rafmacalaba/datause-encoder-data huggingface dataset:
||Thailand from 2020 to<br>2050.|For example, the total electricity production costs in 2050<br>increases by 40% in Indonesia and by 82% in Thailand.|
|---|---|---|
|Würzburg et al. (2013)|A multivariate regression<br>model is used in Germany<br>and Austria.|The changed electricity mix due to the German nuclear<br>electricity generation exit did not affect the size of the<br>merit order effect, where price decreases occur because<br>(additional) RE based electricity bids into the market at<br>lower marginal costs. It also lists several earlier studies<br>analyzing the impacts of RE in the EU electricity market.|

# **4.4 Buildings sector**

Several studies examine the economics of energy-efficient technologies, particularly heating and cooling devices, insulations, lighting devices, cooking appliances, electronic and computer devices in residential and commercial buildings. These technologies reduce the demand for energy and save energy costs; they reduce emissions of GHGs and local air pollutions (e.g., indoor air pollution). Globally, a large investment is made annually in clean technologies in buildings. In 2018, it accounted for the largest share (58%) of the total US$139 billion invested in energy-efficient technologies globally<sup>12</sup> (IEA, 2019). In this section, we highlight recent literature on the economics of energy-efficient technologies in the buildings sector. Several empirical studies show significant savings in energy consumption and energy bills in the household sector (a subset of the buildings sector). Reviewing 100 existing studies analyzing EE improvements in the household sector, McAndrew et al. (2021) report a wide variation of reduction in electricity use ranging from a low of 0.5% to a high of 80% of total electricity use, with a median value of 7.9%. Some selected studies are summarized in Table 5.

With an econometric analysis using data from a field survey of residential energy consumption in Amman and Zarqa regions of Jordan, Abd Alfattah et al. (2017) find the net saving by implementing EE measures (refrigerators and freezers) from 2011 to 2020 ranges from 4,451 GWh to 17,807 GWh depending upon different scenarios, which is equivalent to saving of electricity bills by Jordanian Dinar 320 million to 1,282 million, respectively, roughly US$450 million to 1,808 million in the current exchange rate. Using a multivariate statistical approach on data collected from 1,400 dwellings in France, Belaid et al. (2021) find that low-temperature and condensing boilers, as well as floor insulation, are the most cost-effective EE measures. The

> 12 EE investments in buildings sector include building envelopes, heating, ventilation and air conditioning (HVAC), appliances, and lighting.

30
# **6. Empirical Analysis**

This section firstly describes the data employed and illustrates some descriptive statistics. Secondly, the results from the estimation of the model are presented and discussed.

# **6.1. Data and Descriptive Statistics**

The data used in this paper was obtained from Bolivia’s national household survey MECOVI<sup>14</sup> for 1999, 2000, 2001, 2002, 2005, 2006 and 2007. This survey is conducted at the end of each year, typically in November and December.

The age of entry in primary school is six and the duration of compulsory education is eight years. Therefore, samples of children who are between five and fifteen years<sup>15</sup> are selected in this analysis. The academic year is composed of about forty weeks, five days a week and four hours per day.

The focus of the analysis does not encompass private schools<sup>16</sup> , as the enrollment and attendance behaviors are likely to be driven by different factors.

Table 1 shows the proportions of children for each of the above mentioned characteristics.

# [Table 1 about here]

It is relevant to note that the population is fairly distributed in many of its features as gender, ethnic origin, living area and extreme poverty.

In fact, the proportion of male children in the population is roughly the same of the female one. Moreover, the proportion of indigenous children is slightly higher than the non-indigenous one. Likewise, extremely poor children are faintly more than non-extremely poor ones in all but one year (data in 1999 are out of the average because the definition of poor instead of extremely poor has been adopted). In the last two years, the proportion of children coming from urban areas exceeded the one of those coming from rural ones, albeit maintaining a certain level of symmetry in the population.

Finally, children speaking Spanish are more than 90 percent in all years and it is quite unusual for a child to have an educated head, but it is relatively more common that this is the father instead of the mother.

The data presented in Table 2 shows the proportions of children enrolled in primary public school, attending it and carrying out working activities. It is clear that the percentage of enrolled children has been increasing over the considered years. However, the attendance proportions do not show any clear

> 14 MECOVI is a regional program that aims at standardizing household surveys in the Latin American and Caribbean region, funded by the World Bank, the Inter-American Development Bank, and the United Nations.

> 15 A slightly wider age interval has been used to allow for children that go to school one year before or one year later the traditional age of entry.

> 16 The percentage of those enrolled in private schools is on average below 10 percent.

12
# PATENTS AND PHARMACEUTICAL DRUGS: UNDERSTANDING THE PRESSURES ON DEVELOPING COUNTRIES

# Julio Nogu6s

# I. Introduction

On September 1989--the deadline for a U.S. investigation --Argentina announced that within two years it would submit a draft law extending patent pzotection to pharmaceutical products. One year earlier--on October 20, 1988-the U.S. Federal Register published the list of goods on which the U.S. would retaliate by increasing ad-valorem tariffs rates to 100 percent on imports from Brazil; this country had refused to negotiate patent protection to pharmaceutical drugs. On December 18, 1987, the EC removed Korea from the list of countries eligible for GSP treatment. The reason: Korea had refused to grant EC manufacturers the same intellectual property protection it affords to U.S. exporters. The EC, Japan ar.d the U.S. have all tabled proposals at the Uruguay Round for significantly greater homogeneity and higher protection of Intellectual Property Rights (IPRs)--patent protection to pharmaceuticals probably being the most significant item in this agenda--around the world. The proposals are directed at developing countr5es, which--so far--have resisted This list--which goes on--of bilateral and multilateral actions raises a question: why all these pressures on developing countries? The I appreciate comments received from Bela Balassa and Paul Meo. I also appreciate efficient typing from Maria Teresa Sanchez.

.
As for the second strand of the literature, the World Bank report (2008) emphasizes the importance of securing long-term fiscal sustainability in its basic infrastructure sectors while sustaining the quality of service delivery in them. Moreover, Ragab (2005) argues that better performance of infrastructure and more efficient regulatory framework are critical to improve the business climate and promote private domestic and foreign investment in Egypt.

The third strand of the literature has analyzed the effects of public investment on private capital formation to identify whether public infrastructure investment complements or crowds out private investment in Egypt. The majority of previous studies on this topic find a positive impact of public infrastructure investment on private investment. Shafik (1992) claims that public investment tends to crowd in private investment through infrastructure investment in Egypt. Dhumale (2000) finds a positive effect of public infrastructure investment on private investment in the non oil-exporting countries (including Egypt) within the MENA region, while a crowding out effect in oilexporting countries. In a recent paper, Agenor et al. (2005) investigate the impact of public infrastructure on private investment in three countries in the MENA region (Egypt, Jordan, and Tunisia). They use a vector auto regression (VAR) model that accounts for both the flows and stocks of public infrastructure and controls for simultaneous interactions between these variables and private credit, output, and the real exchange rate.<sup>11</sup> The impulse response analysis indicates that public infrastructure has both flow and stock effects on private investment in Egypt.

# **III. The State of Infrastructure in Egypt: International Context**

## **A.  Cross-Country Comparison Using Current Data**

We start by presenting cross-country data of various infrastructure indicators for different sectors in order to compare the performance of Egypt with the rest of the world.

> 11 They propose two aggregate quality indicators of infrastructure: an “ICOR-based” and “excess demand” measures. They combine these two quality indicators in order to derive the composite index by using the principal component analysis technique.

7
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**Table 9.  FEMALE labor market outcomes by age, education and family structure: IV results**

||||PANEL A||||
|---|---|---|---|---|---|---|
||**Wage Empl.**|<br>**Paid Self- empl.**|<br>**Unpaid work**|**Wage Empl.**|**Paid Self- empl.**|**Unpaid work**|
|**_Sample:_**||**_Age 16-32_**|||**_Age 33-64_**||
|Current hh migrants|-0.297|0.045|0.196|-0.117|-0.543**|0.318*|
||(1.58)|(0.23)|(0.85)|(0.62)|(2.39)|(1.73)|
|Past hh migrants|0.124|0.133|-0.227|-0.196|0.448***|-0.095|
||(0.71)|(0.75)|(1.05)|(1.64)|(3.10)|(0.81)|
|Obs.||1172|||1651||
|||<br>|PANEL B<br> <br>||||
||**Wage Empl.**|<br>**Paid Self- empl.**|<br>**Unpaid work**|**Wage Empl.**|**Paid Self- empl.**|**Unpaid work**|
|**_Sample:_**<br>|**_Pri_**<br>|**_mary education or_**<br>|**_lower_**<br>|**_Seco_**<br>|**_ndary education or_**<br>|**_higher_**<br>|
|Current hh migrants|-0.268|-0.26|0.501*|-1.253|0.512|0.575|
||(0.48)|(0.76)|(0.22)|(0.53)|(0.58)|(0.50)|
|Past hh migrants|0.122|0.312***|-0.345***|-2.489|1.635|0.992|
||(1.63)|(3.29)|(2.76)|(0.61)|(0.58)|(0.57)|
|Obs.||1553|||1251||
|||<br>|PANEL C<br> <br>||||
||**Wage Empl.**|<br>**Paid Self- empl.**|<br>**Unpaid work**|**Wage Empl.**|**Paid Self- empl.**|**Unpaid work**|
|**_Sample:_**||**_Young children(0-_**|**_4)_**|**_N_**|**_oyoung children(0_**|**_-4)_**|
|Current hh migrants|-0.005|-2.446|4.074|-0.163|-0.254**|0.206*|
||(0.24)|(0.20)|(0.18)|(0.41)|(0.84)|(0.99)|
|Past hh migrants|-0.254|2.76|-4.321|-0.108|0.246***|0.082|
||(0.08)|(0.17)|(0.19)|(1.23)|(2.79)|(0.86)|
|Obs.||626|||2197||

_Notes_ : Absolute value of z statistics in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%. All specifications include controls and instruments as in Table 8 and 9.

29
**Table D.18:** Water and Sanitation (z-scored)

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**Water and sanitation index**(SUR)||0.110<br>(0.044)<sup>_∗∗_</sup><br>[0.037]<sup>_∗∗_</sup>|0.066<br>(0.051)<br>[0.210]|0.154<br>(0.051)<sup>_∗∗∗_</sup><br>[0.025]<sup>_∗∗_</sup>|5053|
|(1) Water and sanitation HH index|0.000<br>(1.000)|0.068<br>(0.058)<br>[0.089]<sup>_∗_</sup>|_−_0.022<br>(0.068)<br>[0.362]|0.159<br>(0.063)<sup>_∗∗_</sup><br>[0.040]<sup>_∗∗_</sup>|5053|
|(2) Water and sanitation village index|0.000<br>(1.000)|0.174<br>(0.086)<sup>_∗∗_</sup>|0.131<br>(0.097)|0.218<br>(0.104)<sup>_∗∗_</sup>|5053|
|||[0.073]<sup>_∗_</sup>|[0.298]|[0.040]<sup>_∗∗_</sup>||
|(3) Satisfaction with village sanitation index|0.000|0.087|0.087|0.086|5051|
||(1.000)|(0.043)<sup>_∗∗_</sup>|(0.049)<sup>_∗_</sup>|(0.050)<sup>_∗_</sup>||
|||[0.073]<sup>_∗_</sup>|[0.298]|[0.059]<sup>_∗_</sup>||

_Notes:_ Variables are control-group normalized at endline (z-scored). Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. The household index is comprised of water sources for drinking, for other uses, the existence and type of toilet facilities and the actions households take to make water safe to drink. The village index contains the existence and types of water sources for drinking and general use as well as the existence of public toilets and waste facilities. The satisfaction index consists of questions about sanitation and health services offered as well as village cleanliness. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

**Table D.19:** Economic Outcomes (z-scored)

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**Economic outcomes index**(SUR)||0.053|0.036|0.070|5053|
|||(0.032)|(0.037)|(0.040)<sup>_∗_</sup>||
|||[0.138]|[0.262]|[0.119]||
|(1) Physical asset index|0.000|0.020|_−_0.023|0.063|5052|
||(1.000)|(0.048)|(0.053)|(0.059)||
|||[1.000]|[1.000]|[0.404]||
|(2) Agricultural asset index|0.000|0.120|0.174|0.065|5051|
||(1.000)|(0.044)<sup>_∗∗∗_</sup>|(0.057)<sup>_∗∗∗_</sup>|(0.050)||
|||[0.026]<sup>_∗∗_</sup>|[0.010]<sup>_∗∗∗_</sup>|[0.404]||
|(3) Dwelling materials index|0.000|0.053|0.020|0.087|5052|
||(1.000)|(0.053)|(0.060)|(0.058)||
|||[0.875]|[1.000]|[0.404]||
|(4) Total consumption expenditure|0.000|0.019|_−_0.027|0.066|5053|
||(1.000)|(0.045)|(0.050)|(0.055)||
|||[1.000]|[1.000]|[0.404]||

_Notes:_ Variables are control-group normalized at endline (z-scored). Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Significance: * is significant at the 10% level, ** is significant at the 5% level and *** is significant at the 1% level.

A27
With the new data set we also contribute to a recent wave of studies that use occupational statistics to describe structural change. For example, Newfarmer, Page and Tarp (2018) study various industries in agriculture and services that share important characteristics of manufacturing industries such as tradability, scalability and productivity growth, and name these “industries without smokestacks”. Baccini et al. (2023) stress important variation in activities within the services sector from provision of informal personal services to much more productive formal business services and transport services that are used also for exporting. Duernecker and Herrendorf (2022) show how the share of service occupations within the good sectors increases in later stages of development. Using occupational statistics, Timmer et al. (2019) documented specialization of high-income countries in activities that are knowledge intensive. The _Jobs of the World Project_ is another prominent example of the compilation of a large-scale data set to compare changes in occupational structures across countries and over time. Using this data, Bandiera et al. (2022) show how the nature of jobs vary across countries by stages of development, and within countries by household wealth and gender. In this study we focus specifically on the characteristics of jobs in exports. We show how adding an industry dimension enhances the ability of occupational statistics to capture different types of worker activities. We extend the database of Timmer et al. (2019) increasing the country coverage to 52 countries at all levels of development and adding new data on activities at a higher level of detail. More precisely, activities are identified in the data by cross-classifying 13 occupational classes and 35 industries of work, resulting in 455 distinct activities. This fine-grained dataset allows us to describe for the first time overall development patterns of export specialization from an activity perspective.<sup>2</sup> As a final note we wish to emphasize that the nature of this study is explorative. Our findings are suggestive of important complementarities between various export activities, but we stay agnostic about their precise nature. Co-occurrence of specialization in particular activities might be driven by comparable developments in countries’ endowments such as the buildup of general human capital or the business environment. But it also points to the possibility of spillovers and

> 2 Relatedly, Diodato et al. (2022) enrich export product data with occupational statistics. They assume however that production technologies are the same across countries applying occupation structures found in U.S. production to all countries. We show however that production technologies are not constant around the world such that products will not map one to one into a set of activities.

5
is the case for the coefficient on the 5<sup>th</sup> income quintile and its interaction with initial GDP per capita. Estimates based on the WDI income inequality data thus confirm the message from our baseline regressions: increases in income inequality increase GDP per capita in poor countries but decrease it in rich countries.

Table 4 shows that our main finding holds for the sub-sample of countries located in Latin America and the Caribbean. These economies are notorious for their relatively high levels of inequality. For example, of the top 10 countries with the highest Ginis, 7 are from Latin America and the Caribbean in 2000-2010. Table 4 shows that for the sub-sample of countries located in Latin America and the Caribbean, the coefficient on the interaction between the Gini coefficient and initial GDP per capita is significantly negative; this is also the case for the 5<sup>th</sup> income quintile. On the other hand, the coefficients on the interactions between initial GDP per capita and the income shares of the 1<sup>st</sup> , 2<sup>nd</sup> , 3<sup>rd</sup> , and 4<sup>th</sup> quintiles are significantly positive. We note that in terms of instrument strength the F-statistic continues to be above 10 for the majority of columns in Table 4; however it is below 10 in columns (4) and (5). The lower F-statistic is expected because the sample size is smaller.

Table 5 reports estimates for the pre- and post-1990 period. The split between pre- and post1990 period is of interest because it allows to examine whether the estimated coefficients are stable over time. The year 1990 marks the mid-point in the time period over which the model is estimated so it is a natural point to check for stability of coefficients. The main message of Table 5 is that the heterogeneous effect of income inequality on GDP per capita between rich and poor countries is present in both periods.

# _5.3. Effects on Investment and Human Capital_

In the Galor and Zeira (1993) model the mechanism through which income inequality affects aggregate output is through investment, in particular, investment in human capital. In this section 11
Our second measure of entry and startup costs, tries to capture the actual number of procedures, time, and costs to initiate operations in a country. Data on the procedures required to open banks across countries come from Barth, Caprio, and Levine (2001). They construct an index of bank entry requirements that takes values from 0 to 8 depending on whether banks are required to submit none, some, or all of the following to issue a bank license:

- (1) Draft by-laws.

- (2) Intended organizational chart.

- (3) First 3-year financial projections.

- (4) Financial information on main potential shareholders.

- (5) Background/experience of future directors.

- (6) Background/experience of future managers.

- (7) Sources of funds to be used to capitalize the new bank.

- (8) Intended differentiation of new bank from other banks.

This index is combined using the method of principal components with two other measures of the costs of starting a business that come from the World Bank _Doing Business Indicators._ In particular, we include survey estimates of the actual monetary costs (in dollars) and, separately, the time (in number of days) involved in opening a business across countries. The first measures all identifiable official expenses in setting up any business (not necessarily banking), namely fees, costs of procedures and forms, fiscal stamps, legal and notary charges, etc.<sup>9</sup> The second, the

> 9 As stated in the Doing Business Report “the text of the Company Law, the Commercial Code, and specific regulations and fee schedules are used to calculate the costs [of starting a business]. If there are conflicting sources and the laws are not clear, the most authoritative source is used. The constitution supersedes the company law, and the law prevails over regulations and decrees. If conflicting sources are of the same rank, the source indicating the most costly procedure is used, since an entrepreneur never second-guesses a government official. In the absence of fee schedules, a governmental officer’s estimate is taken as an official source. In the absence of a government officer's estimates, estimates of incorporation lawyers are used. If several incorporation lawyers provide different estimates, the median reported value is applied. In all cases, the cost excludes bribes.” A list of all procedures considered in estimating the costs of starting a business can be found in:

13
adjusting, married females from Cameroon, Lesotho, Rwanda, and Zimbabwe still report using condoms significantly less than unmarried females at the 1 or 5 percent confidence level.

# **Section 5: Discussion**

The results from this analysis confirm a number of findings from earlier studies. The first is that, overall, reported condom use is fairly low, despite the grave dangers posed by HIV throughout Sub-Saharan Africa. Other studies have shown this. Bunnell and others (2008) report that 67% of an HIV positive nationally representative sample in Uganda had never used a condom and nearly half of them were in HIV-discordant relationships. We also corroborate that males generally report using condoms more frequently than females. Lastly, we substantiate that unmarried individuals use condoms more frequently than married individuals with their spouse. This study also highlights a little documented finding that married females often use condoms significantly less than unmarried females in extramarital situations while married males tend to use condoms during extramarital sex at similar rates to unmarried males.

An important aspect of this study is that the data comes from nationally representative samples that are comparable across all the 13 Sub-Saharan African countries included. The data sets come from the most recent standard DHS (Burkina Faso, Cameroon, Ethiopia, Ghana, Guinea, Kenya, Lesotho, Malawi, Rwanda, Senegal, and Zimbabwe) and the AIS (Côte d’Ivoire and Tanzania), which both include similar questions relating to condom use. These provide a powerful tool to understand condom 18