rafmacalaba/datause-encoder-data huggingface dataset:
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE production. Similarly, Arcelor Mittal has agreed to invest in the port of Buchanan and its associated railway line as part of its plans to expand iron ore production in the country.

**Table 4. Benchmarking port indicators: Freeport of Monrovia as compared with selected other ports**<sup>*****</sup>

||**Freeport**<br>**of**<br>**Monrovia**<br>**Liberia **|**Cotonou**<br>**Benin**|**Abidjan**<br>**Cote**<br>**d'Ivoire**|**Tema**<br>**Ghana**|**Apapa**<br>**Nigeria**|**Harcourt**<br>**Nigeria**|**Dakar**<br>**Senegal**|**Lome**<br>**Togo**|
|---|---|---|---|---|---|---|---|---|
|**CAPACITY:**|||||||||
|Containers handled (TEU/year)|**50,000**|158,201|500,119|420,000|336,308|7,900|331,191|460,000|
|General cargo handling capacity<br>(tons/year)|**700,000**|2,500,000||8,500,000|5,000,000|2,000,000|||
|**EFFICIENCY:**|||||||||
|Average container dwell time(days)|**15**|12|12|25|42||7|13|
|Average truck processing time for<br>receipt and delivery of cargo<br>(turnaround time in hours)|**5.5**|6.0|2.5|8.0|6.0||5.0|4.0|
|Average general cargo vessel pre-<br>berth waitingtime(hours)|**3**|48|3|9.6|36|38|24||
|Average general cargo vessel stay<br>(turnaround time in hours)|**3**|48|2|48|41|46|60||
|Container crane productivity*<br>(containersper hour)|||18|13|12||||
|Average general cargo crane/gang<br>productivity (tonnesper hour)|**16**|15|16|14|9|8||23|
|**TARIFFS:**|||||||||
|Average general cargo handling<br>charge, ship to gate (US$/tonne)|**200**|180|260|168|155||160|220|
|Average dry bulk handling charge,<br>ship to gate or rail (US$/tonne)|**10.5**|8.5|13.5|10|8|8|15|9|
|Average liquid bulk handling charge<br>(US$/tonne)|**4**|5|5|3|||5|5|

* Data for the Freeport of Monrovia relate to 2008, but no data were available for the Port of Buchanan. Source: Mundy and Penfold, AICD Background Paper No. 8, 2009. Derived from AICD ports database downloadable from http://www.infrastructureafrica.org/aicd/tools/data TEU = 20-foot equivalent units.

# **Challenges**

Port security is an issue for Liberia. The National Port Authority’s Work Plan for 2008 included obtaining ISPS certification for the port of Monrovia as one of its objectives, but as of mid-2009 this certification had not been obtained. No ports in Liberia possess ISPS certification. In 2005, the port of Monrovia was removed from the US Coast Guard restricted list after the United Nations Mission in Liberia assumed overall responsibility for it. Security remains crude at the ports, although efforts are underway to achieve ISPS compliance at the port of Monrovia.

12
Psacharopoulos, G., J. P. Tan, and E. Jimenez. 1986. Financing Education in Developing Countries: An Exploration of Policy Options. Washington, D.C: World Bank.

Romer, Paul. 1986. "Increasing Returns and Long Run Growth." Journal of Political Economy (October) 1002-37.

. 1990. "Endogenous Technological Change." Journal of Political Economy 98(5 ):s71-102.

. 1993. "Two Strategies for Economic Development: Using Ideas and Producing Ideas." Proceedings of the Annual World Bank Conference on Development Economics 1992 Supplement. World Bank Economic Review. Washington, D.C.

Schultz, T.W. 1961. "Investment in Human Capital. " American Economic Review 51(l):1-17.

Spiegel, Mark. 1994. "Determinants of long-run productivity growth: A selective survey with some new empirical results." mimeo.

Summers, Robert and Alan Heston. 1991. " The Penn World Table (Mark 5): An expanded set of international comparisons. 1950-88." Quarterly Journal of Economics. 106(2):327-368.

Stiglitz, Joseph E. And Marilou Uy. 1996. "Financial Markets, Public Policy, and the East Asian Miracle." The World Bank Research Observer, I 1(2):249-76.

Tilak, J.B. G. 1989. "Education and its Relation to Economic Growth, Poverty, and Income Distribution: Past Evidence and Further Analysis." World Bank Working Papers 46.

Thomas, Vinod and Yan Wang. 1996. "Distortions, Interventions and Productivity Growth: Is East Asia Different?" Economic Development and Cultural Change, 44(2):265-288.

. 1997. "East Asian Lessons from Reforms." In Wing T. Woo, Stephen Parker, and Jeffrey D. Sachs, eds. Economies in Transition: Comparing Asia and Eastern Europe. Cambridge Mass: MIT Press.

World Bank. 1991. World Development Report 1991: Challenge of Development. New York: Oxford University Press for the World Bank.

World Bank. 1995. World Development Report 1995: Workers in an Integrating World. New York: Oxford University Press for the World Bank.

Young, Alwyn. 1991. "Learning by doing and the Dynamic Effects of International Trade." Quarterly Journal of Economics. May. p.369-405.

26
- Hjort, J., and J. Poulsen (2019): “The Arrival of Fast Internet and Employment in Africa,” _American Economic Review_ , 109, 1032–1079.

- Hjort, J., and L. Tian (2021): “The Economic Impact of Internet Connectivity in Developing Countries ,” _INSEAD Working Paper, SSRN_ .

- Holl, A. (2004): “Transport Infrastructure, Agglomeration Economies, and Firm Birth: Empirical Evidence from Portugal,” _Journal of Regional Science_ , 44, 693–712.

- Houngbonon, G. V., J. T. Mensah, and N.-. Traore (2022): “The Impact of Internet Access on Innovation and Entrepreneurship in Africa,” Policy Research Working Paper Series 9945, The World Bank.

- Jedwab, R., and A. Storeygard (2021): “The average and heterogeneous effects of transportation investments: Evidence from Sub-Saharan Africa 1960-2010,” _Journal of the European Economic Association_ .

- Juhn, C., G. Ujhelyi, and C. Villegas-Sanchez (2013): “Trade Liberalization and Gender Inequality,” _American Economic Review_ , 103, 269–273.

- Khanna, R., and C. Sharma (2018): “Testing the effect of investments in IT and RD on labour productivity: New method and evidence for Indian firms,” _Economics Letters_ , 173, 30–34.

- Kho, K., L. Lakdawala, and E. Nakasone (2018): “Impact of Internet Access on Student Learning in Peruvian Schools,” Working Papers 2018-3, Michigan State University, Department of Economics.

- Kuhn, P. J., and H. Mansour (2011): “Is Internet Job Search Still Ineffective?,” IZA Discussion Papers 5955, Institute of Labor Economics (IZA).

- Lebrand, M. (2022): “Infrastructure and structural change in the lake Chad region,” Policy Research Working Paper Series WPS 9899, The World Bank.

- Lederman, D., and M. Zouaidi (2020): “Incidence of the Digital Economy and Frictional Unemployment : International Evidence,” Policy Research Working Paper Series 9170, The World Bank.

- Leuven, E., A. Akerman, and M. Mogstad (2018): “Information Frictions, Internet and the Relationship between Distance and Trade,” Memorandum 1/2018, Oslo University, Department of Economics.

- Li, H., and Z. Li (2013): “Road investments and inventory reduction: Firm level evidence from China,” _Journal of Urban Economics_ , 76, 43–52.

42
where Fj is a dummy variable equal to 1 if the fi rst child in family j is born in an auspicious year, and 0 otherwise; Xj is a vector of parental (and commune-level) characteristics. Table 5 panel A shows the results from the estimation of (3).<sup>9</sup> We fi nd that when parents are older, the fi rst child is relatively less likely to be born in an auspicious year for either boys or girls, or just for boys (columns (1) and (5)). This is consistent with the idea that older parents have less time ahead of them to choose the year of birth of their fi rst child. More interestingly, we fi nd that when mothers are more educated and fathers are less so, then the fi rst child is more likely to be born in an auspicious year. The effect is not signi fi cant for fathers’ education in the simplest speci fi cations (columns (2) and (6)) but holds when we control for family per-capita expenditure (columns (4) and (8)). While other mechanisms could explain these fi ndings, the results are consistent with the commonly-shared view in Vietnam that among parents, women are the ones who are superstitious. The signs of the coefficients on mother’s and father’s education then re fl ect the extent of relative bargaining power in the household when it comes to, among other things, fertility decision.<sup>10</sup> Surprisingly, no effect of real per capita expenditure is detected (columns (3) and (7)).

# 5.1.2 Astrology and Family Size

We push the analysis further by looking at how superstition correlates with family size. To test whether the channels discussed in section 4 can be detected in the data, we propose the following model: in which Hj is family j<sup>�</sup> s number of children, and Fj is the dummy which is equal to 1 if the fi rst child in family j is born in an auspicious year. Note that family will refer to the nuclear household (parents and children), while household refers to the more standard survey de fi nition. To test the fi rst channel described above, Fj will be the dummy variable which equals 1 if the fi rst child in family j is born in a year auspicious for either boys or girls (G<sup>or</sup> = 1), or alternatively lucky for boys<sup>�</sup> G<sup>b</sup> = 1<sup>�</sup> . For the second channel, Fj will be equal to 1 if and only if the fi rst child in family j, is born lucky, i.e. she is a girl born in a year auspicious for girls, or he is a boy born in a year auspicious for boys.

> 9 We also ran probit speci fi cations, without any noticeable differences.

> 10 An alternative explanation is the more educated women are more able to control fertility. However such argument could not at the same time explain the negative effect of fathers’ education.

14
Table 11: Disaster Preparedness and Management Data

|Country|Agency|Year|Annual<br>Equivalent<br>(Million$US)|
|---|---|---|---|
|Armenia|EmergencyManagement Administration|2006|7.0|
|Bangladesh|Food and Disaster Management Budget|Annual|500.0|
|China|Various agencies|2005|217.7|
|Indonesia|Contingencybudget for disaster response|Annual|125.8|
|India|CalamityRelief Fund|2000-2005|5.1|
|Japan|Budget for disaster risk reduction|Annual|34,000.0|
|Kazakhstan|For debris flows|1999|200.0|
|Republic of Korea|National EmergencyManagement Agency|Annual|300.0|
|Thailand|Department of Disaster Prevention and Mitigation|2003|25.6|
|Thailand|Department of Disaster Prevention and Mitigation|2006|63.9|
|Thailand|Department of Disaster Prevention and Mitigation|2005|46.0|
|Thailand|Department of Disaster Prevention and Mitigation|2004|32.4|
|Mongolia|Total Buget|2006|12.5|
|Malaysia|Disaster Relief Fund|Annual|15.5|
|Nepal|EmergencyFund|2006|0.015|
|Pakistan|Ten Year Perspective Development Plan|2001-2011|18.8|
|Philippines|National CalamityFund|2005|12.8|
|Russian Federation|Fund for prevention and elimination of emergency<br>situations|2003|687.4|
|Tajikistan|activities for disaster management|Annual|5.5|

Source: Asian Disaster Reduction Center, Country Reports <u>http://www.adrc.asia/</u> 33
Table 7: Expectations on future sales and loan application

|Dependent variable:||Loan ap|plication||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|E(Sales growth): Stable|-0.0343<br>[0.0352]|-0.00274<br>[0.0929]|||
|E(Sales growth): Stable _×_ Size||-0.00864<br>[0.0258]|||
|E(Sales growth): Positive|-0.0165<br>[0.0333]|0.145*<br>[0.0784]|||
|E(Sales growth): Positive _×_ Size||-0.0462**<br>[0.0210]|||
|E(Sales growth)|||-0.0393<br>[0.0531]|0.225*<br>[0.121]|
|E(Sales growth)_×_ Size||||-0.0745**<br>[0.0308]|
|Size|0.0319***<br>[0.00943]|0.0623***<br>[0.0181]|0.0328***<br>[0.00939]|0.0346***<br>[0.00941]|
|Model|Logit|Logit|Logit|Logit|
|Time FE|yes|yes|yes|yes|
|Geographic area FE|yes|yes|yes|yes|
|Additional controls|yes|yes|yes|yes|
|Observations|1263|1263|1263|1263|
|Pseudo R2|0.189|0.189|0.189|0.193|

_Notes:_ logit marginal effects. The dependent variable is a dummy measure for loan application in the last year. Because of data availability, expectations refer to sales growth for the following year. Other regressors are timed consistently with previous analyses. Unreported controls follow the specification in Table 4. Variables are defined in Table B4. Robust standard errors in brackets. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

firms. While in general increased competition between firms is expected to be welfare enhancing, this is not obvious when competition is between formal and informal firms. In this case, competition may indeed be detrimental to formal firms and to the overall economy (Friesen and Wacker, 2019). As noted by (Rozo and Winkler, 2021), a larger informal sector may end up hurting formal firms’ performance in several ways, including (unfair) cost competition and by reducing the quality of local public goods. Our results indicate another negative effect due to informal competition: by worsening (sales growth) expectations it reduces the demand for credit by the firm.

Taken together, these results suggest that a mechanism explaining the poor employment performance of formal firms in MENA countries is due to the negative effect of informal competition on access to finance, the latter being an important determinant of employment creation.

> we make sure that we are not capturing a spurious relationship.

> 17Interestingly, our results are in line with previous analyses of the relationship between competition from informal firms and firm size. La Porta and Shleifer (2014) highlights that firms perceive the threat from informal firms as a different obstacle to their business depending on firm characteristics. Gonzalez and Lamanna (2007) show that the formal firms most affected by informal competition are those that resemble informal firms the most. Our results confirm these predictions. By showing that the induced reduction in loan application due to lower expected sales growth is differentially larger for small firms, we provide evidence that the negative effect of informal competition varies depending on firm’s characteristics and it i is more negative for smaller firms, i.e. those more similar to informal firms.

17
Table D3: Differences in Absolute Score and Relative Ranking of Early Prenatal Care

||Absolu<br>|te Score<br> <br>|Relati<br>|ve Ranking<br>|
|---|---|---|---|---|
||(1)<br> OLS|<br>(2)<br>OLS‐IPW|(3)<br>OLS|(4)<br>OLS‐IPW|
|Difference (Treatment – Control)|0.20|0.13|0.10|0.14|
||(0.22)|(0.92)|(0.21)|(0.89)|
|Large Sample_p_‐value|0.38|0.89|0.65|0.88|
|Permutation_p_‐value|0.35|1.00|0.46|0.99|
|Observations|20|20|20|20|
|Control group mean|4.57|1.88|4.66|1.88|

Notes: Column (1) shows the differences between treatment and control clinics in the absolute score assigned to the practice of early prenatal care without any adjustment of sample loss. Column (2) adjusts for sample loss by Inverse Probability Weighting. Column (3) shows the differences between treatment and control clinics in the relative ranking assigned to early prenatal care among seven different practices. Column (4) is the same as Column (3) but adjusts for sample loss by Inverse Probability Weighting. (Wooldridge 2007) The coefficients are obtained from an OLS regression of each outcome against a treatment binary indicator. The third row shows the _P_ ‐value obtained from permuting the data using a random sample of 10,000 permutations. Standard errors are in parentheses. We lose one observation in each case because of missing data in each specific question.

58
Figure 5: Language requirements per occupation and share of at-risk jobs Panel a. % of jobs with income risks Panel b. % of jobs with health risks <!-- Start of picture text --> 910911 961 833 222<br>513 141 532 511 54 32101 226 220 221<br>325<br>9129418157527537219327517581814816523 812 821 0 813722818720512514723 510 832 322<br>910713911834712710521931930731524754515933520711313 741312522732740 742310540 140265 516 234<br>432 311422 331 3 2<br>962 611 541321<br>921 961830613832610612831531833532441421430343516351315511133431352142340325411342 2152523323143413313222502513301124103 3 130412216345111214241143121226210211134120240413220234243242322221 2 2602132263235221 261233230 264231 531 232 235 230<br>912 9 81713427 1 753 5 2834721932 750 81962718148165 8 1 2 721 8135 93 3524 121 73100 78 72659352 516 18367 12 415 10 35 3 020 1 2113723 4 83325 1 73510 4 34573241 12 740212430343351 52742 31 1431 33 50142411342401413114 2 2522153314 3 140 332 433011226502 13 41013 5 2 3 130421345 1 111 2 62411432 1 421134 1 413240200211243242122260213263 261233 264231<br>30 40 50 60 70 80 30 40 50 60 70 80<br>Language intensity per occupation (0-100) Language intensity per occupation (0-100)<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>Share of income-risk jobs Share of health-risk jobs<br>.2<br>.2<br>0<br>0<br><!-- End of picture text --> Source: Own elaboration based on the EU-LFS 2018 and the ad-hoc migration module of 2014. Note: The size of the circles represents the share of each occupation in the total employment in the EU.

Figure 6: Correlation between occupational choices of migrants in the EU in 2005 and 2018 <!-- Start of picture text --> 0 .1 .2 .3 .4 .5<br>% of migrants in each occupation in 2005<br>.3<br>.2<br>.1<br>0<br>% of recently arrived migrants in each occupation (2018)<br><!-- End of picture text --> Source: Own elaboration based on the EU-LFS 2005 and EU-LFS 2018.

Note: Share of migrant workers in each occupation for each NUTS2 division of residence. For 2018, migrants are restricted to those recently arrived in the country (less than ten years) so there is no overlap with the migrants in the country in 2005.

38
**Table B5. Simulations – FUNPI and complementary** **<u>policies (</u>** **_ε=0.25_** **<u>)</u>**

|**Policy**<br>|**(I) Base**<br>**Scenario -**<br>**no FUNPI**<br>|**(II) = (I) +**<br>**FUNPI**<br>|**(V) = (I) + FUNPI +**<br>**10% reduction in**<br>**exporters costs**<br>|**(VI) = (I) + FUNPI**<br>**+ 10% reduction**<br>**in traders costs**<br>|**(VII) = (I) + FUNPI**<br>**+ 10% reduction**<br>**in exporters and**<br>**traders costs**<br> <br>|**(VIII) = (I) + FUNPI**<br>**+ 48% reduction**<br>**in exporters and**<br>**traders costs**<br>|**(IX) = (I) + FUNPI**<br>**+ 2 efficent**<br>**exporters**<br> <br>|**(X) = (I) + FUNPI +**<br>**45 efficent**<br>**exporters**<br>|
|---|---|---|---|---|---|---|---|---|
|_International Price (USD/ton)_|799.88|799.88|799.88|799.88|799.88|799.88|799.88|799.88|
|_Trade taxes (USD/ton)_|93.50|199.88|199.88|199.88|199.88|199.88|199.88|199.88|
|_International Price net of taxes (USD/ton)_|706.38|600.00|600.00|600.00|600.00|600.00|600.00|600.00|
|_Intermediate Price (USD/ton)_|523.71|426.66|440.59|426.39|440.35|495.59|445.55|523.74|
|_Farm-gate Price (USD/ton)_|441.47|351.00|363.97|356.06|369.09|441.53|368.59|441.50|
|_Exporters + cashew processors profit (USD/ton)_|71.73|67.20|63.35|67.33|63.46|47.26|61.29|20.56|
|_of which, exporters (USD/ton)_|68.00|61.78|58.12|61.93|58.25|42.82|56.12|16.13|
|_of which, cashew processors (USD/ton)_|344.09|441.14|427.21|441.41|427.45|372.21|422.25|344.06|
|_HHI exporters and cashew processors_|480|510|487|511|487|388|557|208|
|_Traders profit (USD/ton)_|40.39|34.66|35.49|33.39|34.21|32.30|35.79|40.39|
|_HHI traders_|256|256|256|256|256|256|256|256|
|_Quantity (tons)_|148261|140000|141276|140502|141770|148266|141722|148263|

**Table B6. Simulations – FUNPI with cashew processing or subsidies to farmer inputs (** **_ε=0.25_** **<u>)</u>**

|**Policy**|**(I) Base**<br>**Scenario -**<br>**no FUNPI**|**(II) = (I) +**<br>**FUNPI**|**(XIV) = (I) + FUNPI**<br>**+ 4 cashew**<br>**processors (200**<br>**ton each)**<br>|**(XV) = (I) + FUNPI**<br>**+ 1 cashew**<br>**processor (20,000**<br>**ton)**|**(XVI) = (I) + FUNPI**<br>**+ 10% reduction**<br>**in cost of farmer**<br>**services**|
|---|---|---|---|---|---|
|_International Price (USD/ton)_|799.88|799.88|799.88|799.88|799.88|
|_Trade taxes (USD/ton)_|93.50|199.88|199.88|199.88|199.88|
|_International Price net of taxes (USD/ton)_|706.38|600.00|600.00|600.00|600.00|
|_Intermediate Price (USD/ton)_|523.71|426.66|427.18|439.83|424.93|
|_Farm-gate Price (USD/ton)_|441.47|351.00|351.49|363.26|348.70|
|_Exporters + cashew processors profit (USD/ton)_|71.73|67.20|68.20|88.82|68.00|
|_of which, exporters (USD/ton)_|68.00|61.78|61.47|54.05|62.70|
|_of which, cashew processors (USD/ton)_|344.09|441.14|440.62|427.97|442.87|
|_HHI exporters and cashew processors_|480|510|505|586|511|
|_Traders profit (USD/ton)_|40.39|34.66|34.69|35.45|34.90|
|_HHI traders_|256|256|256|256|256|
|_Quantity (tons)_|148261|140000|140049|141207|143264|

41
||**Number of**<br>**people**<br>**(millions)**|**Percent of**<br>**population**<br>**(over 15)**|**Trends**|
|---|---|---|---|
|**Americas**|9.08|1.50|Mostly stable|
|- United States|5.79|2.60|Declining|
|- Mexico|0.33|0.50|Stabilizing|
|- Argentina|0.51|1.90|Increasing|
|- Colombia|0.34|1.20|Some increase|
|- Peru|0.17|1.00|Some increase|
|- Bolivia|0.05|0.90|Stable|
|- Other|1.89|0.79|Mixed|
|**Europe**|3.71|0.57|Mostly increasing|
|- United Kingdom|0.95|2.00|Increasing|
|- Other West Europe|2.48|0.93|Mostly increasing|
|- East Europe|0.29|0.09|Mostly increasing|
|**Oceania**|0.23|1.03|Stable|
|**Africa**|0.91|0.20|Stable or increasing|
|**Asia**|0.15|0.01|Stable or decreasing|
|**Global**|14.08|0.33|Stable to increasing|

Source: UNODC (2003, pp. 129-135, 337); U.N. World Population Prospects 2002 Revision

# **Table 2: Annual Prevalence Estimates of Cocaine Abuse, 2000-2001**

## **PRODUCING COUNTRIES**

A small number of nations account for the bulk of the production of coca and opium. According to official estimates by the UNODC (2003), three countries, Bolivia, Colombia, and Peru, account for the entirety of commercial coca production. There are reports of small amounts of coca being produced in Brazil and Venezuela, constituting a miniscule portion of world production. Table 3 displays the global production of dry leaf coca for various years between 1990 and 2004.<sup>10</sup> As shown, most coca is currently produced in Colombia, although Peru was 10 A kilogram of cocaine requires approximately 400 kilograms of leaf as input. The precise figure varies, depending on the alkaloid content. There is variation among regions within the Andes; however 5
The Drivers and Impacts of Water Infrastructure Reliability – A Global Analysis of Manufacturing Firms<sup>1</sup> Asif Islam<sup>†</sup> and Marie Hyland<sup>††</sup> † Asif Islam, Enterprise Analysis Unit, Developing Economics Indicators Group, The World Bank. Email: aislam@worldbank.org †† Marie Hyland, Enterprise Analysis Unit, Developing Economics Indicators Group, The World Bank. Email: mhyland@worldbank.org JEL codes: H54, H41, Q25 Keywords: Water infrastructure, drought, firm productivity, firm-level analysis to the vagaries of rainfall.

> 1 The study was developed as a background paper for the World Bank Water Global Practice flagship report titled “Uncharted Waters: The New Economics of Water Scarcity and Variability.” The authors are indebted to Richard Damania, Sébastien Desbureaux, Aude-Sophie Rodella, Jason Russ, and Esha Zaveri for invaluable comments. The findings of the report were also presented at an Oxford University Workshop and the authors thank the participants for their comments - Felix Pretis, Jacquelyn Pless, Michael Gilmont, Emily Barbour, Johanna Koehler, and Jesper Svensson. All remaining errors are the responsibility of the authors.