rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1.  Imports from EU: top 20 sectors by value**

|Sector (ISIC-3 digits)|Imports from EU<br>[million US$]|Total imports<br>[million US$]|EU<br>Share|
|---|---|---|---|
|241 - Basic Chemicals|789|2,394|33.0|
|291 -Generalpurposemachinery|783|1,529|51.2|
|292 -Special PurposeMachinery|692|2,148|32.2|
|341 - Motor Vehicles|593|1,527|38.8|
|323 - TV & radio receivers, sound or video recording apparatus|513|644|79.7|
|271 - BasicIronand Steel|486|1,315|37.0|
|152 - DairyProducts|475|658|72.2|
|242 -OtherChemical Products|456|1,110|41.1|
|311-Electric Motors, Generators and Transformers|362|777|46.6|
|011 -Growing ofcrops andhorticulture|343|1,436|23.9|
|151 - Production, Processing and Preservation of Meat, Fish, Fruit,<br>Vegetables, Oils andFats|330|952|34.7|
|272-Basic Precious and Non-ferrous Metals|290|577|50.3|
|232 - Refined petroleumproducts|260|560|46.4|
|322 - TV&radio transmitters and apparatusfor line telephony|232|505|45.9|
|154-Other Food Products|229|430|53.3|
|351 - Building andrepairing ofships and boats|208|372|55.9|
|210- PaperandPaper Products|191|522|36.6|
|269- Non-metallicMineral Productsn.e.c.|157|540|29.1|
|300-office, accounting and computingmachine|120|260|46.2|
|281-Structural Metal Products, Tanks, Reservoirs etc|117|341|34.3|
|Totalallsectors|8,579|22,653|37.9|

Source: Computed by authors based on TRIST database For many of the sectors shown in Table 1, EU suppliers have a dominant position. There are also a number of other sectors where imports are smaller but the EU share is high, such as, furniture. Preferential liberalization in sectors in which the EU has a substantial initial market share may lead to more trade creation than trade diversion, if the high initial share indicates that the EU is a competitive/efficient source in those sectors. However, in the case of some food products, such as dairy, it could reflect EU production and export subsidies. On the other hand, if there are only a few firms behind such a dominant position then the tariff reduction may not be passed on to consumers but the rents may be appropriated by the EU companies. Categories with significant import values and important EU market shares are television and radio equipment (US$512 million from EU accounting for almost 80 percent of total imports), dairy products (US$475 6
**<u>Table 6: Millet and sorghum income in Mali for different household groups, 2006</u>**

||Percentage o|f households re<br>income|ceiving|Average i|ncome for all ho|useholds|Average income|for households wit|hpositive income|
|---|---|---|---|---|---|---|---|---|---|
||Auto|||Auto|||Auto|||
||consumption|Sales|Total|consumption|Sales|Total|consumption|Sales|Total|
|**All**|52.90%|13.97%|53.80%|78,094.34|14,987.44|93,081.79|147,703.45|107,315.80|173,018.60|
|**Residence area**||||||||||
|Urban|26.90%|3.28%|27.42%|21,316.04|3,574.41|24,890.45|79,230.87|108,924.00|90,777.55|
|Rural|68.40%|20.34%|69.54%|111,982.84|21,799.39|133,782.20|163,784.73|107,161.00|192,372.00|
|**Region**||||||||||
|Kayes|67.70%|20.57%|68.14%|122,121.98|24,916.44|147,038.40|180,406.70|121,154.60|215,799.40|
|Koulikoro|73.70%|8.84%|74.76%|134,332.16|8,392.64|142,724.80|182,160.38|94,992.07|190,899.90|
|Sikasso|64.30%|13.47%|66.21%|91,588.62|12,829.96|104,418.60|142,516.41|95,216.78|157,701.40|
|Ségou|55.00%|23.48%|56.27%|80,295.56|30,793.69|111,089.30|145,887.83|131,140.40|197,424.10|
|Mopti|51.70%|17.49%|51.84%|74,001.27|13,702.06|87,703.33|143,248.41|78,337.38|169,165.00|
|Tombouctou|39.70%|12.92%|41.48%|24,439.19|11,937.62|36,376.80|61,624.04|92,371.10|87,696.58|
|Gao|26.30%|5.04%|27.08%|14,043.52|3,226.20|17,269.72|53,335.23|63,953.33|63,767.09|
|Kidal|5.70%|0.00%|5.67%|1,416.54|0.00|1,416.54|24,992.43|0.00|24,992.43|
|Bamako|16.40%|0.59%|16.51%|7,722.35|909.88|8,632.23|47,105.36|154,417.50|52,286.93|
|**Quintile**||||||||||
|Q1|73.40%|24.76%|76.39%|91,306.84|22,964.82|114,271.70|124,468.88|92,734.27|149,582.40|
|Q2|70.50%|20.83%|71.35%|105,180.62|17,906.77|123,087.40|149,173.31|85,954.71|172,511.20|
|Q3|64.80%|16.04%|65.36%|111,379.59|16,461.86|127,841.50|172,013.97|102,640.10|195,584.30|
|Q4|48.00%|12.41%|48.51%|79,811.85|15,621.36|95,433.21|166,220.25|125,917.10|196,730.40|
|Q5|26.90%|3.90%|27.41%|29,760.69|7,485.61|37,246.30|110,452.22|191,796.90|135,900.90|

Source: Authors’ estimation using ELIM 2006.
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Further investment will help Cameroon diversify its energy portfolio and enhance the availability of reliable power in the country. At present, 70 to 80 percent of Cameroon’s power is derived from hydropower sources, with the remainder from conventional thermal. Cameroon’s first independent power producing agreement (IPP) will add 216 MW in power generation and trigger the development of Cameroon’s gas reserves, as yet unexploited. Also, Cameroon will further increase its generation capacity when the new Lom Pangar plant becomes fully operational.

# **Challenges**

In spite of recent increases in generation capacity, power generation continues to be expensive in Cameroon. At $0.17 per kilowatt-hour, power costs in Cameroon, are among the highest in Africa, inviting comparison with costs from small-scale thermal systems (figure 19). Two factors explain the high costs. First, hydropower resources are seasonal and subject to fluctuation. Second, prices are high for the diesel on which the country relies for back-up generation in the dry season (figure 20). Despite Cameroon’s oil resources, the country lacks a refinery, and prices reflect that lack. The costs of transporting oil along Central Africa’s corridors are high ($0.13 per tonne-kilometer).

**Figure 19.Costs of power production in Sub-Saharan Africa**

<!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>US cents per KWh<br>Mali Burkina Faso Congo Benin Cape Verde Cameroon Rwanda Niger Angola Madagascar Kenya Tanzania Botswana Chad Ghana Senegal Namibia Cote d'Ivoire Lesotho Uganda Nigeria Malawi Mozambique Ethiopia DRC Zambia South Africa<br><!-- End of picture text --> _Source_ : Briceño-Garmendia and Shkaratan 2010 **.** The average power tariff in Cameroon, at $0.10 per kilowatt-hour, fails to recover production costs. Whereas tariffs are in the middle of the price distribution in Sub-Saharan Africa (figure 21), they mask generous cross-subsidies to the aluminum smelter, Alucam. The low- and medium-voltage consumer paid between $0.11 and $0.14 cents in 2009, whereas Alucam benefitted from a tariff cap of 7 CFA francs per kilowatt-hour (less than $0.02) up to 2009, until recent revisions took effect (Husband, McMahon, and van der Veen 2009) (box 2).

32
Martin, Philippe, and Helene Rey (2004) Globalization in Emerging Markets: With and Without Crash, _American Economic Review_ Vol. 96(5), 1631-1651.

McKinnon, Ronald and Huw Pill, (1996). “Credible Liberalizations and International Capital Flows: The “Overborrowing Syndrome,” in T. Ito and A. Krueger, _Financial Deregulation and Integration in East Asia_ , NBER-EASE Volume 5 (Chicago: University of Chicago Press), 7-50.

Mendoza, Enrique and Marcos Terrones (2012) “An Anatomy of Credit Booms and Their Demise”, NBER Working Paper 18379, September.

Meyer, Josefin, Carmen M. Reinhart, and Christoph Trebesch (2019). “Sovereign Bonds Since Waterloo” NBER Working Paper 25543, February.

Pazarbasioglu, Ceyla and Carmen M. Reinhart, (2021). “Key to Resolving COVID’s Global Debt Crunch: Transparency,” Bloomberg, March 7.

Obstfeld, Maurice and Alan M. Taylor. (2004) Global capital markets: integration, crisis, and growth. (Cambridge and New York: Cambridge University Press.) Pitchford, R., and Wright, M., (2012) “Holdouts in Sovereign Debt Restructuring: A Theory of Negotiation in a Weak Contractual Environment,” _The Review of Economic Studies_ , Vol. 79(2), April, 812-837.

Reinhart, Carmen M., (2018). “International Financial Crises,” Nobel Symposium on “Money and Banking” May 26-28, 2018, Stockholm.

Reinhart, Carmen M., and Kenneth S. Rogoff, (2004). “The Modern History of Exchange Rate Arrangements,” _Quarterly Journal of Economics,_ Vol. 119(1), February, 1-48.

Reinhart, Carmen. M., Kenneth S. Rogoff, and Miguel A. Savastano, (2003). “Debt Intolerance,” _Brookings Papers for Economic Activity,_ Vol.1, Spring, 1-74.

Reinhart, Carmen M., and Kenneth S. Rogoff, (2013). “Banking Crises: An Equal Opportunity Menace,” _Journal of Banking & Finance_ Vol. 37, November, 4557–4573.

Reinhart, Carmen M., and Kenneth S. Rogoff, (2009). _This Time It’s Different: Eight Centuries of Financial Folly._ (Princeton: Princeton University Press).

Reinhart, Carmen M., and Kenneth S. Rogoff, (2011). “From Financial Crash to Debt Crisis,” _American Economic Review_ , Vol. 101, No. 5, August, 1676-1706.

Reinhart, Carmen M., and Kenneth S. Rogoff, (2011). “The Forgotten History of Domestic Debt,” _Economic Journal_ , Vol. 121, Issue 552, May 2011, 319-350 _._ Reinhart, Carmen M., and Kenneth S. Rogoff, (2014) Recovery from Financial Crises: Evidence from 100 Episodes, American Economic Review, Vol. 104(5), May, 50-55.

Reinhart, Carmen M. and Belen Sbrancia (2015) The Liquidation of Government Debt, Economic Policy, Vol. 30(82), March, 291-333.

36
Table 4. Debt Forgiveness by Finance Commission

|Finance Commission|Year of<br>report|Rs.<br>billion|GDP<br>Rs.billion|% of<br>GDP|
|---|---|---|---|---|
|Sixth|1974|20|667|2.95|
|Seventh|1979|22|1025|2.11|
|Eighth|1984|23|2223|1.03|
|Ninth|1989|10|4357|0.22|
|Tenth|1995|5|10672|0.05|
|Eleventh|2000|34|20050|0.17|
|Twelfth|2005|535|31494|1.70|

Source: McCarten (2001) and Report of Thirteenth Finance Commission, (2009) To implement the recommendations of the Twelfth FC, the Debt Consolidation and Relief Facility was introduced during 2005–06, which provided debt relief through consolidation, rescheduling repayments for a fresh term of 20 years, and lowering of the interest rate on the debt to 7.5 percent. All states were eligible to obtain relief from the year they enacted fiscal responsibility legislation. This amounted to Rs 187 billion in terms of lower interest payments, and Rs 211 billion in terms of lower repayments, totaling Rs 398 billion (US$8.9 billion<sup>35</sup> ) during 2005–06 to 2009–10. In addition, repayments due during 2005–10 on central loans contracted up to March 31, 2004, (after consolidation and rescheduling) were eligible for write-off subject to the reduction in revenue deficits. The debt write-off would also be subject to containment of the fiscal deficit to the 2004–05 level. Subject to these provisions, if the revenue deficit were brought down to zero by 2008–09, the entire repayments during 2005–10 would be written off.

Carrying forward the momentum to support states toward urgent fiscal correction, the Thirteenth FC worked out a differentiated fiscal adjustment road map (described in the previous section), with a state-specific approach based on past fiscal performance (using 2007–08 as the base year) for different groups of states. A key requirement is that all states eliminate their revenue deficits (the deficit on current balance), but they can have a fiscal deficit of 3 percent of

> 35 The average of the quarterly average exchange rates from the 2nd quarter of 2005 to the 1st quarter of 2010 is used (from the IFS), which is Rs 44.64 = US$1.0.

23
| POLICY RESEARCH WORKING PAPER 2069

# Summary findings

Gonzalez and Maloney derive a methodology for analyzing logit models in a rotating panel context. They then apply the technique to test two theories of why and when salaried workers enter the informal self-employed sector.

In the traditional view, workers fired from formal jobs queue in the informal sector to reenter the formal sector. Gonzalez and Maloney argue that for many, selfemployment is a desirable goal, but that credit constraints often dictate that they work in the formal sector until enough start-up capital is accumulated.

They model the decision to move as a stopped Markov process in which, in each period, the worker compares accumulated savings with the target level for switching sectors dictated by the forecasted stream of discounted utility arising from employing labor and cal ital in each sector.

They test and find support for the model using the new logit methodology and rotating panel data from Mexico.

- This paper a product of the Poverty Reduction and Economic Management Sector Unit, Latin America and Caribbean - Region is part of a larger effort in the region to understand the functioning of developing country labor markets. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Tania Gomez, room 18-102, telephone 202-473-2127, fax 202-522-2119, Internet address tgomez@worldbank.org. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/html/dec/Publications/Workpapers/ home.html. The authors may be contacted at wmaloney@worldbank.org or paroca@socompa.cecun.uncn.cl. February 1999 (19 pages).

_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 view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
# **Appendix C: Robustness Tests**

Table C.1. Excluding Border Departments

||_Likeliho_|_od of having chil_|_dren of_||
|---|---|---|---|---|
||(1)<br>0years of age|(2)<br>1year of age|(3)<br>2years of age|(4)<br>3years of age|
|PEP [=1]|-0.034***|-0.072***|-0.015|-0.002|
||(0.011)|(0.013)|(0.010)|(0.002)|
|Observations|3,588|3,588|3,588|3,588|
|Observations by wave|1,196|1,196|1,196|1,196|
|Wave FE|Yes|Yes|Yes|Yes|
|HH FE|Yes|Yes|Yes|Yes|
|Department_×_wave|Yes|Yes|Yes|Yes|
|Geographic Sampling<br>|Yes|Yes|Yes|Yes|
|Pre-migration controls_×_wave|Yes|Yes|Yes|Yes|

_Notes:_ The table presents the estimates of the specification described in equation (1). The analysis excludes migrants in the departments bordering Venezuela. Department corresponds to the five departments in which the sample was collected and geographic sampling corresponds to the four geographic levels at which the sample is representative, including three main cities and a fourth group that accounts for nine smaller urban centers with prevalent migration from Venezuela. Premigration control variables include: (i) individual controls for the head of household (gender, age, and education); (ii) labor history for the head of household (probability of being employed, type of job, probability of having a written contract, and the time gap between the last job and the migration episode); (iii) household characteristics (number of children, household size, access to public services, owning dwelling, and having a smartphone); and (iv) networks prior to migration episode (had family and friends in Colombia, knew of job opportunities before migrating, and migrated for health-related reasons). Standard errors clustered at the household level in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

31
**Table 6: Conditional Transitions  for Population (21-25 years old) by Key Education Thresholds and Selected Socio-Economic Quintiles**

|**by Ke**|**y Educ**<br>**Pri**|**ation Thres**<br>**mary**|**holds and**<br>**Seco**|**Selected S**<br>**ndary**|**ocio-Econom**<br>**Post-Sec.**|**ic Quintiles**<br>**Memo**<br>|
|---|---|---|---|---|---|---|
|||||||**Unconditional**|
||**Start**|**Finish**<sup>**2**</sup>|**Start**<sup>**2**</sup>|**Finish**<sup>**2**</sup>|**Start**<sup>**2**</sup>|**Post-Secondary**|
||**A**|**B**|**C**|**D**|**E**|**F=A*B*C*D*E**|
|**Costa Rica**|||||||
|Lowest|94%|78%|**63%**|39%|51%|9%|
|Richest|100%|99%|**94%**|**82%**|83%|**63%**|
|Total|98%|**91%**|**76%**|60%|71%|29%|
|**Panama**|||||||
|Lowest|93%|87%|**63%**|44%|38%|8%|
|Richest|100%|99%|97%|87%|72%|61%|
|Total|98%|96%|**86%**|68%|58%|32%|
|**Guatemala**|||||||
|Lowest|**69%**|**48%**|**53%**|32%|29%|2%|
|Richest|98%|89%|86%|61%|50%|23%|
|Total|86%|69%|69%|47%|39%|7%|
|**El Salvador**|||||||
|Lowest|86%|59%|78%|34%|33%|5%|
|Richest|98%|94%|96%|77%|61%|41%|
|Total|93%|78%|89%|53%|44%|15%|
|**Honduras**|||||||
|Lowest|**87%**|**63%**|**37%**|**37%**|**36%**|3%|
|Richest|99%|94%|**83%**|70%|60%|32%|
|Total|94%|82%|**64%**|58%|48%|14%|
|**Nicaragua**|||||||
|Lowest|**86%**|**79%**|**84%**|**50%**|49%|**14%**|
|Richest|97%|**96%**|95%|**77%**|72%|**49%**|
|Total|**95%**|**89%**|**88%**|**63%**|60%|**28%**|
|Note: numbers in<br>percent.<br>2Conditional on a|bold (green<br>chievement|) denote an incre<br>of theprevious th|ase of 5 percent o<br>reshold.|ver 2001 level, h|ighlighted (red) numb|ers indicate a decline in 5|
|Source: Authors’|calculations|based on several h|ousehold survey|s circa 2001 and|circa 2009||

28
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45
2007/08, Peru during 2005- 2006, the US during 2007-2009, and Vietnam during 2006- 2008 in Table 3.<sup>27</sup> Estimates for earlier survey rounds for the last three countries provided in Appendix 2, Table 2.2 offer qualitatively similar results.

We provide two ways to evaluate the goodness-of-fit for estimation results. First, we consider the precision of the synthetic panel point estimates by enumerating the number of times they fall within the 95 percent confidence interval around those from the actual panels. A more demanding test is to consider a similar statistics on a narrower band of one standard error around the actual panel estimates. Second, we consider the efficiency of the synthetic panel estimates by looking at the share of the overlap between the 95 percent confidence intervals of the synthetic panel estimates and the true estimates over the former’s 95 percent confidence interval. This test focuses on the proportional contribution of its variance in the total mean squared error and scores the accuracy of each estimate on a 100 percent scale.<sup>28</sup> Our estimation results using actual panel data and synthetic panel data are respectively displayed under the columns “Actual Panel” and “Synthetic Panel” in Table 3, and the goodness-of-fit results are shown in Table 4.<sup>29</sup> Results appear very encouraging with the synthetic panel point estimates being close to the true point estimates and lying within the 95 percent confidence intervals around the true estimates for almost all the cases (i.e., 19 out of 20). In fact, more than half of the synthetic panel point estimates fall within one standard error of the actual panel estimates (i.e., 11 out of 20; not shown). Furthermore, the difference between the true rates and our estimates seems negligible for certain mobility categories; for

> 27 Unless otherwise noted, we use data in the second survey round (xi2)—as the base year—for predictions. Estimates corresponding to those in Table 2 but use data in the first survey round are provided in Appendix 2, Table 2.3. Weighted regressions provide qualitatively similar but somewhat less accurate results, thus we use unweighted regressions.

> 28 See also Dorfman (2011) for a related test for the bias of the point estimate in the context of small area estimation.

> 29 Estimated parameters for equations (1) and (2) are provided in Appendix 2, Table 2.1.

31
- 11 -

**Figure 9. Historical monthly precipitation data: Selected Districts**

<!-- Start of picture text --> 1,800 1,800<br>District: Banke District: Makwanpur<br>1,600 1,600<br>1,400 1,400<br>1,200 1,200<br>1,000 1,000<br>800 800<br>600 600<br>400 400<br>200 200<br>0 0<br>1,800<br>District: Siraha<br>1,600<br>1,400<br>1,200<br>1,000<br>800<br>600<br>400<br>200<br>0<br>Source: Author’s calculation based on data from the Ministry of Environment, Nepal.<br>Figure 10. Actual precipitation and AR-predicted expectations during the original work schedule<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br>0 1000 2000 3000 4000 5000<br>Rain eAR<br>Raina<br>Monthly precipitation (mm) Monthly precipitation (mm)<br>Jan-00 Jan-05 Jan-00 Jan-05<br>Monthly precipitation (mm)<br>Jan-00 Jan-05<br>Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09<br>Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09<br><!-- End of picture text --> Source: Author’s calculation based on data from the Ministry of Environment, Nepal.

Finally, in Equations (1) and (2), _X_ controls for contract-specific heterogeneity, such as length of roads ( _length_ ) and amounts of materials used ( _gravel_ , _bitumen_ , _cement_ , etc.). To