rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Impacts on Total Cash Savings, Debt, and Savings Net Debt - Heterogeneity by Baseline Overdraft Behavior

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||||Savings|||De|bt||
||Savings<br>at NVRB<br>(Admin)|Savings<br>at NVRB<br>(Survey)|Other<br>Formal<br>Savings|Informal<br>Savings|Total<br>Savings|Took Any<br>New Debt|Total<br>Debt|Savings<br>Net Debt|
|Treatment _×_ During _×_ Light Overdrafter|322.4<sup>_∗∗∗_</sup>|326.6<sup>_∗∗∗_</sup>|341.7<sup>_∗∗_</sup>|-81.2<sup>_∗∗_</sup>|573.8<sup>_∗∗∗_</sup>|0.043|72.5|621.8|
||(76.8)|(71.4)|(166.5)|(36.4)|(200.2)|(0.028)|(369.9)|(471.3)|
|Treatment _×_ After _×_ Light Overdrafter|101.2|54.7|567.3<sup>_∗∗∗_</sup>|-8.97|596.4<sup>_∗∗_</sup>|0.021|-76.5|794.4<sup>_∗_</sup>|
||(97.4)|(85.7)|(205.1)|(63.9)|(242.9)|(0.036)|(338.1)|(459.1)|
|Treatment _×_ During _×_ Heavy Overdrafter|284.2<sup>_∗∗∗_</sup>|251.2<sup>_∗∗∗_</sup>|-428.6<sup>_∗∗∗_</sup>|38.7|-136.6|0.11<sup>_∗∗_</sup>|492.6|-776.1|
||(43.0)|(46.8)|(158.6)|(26.1)|(185.8)|(0.050)|(426.0)|(472.2)|
|Treatment _×_ After _×_ Heavy Overdrafter|81.0<sup>_∗∗_</sup>|-4.04|-312.0<sup>_∗_</sup>|15.3|-297.4|0.069|463.3|-771.4|
||(39.4)|(51.8)|(186.9)|(65.9)|(221.5)|(0.051)|(418.0)|(499.4)|
|P-value: Heavy=Light, During|0.665|0.382|0.001<sup>_∗∗∗_</sup>|0.009<sup>_∗∗∗_</sup>|0.009<sup>_∗∗∗_</sup>|0.253|0.456|0.031<sup>_∗∗_</sup>|
|P-value: Heavy=Light, After|0.847|0.561|0.002<sup>_∗∗∗_</sup>|0.793|0.006<sup>_∗∗∗_</sup>|0.437|0.316|0.017<sup>_∗∗_</sup>|
|P-value: Treat=0, Light|0.000<sup>_∗∗∗_</sup>|0.000<sup>_∗∗∗_</sup>|0.021<sup>_∗∗_</sup>|0.079<sup>_∗_</sup>|0.011<sup>_∗∗_</sup>|0.308|0.892|0.221|
|P-value: Treat=0, Heavy|0.000<sup>_∗∗∗_</sup>|0.000<sup>_∗∗∗_</sup>|0.026<sup>_∗∗_</sup>|0.333|0.406|0.060<sup>_∗_</sup>|0.440|0.195|
|Control Mean (During, Light)|292.1|293.2|780.2|234.1|1311.7|0.081|2552.9|-1257.1|
|Control Mean (After, Light)|336.7|349.4|731.2|246.9|1328.1|0.088|2322.0|-989.5|
|Control Mean (During, Heavy)|42.0|137.9|1174.4|113.9|1442.0|0.29|3590.2|-2114.5|
|Control Mean (After, Heavy)|65.9|165.3|1105.9|225.7|1518.8|0.20|2943.2|-1496.9|
|N|1528|1499|1518|1527|1488|1528|1519|1481|
|Baseline Control?|Yes|Yes|Yes|Yes|Yes|No|Yes|Yes|

Notes: All regressions include strata fixed effects, time period fixed effects, interactions between overdrafter status and time period, and the baseline outcome of the dependent variable whenever possible. Robust standard errors clustered at the individual level in parentheses. Administrative NVRB savings is the balance on the relevant survey day, with negative balances coded to zero. All variables in Ghanain Cedis top coded at the 99th percentile. In 2013 GHS 2.15 _≈_ USD 1. * p _≤_ 0 _._ 10, ** p _≤_ 0 _._ 05, *** p _≤_ 0 _._ 10.
Laeven, L., & Valencia, F. (2013). Systemic Banking Crises Database: An Update. IMF Economic Review, 61, 225–270. https://doi.org/10.5089/9781475505054.001 Ong, L. L. (2014). A guide to IMF stress testing. International Monetary Fund. https://doi.org/10.5089/9781484368589.071 Ong, L. L., Maino, R., & Duma, N. (2010). Into the great unknown: Stress testing with weak data. IMF Working Papers (WP/10/282). International Monetary Fund. https://doi.org/10.5089/9781455210893.001 Reinhart, C. M. (2021a). From health crisis to financial distress. World Bank Policy Research Working Paper (No. 9616). The World Bank. http://hdl.handle.net/10986/35411 Reinhart, C. M. (2021b). The quiet financial crisis. Project Syndicate. https://www.projectsyndicate.org/onpoint/covid19-balance-sheets-quiet-financial-crisis-by-carmen-reinhart-202101?a_la=english&a_d=5ff70aa06140ad371c66e00b&a_m=&a_a=click&a_s=&a_p=%2Fcolumnist%2Fca rmen-reinhart&a_li=covid19-balance-sheets-quiet-financial Reinhart, C. M., & Rogoff, K. S. (2013). Banking crises: An equal opportunity menace. Journal of Banking and Finance, 37(11), 4557–4573. https://doi.org/10.1016/j.jbankfin.2013.03.005 Yuuki, I., Will, K., Ulf, L., & Christian, S. (2021). Covid-19 and bank resilience: where do we. BIS Bulletin (No. 44). Bank for International Settlements. https://www.bis.org/publ/bisbull44.pdf World Bank (2021). Global Economic Prospects. January 2021. World Bank. <u>http://hdl.handle.net/10986/34710</u> 14
**Figure 5: Energy income elasticity estimates using controls and robustness checks**

<!-- Start of picture text --> Elasticity<br>1.6<br>1.2<br>0.8<br>0.4<br>0.0<br>Controls Robustness checks<br>Source : Authors’ calculations based on model’s parameter estimates.<br>Notes : Diamonds denote elasticities at $ 9,900 per capita income (sample median). The lower and upper ends<br>denote elasticities at $ 28,000 and $ 9,900 per capita income, respectively. Definitions of Controls and Robustness<br>checks can be found in Table C1 (Appendix C).<br>Figure 6: Metals income elasticity estimates using controls and robustness checks<br>Elasticity<br>1.6<br>1.2<br>0.8<br>0.4<br>0.0<br>Controls Robustness checks<br>Base specification Investment share Urbanization Population density Trend Price/RER All controls 1965‐97 1998‐2017 Ex‐China Ex‐US Ex‐G7 Ex‐LIC All energy AIC<br>Base specification Investment share Urbanization Population density Trend Price/RER All controls 1965‐97 1998‐2017 Ex‐China Ex‐US Ex‐G7 Ex‐LIC Weighted Limited AIC<br><!-- End of picture text -->

**Source** : Authors’ calculations based on model’s parameter estimates. **Notes** : Diamonds denote elasticities at $ 9,900 per capita income (sample median). The lower and upper ends denote elasticities at $ 28,000 and $ 9,900 per capita income, respectively. Definitions of Controls and Robustness checks can be found in Table C1 (Appendix C).

**Figure 6: Metals income elasticity estimates using controls and robustness checks**

**Source** : Authors’ calculations based on model’s parameter estimates. **Notes** : Diamonds denote elasticities at $ 9,900 per capita income (sample median). The lower and upper ends denote elasticities at $ 28,000 and $ 9,900 per capita income, respectively. Definitions of Controls and Robustness checks can be found in Table C2 (Appendix C).

_— 23 —_
khandsari to plantation white sugar and finer khandsari sugars. As noted previously, the quality of khandsari has improved to the extent that a growing proportion is difficult to distinguish from plantation white sugar, and some of it sells at about the same price or at only a small discount from mill sugar.

# **_Exports and_** _imports of_ **_sugar_**

India started exporting sugar in 1957. It became a member of the International Sugar Agreement (ISA) in 1961 and joined the Commonwealth Sugar Agreement in 1965. As a member of ISA it obtained a preferential quota for the US market. Its export quotas under these agreements are shown in Table 5.

Table 5: **Preferential quotas of India with major importers**

|Agreements|Year of|Original|Year ended or|
|---|---|---|---|
||agreement|quota|present quota|
|Statutory quota allocations|1962|175,000 MT|1975|
|under US Sugar Act|1982|20,000 MT|10,571 MT|
|Commonwealth /EEC-ACP Sugar|1965|||
|Agreement - Overall Agreement Quota||100,000 MT||
|- Negotiated Price Quota|1975|25,000 MT|10,000 MT|
|International Sugar Agreement|1968|250,000 MT|1977|
||1977|824,000 MT|1982|
||1983|700,400 MT|1987|

Source and Notes: Extreme Focus Group (1992).

The Commonwealth Sugar Agreement was replaced by the EEC-ACP Sugar Agreement in 1975. ISA had no economic provisions after 1987.

The ISA quotas were the maxima for free market exports as part of ISA's attempts to stabilize the world free market price, but gave no price advantage. They had no real impact on Indian exports and were abandoned after 1987. In most years the US and CommonwealthlEEC-ACP quotas were at higher prices than prevailing free market prices, but they were reduced if a country did not fulfill its commitments, and once reduced they were not revised upwards. In an attempt to meet these commitments, India made elaborate arrangements to cross subsidize exports and in this way continued exporting even when domestic prices exceeded the export quota prices. Despite this it did not export the required amounts and its preferential quotas have declined over time to almost negligible levels.

Data S9 gives the origins of Indian sugar imports and the destinations of Indian sugar exports for the period 1985-93. Between 1985 and 1990 there were substantial imports from a variety of countries including the EU, the USA, Brazil, China, Cuba and others. At the same time as sugar was imported from the EU and the US, there were small Indian exports to these same 19
53

# Table III.4

Source of Contraceptives, by Age, for Selected Countries

||||Source of Co|ntracepti|vesa|
|---|---|---|---|---|---|
|Country|Contracepti<br>Prevalence|ve<br>Government<br>(Percent)|conuercial<br> <br>(Percent)|b NGO<br>(Percent|OtherC<br>) (Percent)|
|Colombia<br>(1986)||||||
|All Women|40.8|34.0|42.5|22.1|1.4|
|15-24|32.8|25.1|54.3|19.6|0.9|
|25-34|50.3|29.0|48.1|21.7|1.2|
|35+|36.9|45.9|28.1|24.1|1.9|
|Egypt (1984)||||||
|All Women|28.7|28.2|69.4|1.3|1.1|
|15-24|12.4|23.9|75.4|0.4|0.4|
|25-34|34.6|24.6|72.9|1.5|1.0|
|35+|33.1|32.8|64.4|1.4|1.4|
|Morocco (1984)||||||
|AllWomen|21.2|58.3|39.9|-|1.8|
|15-24|16.6|55.1|42.6|-|2.3|
|25-34|25.5|56.3|42.3|-|1.3|
|35+|19.7|62.2|35.7|-|2.1|
|Thailand<br>(1984)||||||
|All Women|54.0|78.0|19.7|0.7|1.6|
|15-24|42.1|68.2|27.8|0.9|3.1|
|25-34<br>|61.2<br>|77.6<br>|20.3<br>|0.9<br>|1.3<br>|
|35+|52.8|82.7|15.7|0.4|1.2|

Source: CPS; DHS

- a. Sample is currently married women. b. Includes private physicians, hospitals, pL;rmacies, and any other private, non-NGO.

- c. Unspecified source, may encompass NGOs when private, nonprofits are not a category, and may include commercial where it is not a separate category. Other also includes don't know and not stated.

- **VP:** Table **X11.4**
15

# **Table** **<u>6 Djibouti: Selectivity Corrected Wage Equations</u>**

|**Variables**|**All**||**All(years o**|**f edu.)**|**M**|**ale**||**Fem**|**ale**||
|---|---|---|---|---|---|---|---|---|---|---|
|age|0.095 ***|(0.03)|0.103 ***|(0.03)|0.133|***|(0.03)|0.105|*|(0.06)|
|age squared/100|-0.104 ***|(0.03)|-0.109 ***|(0.03)|-0.144|***|(0.04)|-0.131|*|(0.08)|
|female dummy|-0.875 ***|(0.09)|-0.783 ***|(0.09)|||||||
|married dummy|0.260 ***|(0.05)|0.270 ***|(0.05)|0.273|**|(0.12)|0.067||(0.23)|
|primary school|0.597 ***|(0.06)|-<br>-|-|0.492|***|(0.07)|0.908|***|(0.14)|
|middle school|1.003 ***|(0.09)|-<br>-|-|0.787|***|(0.10)|1.533|***|(0.20)|
|high school|1.328 ***|(0.13)|-<br>-|-|1.184|***|(0.14)|1.795|***|(0.31)|
|vocational training|1.348 ***|(0.13)|-<br>-|-|1.166|***|(0.15)|1.860|***|(0.27)|
|university|1.596 ***|(0.14)|-<br>-|-|1.500|***|(0.14)|1.923|***|(0.45)|
|constant|2.923 ***|(0.67)|-<br>-|-|2.177|***|(0.79)|1.838||(1.55)|
|selection term|0.345 **|(0.17)|0.367 **|(0.16)|0.377|**|(0.18)|0.470||(0.41)|
|years of education|-<br>-|-|0.116 ***|(0.01)|||||||
|Chi square|2006.|10|2027.4|9|14|97.17||779|.65||
|No. of uncensored obs.|2011||1997||1|293||71|8||

_Note: Standard errors are in brackets. *** denotes statistical significance at 1% level, ** at 5%, and * at 10%. Source: Authors' estimates based on EDAM 1996._

**Table 7 Djibouti: Selectivity-Corrected Annual Private Rate of Returns to Schooling, 1996**

|**Education Level**|**All**|**Males**|**Females**|**Public**|**Private**|
|---|---|---|---|---|---|
|Primary|10.0|8.2|15.1|2.4|6.7|
|Middle|10.2|7.4|15.6|5.8|8.9|
|Secondary|10.8|13.2|8.7|9.9|6.6|
|Vocational|11.5|12.6|10.9|9.5|2.0|
|University|8.9|10.5|4.3|9.0|0.8|
|years of education<sup>3</sup>|11.6|||||

_Note. Estimates for primary category is the earnings premium over no-formal education. Similarly, middle school category shows earning differential over primary school. Secondary category shows differential over middle school, vocational shows differential over middle school and university is the differential over secondary school. Source: Authors' calculations based on wage regressions from Tables 3 and 6_ An additional year of schooling, corrected for worker’s sector allocation, raises earnings by 11.6 percent - higher than the world average of 10 percent<sup>8</sup> . This figure may be overstated as it does not account for years of repetition which is high in Djibouti, particularly for secondary education. When compared to other African countries such as Ghana, Cote D’Ivoire and Kenya, returns in Djibouti to secondary and university schooling are lower (Schultz, 2003). In addition, the analysis of public and private wages in Djibouti revealed that private returns depend whether the worker is employed in the public sector or in the private sector **.** Estimates on private returns to education should be treated with caution, and future work should provide more accurate estimates by sector as well as control for selection bias.

8 See Psacharopoulos (1994) for world averages.
Figure 38: Performing Bank Credit to GDP and to Deposits by Mexican State (2003) <!-- Start of picture text --> 40% 140%<br>35%<br>120%<br>30%<br>100%<br>25%<br>80%<br>20%<br>60%<br>15%<br>40%<br>10%<br>20%<br>5%<br>0% 0%<br>Performing Bank Credit /GDP Performing Bank Credit/Deposits<br>Aguascalientes Baja California Coahuila de Zaragoza Campeche BCS Colima Chiapas Distrito Federal Chihuahua Durango Guanajuato Guerrero Hidalgo Jalisco México Michoacán Morelos Nuevo León Nayarit Oaxaca Puebla Quintana Roo Querétaro SLPSinaloa Sonora Tabasco Tamaulipas Tlaxcala VeracruzYucatán Zacatecas<br>Performing Bank Credit/GDP<br>Performing Bank Credit/Deposits<br><!-- End of picture text --> Source: CNBV and INEGI.

Note: Performing bank credit is based on CNBV data and excludes restructured loans.

37
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However, when one looks at the independence from the government, impartiality and integrity of the judicial system (all provided by the 2003 Annual Report of Economic Freedom of the World, a compilation of objective and survey data), those two countries, precisely, score last in the ECA countries ranking (figure 2.4).

Hence, it is not enough to look at the efficiency of the courts to assess the quality of the legal system. One has also to explore the independence of the courts and the <u>protection to property rights to get a complete picture.</u>

# **_Tax system_**

Apart from the level of taxes, potential entrepreneurs may be deterred by the frequency of changes in the fiscal law, which enhances business’ uncertainty. Together with the frequent and over-inquisitive tax inspections, this practice results in entrepreneurs spending the bulk of their resources, be them money or time, trying to comply with the tax law rather than to business planning.

Hence both the tax burden and the tax law (transparency and predictability) are important factors affecting the survival and growth of business.

# **_Summary_**

The main sets of institutions affecting the entry of new firms are: the administrative burdens on firm creation, the access to finance, and the exit costs.

Survival and growth depend on two large set of institutions. The first set deals with the interference of governments into the day-to-day business operations. It includes a wide range of factors, going from business regulations to red tape. They have been grouped in four categories: legal system, market regulations, size of government and corruption (and informal economy, very related to the latter).

Finally, the tax burden and unpredictability of the tax law have also been identified as important determinants of the probability of survival and growth of new companies.

9
**Table 6. Probabilities of transition across deciles of per capita expenditure, from SI (rows) to MI (columns)**

|||||||**Tehera**<br>Decil|**n urban**<br>es MI|||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|||1|2|3|4|5|6|7|8|9|10|
||1|0.86|0.13|0|0|0|0|0|0|0|0|
||2|0.05|0.70|0.25|0|0|0|0|0|0|0|
||3|0|0.08|0.63|0.29|0|0|0|0|0|0|
||4|0|0|0.12|0.64|0.21|0|0|0|0|0|
|Deciles|5|0|0|0|0.25|0.57|0.18|0|0|0|0|
|SI|6|0|0|0|0|0.19|0.61|0.19|0|0|0|
||7|0|0|0|0|0|0.29|0.59|0.12|0|0|
||8|0|0|0|0|0|0|0.28|0.67|0.06|0|
||9|0|0|0|0|0|0|0|0.14|0.84|0.02|
||10|0|0|0|0|0|0|0|0|0.07|0.93|
|||||||**Northea**|**st rural**|||||
|||||||Decil|es MI|||||
|||1|2|3|4|5|6|7|8|9|10|
||1|0.98|0.02|0|0|0|0|0|0|0|0|
||2|0.25|0.73|0.02|0|0|0|0|0|0|0|
||3|0|0.37|0.57|0.06|0|0|0|0|0|0|
||4|0|0.02|0.27|0.57|0.14|0|0|0|0|0|
|Deciles|5|0|0|0.03|0.22|0.59|0.16|0|0|0|0|
|SI|6|0|0|0|0.01|0.22|0.54|0.22|0|0|0|
||7|0|0|0|0|0.03|0.18|0.56|0.23|0|0|
||8|0|0|0|0|0|0.04|0.13|0.6|0.23|0|
||9|0|0|0|0|0|0|0.03|0.14|0.69|0.13|
||10|0|0|0|0|0|0|0|0|0.12|0.87|

_Source_ : Authors calculation based on HEIS 2019 36
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 19. Expansion of the Internet market in Senegal**

<!-- Start of picture text --> a. Internet service trends, 2000–08<br>0.9 20<br>0.8 18<br>16<br>0.7<br>14<br>0.6<br>12<br>0.5<br>10<br>0.4<br>8<br>0.3<br>6<br>0.2 4<br>0.1 2<br>0.0 0<br>2000 2001 2002 2003 2004 2005 2006 2007<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person)<br>Internet users<br>International internet bandwidth<br><!-- End of picture text --> <!-- Start of picture text --> b. Internet service, ECOWAS, 2008<br><!-- End of picture text --> <!-- Start of picture text --> 14 350<br>12 300<br>10 250<br>8 200<br>6 150<br>4 100<br>2 50<br>0 0<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person)<br>Internet users<br>International internet bandwidth<br>Sierra Leone Liberia Niger Guinea-Bissau Mali Burkina Faso Mauritania Gambia Senegal Benin Ghana Cote d'Ivoire Cape Verde Togo Nigeria<br><!-- End of picture text --> _Source:_ World Bank, including the Information and Communications for Development database. _Note:_ ECOWAS = Economic Community of West African States.

Prices for telephony remain modest, particularly via the Internet; end-user prices are below those of countries both with and without access to the submarine cable (table 16). Such prices are a result of the advanced roaming arrangements for mobile services that exist in the Economic Community of West African States (ECOWAS) area. The West African region, by comparison with the rest of Africa, has made significant progress in promoting preferential intraoperator roaming arrangements. Subscribers who belong to one of these networks can use their mobile handsets in other countries, where they do not pay for incoming calls and are charged local rates for outgoing calls. Prepaid users can also recharge their phones in the country that they are roaming. Two factors explain the relatively advanced state of ECOWAS’s regional integration with regard to mobile roaming arrangements. One is the existence of a proactive regional regulatory association for ICT—the West Africa Telecommunications Regulators Association (WATRA)—the other, the existence of many large mobile operators with a presence across multiple ECOWAS countries. Seven large mobile groups with a multicountry presence dominate the regional telecommunications market.<sup>13</sup> These multicountry networks have provided the basis for the regional roaming arrangements that essentially collapse into three roaming areas: Orange Zone, Zain One, and One World (table 17). Within these areas, roaming charges are very modest.

> 13 Etisalat, France Telecom, Maroc Telecom, Millicom, MTC (Zain), MTN, and Comium.

33
Village xed eects are included in all specications. I start estimation with no additional controls (column 1) and progressively include extra controls. Column 3 is the full baseline specication. The estimated dierences-in-dierences for cohort 22 to 29 are close to 0 in size and statistically not dierent from 0 across all specications and measures of height. This provides strong evidence that the dierence-in-dierences coecients of interests are not driven by inappropriate identication assumptions.

Exposure to civil conict only during the pubertal spurt appears to have no signicant eect on adult height. Across all specications, conict had statistically zero impact on height of the women aged 9 to 15 in 1996 compared to women in the control group. On the surface, this nding is slightly at odds with Akresh et al.'s (2018) analysis of Biafran war, where they nd conict intensity during women's adolescent years to have signicantly negative impact on their adult stature. However, as illustrated in Figure 1, Nepal's conict started as a small-scale rebellion and only after 2001 developed into countrywide brutal civil war. Unlike the other two treatment cohorts in the paper, most women in this age group would have escaped the most brutal phase of the civil conict during their growing period. In addition, my identication strategy diers from Akresh et al.'s (2018) in that women in this age group start facing conict only during their adolescent years.

Women aged 4 to 8 in 1996, on the other hand, would have just entered or already be in their adolescent spurt years when the conict started to intensify in 2001.Therefore, it is not surprising to see the cumulative violence, which was at a lower level in the second growth stage and intense in the third stage, has signicant and negative impact on their adult stature. Eects vary between 0.67 to 0.71 millimeters (0.011 to 0.012 sd) across the specications. The coecient in the full model can be interpreted as: an additional month of exposure to civil war during the latter two stages of the growing period decreases nal adult height by 0.71 millimeters or 0.012 sd.

Besides being a period of a rapid growth, the rst three years are also the most sensitive period to environmental inuences on height in human beings (Schmidt et al., 1995). Early childhood height at ages 2 (Luo and Karlberg, 2000) and 5 (Satyanarayana et al., 1986), is a strong predictor of nal adult height. Similarly, the pubertal growth spurt plays an important role in determining the nal adult stature (Case and Paxson, 2008). Women in the age group 0 to 3 in February 1996, who would have been exposed to conict during all three phases of growth, therefore would suer the most from the conict compared to the other age groups. On average, girls aged 0 to 3 in 1996 suered 18