rafmacalaba/datause-encoder-data huggingface dataset:
25

# Bibliocsraihy

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- Dollar, D., 1990, "Outward-oriented countries really do grow more rapidly: Evidence from 95 LDCs, 1976-85," mimeo.

- Edwards, S., 1987, "Real exchange rate variability: An empirical analysis of the developing country case," International Economic Journal, Vol. 1, 1, Spring.

- Edwards, S., 1989, Real Exchange Rates. Devaluation, and Adjustment. MIT Press.

- El Badawi, I., 1991, "Real Overvaluation, terms of trade shocks and the cost to agriculture in Sub-Saharan Africa," mimeo.

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- Kamin, S., 1988, "Devaluation, external balance, and macroeconomic performance: A look at the numbers," Princeton Studies in International Finance, #62.

- Kendall, M., and A.S. Stuart, 1968, The advanced theory of statistics, 2' ed., Hatner. a

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|P042927|2001|2011|1/5/2011|17 Closed|Vietnam|
|---|---|---|---|---|---|
|P042927|2001|2015|11/26/2014|18 Closed|Vietnam|
|P042940|1998|1998||1 Closed|Algeria|
|P042940|1998|1999||3 Closed|Algeria|
|P042940|1998|2000|5/11/2000|5 Closed|Algeria|
|P042940|1998|2001|6/25/2001|9 Closed|Algeria|
|P042940|1998|2002|6/19/2002|12 Closed|Algeria|
|P042940|1998|2003|2/19/2003|14 Closed|Algeria|
|P042940|1998|2020|9/12/2019|1 Closed|Algeria|
|P043021|2005|2019|5/7/2019|1 Dropped|India|
|P043101|1998|1998||2 Closed|Yemen,Republic of|
|P043101|1998|1999||5 Closed|Yemen,Republic of|
|P043101|1998|2000|6/9/1999|5 Closed|Yemen,Republic of|
|P043101|1998|2019|5/7/2019|6 Closed|Yemen,Republic of|
|P043102|1996|1996||1 Closed|Egypt,Arab Republic o|
|P043102|1996|1997||3 Closed|Egypt,Arab Republic o|
|P043102|1996|1998||5 Closed|Egypt,Arab Republic o|
|P043102|1996|1999||9 Closed|Egypt,Arab Republic o|
|P043102|1996|2000|6/30/2000|15 Closed|Egypt,Arab Republic o|
|P043102|1996|2001|6/4/2001|18 Closed|Egypt,Arab Republic o|
|P043109|1996|1996||1 Closed|Yemen,Republic of|
|P043109|1996|1997||4 Closed|Yemen,Republic of|
|P043109|1996|1998||7 Closed|Yemen,Republic of|
|P043109|1996|1999||9 Closed|Yemen,Republic of|
|P043109|1996|2000|6/26/2000|11 Closed|Yemen,Republic of|
|P043124|1998|1998||2 Closed|Eritrea|
|P043124|1998|1999||3 Closed|Eritrea|
|P043124|1998|2000|6/27/2000|6 Closed|Eritrea|
|P043124|1998|2001|5/4/2001|8 Closed|Eritrea|
|P043124|1998|2002|4/30/2002|10 Closed|Eritrea|
|P043124|1998|2003|6/2/2003|13 Closed|Eritrea|
|P043124|1998|2004|3/22/2004|15 Closed|Eritrea|
|P043124|1998|2005|12/29/2004|17 Closed|Eritrea|
|P043124|1998|2019|5/7/2019|18 Closed|Eritrea|
|P043163|1997|1997||0 Closed|Mexico|
|P043163|1997|1998||1 Closed|Mexico|
|P043163|1997|1999||2 Closed|Mexico|
|P043178|1999|1999||1 Closed|Albania|
|P043178|1999|2000|4/14/2000|3 Closed|Albania|
|P043178|1999|2001|2/22/2001|5 Closed|Albania|
|P043178|1999|2002|6/27/2002|8 Closed|Albania|
|P043178|1999|2003|6/18/2003|10 Closed|Albania|
|P043178|1999|2004|6/16/2004|12 Closed|Albania|
|P043178|1999|2005|5/2/2005|14 Closed|Albania|
|P043195|2004|2004|5/20/2004|4 Closed|Croatia|
|P043195|2004|2005|4/1/2005|6 Closed|Croatia|
|P043195|2004|2005|6/8/2005|7 Closed|Croatia|
## **7.1.2 Household survey outcomes**

Panel A of Table 15 reports estimates of the effect of the savings default on total spending in the week following the transfer for male-headed households, for households with any bank activity before the experiment, and for those above the median for the other three characteristics; Panel B reports the corresponding estimates for female-headed households, for those without bank activity before the experiment, and those below the median for the other characteristics. For example, the average male-headed household that received a cash transfer spent MK 16,132 in the week following the survey. The savings default increased spending for male-headed households by MK 959. The average female-headed household that received a cash transfer spent MK 13,472, and savings default recipients spent MK 1,227 less. Neither effect is statistically different from zero. In fact, of the ten coefficients reported in Table 15, none is significant. Moreover, in each case, the magnitude of the coefficient is small relative to the mean for cash recipients.

We reject the null hypothesis that savings defaults affected pairs of subgroups equally for one pair. Savings defaults decreased total spending for households that had previously used their accounts and decreased it for households that had not (column 2), so while neither coefficient is significantly different from zero, the p-value for the test that they are equal (reported in the bottom row of the table) is 0.063. The p-value for the joint test that savings defaults affect all ten subgroups equally is 0.459 (not shown).

We analyze spending on each of the four categories of consumption in a similar fashion.<sup>15</sup> In total, we estimate 40 coefficients, and only two are individually significantly different than zero. In 20 tests of equal effects between subgroups, we reject the null only once: spending on fees and transfers increases for patient respondents and falls for impatient respondents, but in either case, the effect is less than three percent of total weekly spending. Consumption is not significantly affected by mental accounting or defaults for any of the subgroups we examine, and it is therefore unlikely that the null effect we describe above masks large positive effects for any subgroup.

# **7.2 Payment timing**

We conduct analogous tests for heterogeneous effects of delayed payments, though since the design includes two payment delay treatments, these estimates are imprecise. We report them for completeness, and because we can examine the sign and pattern of coefficients even when they are estimated imprecisely.

Table 16 examines effects on total spending in the week following transfer receipt, using

> 15See Tables A1 to A4.

19
# **What Is Factoring?**

a factoring relationship that specifies a 70 percent advance and 2 percent commission, the factor would advance $68 and acquire ownership of the receivables on the day the invoices are generated. If the invoices are paid in 31 days, the factor remits $30 to the client on the 31<sup>st</sup> day (Figure 1).

Factoring usually involves additional fees beyond the commission. For example, interest may be charged on the outstanding balance of receivables or on the balance of receivables outstanding more than a fixed number of days.<sup>9</sup> Alternatively, interest can be fixed and included in the commission. Factoring may also include an application fee, credit checking fees, an origination fee, pre-contract due diligence fees (to conduct onsite and offsite reviews of client books and records), and fees to search public records for tax liens or judgments, lawsuits, bankruptcy filings, administrative actions, news items, press releases, and the like (Worthy 2003).

<!-- Start of picture text --> Figure 1. Basic Mechanics of Factoring<br>Ongoing Process: Credit Information<br>Third 6) Day 1:<br>Parties • Purchase Invoice @<br>2% Commission<br>Ongoing Process: Credit Information<br>6) Day 1:<br>Customer • 7 0% Advance (less<br>7) Day 30: Factor<br>(Debtor) • Invoice Paid ($100) commission) sent to Client<br>(Borrower)<br>8) Day 31:<br>• Balance of Invoice paid<br>to Client (Borrower)<br>Client<br>(Borrower)<br>Source: Worthy 2003<br>5)<br>4)<br>1)<br> Invoice ($100)<br> Ship Product<br> Issue Order<br> Submit Order for Credit Approval<br>2)<br> Issue Credit Approval Invoice ($100) $68 - Day 1<br>3) 5) 6)<br>* $68 - Day 31<br>Ongoing Process: Reports<br><!-- End of picture text --> The specific fees will be defined in the factoring agreement. This agreement will also cover a broad range of other issues associated with the relationship between the factor and the client, including the effective date of agreement, initial terms, renewal options, exclusivity of the factoring relationship, invoices required to be factored, minimum factored volume requirement, maximum cash advances available to client, purchase price of invoices, percentage of purchase price to which client is entitled to be paid in advance, minimum required reserve percentage or amount, and termination provisions.<sup>10</sup>

> 9 In some factoring relationships, known as maturity factoring, the factor advances funds only on invoices outstanding more than a specified period, such as 30 days.

> 10 For more detail on the practice of factoring see Salinger (1999).

**6**

**THE WORLD BANK**
# **Table A.4a: Difference in Difference Results for Retail-World Margin** **<u>(Margins Based on Current World Price of Crude Palm Oil)</u>**

|||**R**|**etail-World**|**Price Margin**|**s**||
|---|---|---|---|---|---|---|
||Full S|ample|Restricted|Sample|Matched|Sample|
|**Effect of Announcement**|(1)|(2)|(1)|(2)|(1)|(2)|
|Announcement Dummy|-5.986***|-5.543***|-7.164***|-5.064***|-6.178***|-5.702***|
||(-11.03)|(-8.833)|(-12.84)|(-10.47)|(-9.778)|(-9.127)|
|**Announcement*Palm**|||||||
|**(Intercept)**|**36.40***|**27.75**|**40.45****|**28.34**|**35.31***|**27.22**|
||(1.841)|(1.460)|(2.055)|(1.505)|(1.811)|(1.462)|
|**Announcement*Palm*World**|||||||
|<br>**Price (Slope)**|**-0.321**|**-0.256**|**-0.346**|**-0.281**|**-0.344**|**-0.245**|
||(-1.313)|(-1.089)|(-1.419)|(-1.209)|(-1.426)|(-1.066)|
|**Effect of Implementation**|||||||
|Policy Implement. Dummy|-4.087***|-4.727***|-5.772***|-3.564***|-3.129***|-3.968***|
||(-7.282)|(-7.812)|(-9.241)|(-5.893)|(-5.074)|(-5.816)|
|**Policy Implement.*Palm**|||||||
|<br>**(Intercept)**|**35.41*****|**31.41*****|**37.22*****|**29.57*****|**35.16*****|**37.29*****|
||(11.10)|(9.214)|(11.32)|(8.065)|(10.82)|(10.36)|
|**Polic Imlement*Palm*World**|||||||
|**y p.**<br>**Price (Slope)**|**-0.324*****|**-0.308*****|**-0.320*****|**-0.311*****|**-0.358*****|**-0.381*****|
||(-7.838)|(-7.079)|(-7.514)|(-6.857)|(-8.407)|(-8.171)|
|World price*palm|0.276***|-0.189***|0.282***|0.0952|0.292***|-0.00356|
||(6.600)|(-3.607)|(4.712)|(1.400)|(6.044)|(-0.0497)|
|World Price|-0.632***|-0.185***|-0.627***|-0.491***|-0.649***|-0.387***|
||(-14.18)|(-3.441)|(-9.615)|(-6.527)|(-12.97)|(-5.276)|
|Palm|18.29***|33.64***|15.47***|29.30***|21.03***|30.19***|
||(17.80)|(19.41)|(14.00)|(16.18)|(14.72)|(12.46)|
|Intercept|25.65***|10.48***|24.22***|25.79***|24.97***|8.904***|
||(26.34)|(3.976)|(15.25)|(5.078)|(22.87)|(3.185)|
|Observations|1,932|1,932|1,374|1,374|1,312|1,312|
|Year and Quarter dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Ramadan Dummies|No|Yes|No|Yes|No|Yes|
|Relative Prices|No|Yes|No|Yes|No|Yes|
|Fuel Price|No|Yes|No|Yes|No|Yes|
|Dummyif World Price rising|No|Yes|No|Yes|No|Yes|
- World Bank. 2007. Logistic Performance Index database, available at

- http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTTRANSPORT/E XTTLF/0,,contentMDK:21514122~menuPK:3875957~pagePK:210058~piPK:21 0062~theSitePK:515434,00.html.

- _____. 2008. Doing Business database, available at http://www.doingbusiness.org/. _____. 2008. Global Monitoring Report 2008. Yeats, Alexander. 1990a. “On the Accuracy of Economic Observations: Do Sub-Saharan Trade Statistics Mean Anything?” _The World Bank Economic Review_ 4(2).

- _____. 1990b. “Do African Countries Pay More for Imports? Yes.” _The World Bank Economic Review_ 4(1): 1-20.

37
<!-- Start of picture text --> Ce<br>100 4<br>OPKs<br>o<br>sy on © Cantbean 510s<br>=)= is | * Tuvalu — bati | Other countries<br>£ ® Ah<br>5<br>zB<br>g 4 = —<br>E Tanga ~.. naar<br>* Solomon stances —~<br>é 0<br>4 o4 - — —-.. — |<br>: _1# ° CREE<br>fal) OF wo nian}<br>: p.04 16 GODPRERTSL\<br>: 6 RG eer )<br>: © 0 ORR<br>: COGATR IAL<br>ba C CU<br>2 \<br>0.0001 . !<br>0.01 O.1 i 10<br>Geographical scale of population digpersion (O00 km: log scale)<br>Note: Marker size reflects population size.<br>CR<br><!-- End of picture text -->
case, we would expect income to have a positive (not a negative) impact on flood frequency in developing countries. Better reporting cannot satisfactorily explain the nonlinear impacts of governance either. Moreover, our regressions include country and time fixed effects. A caveat noted explicitly by the DFO is that the quality of the floodevent information varies from nation to nation: ―[N]ews from floods in low-tech countries tend to arrive later and be less detailed than information from ̳first world‘ countries.‖ In addition, less democratic countries might systematically underreport the number of casualties. Both these effects are captured by the country-specific effects as long as they are constant over time. If reporting improves over time, however, country fixed effects will not pick up the differences in reporting. Time fixed effects will pick them up as long as these improvements are driven by, say, technological change (e.g., improvements in remote sensing, which DFO uses extensively) or international initiatives that are common to all the countries.

The result that income and governance matter only when we include high-fatality floods parallels a finding that these variables have larger effects on earthquake fatalities when the sample is limited to large earthquakes (Keefer et al. 2010). It could be due to the aggregate nature of our measures of income and governance, which are measured at the national level. Big events that cause many fatalities are more likely to attract the attention of national governments than small events, which are more likely to be left to state and local governments to manage. If so, then it makes sense that our income and governance variables are significant when we include the larger events but not when we exclude them, as they do not reflect the differences in income and governance that exist between different parts of a country and are important for explaining subnational responses to smaller floods.

28
https://www.radiookapi.net/2018/05/22/actualite/societe/kinshasa-le-gouverneur-andrekimbuta-insiste-sur-le-respect-des-prix-du

# RFI. (2018, May 4). _RDC: Augmentation des prix des transports à Kinshasa_ . RFI.

https://www.rfi.fr/fr/afrique/20180504-rdc-augmentation-prix-transports-kinshasa Ricky Burdett, Ömer Çavuşoğlu, Savvas Verdis, Thomas Matussek, Deyan Sudjic, David Harvey, Susan S.

Fainstein, Edgar Pieterse, Richard Sennett, Rahul Mehrotra, Enrique Peñalosa, Andy Altman, & Catarina Heeckt. (2013). Urban Age—City Transformations Conference—Rio de Janeiro—24-35 October 2013. _City Transformations_ . https://lsecities.net/wp-content/uploads/2013/10/citytransformations-newspaper_en.pdf Sims, R., & Schaeffer, R. (2014). _IPCC - AR5 Climate Change 2014: Mitigation of Climate Change_ . IPCC. https://www.ipcc.ch/site/assets/uploads/2018/02/ipcc_wg3_ar5_chapter8.pdf SSATP. (2001). _SSATPWP54.pdf_ (Working Paper No. 54). The World Bank and Economic Commission for Africa. https://www.ssatp.org/sites/ssatp/files/publications/SSATPWorkingPapers/SSATPWP54.pdf Telaye, A., Benitez, P., Tamru, S., Medhin, H., & Toman, M. (2019). _Exploring Carbon Pricing in Developing Countries: A Macroeconomic Analysis in Ethiopia_ . World Bank. https://openknowledge.worldbank.org/handle/10986/31717 Venables, A. J. (2017). Breaking into tradables: Urban form and urban function in a developing city. _Journal of Urban Economics_ , _98_ , 88–97. https://doi.org/10.1016/j.jue.2017.01.002 World Bank. (2018). _Democratic Republic of Congo Urbanization Review: Productive and Inclusive Cities for an Emerging Congo_ . World Bank. https://doi.org/10.1596/978-1-4648-1203-3 World Bank. (2019, October 17). _Democratic Republic of Congo—Overview_ [Text/HTML]. World Bank. https://www.worldbank.org/en/country/drc/overview World Bank. (2022). _Carbon Pricing Dashboard_ . https://carbonpricingdashboard.worldbank.org/ WorldPop. (2022). _WorldPop_ . https://www.worldpop.org/ Wu, H., Avner, P., Boisjoly, G., Braga, C. K. V., El-Geneidy, A., Huang, J., Kerzhner, T., Murphy, B., Niedzielski, M. A., Pereira, R. H. M., Pritchard, J. P., Stewart, A., Wang, J., & Levinson, D. (2021). Urban access across the globe: An international comparison of different transport modes. _Npj Urban Sustainability_ , _1_ (1), Article 1. https://doi.org/10.1038/s42949-021-00020-2 ZoomEco, L. (2018, December 29). _RDC: Carburant, le prix du litre a augmenté de 29,5% en 2018 !_ https://zoom-eco.net/economie/rdc-carburant-le-prix-du-litre-a-augmente-de-295-en-2018/ 34
orthogonal with the error term ν it. This is a weak assumption given the randomized study design and the absence of observable differences between the two groups at baseline. The first-stage relationship for these estimates is: The estimate of γ is 0.361, with a robust standard error of 0.027, so the estimates of program impact in (3) can be expected to be roughly two-and-a-half to three times as large (1/0.363) as the corresponding lottery effects estimated in (1). As is well known, instrumental variables estimate Local Average Treatment Effects (LATE). These measure the effect of BDH transfers on outcomes for individuals whose probability of receiving transfers was affected by the lottery—“compliers”, in the language of Angrist, Imbens, and Rubin (1996). A comparison of Tables 2 and 3 suggests that complier households have lower education levels and fewer members than the average household in our sample.

# **4. Results and discussion**

# _A. Main results of program impact_

The main results in this paper are summarized in Tables 4 and 5. To help in interpreting the magnitude of the regression coefficients, the first column presents the mean and standard deviation (in parenthesis) of the dependent variable for the control group, and the second column gives the sample size for the regression.<sup>10</sup> The third through fifth columns in the table correspond to specifications with different sets of controls. The first specification includes only controls for the gender and age of the child (dummy variables by single years of age). These are included because they add precision—results are very similar when no controls are included in the regression. The second specification includes an “extended” set of controls for baseline child and household characteristics; the third specification supplements these controls with 27 cantonlevel fixed effects. Standard errors in all regressions are corrected for within-sibling correlations.

> 10 Note that the sample size for (1), the model that only uses the follow-up survey, is larger than the sum of the sample sizes for the two models that use both the baseline and follow-up surveys.  This is because some children who were younger than six at baseline would be included in the estimation of (1), which only includes the follow-up survey, but not of (2) and (3), which include both surveys.

13
# **Table 8 Income Effects,**

# **MFN Liberalization-based Preference Erosion and the ATC Phaseout**

||change in annua<br>Total<br>preference loss,<br>unadjusted|l national income<br>Total<br>preference<br>loss,adjusted|, $US million<br>Total ATC<br>quota<br>elimination|
|---|---|---|---|
|_African LDCs_|_-110.5_|_6.3_|_-71.8_|
|Botswana|16.4|16.0|0.2|
|Madagascar|9.8|17.0|-7.0|
|Malawi|-7.0|-2.5|0.0|
|Mozambique|-14.3|-14.3|-1.7|
|Tanzania|1.5|20.9|3.2|
|Uganda|-4.3|1.6|-0.3|
|Zambia|-21.3|-20.9|1.7|
|Other Sub-Saharan Africa LDCs|-91.3|-11.4|-68.0|
|_Asia/other LDCs_|_-87.4_|_-25.1_|_-661.9_|
|Bangladesh|-138.2|-77.2|-205.1|
|Other Central/South Asia LDCs|50.8|52.2|-456.8|
|_Other Low-income_|_2050.5_|_1970.5_|_-274.1_|
|India|275.8|267.9|-478.5|
|Vietnam|1774.8|1702.6|204.4|
|TOTAL|1852.6|1951.7|-1007.9|

Source: Table 7, and Francois, Spinanger, and Woerz (2005).

33
<!-- Start of picture text --> — Noton Sensitive List === On Sensitive List 1S<br>i<br>H<br>i<br>vat<br>a<br>|<br>a We<br>yyy We a<br>wil iy AN<br>a ae<br>0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80<br><!-- End of picture text -->

Ad Valorem Preferential Tariff Rates (%)

> z S)=

Source: Department of Customs, Nepal
Figure 2. **Trends in municipal expenditure, 1998-2003** In per cent of municipal current expenditure, weighted averages<sup>1</sup> <!-- Start of picture text --> Brazil Small municipalities 2 Large municipalities 3<br>A. Investment B. Personnel<br>25 52<br>51<br>20<br>50<br>15<br>49<br>10 48<br>47<br>5<br>46<br>0 45<br>1998 1999 2000 2001 2002 2003 1998 1999 2000 2001 2002 2003<br>C. Transport D. Debt service: interest and<br>amortization<br>14 10<br>12 9<br>8<br>10<br>7<br>8 6<br>5<br>6<br>4<br>4 3<br>2<br>2<br>1<br>0 0<br>1998 1999 2000 2001 2002 2003 1998 1999 2000 2001 2002 2003<br>E. Health care and sanitation F. Housing and urbanisation<br>35 18<br>16<br>30<br>14<br>25<br>12<br>20 10<br>15 8<br>6<br>10<br>4<br>5<br>2<br>0 0<br>1998 1999 2000 2001 2002 2003 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> _Source_ : National Treasury, and author's calculations.

1. The number of municipalities varies between 3 807 in 1998 and 4 881 in 2001.

2. Refers to the municipalities with population less than 5 102 inhabitants in 2000, which are in the lowest quartile of the distribution.

3. Refers to the municipalities with population higher than 22 425 in 2000, which are in the highest quartile of the distribution.

16
The model incorporates technological progress as exogenous improvements in energy factor productivity, as well as the cost reduction trend in renewable electricity generation.

# **7. Parameters and Calibration Methods**

In this section, we provide a brief overview of the calibration process for our model. For a more comprehensive understanding of the data sources, data processing methods, and calibration techniques, we recommend referring to Goulder _et al_ . (2023).

For any CES function of the form in Equation (A1), the Lagrangian equation for obtaining the composite _V_ at minimum cost is given by: where _pi_ is the price of input<sup>_v_</sup> _i_<sup>. From this minimization problem, the optimal demand of input</sup><sup>_v_</sup> _i_<sup>per unit of the composite</sup><sup>_V_is derived as:</sup> Therefore, the share parameters of CES functions that have the functional form of Equation (A1), α _i_ can be calibrated by inverting the optimal input intensity function:

> where α _i_ is the share parameter of the CES production function, _V_ the output quantity,<sup>_v_</sup> _i_<sup>the</sup> quantity of input _i_ ,<sup>_p_</sup> _i_<sup>the benchmark price of input</sup><sup>_i_and</sup><sup>_p_the benchmark price of output.</sup>

Data for calibration includes the 2017 China’s input-output table and a firm-level dataset<sup>_v_</sup> _<u>i</u>_ from the Ministry of Ecology and Environment of 2017, which can be used to derive the “ _V_<sup>”</sup> component in Equation (A4). The elasticities of substitution ( σ ) at different levels of the nested CES structure are obtained from calibrations and various sources. Table B2 provides the 56