rafmacalaba/datause-encoder-data huggingface dataset:
_Appendix Table 7: Program impact on 6-36-month-old beneficiary children's physical growth, overall and by program saturation_

||Height-for-Age<br>Z score|Stunted|Severely stunted|Weight-for-Age Z<br>score|Underweight|
|---|---|---|---|---|---|
|_Panel A: Interaction with satu_<br>|_ration_<br>|||||
|Program village|0.324|-0.369**|-0.099|-0.394|0.086|
||(0.442)|(0.153)|(0.113)|(0.471)|(0.146)|
|Saturation|-0.264|-0.231|0.363*|-1.384**|0.243|
||(0.678)|(0.249)|(0.195)|(0.595)|(0.203)|
|*|-0.115|0.532**|0.016|0.827|-0.175|
|Program villageSaturation|(0.691)|(0.248)|(0.182)|(0.744)|(0.233)|
|_Panel B: Interaction with abov_|_e median saturation_|||||
|Program village|0.098|-0.084|-0.095*|0.099|0.020|
||(0.206)|(0.074)|(0.055)|(0.197)|(0.068)|
|Above median saturation|-0.033|-0.127|0.019|-0.050|-0.016|
||(0.297)|(0.096)|(0.072)|(0.244)|(0.077)|
|Program village*Above|0.313|0.097|0.004|0.052|-0.086|
|median saturation|(0.295)|(0.107)|(0.082)|(0.302)|(0.094)|
|_Panel C: Interaction with top_|_quartile of saturation_|||||
|Program village|0.335*|-0.071|-0.120**|0.022|-0.014|
||(0.191)|(0.062)|(0.050)|(0.163)|(0.056)|
|Fourth quartile of saturation|0.225|-0.065|0.013|-0.104|0.018|
||(0.265)|(0.087)|(0.081)|(0.258)|(0.073)|
|Program village*Fourth|-0.264|0.120|0.092|0.343|-0.032|
|quartile ofsaturation|(0.332)|(0.125)|(0.093)|(0.361)|(0.107)|
|Control observations|162|162|162|186|186|
|Treated observations|181|181|181|201|201|
|Control mean|-1.800|0.481|0.216|-1.207|0.280|

*** p<0.01, ** p<0.05, * p<0.1.

All specifications include municipality fixed effects. All specifications include quadratic terms for child age (in months). Standard errors are clustered by village; there are 130 villages.

Source: 2011 _Pantawid_ impact evaluation survey 56
**Table** **_5._ Subsidies to Petroleum Products (billion US. $)'**

||Gasoline|Kerosene|Diesel|HFO*|<br>LFO*|<br>Total|
|---|---|---|---|---|---|---|
|Former USSR|19.0<br>(0.54)|1.0<br>(0.40)|30.0<br>(0.30)|5.0<br>(0.68)|10.0<br>(0.23)|65.0|
|China|||1.5-<br>(0.70)|0.6<br>(070)|2.5<br> <br>(0.79)|4.6|
|Czechoslovakia|(2.34)||(1.88)|(1.05)|0.38<br>(0.74)|0.38|
|Brazil|(1.95)|0.1<br>(0.74)|0.4<br>(0.94)|(1 41)|0.4<br>(0.71)|0.9|
|Venezuela|2.3<br>(0.25)|0.2<br>(0.47)|0.6<br>(0.18)|0.1<br>_(0.55)_|0.4<br>(0.19)|3.6|
|Mexico|(1.06)|0.2<br>(0.72)|0.85<br>(0.71)|0.4<br>(0.84)|0.1<br>(0.75)|1.55|
|India|(2.09)|2.55<br>(0.33)|1.3<br>(0.79)|(1.60)|0.4<br>(0.61)|4.25|
|Indonesia|0.6<br>(0.65)|1.8<br>(0.28)|1.3<br>(0.39)|(1.00)|1.4<br>(0.36)|5.1|
|Saudi Arabia|1.1<br>(0.65)|0.1<br>(0.22)|0.8<br>(0.13)|0.5<br>(0.47)|2.5<br>(0.12)|_5.0_|
|South Korea|(1.84)|0.3<br>(0.74)|0.3<br>(0.83)|(1.05)|_0.5_<br>(0.68)|1.1|
|Egypt|0.6<br>(0.34)|1.0<br>(0.05)|0.2<br>(0.05)|0.1<br>(0.95)|1.1<br>(0.40)|3.0|
|Iran|1.7<br>(0.29)||3.8<br>(0.05)|1.5<br>(0.07)|2.1<br>(0.04)|9.1|
|Bugaria|(1.29)|0.15<br>(0.60)|0.1<br>(0.90)|(1.80)|0.2<br>(0.75)|0.45|
|Total|25.3|7.4|41.2|8.3|22.0|104.1|

# Source: Author's calculations.

* Heavy Fuel Oils and Light Fuel Oils respectively.

** Primarily to agriculture (no diesel subsidies to transport).

I/Figures in parentheses are ratio of domestic prices to world prices.
19

**Figure 12: Gabon’s Exports of Logs and Processed Wood Products to France (roundwood equivalent in 1,000 m**<sup>**3**</sup> **)**

<!-- Start of picture text --> 600<br>500<br>400<br>300<br>200<br>100<br>0<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br>Logs Processed wood<br><!-- End of picture text --> _Source:_ Elaborated from FAOSTAT Forestry Trade Flow data, accessed November 2009, and WRI (2000) The EU market for timber products saw four important changes after the 1980s. First, the EU’s long-term dominance of global wooden furniture exports was eroded, particularly by expanding exports from China. In 1992, Chinese furniture exports were only 14 percent of Italian and 19 percent of German exports (the two leading global furniture exporters). In 2008, Chinese furniture exports were 2.3 times those of Italy and 2.6 times those of Germany (calculated from COMTRADE, accessed December 2009). Second, as wage levels rose in the EU, so the competitiveness of its wood processing industries fell in comparison to low-income economies, who at the same time had developed their capabilities significantly in these processing sectors. This led to a migration of wood processing activities out of high-income economies as the timber furniture value chain extended. Third, governments became increasingly aware of health and safety, and regulations were instituted in many sectors to ensure higher levels of safety for consumers. Finally, the growth of per capita incomes led to greater consumer concerns with the environment and with ethical standards in value chains. In the timber sector, organizations such as the World Wildlife Fund and Friends of the Earth increasingly exerted pressure on producers to promote biodiversity and sustainability in forestry (Gulbrandsen and Humphreys, 2006; Stringer, 2006).

The growing prevalence of these standards in the EU market is reflected in Box 3 which summarizes the major trends in Europe with regard to the preferences of buyers in their purchasing of timber and the standards governing access to EU markets.
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE In the water sector, it is estimated that as of 2008 the average total cost of producing utility water was $1.15 per m<sup>3</sup> (including capital costs), while the average residential effective tariff was $1.22. As a result, ONEA managed to cover 100 percent of its operating and capital costs (figure 28).

**Figure 28. Underpricing of power in Burkina Faso relatively low but still material**

Financial burden of underpricing in 2006, as percentage of GDP <!-- Start of picture text -->
1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Power Water<br>Burkina Faso LIC-Nonfragile<br>Source : Derived from Briceño-Garmendia , Smits, and Foster (2009).<br>Note:  GDP = gross domestic product; LIC = low-income country.<br>Percentage of GDP<br><!-- End of picture text -->

In the power sector, the burden of 0.4 underpricing was 0.55 percent of GDP; 0.2 average effective tariffs, at $35 cents per 0.0 kWh, were not enough to cover operating Power Water Burkina Faso LIC-Nonfragile and capital costs (the estimated cost- _Source_ : Derived from Briceño-Garmendia , Smits, and Foster (2009). recovery tariff is $0.42 per kWh). Looking _Note:_ GDP = gross domestic product; LIC = low-income country. ahead to the future, the long-run marginal cost of power in Burkina Faso could fall from 26 cents to 25 cents once power-trading agreements materialize. These developments should lower prices charged to end users as well as ease the pressure on national budgets.

Access to electricity and safe water services in Burkina Faso is not equitable (figure 29). The poorest quintiles of the population do not have access to utility water, whereas 34 percent of the richest quintile has access to piped water and 47 percent to stand posts. On the other hand, the poorest households are served mainly by wells or boreholes (65 percent of the quintile 1 and 83 percent of the quintile 2). The poorest people are the ones using the most surface water (34 percent of the quintile 1 and 16.5 percent of the quintile 2), whereas the richest population barely uses surface water (0.04 percent of the quintile 5). On the sanitation side, less than 1 percent of the poorest quintile of the population has access to any form of sanitation, which implies that 99.6 percent uses open defecation. The richest quintile is the one with the highest access to flush toilets (at 9.02), traditional latrines (at 10.34), and VIP/San Plat/chemical latrines (at 74.35). Fifty-seven percent of households with access to the power grid are in the richest quintile (figure 29).

39
I is a k x 1 vector of unknown parameters; xi is a k x 1 vector of explanatory variables.

The empirical model, therefore, takes the following form: _INEFF = 0 +/3,CORRflDEX +/ 2 GOVERIVDEX +/3 3DUM + 5 (13)_ where: INEFF is the inefficiency score; CORRINDEX is the corruption index; GOVERNINDEX is the govemance index; DUM is a dummy variable: = 1 if the water utility is private; = 0 otherwise. Statistical analyses are performed using STATA 5 statistical software (Statacorp 1997).

Results are displayed in Table 7. An important feature of the results is that institutional variables are statistically significant at the five percent level; their signs are also as expected. An interpretation of these results corroborate the fact that corruption is negatively linked to efficiency while govemance is positively linked. As a consequence, water utilities should also focus on institutional variables when trying to improve their efficiency scores.

**Table 5: Inefficiency and Institutional Variables**

|Independent variable|Coefficient|Standarddeviation|
|---|---|---|
|Constant|1.989**|0.508|
|Corruption|0.0344***|0.048|
|Governance|0.041**|0.0247|
|Dummy(Privatelyoperated=1)|-0.028**|0.015|
|LR tes<br>|35.28***||
|McFadden's**R**<sup>**2**</sup>|0.348||

Notes: Numbers in parentheses are standard errors. Single, double, and triple asteriks **(*)** denote significance<sup>atthe</sup> 10%, **_5%_** and 1%, respectively.

A final interesting result is the effect of ownership on the efficiency of the firm.. In the present context, the dummy variable which captures the effect of privatization is statistically significant at the five percent level. In terms of policy implications, this suggests that privatization has had an impact on water utilities in the African context. This is in contrast to the results found by Estache and Rossi (2001) for Asia where no significant difference was found.

16
# **1. Introduction**

The Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPs) requires a number of countries to strengthen their patent and other intellectual property rights (IPR) systems.<sup>2</sup> Even though policymakers have committed to significant reforms, the implementation of this agreement remains contentious. On one side, many policymakers believe that this mandated policy change will work against their national economic interests, transferring rents to multinational corporate patent holders headquartered in the world’s most advanced countries, especially the United States.<sup>3</sup> Advocates for strong IPR counter that strengthening IPR will induce more innovation, thereby fostering more rapid economic growth. These advocates also believe that a strengthening of IPR will accelerate the transfer of technology between countries, ensuring a relatively equal distribution of gains from this policy change.

Despite this heated policy debate, the economics literature has only started to confront alternative viewpoints with facts. While theoretical work provides valuable frameworks that detail the circumstances under which IPR enhances and reduces welfare, many hypothesized effects of IPR have not been held up to empirical scrutiny. This paper uses affiliate-level data on U.S. multinational firms and aggregate patent data to test whether legal reforms that strengthen IPR increase the transfer of technology by multinationals to reforming countries. Throughout the 1980s and 1990s, a number of countries undertook reforms of their intellectual property systems. Analysis of data from the Bureau of Economic Analysis’s survey of U.S. multinational firms reveals evidence

> 2   Implementation of the terms of this agreement is ongoing.  Some developing countries have until 2016 to fully comply. 3   For a more academic treatment of some of these claims see Lanjouw (1997) and McCalman (2001).

1
indicator. For the price-wage variable, t-statistics exceed 2 when used with the SPO estimate of unemployment and registered unemployed'and when used with the application/placement ratio and the - (application placements)/applications rate at least higher than 1. For the labor market indicator, however, t-values are systematically below 1 except for the applications/placements ratio for which it is about **11.** DWs are generally satisfactory but the adj.R<sup>2</sup> s are very low, 0.05 to 0.14, and F-values indicate insignificance of all specifications.

For the Gordon specification a simple one-lag with a three-lag version were run. Results are presented in Table '.2. For the three-lag version only the sum of coefficients are snown. Even for the one-lag version, adj.R s are in all cases around 0.5 and F-values indicate significance at the 5 percent level. DWs are generally not very satisfactory.Cochrane-Orcutt transformations in many cases lead to sign changes and low t and adj. R<sup>2</sup> values. In the three-lag version, adj.R<sup>2</sup> s are never below 0.76 and in two cases even of the order 0.95. F-values show significance at the 1 percent level.

Considering the individual independent variables, we find in the one-lag version correct sign for the lagged labor cost variable in all cases and the value is in some cases close to one. t-values are in nine cases out of ten above 2 and in five cases even above 3. The share variable has systematically negative sign, meaning that prices have a positive effect on the

- 68 -
Figure 7 – Great Britain, by survey <!-- Start of picture text --> GreatBritain<br>Relative inequality of opportunity Return to education<br>1990 1995 2000 2005 2010 2015<br>survey<br>st.deviation logs mean log dev. 1990 1995 2000 survey 2005 2010 2015<br>Parental networking Intergenerational persistence in education<br>1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br>survey survey<br>.5 .35 .25<br>.3<br>.45<br>.25 .2<br>.4<br>.2<br>st.deviation logs mean log dev.<br>.35 .15<br>.15<br>.3 .1<br>.1<br>.025 .15<br>.02 .14<br>.015<br>.13<br>.01<br>.12<br>.005<br>0 .11<br><!-- End of picture text --> Regressors include gender, age, age2, born in England and foreign citizenship Data trimmed at 99.5th centile - vertical dashed line indicates change of survey Figure 8 – Great Britain, age-cohort decomposition <!-- Start of picture text --> GreatBritain<br>Inequality of opportunity - Standard deviation of logs personal incomes Return to education<br>30 40 50 60 70 80 30 40 50 60 70 80<br>Age Age<br>. .<br>1920 1940 1960 1980 2000 1920 1940 1960 1980 2000<br>Born in... Born in...<br>. .<br>Parental networking Intergenerational persistence in education<br>30 40 50 Age 60 70 80 30 40 50 Age 60 70 80<br>. .<br>1920 1940 Born in...1960 1980 2000 1920 1940 Born in...1960 1980 2000<br>. .<br>Regressors include gender, age, age2, born in England and foreign citizenship - Age and cohort effects obtained from Deaton's decomposition<br>.4 .15<br>.3<br>.1<br>beta1 .2 beta1<br>.1 .05<br>0 0<br>.8 .25<br>.6 .2<br>beta1 .4 beta1 .15<br>.2 .1<br>0 .05<br>.005 .03<br>0 .02<br>beta1 -.005 beta1<br>.01<br>-.01<br>-.015 0<br>0 .15<br>-.01 .1<br>-.02<br>beta1 beta1 .05<br>-.03<br>-.04 0<br>-.05 -.05<br><!-- End of picture text --> 17
The business grant of $360 is equivalent to approximately two months of baseline average revenue and five months of average profits in our sample. It was electronically distributed to the youth in the assigned treatment groups in two tranches, in March and June 2020. Youth were required to attend a short orientation session in order to receive the grant, where they were provided information about the program’s purpose and follow-up requirements. Specifically, youth were told that they needed to attend the second orientation session in order to receive the second tranche, and that there would be some telephone and in-person monitoring in the intervening period. In practice, the monitoring was relatively light touch and primarily over the telephone due to COVID-19 restrictions.

The BDS comprises a multi-faceted approach to teaching managerial skills to youth entrepreneurs, and includes four days of classroom training, four months of access to a digital BDS repository, and seven individual visits by a trained financial counselor. The curriculum covers the topics of business idea formation and business models; funding sources; legal registration; marketing and pricing; and record-keeping. Its worth noting that the curriculum does not include any explicit content related to dealing with economic shocks as it was developed prior to COVID-19 as part of the original study. Nevertheless, the hypothesis that we test in this paper is whether standard business knowledge and growth tools can sustain businesses for longer and allow youth entrepreneurs to better cope with the impacts of the crisis.

In terms of timing of BDS, the classroom sessions took place in February 2020, and the digital repository became available starting in August 2020. The counseling sessions were conducted between June and December 2020.

For the purposes of this paper and for statistical power purposes, we pool all forms of BDS into a single composite treatment that we label “Any BDS”. The results are not different if we disaggregate the analysis.

# **<mark>3.2. Data Description</mark>**

The analysis presented in this study is based on two sources of data: (i) an in-person baseline survey that was conducted in January and February 2020, prior to any COVID-19 related restrictions; and (ii) phone surveys that were conducted in 3 rounds between June and November 2020 to retrospectively collect data from February to October on the impact of COVID-19 on business and individual outcomes.

The in-person baseline survey comprised 9,380 respondents from 15 counties throughout Kenya, whereas the phone surveys comprised 1,054 respondents from 5 counties where the study interventions had started to roll out.

8
- 22 -

discussion in the literature. Deb and Filippini (2013) found that the service quality, defined by density of transport routes, has the highest demand elasticity. Shorter waiting time can also increase the demand for public transportation (e.g., Hensher and Rose 2007).

Safety matters. As discussed, there is a concern about safety of minibus operations in Antananarivo. Over 6,500 people died in road crashes in the city in 2016, of which more than 20 percent involved minibuses (World Bank 2019). The coefficient of _dUNSAFE_ is negative and significant. When minibus operations are unsafe (though judgements are fundamentally subjective), the demand declines. This is consistent with increasing concern among users. On the other hand, there is no significant result for comfortability of minibus operations ( _dUNCOMF_ ).

For the individual effects, it is found that informal minibus transportation in Antananarivo is a “normal good” (as opposed to inferior good). The estimated coefficients of the income group dummy variables tend to increase with income level ( **Figure 21** ). This is contradictory to earlier studies in the literature, which generally show that the income elasticity is negative (e.g., Deb and Filippini, 2003; TRL 2004; Paulley et al. 2006). In developed countries, the demand for public transportation could decrease with household income because people with high income can own and use their private cars. However, in developing countries, such as Madagascar, buying a car is not an immediate, viable option. Private vehicles are still expensive for most people. As shown above, the private vehicle ownership is only 6 percent in Antananarivo. Therefore, people still tend to continue using minibuses even if their income increases. A policy implication is straightforward: It continues to be important for the government to make increased efforts toward improving efficiency in this public transport sector.

Still, relatively speaking, private car owners are less likely to use minibuses than those who do not own a car. After all other characteristics, including household income, are controlled for, the coefficient of _dCAR_ is significantly negative. This is consistent with the literature.
2. Assume an increase in the imputed interest rate of 4% or 8%, depending on scenario.<sup>10</sup> 3. Recalculate new interest payment assuming increased interest rate (i.e. new interest payments equals new interest rate times total debt).

4. Recalculate new interest coverage ratios using new interest expense. Recalculate new proportion of firms with interest coverage below 0.75 and new uncovered debt ratio.

5. Compare new uncovered debt ratios for shocks scenarios of increased interest rates by 4% and 8% to the baseline (i.e. actual 2006 data).

The results of this simulation exercise are reported in Table 8. The results of the simulation exercise show that most vulnerable sectors are: construction, finance, industry and trade.

**Table 8.  Simulation of Interest Rate Shocks**

|**Sector**|**Baseline**|**UDR**<br>**Interest**<br>**rate**<br>**shock**<br>**4%**|**Interest**<br>**rate**<br>**shock**<br>**8%**|**Proportion**<br>**below 0.75**<br>**Baseline**|**of firms w**<br> <br>**Interest**<br>**rate**<br>**shock**<br>**4%**|**ith ICR**<br>**Interest**<br>**rate**<br>**shock**<br>**8%**|
|---|---|---|---|---|---|---|
|1. Mining|0.5%|0.5%|0.5%|9%|9%|9%|
|2. Industry|19%|31%|32%|25%|32%|34%|
|3. General|||||||
|Trade/Services|21%|28%|28%|24%|28%|28%|
|4. Construction|27%|27%|52%|22%|33%|33%|
|5. Transportation|3%|3%|3%|30%|30%|30%|
|6. Tourism and Hotels|7%|7%|7%|12.5%|12.5%|12.5%|
|7. Utilities and Energy|0%|12%|68%|0%|25%|50%|
|8. Financial|24%|39%|45%|22%|28%|32%|

Figure 3 shows the same results for UDR in a more visual form. This analysis so far shows that even a small interest rate increase of 4% will be dangerous to several firms in construction, finance, utilities and energy, industry or trade.

> 10 Assume that firms that report zero interest payments, and hence have zero implied interest rate continue to have zero interest rate (i.e. interest rate increase affects only those borrowers who report non-zero interest expense). Also, a few outlier observations for which reported interest rate was above the 99% percentile of the interest rate distribution were removed and not used in the analysis.

19