rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 12. Levels and inequalities** in **under-five mortality**

<!-- Start of picture text --> Average U5MR per 1000<br>0.0 50.0 100.0 150.0 200.0 250.0 300.0 350.0<br>0.05 -<br>0.00 - s, Kazakstan 1995 , Chad 1996/7<br>-0.05 Uzbekistan W6 Tanzania 1996 * Burkina F,so 1993<br>-_0.05 - *z J'amibia 1992 Italit 1994/5 Malawi 1992 * Niger 1998<br>u Zimbabwe 1994<br>Pakistan 1990/1 Bangladesh .CJt7a,lZdambia 1996<br>Comores 1996, * Togo 1998 nin 1996 * Mali 1995/6<br>c010 Kyrgyzstan 1997 Nepal 1996 CAR 1994/5<br>° Colombia Colomia 1995, 195, Nicar'.uatema/895 *Guatemala 1995 Ct C&* * d'Ivoire aaacr 19V eaMo9°zambique 1997 9719 o<br>.0 ~ ~ ~ ~~iaagua 1997/8 Coe 'vr igera.199<br>4i0* *Paraguay 1990 *Ghana 1993 9<br>ME -0.15 Morocco 193 Kenya 1998<br>c * -* Cameroun 1991<br>Vietnam 1997 India 1992/3 Senegal 1997<br>-020 - * Philippines 1998<br>Dom Rep 1996 * * * Turkey 1993<br>Indonesia 1997 * Bolivia 1998<br>* Peru 1996<br>-0.25 - * Brazil 1996<br>-0.30<br><!-- End of picture text -->

**Figure** 13 illustrates the idea in the case of under-five mortality for the countries in the grouping labelled "Asia, the Near East and North Africa." India has a lower under-five mortality rate than Bangladesh (119 compared to 128 per 1,000), but the inequality between the poor and the better-off is higher in India. A country's achievement index captures both these considerations. Higher mortality rates among the poor push the achievement index (or, more correctly in this case, the _disachievement_ index) above the sample mortality rate. The bigger the inequality, the greater the proportional "wedge" between achievement and the sample mean. India's concentration index for under-five mortality is -0.169, so that its achievement index for under-five mortality is 117 percent of 119, or 139 per 1,000. By contrast, Bangladesh's concentration index is only -0.084, so its achievement index is 108 percent of 128, or 139 per 1,000-the same as India's, despite its higher average rate.

_28_
# **_3.2. Africa level estimates of trends in access_**

One difficulty in providing an Africa-wide trend in access rates stems from the fact that the panel of countries available through the DHS surveys is not balanced for each period. Countries have observations for different years. Therefore three alternative methods were used to estimate overall access trends. The first method includes only the 11 countries for which there are data for three time periods, 1990–95, 1996–2000, and 2001-05 are available. The second method includes countries with data for only one or two time periods. For countries with data for only one time period the data are used for all three time periods, assuming no change over time in access. If data are available for two periods, the annual growth rate in coverage between the two periods is used to estimate the rate for the third period. The third method is similar but assumes that access rates cannot fall more than population growth. If access rates in the third period drop by more than what would be observed assuming no growth in the total number of connections, the survey data for the third period are replaced with the coverage rate in the second period times the ratio of the population in the second period divided by the population in the third period. In the case of landlines, due to a smaller number of observations, and increasing access in most countries, only the second method is applicable.

Given some issues of comparability between surveys in selected countries and the resulting need to correct for some outliers, our preferred estimates for the analysis are obtained from the third method. Yet the results provided in table 8 from all three methods are broadly similar. They suggest that access rates for electricity and flush toilets have improved slightly over time but that access to piped water has not. Access rates within urban areas have either declined or remained stable and rural areas have not changed much or increased which suggests that migration from rural to urban areas has contributed to the higher access rates. Finally, the gains in access to electricity have been better shared across wealth groups (except for the very poor) than have the gains in access to flush toilets, which tend to have benefited the richest households the most. Among the poorest quintile access to all three basic infrastructure services remains virtually inexistent.

How far away is universal access? While this may seem to be a misplaced question given the low coverage rates for many services in most countries, it helps in showing how slow progress will be if substantial investments are not made in order to improve coverage faster. The 10
government can reclaim title and grant licenses itself. (These are the so-called “march-in” rights.)

- Universities must share royalty and license income from the invention with the inventor.  It must use the remaining income to cover the cost of maintaining a university technology transfer office and to support scientific research and education.<sup>68</sup>

Bayh-Dole had a major impact on the commercialization of government-financed inventions. For example, in 1980, approximately 25-30 universities were engaged in technology transfer. Between 1974 and 1984, 84 universities applied for 4105 patents and received 2944 patents. Licensing income reported by 112 universities in 1986 amounted to $30 million. By comparison, in 1999 alone, 190 universities, hospitals and nonprofit research organizations reported<sup>69</sup> :

- Approximately $41 billion of economic activity, supporting 271,000 jobs was attributed to the results of academic licensing;

- Adjusted gross license income was $862 million;

- 5545 US patent applications were filed and 3661 patents were issued;

- 3914 new licenses were issued and 18,617 licenses were outstanding.  Almost 2/3 of the new licenses were issued to small businesses;

- The business activity associated with the sale of licensed products is estimated to generate approximately $5 billion of federal, state and regional tax revenue.

# **B. Stevenson-Wydler**

Whereas Bayh-Dole concerns the ownership and commercialization of governmentfunded inventions created in universities, Stevenson-Wydler addresses the ownership of inventions created in the course of cooperative research ventures between private enterprises and government laboratories. The basic rationale for the legislation was an attempt to create closer linkages between federal laboratories conducting basic research

> 68 Excellent descriptions of university technology licensing procedures and the operation of university technology management offices can be found in <u>University Technology Transfer: Questions and Answers, Council on Governmental Relations, November 30, 1993 (available at the web site http://www.cogr.edu/qa.htm</u> .  Another excellent source of information is available at the web site of the Association of University Technology Managers <u>http://www.autm.net</u>

> 69

> 1999 data are from The Association of University Technology Managers, Inc., report entitled, <u>AUTM Licensing Survey, FY 1999: A Survey Summary of Technology Licensing (and Related) Performance for U.S. and Canadian Academic and Nonprofit Institutions and Patent Management Firms.</u>

78
42

# **Brazil**

The official position of the Government of Brazil is that the downstream petroleum sector was completely deregulated effective January 2002. The government has nevertheless continued its informal policy of keeping domestic prices of petroleum products artificially low through losses suffered by Petrobras, a semi‐public oil company that controls more than 99 percent of the refining capacity in the country.

A history of producer prices of diesel is shown in Figure 9. Also shown are the spot prices of diesel on the U.S. Gulf Coast—which has the lowest prices of three benchmark markets in the United States, the other two being the West Coast and New York Harbor—during the same period. Brazil is a net importer of diesel. Therefore, import‐parity prices should form the basis for economic pricing, which means that shipping costs should be added to the U.S. Gulf Coast prices. In 2014, according to the annual statistics provided by the regulatory agency, 44 percent of diesel imports was from the United States and another 40 percent was from India. It is clear that, for many months since 2011 and until the recent precipitous drop in world oil prices, producer prices were lower than even FOB prices on the U.S. Gulf Coast and consistently lower than import‐parity prices. Producer prices of gasoline and diesel did not change between February and September 2015.

**Figure 9: Comparison of producer prices of diesel fuel in Brazil with U.S. Gulf Coast FOB diesel prices**

<!-- Start of picture text --> 2.50<br>R$/liter<br>2.00 US$/liter<br>U.S. Gulf Coast FOB<br>1.50<br>1.00<br>0.50<br>0.00<br>Jan 11 Jul 11 Jan 12 Jul 12 Jan 13 Jul 13 Jan 14 Jul 14 Jan 15 Jul 15<br>R$ or US$/liter<br><!-- End of picture text --> _Sources_ : <u>http://www.anp.gov.br/?id=2368 for Brazilian prices, http://www.eia.gov/petroleum for U.S. Gulf Coast.</u> _Note_ : Brazilian diesel prices are for diesel to December 2012 and diesel with 10 parts per million sulfur since January 2013 for direct comparison with U.S. Gulf Coast prices.

This policy has contributed substantially to the large losses suffered by the downstream arm of Petrobras since 2011 (Figure 10). The losses grew in periods of increasing imports, because Petrobras had to import refined products at world prices and sell below cost. In addition to being a net importer of diesel, Brazil in 2011 became a net importer of gasoline (ANP 2015a). The net loss in 2014 is fundamentally different from other years in that Petrobras wrote off US$12.8 billion for overvalued assets and another US$1.4 billion for “incorrectly capitalized” overpayments, or a total of US$14.2 billion out of the net income loss of US$15.4 billion shown in Figure 10.
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 1a. Infrastructure’s historic contribution to economic growth, 1990–95 vs 2000–05**

<!-- Start of picture text --> 2<br>1.5<br>1<br>0.5<br>0<br>-0.5<br>-1<br>Congo, Rep Kenya East Africa Cameroon Cote d'Ivoire Tanzania Uganda Sudan<br>ICT Power Roads<br>Changes in per capita growth<br><!-- End of picture text --> _Source:_ Calderón 2009. _Note:_ ICT = information and communication technology.

Infrastructure could contribute more to all parts of Sudan’s infrastructure in the future than it has in the past (figure 1b). Simulations suggest that if Sudan’s infrastructure were to be upgraded to the level of the best-performing country in Africa (Mauritius), the impact on per capita economic growth would be on the order of 3.5 percentage points. While all areas of infrastructure—ICT, power, and transport—need to be upgraded, improvements in roads can impact growth the most—by around 1.5 percent. Improvements in power infrastructure will add another 1 percent to per capita growth. Infrastructure’s immediate potential contribution to growth is less than it is in several other East African countries.

**Figure 1b. Infrastructure’s potential future contribution to economic growth**

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>Cote d'Ivoire Kenya Cameroon Tanzania Sudan Congo, Rep of Uganda East Africa<br>ICT Power Roads<br>growth<br>Potential contributions of per capita<br><!-- End of picture text --> _Source:_ Calderón 2009. _Note:_ ICT = information and communication technology.

> <sup>4</sup>
Table A4: Heterogeneity in Effects of ACASI on Placebo Questions (Malawi)

|(1)|(2)|(3)|(4)|
|---|---|---|---|
|Farm work<br>(past year)|Market visit<br>(past week)|Maize/Rice<br>(next week)|Meat<br>(next week)|
|**Panel A. Primary education completion**||||
|SI _×_ Primary Educ (_β_)<br>-0.02|0.10***|0.06|0.07*|
|(0.01)<br><br>|(0.03)<br>|(0.04)<br>|(0.04)<br>|
|SI _×_ No Primary Educ (_γ_)<br>-0.01|0.07**|0.10***|0.16***|
|(0.02)|(0.03)|(0.04)|(0.03)|
|Primary Educ<br>0.03|0.03|0.04|0.10*|
|(0.02)|(0.05)|(0.06)|(0.05)|
|FTFI _×_ No Primary Educ mean<br>0.93|0.41|0.52|0.22|
|_p_-value (_β_ =_γ_)<br>0.749|0.659|0.404|0.096|
|Observations<br>1,718|1,713|1,345|1,228|
|**Panel B. Access to mobile phone**||||
|SI _×_ Mobile (_β_)<br>-0.01|0.13***|0.09**|0.09**|
|(0.02)|(0.03)|(0.04)|(0.04)|
|SI _×_ No Mobile (_γ_)<br>-0.02|0.05|0.07*|0.14***|
|(0.02)|(0.03)|(0.04)|(0.03)|
|Mobile<br>-0.02|-0.02|-0.01|0.00|
|(0.02)|(0.03)|(0.04)|(0.04)|
|FTFI _×_ No Mobile mean<br>0.95|0.46|0.53|0.26|
|_p_-value (_β_ =_γ_)<br>0.549|0.096|0.763|0.314|
|<br>Observations<br>1,718|1,713|1,345|1,228|
|**Panel C. Able to read/write in English**||||
|SI _×_ English (_β_)<br>-0.02*|0.10***|0.06*|0.10***|
|(0.01)|(0.03)|(0.03)|(0.03)|
|SI _×_ No English (_γ_)<br>-0.01|0.06|0.12***|0.15***|
|(0.02)|(0.04)|(0.05)|(0.04)|
|English<br>0.03|-0.04|0.01|-0.00|
|(0.02)|(0.05)|(0.05)|(0.05)|
|FTFI _×_ No English mean<br>0.92|0.40|0.51|0.23|
|_p_-value (_β_ =_γ_)<br>0.499|0.385|0.239|0.334|
|<br>Observations<br>1,718|1,713|1,345|1,228|
|**Panel D. Age**||||
|SI _×_ Below-median Age (_β_)<br>-0.03*|0.07**|0.04|0.10***|
|(0.02)<br><br>|(0.03)<br>|(0.04)<br>|(0.04)<br>|
|SI _×_ Above-median Age (_γ_)<br>-0.00|0.11***|0.12***|0.13***|
|(0.02)|(0.03)|(0.04)|(0.04)|
|Below-median Age<br>-0.00|0.05|0.05|0.09*|
|(0.02)|(0.05)|(0.05)|(0.05)|
|FTFI _×_ Above-median Age mean<br>0.94|0.41|0.52|0.27|
|_p_-value (_β_ =_γ_)<br>0.188|0.397|0.120|0.511|
|Observations<br>1,718|1,713|1,345|1,228|

Note: Regressions include individual controls (including all variables in Table 1). Standard errors clustered at individual level in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively.

27
between being formerly married and HIV status both for females and males. Ceteris paribus, being formerly married increases the probability of being HIV positive by 13.9 percentage points for men and 26.2 percentage points for women. It is possible that marital disruption is a consequence rather then a cause of HIV infection – a case of reverse causality, so these coefficients must be interpreted with caution. Surprisingly, being a widow/er and having been in successive marriages does not affect significantly the likelihood of being HIV infected. It should be noted that the widow/er variable is constructed as an interaction with formerly married, so the result should be interpreted as indicating that compared to other formerly married individuals, widows or widowers are not more likely to be HIV positive.

The probability of being HIV positive decreases with education, suggesting an important role for education in preventing HIV infection. This finding is particularly interesting because the question of the effect of education on HIV infection has different answers depending on the countries analyzed. For example, an analysis of five Demographic and Health Surveys in Burkina Faso, Cameroon, Ghana, Kenya and Tanzania did not find any correlation between HIV and education (De Walque, 2006), while in a rural cohort in Uganda a negative gradient between schooling and HIV infection appeared over time among young women (De Walque, 2007b). However, the coefficient is statistically significant only for males who have attended primary school.

Wealth tends to be positively associated with HIV infection, but only until the fourth quintile. Table 3 also shows no significant association between HIV prevalence and religion.

[Insert table 4] Some individuals who were randomly selected to be tested in the survey do not have a test result either because they refused to be tested or because they were absent. Table 4 reports the marginal effect of probit estimates of the probability of being tested for HIV during the survey. Urban status is strongly negatively associated with the probability of being tested. The coefficient is always significant at the 1% level. One potential reason might be that people in urban areas are less likely to be present at the time of the test. Widowers are also less likely to be tested, while the probability of 12
corporate governance mechanisms to legal authorities and the public. India’s score of 0.92 is higher than the averages of all LLSV subgroups of countries, including the English origin countries, suggesting that Indian firms must disclose a large amount of information. However, this does not imply the quality of disclosure is also good. In terms of the degree of earnings management (higher score means more earnings management; Leuz, Nanda and Wysocki 2003), India’s score is much higher than the average of English origin countries, and is only lower than the German origin countries, suggesting that investors have a difficult time in evaluating Indian companies based on publicly available reports.

The fourth and last category of Table 2-D is the legality index, a composite measure of the effectiveness of a country’s legal institutions. It is based on the weighted average of five categories of the quality of legal institutions and government in the country (see Berkowitz, Pistor and Richard 2003). Consistent with other measures, India’s score is lower than the averages of all the subgroups of LLSV countries, suggesting that India’s legal institutions are less effective than those of many countries, and that it will be more difficult for India to adopt and enforce new legal rules and regulations than other countries.

Finally, as for the business environment in India, a recent World Bank survey found that, among the top ten obstacles to Indian businesses, the three which the firms surveyed considered to be a “major” or “very severe” obstacle and exceeding the world average are corruption (the most important problem), availability of electricity, and labor regulations. Threat of nationalization or direct government intervention in business is no longer a major issue in India. With rampant tax evasion, the shadow economy in India is significant. It is estimated to be about 23% of GDP.<sup>12</sup> In keeping with its socialist moorings during the first three decades after independence, India created a

> 12 This figure is 22.4% according to Schneider and Enste (2000), and 23.1% by Schneider (2002) (World Bank). Popular perception, however, would put it significantly larger, particularly given that the average figure of OECD countries themselves is about 12%.

11
# 5.1 Alternative instruments

Table 6 presents the results for the case of alternative instrumental variable, where we employ multilateral and bilateral loans. The results show that a dollar increase in public investment increases private investment by about 2 dollars on impact in the case where predicted disbursements from multilateral creditors is used as instrument for public investment (Panel A, Table 6).

**Table 6: The response of private investment to government investment, alternative instruments**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Cu<br>|mulative Mult<br>|ipliers (horiz<br>|on h in year<br>|s)<br>|
||h=0|h=1|h=2|h=3|h=4|
|||Panel A: M|ultilateral N|= 109||
|Change in government investment|2.146**|2.045**|2.476**|1.708|1.622|
||(0.860)|(0.802)|(1.216)|(1.365)|(1.493)|
|Observations|3,429|3,344|3,237|3,131|3,024|
|F-statistic|7.405|7.501|6.976|7.118|8.106|
|KP Underid stat|0.0110|0.00998|0.0128|0.0119|0.00733|

|||Panel B:|Bilateral N =|109||
|---|---|---|---|---|---|
|Change in government investment|1.593|1.280|2.356|2.983*|2.490|
||(1.076)|(0.883)|(1.698)|(1.786)|(1.640)|
|Observations|3,404|3,317|3,211|3,106|3,000|
|F-statistic|5.185|6.048|5.094|5.928|7.929|
|KP Underid stat|0.0267|0.0163|0.0264|0.0165|0.00637|

_Notes_ : *** p <0.01, ** p <0.05, * p <0.1. Dependent variable is change in private investment to lagged GDP. Robust standard errors in parentheses are Driscroll-Kraay standard errors to control for the presence of error cross-sectional dependence a ́ la Boehm (2020). All estimations include country and year fixed effects and one year lagged dependent variable. “KP” is Kleibergen and Paap underidentification test.

Moreover, the crowding-in effect after one year of the public investment shock (h=1) is about 2.5 and is statistically significant at the 5 percent level. Albeit slightly larger than the crowding-in effect from our main results, the evidence of crowding-in of private investment by public investment is consistent with the main results in the previous section. For longer horizons, however, the multiplier is statistically not different from zero. In the case of using predicted disbursements from bilateral loans only one horizon estimate (h = 2) is found to be statistically significant, and the effective F-statistics are lower still. Our results are somewhat in line with Kraay (2014) who finds a greater strength of identification from 18
and other). Commercial loans thus constructed exclude loans to financial institutions and government, so total credit in this case refers to credit to the nonfinancial private sector. To complete the loans by purpose data, consumer and mortgage loans data were readily available as separate line items.

Armed with these data, we constructed a country-level data set on the shares (over total loans) of bank loans by sectoral distribution and loan maturity, aggregating individual bank data provided in the survey, or using aggregate data publicly available from websites. These shares are defined by dividing a particular maturity or credit purpose value by the total reported credit in a country for a given year. In the case of individual bank data provided by ASBA, the shares were calculated by aggregating total gross loans to the nonfinancial sector by purpose/maturity and dividing by the sum of the aggregates by sector/maturity so the shares add up to one.<sup>9</sup> Bank-level information included information from all supervised institutions, including public banks. Some of these public banks are development financial institutions focused on providing long-term finance directly to borrowers or by lending long-term funds to private commercial funds. To avoid overestimating the share of long-term lending, second-tier operations or wholesale funding by public banks were excluded from the aggregation.<sup>10</sup> Data obtained directly from supervisors’ website were already aggregated at the banking sector level and should net out public sector lending on second-tier basis to avoid double counting of loans. In the cases where data were obtained from the notes to individual bank statements (available in PDF), information was obtained only for the top three or four largest banks in the country, constructing the country share as the share aggregating the information on those banks. Country-level observations with less than five years of available data were excluded from the data set. **Error! Reference source not found.** shows the details on country-year observations for the shares of loans by maturity and by purpose. Table 2 expands on how the information was calculated.

To obtain information on the U.S. dollar amount of bank loan portfolios by maturity and purpose, we multiplied the shares constructed as described earlier by the IFS Domestic Private

> 9 In almost all the cases, the sum within a country-year of the total of the three categories of loan purpose or the total of the two or three maturity groupings was smaller than the sum of total gross loans to the nonfinancial sector. The difference was small and averaged 92.4 percent for the constructed purpose total over the reported total and 96.6 percent for the constructed maturity total over the reported total.

> 10 For Argentina’s BICE, Brazil’s BNDES, Chile’s Banco Estado and Nicaragua’s Banco Produzcamos credit data are for their direct loans only.

8