added and intermediate supply. Value-added should be derived from labor and capital, or OPEX and CAPEX. Unfortunately, capital and labor data for the electricity sector in most countries are not available (even if value-added is). The production of electricity thus takes the same form as the modeling of the other sectors as in the previous section. This is an obvious shortcoming since making sector specific investment choices are not that easy in this setup and keeping track of different capital vintages by electricity production unit is not feasible (i.e., modeling stranded assets is not straightforward). In the model, excess demand is imported up until the point capacity is met. I.e., capacity is a constraining factor and is assumed to be exogenous. These assumptions are relaxed in the soft-linking approach in the next section. The soft-linking approach tracks CAPEX and OPEX costs explicitly and hence also capital vintages.

Aggregate economic output is produced using capital ( _K_ ), labor ( _N_ ) and electricity ( _Q_<sup>�</sup><sup>_E_</sup> _t_<sup>).Theunitsforcapitalareinlocalcurrencyunits(LCU),foremploymentweusethe</sup> number of people employed and for electricity we use the amount of _kWh_ available for supply (installed capacity). In the modeling it is assumed that electricity generation and capital are complements. This means that disruptions in electricity supply have a direct and indirect effect on output.
# **References**

- **Agarwal, Sumit, Chunlin Liu, and Nicholas S. Souleles** , “The Reaction of Consumer —

- Spending and Debt to Tax Rebates Evidence from Consumer Credit Data,” _Journal of Political Economy_ , 2007, _115_ (6), 986–1019.

- **Ali, Merima, Abdulaziz B. Shifa, Abebe Shimeles, and Firew Woldeyes** , “Building Fiscal Capacity in Developing Countries: Evidence on the Role of Information Technology,” _National Tax Journal_ , 2022, _74._

- **Arango, Carlos and Varya Taylor** , “Merchants’ Costs of Accepting Means of Payment: Is Cash the Least Costly?,” Technical Report Winter 2008-2009, Bank of Canada Review 2008.

- **, Kim P. Huynh, and Leonard Sabetti** , “Consumer payment choice: Merchant card acceptance versus pricing incentives,” _Journal of Banking & Finance_ , 2015, _55_ , 130–141. Global Governance and Financial Stability.

- **Banerjee, Abhijit, Esther Duflo, Clement Imbert, Santhosh Mathew, and Rohini Pande** , “E-governance, Accountability, and Leakage in Public Programs: Experimental Evidence from a Financial Management Reform in India,” _American Economic Journal: Applied Economics_ , 2020, _12_ (4), 39–72.

- **Beck, Thorsten, Haki Pamuk, Ravindra Ramrattan, and Burak R. Uras** , “Payment instruments, finance and development,” _Journal of Development Economics_ , 2018, _133_ , 162 – 186.

- **Bellon, Mr Matthieu, Jillie Chang, Ms Era Dabla-Norris, Salma Khalid, Frederico Lima, Enrique Rojas, and Pilar Villena** , “Digitalization to improve tax compliance: evidence from VAT e-Invoicing in Peru,” IMF Working Paper, International Monetary Fund 2019.

- **B ́ergolo, Marcelo L, Martin Leites, Ricardo Perez-Truglia, and Matias Strehl** , “What Makes a Tax Evader?
floating. Table 1 shows that 78.5% of borrowers are located in countries with flexible exchange rates. Next, Credit constraints (in), is a dummy variable indicating the presence of restrictions on the inflow of commercial credit in the country of the borrower based on the data from Fernández, Klein, Rebucci, Schindler and Uribe (2015). A share of 14.6% of borrowers face credit constraints on credit inflows into their countries.

In some specifications, we control for proxies of economic and financial development. Among these, GDP per capita is GDP per capita calculated at constant 2005 US dollar prices with a sample mean of US$31,363. Next, Credit is domestic credit to the private sector by banks as a percentage of GDP with a mean of 81.8%; Domestic credit is domestic credit provided by the financial sector relative to GDP with a mean of 153.1%; Market cap is the market capitalization of listed companies relative to GDP with a mean of 115.9%; and Turnover ratio is the total value of shares traded during the period as a percentage of the average market capitalization for the period with a mean of 119.3%. These variables are from the WDI database. Finally, the rate of inflation and the rate of GDP growth are macroeconomic control variables.

# **3. The transmission of monetary policy and borrower creditworthiness**

This section presents empirical evidence of the impact of lender-country monetary policy on the volume of cross-border syndicated lending and on other credit terms. We specifically present results on how his transmission depends on borrower creditworthiness. Loan volume and other credit terms are considered in turn.

# **3.1 Loan volumes**

In the loan volume regressions, the dependent variable is the logarithm of the amount of cross-border lending to a particular firm by a particular bank. The regressions include borrower*time fixed effects to control for variation in firm-level loan demand. The main 11
approximation in most cases,<sup>17</sup> and the author’s view is that a value indicator would introduce significant additional noise. For the same reason, introducing a measure relative to GDP or population size would introduce the same source of distortion. An alternative could be a count variable of the number of FDI projects, but it would heavily bias the measure against small countries. The constructed binary variable is our dependent variable in the empirical analysis.

# II. <u>Trade data</u>

We use the BACI data set prepared by CEPII to construct a measure of the revealed comparative advantage at the disaggregated 4-digit sector level.<sup>18</sup> The BACI data set provides disaggregated data on bilateral trade flows for more than 5,000 products and 200 countries. The BACI database is built from data directly reported by each country to the United Nations’ COMTRADE database, where additional procedures are applied by CEPII to reconcile the declarations of the exporter and the importer and provide more accurate measurement of trade flows relative to COMTRADE. The data set is available at annual frequency. The 4-digit level of disaggregation was chosen because it offers a good compromise between data set size and use of results for the construction of the benchmarking values.<sup>19</sup> We use trade data to calculate the measure of the revealed comparative advantage (RCA) index (Belassa, 1967) for each country/sector pair. Based on the calculated RCA for each sector/country pair over the specified period, we construct the binary trade success variable which takes value 1 if the estimated RCA for country/sector pair is above 1 and 0 otherwise. We consider the use of a binary RCA variable as the most appropriate (though imperfect) approximation of whether a country has successfully exported a given product, relative to the rest of the world and export basket composition. The definition of export success in this case does not distinguish between instances of artificial competitiveness (for instance
29 how to count food purchases and exchanges by non-family memn,bers within the household (such as servants and hired labor), household budget surveys may tend to overestimate the actual level of calorie availability. In this sense, it might be more accurate to measure calorie availability (and intake) by a 24hour recall survey.

30According to Tables 3 and 7, mean per capita expenditures on baladi wheat flour for the lowest quintile group in rural areas are six times what they are for this group in urban areas (LE 0.64 vs. LE 0.10).

31For more on this pointi, see Richard Adams, Jr., Development and Social Change in Rural Egypt (Syracuse Uni versity Press, 1986) 32According to Tablles 5 and 8, mean total per capita household expenditures per week (on all food and nonfood items) are about 77 percent higher in urban areas: 57.8 LE in urban areas versus 32.3 LE in rural areas.

33Howarth Bouis, "A Food Demand System Based on Demand for Characteristics: If There is Curvature in the Slutsky Matrix, What do the Curves Look Like and Why?," Journal of Development Economics 51 (December 1996), 239-266.

34 Bouis and _ALhmed_ (note (25) above).

35For a full lisit of the own-price and cross-price elasticities generated by the model, see Bouis and Ahmed (note (25) above).

36 Since there are currently no one piaster (LE 0.01) coins in Emypt, it would be quite difficult to increase the price cf subsidized baladi bread by anything less than five piaster (LE 0.05) increments.
and Martin Ravallion. 2003. “Estimating the benefit incidence of an antipoverty program by propensity-score matching.” _Journal of Business & Economic Statistics_ 21(1): 19–30.

- Jensen, Robert, and Nolan Miller. 2017. “Information, Demand and the Growth of Firms: Evidence from a Natural Experiment in India” Wharton School Working paper.

- Kassie, Menale, Bekele Shiferaw, and Geoffrey Muricho. 2011. “Agricultural technology, crop income, and poverty alleviation in Uganda.” _World Development_ 39(10): 1784–1795.

- Markelova, Helen, Ruth Meinzen-Dick, Jon Hellin, and Stephan Dohrn. 2009. “Collective action for smallholder market access.” _Food policy_ 34(1): 1–7.

- Marshall, Alfred. _Principles of Economics_ (London, 1920).” _Book VI_ (1920): 618–619.

- McKenzie, David, Steven Stillman, and John Gibson. 2010. “How important is selection? Experimental vs. non-experimental measures of the income gains from migration.” _Journal of the European Economic Association_ 8(4): 913–945.

- Ministry of Commerce of the People’s Republic of China, 2016. “E-commerce in China.” 4 Feb 2019, http://dzsws.mofcom.gov.cn/article/ztxx/ndbg/201706/20170602591881.shtml.

- Nanjing University and AliResearch. 2018. “China Taobao Village Development Report (2014– 2018).” URL: http://i.aliresearch.com/img/20181216/20181216145248.pdf.

26
Using the normalized task index of occupations, Figure 7 shows that, for Germany and Spain, the intensity of non‐routine cognitive tasks is higher for jobs at the top of the wage distribution while routine task intensity is higher in the middle of the distribution. Low pay jobs are more intensive in non‐routine manual occupations.

In sum, no matter what the criteria for ordering of occupations is used – either the average wage paid in each occupation, or the intensity of their task content – there is evidence of a hollowing of the middle of the distribution of occupations, or of its polarization. For the three countries under study, jobs entailing a high degree of routine tasks and paid around the middle of the wage scale are declining.

19
At the individual level, researchers measure migration and define individuals as migrants in a number of ways, for example based on time spent away, destination location or links to those remaining behind (Smith and Thomas 2003). Correspondingly, there are even more ways to characterize migration at the household level and to define a “migrant household” (Carletto and de Brauw 2007). Since individuals can leave or return over time, the definition is further complicated when considering longer timeframes. For example, in recent decades there has been both short- and long-term international migration from the region, comprising short-term seasonal migration (e.g., for harvest season) to nearby Chiapas, Mexico and longer-term, though not necessarily permanent, migration to the United States.<sup>vi</sup> The latter migrants are typically undocumented and commonly spend several years away, with some returning for extended visits in between periods of migration (Saenz de Tejada 2009).

Researchers are often constrained in their characterizations of migration status by the limited information available in a general-purpose survey not explicitly designed with migration in mind and are therefore unable to represent well the various patterns. For this research, we piloted and then implemented comprehensive migration modules designed for the specific context. To identify both past and current migrants, we collected detailed migration histories for all current household members 15 years and older. We began with a standard household definition, including all the individuals who ate and slept in the same dwelling. We defined an individual as a household member if they had been present for at least one of the previous 12 months. Many household surveys such as the Guatemalan ENCOVI use a longer, more restrictive, three-month minimum residence period. The one-month period facilitated the inclusion of migrants who had made return visits in the past year, which is particularly common during the year-end holidays. The histories included the first and the most recent migration events for each of these current household members. This allowed us to incorporate individuals who were currently present but may have
**_<u>________________________________________________________</u>_**

At the sub-sectoral level, consensus on a set of core measures for each transport sub-sector has been achieved which constitute realistic and meaningful frames in order to monitor the performance of each sub-sector (roads, railways, international ports, waterways, and air transport).

There are different requirements at each level which are related to significant variations in the objectives of different key stakeholders and, therefore, in the information priorities set by each and the ways in which they use data. The differences are particularly pronounced in the road transport sub-sector which, universally, has very little vertical integration between the delivery of transport service and the provision and maintenance of the related infrastructure.

In the short-term, some of the necessary data will not be available for all client countries. Pending collection of the necessary data, "best estimates" of the relevant indicators will be made in consultation with specialists having the necessary country experience.

Indicators for road transport (road network, administration, finance, usage etc.), ports etc. have been summarized on the basis of extensive consultation amongst transport specialists in the World Bank. (See <u>Annex G</u> for the template on trade logistics indicators).

# <u>Transport Web Library</u>

The main objective of the Library is to make available some of the documents gathered from outside partners or produced by the World Bank on Transport issues, mostly related to ECA. Documents are searchable by Country, Document Type, Modal Themes, Sub-region and topics.

# _<u>Transport CD</u>_

Combines the databases of three research organizations: OECD's International Road Research Documentation (IRRD); the ECMT TRANSDOC database; and the TRB Transportation Research Information Services database (TRIS). Special emphasis is on road transportation and transportation economics. Coverage: 1988 to the present.

# _<u>UN Economic Commission of Latin America and Caribbean (ECLAC)</
<!-- Start of picture text --> FDH FDH<br>0 .2 .4 .6 .8 1 .4 .6 .8 1<br>score score<br>Unorthogonalized Public Expenditure Orthogonalized Public Expenditure<br>8 15<br>6<br>10<br>4<br>Density Density<br>5<br>2<br>0 0<br><!-- End of picture text -->

**Figure 5. Density of Efficiency Scores – Gross Primary School Enrollment**

This paper uses nine indicators of education output and four indicators of health output.<sup>6</sup> The education indicators are: primary school enrollment (gross and net), secondary school enrollment (gross and net), literacy of youth, average years of school, first level complete, second level complete, and learning scores. Though the ideal educational output indicator are comparable learning scores, international assessments are based on samples mostly composed of developed nations, limiting the applicability to the present paper. However, Crouch and Fasih (2004) recently combined several international assessments to obtain a larger sample of comparable results.<sup>7</sup> Unfortunately they only do it for one period. The correlation between the learning scores and other output variables is high (.81 with net secondary school enrollment and .76 with average years of school), as shown in Figure 6<sup>8</sup> . The health output indicators are: life expectancy at birth, immunization (DPT<sup>9</sup> and measles), and the disability-adjusted life expectancy (DALE).

The cross-country comparisons with this set of indicators assume some form of data homogeneity, which might be problematic given the diversity of counties in the sample considered. Even for a more homogeneous group of countries, such as the OECD, there is call for caution when comparing expenditure levels in member countries (