rafmacalaba/datause-encoder-data huggingface dataset:
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<u>Table 3. Summary Statistics</u>

|Variable|Abbr.|Obs.|Mean|Std.Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Bid amount 1/|_b_|131|50.61|73.77|0.22|435.49|
|Number of bidders|_n_|131|5.47|3.67|2.00|18.<br>00|
|Dummy for technical evaluation|_D _<sup>_TE_</sup>|131|0.67|0.47|0|1|
|Dummy for dam contracts|_D_<br>_DAM_|131|0.10|0.30|0|1|
|Dummy for civil work|_D _<sup>_CIVIL_</sup>|131|0.65|0.48|0|1|
|Installed generation capacity (MW)|_GCAP_|131|125.99|284.51|0|1,200.00|
|Length of transmission lines (km)|_LINE_|131|50.69|153.39|0|765.00|
|Max capaity of substations (kV)|_SUBS_|131|65.46|136.48|0|500.00|
|Engineering cost estimate 1/|_COST_|131|46.93|67.80|0.36|406.61|
|Past transport ODA in the world 1/|_WAID_|131|6,980|615|6,622|9,807|
|Past transport ODA in each country 1/|_CAID_|131|413|341|15|1,339|

1/ In millions of constant 2005 U.S. dollars.

Source: Author’s calculation.

Regarding additional exogenous variables _Zj_ , two numbers are created: _CAID_ is the amount of gross ODA disbursement in the energy sector that each project host country received for the three years before the award of a particular contract. _WAID_ is similarly defined as the total ODA amount distributed to the energy sector all over the developing world. Both are in constant 2005 U.S. dollar terms. These are lagged variables and considered independent of firms’ bidding behavior in individual current auctions. But they would still likely influence firms’ capabilities to enter the competition. Our sample data represent only some 10 percent of total energy-sector ODA (Table 4). Several domestic and multinational enterprises have repeatedly been participating in the ODA-related infrastructure market.<sup>16</sup> If firms are already engaged in a large number of similar development projects, fewer bidders would be willing to bid on further works. It is also noteworthy that _CAID_ and _WAID_ are not invariant across auctions within a country, if contracts are awarded in different years. In the estimation models, they are in logarithm. The aid data originate from the OECD Creditor Reporting System database.

Table 4. Total Transport-Sector ODA Projects <u>(Millions of constant 2005 U.S. dollars)</u>

||2000|2001|2002|2003|2004|2005|2006|2007|
|---|---|---|---|---|---|---|---|---|
|Total amount of ODA|55,112|54,580|62,207|76,362|73,935|94,407|95,286||
|Of which, infrastructure 1/|10,817|9,829|8,119|8,344|13,764|13,063|11,979||
|Of which, energy|1,758|2,103|2,706|3,467|6,024|3,238|3,720||
|Total cost estimates of our sampled projects|294|407|328|306|632|508|29|89|

1/ Including energy, water and sanitation, transport and storage, and communication.

Source: Author’s calculation based on own data and OECD.Stat.

16 There is one firm which solely or jointly obtained five large contracts in our sample. Nonetheless, the market concentration generally appears modest. The concentration ratios of the top five firms in terms of the number of contracts awarded in our sample is 22 percent in electricity project procurements (Estache and Iimi, 2008c).
pattern in the 26 countries he studies is of a preference for at least one son and one daughter. He finds some weak evidence for son preference in North Africa and Sri Lanka. Arnold (1997) analyzes data from 44 countries, but largely focuses on the effect of sex ratios on _stated_ fertility preferences and on some fertility behaviors such as current pregnancy status and average birth spacing. He finds regional variation in the extent of an association between sex ratios and the outcomes he analyzes, with the strongest results suggesting son-preference for the Asian and North African countries.

In this paper, we use information on 5 million births from 1.3 million mothers in 65 countries to analyze how the gender mix of children already born affects fertility decisions in the developing world. We extend the literature in a number of important ways. Our analysis includes a large number of developing countries from disparate regions. We document not only regional patterns in son-preferred differential stopping behavior, but also within-country differences by location (urban versus rural), education (women who have completed primary school versus those with less schooling), wealth levels (above and below the median of a composite measure of assets), and over time (by comparing different birth cohorts of mothers). We analyze the extent to which observed patterns in son-preferred differential stopping behavior strengthen or weaken as the total number of children decreases. And, finally, we link these results to the wider literature on gender composition and resource dissolution in larger families.

# **II. Methods and data**

_Estimating the impact of gender balance on fertility behavior_ We begin our analysis by estimating the impact of gender balance of children on the probability of subsequent births. The basic model estimates: 3
# **Labor Force Participation**

It is not surprising that labor force status is strongly correlated with poverty outcomes in Eastern Europe. In all countries, wage earners and the self-employed have the lowest poverty incidence and poverty gap (Table 4). Which of these two groups does best depends on the stage of transition. In Hungary, with perhaps the best developed private sector, and the earliest initiation of transition, the self-employed have the lowest poverty incidence-slightly more than half the national rate. Elsewhere though, wagework still provides the better alternative.

Table 4 also shows though that being a pensioner sharply increases the odds of being poor, except in Poland, and in all countries pensioners have above average poverty gaps. The favorable situation of pensioners in Poland is due to the generosity of the Polish pension system. Of all East European countries, Poland increased spending on pensions the most: between 1988 and 1993, pension spending rose from 6.9 percent to 14.7 percent of GDP (Perraudin and Pujol, 1994). One reason for this was the sudden swelling of the ranks of pensioners by 1.5 million early retirees in the period 1989-1992. Furthermore, in 1992-93, the average pension in Poland was 64 percent of the average wage-the highest ratio in Eastern Europe. Polish pensions were at that time also fully indexed (Milanovic, 1995). 10

**I0** The pension systen in Poland is discussed in detail in World Bank (1993) and Peffaudin and Pujol (1994). For a more general discussion of pension systems in transition economies, see World Bank (1994).

30
**Table 4:** Patient Deaths

|Total Cases in|Predicted Deaths|Predicted Deaths|Difference|
|---|---|---|---|
|Last 2 Weeks|in Control|in Pooled||
|2 reported cases|0.49|0.36|0.13|
||(0.04)|(0.05)|(0.06)<sup>_∗∗_</sup>|
|5 reported cases|1.23|0.80|0.43|
||(0.11)|(0.17)|(0.19)<sup>_∗∗_</sup>|
|10 reported cases|2.45|1.53|0.92|
||(0.21)|(0.36)|(0.40)<sup>_∗∗_</sup>|

_Notes_ : Standard errors clustered on section. Treatment effects estimated using OLS including matching-triplet and week fixed effects. Predicted deaths based on estimates in Table E.4 model 1. Significance: *p < 0.10, ** p < 0.05, and *** p < 0.01.

crisis conditions.

# **4.2.1 Additional Checks within Ebola Sample**

Table E.13 reports balance checks for the 160 sections in our Ebola sample. We find negligible differences along most baseline measures relative to levels in control; the imbalance observed in the full sample carries over to this subset. As a robustness check, we aggregate unbalanced baseline indicators to the section-level and includes these as controls (Table E.26). In Table E.20, we also look at whether treated sections are more exposed to the epidemic due to their proximity to index cases in Guinea or Sierra Leone or their road density. We find that, if anything, treated sections are slightly further from index cases.

We look for evidence of spillovers between treated and control sections, particularly indications that patients traveled from control to treated sections. Such reallocation within our sample would amplify our treatment effects on Ebola reporting. Assuming that patients minimize travel costs, spillovers should be largest in treated sections that border (populous) control sections. In Table E.21, we interact our treatment indicator, first, with the number of bordering control sections and, second, with the population (based on 2004 census data) in bordering control sections. If patients from control sections report in adjacent treatment areas, the coefficients on these interactions will be positive; yet, our estimates are negative and insignificant.<sup>37</sup> Following a similar logic, spillovers could occur via road networks. Using data from Open Street Map, we count how many control sections a treatment section is connected to via the road network (see Figure E.8). Table E.23 interacts this variable with our treatment indicator and, again, finds no indication

> 37 We also calculate each clinic’s proximity to the next nearest control clinic in the full sample. (We do not have exact coordinates of clinics, and thus geolocate clinics using the centroids of the census enumeration areas that contain the clinics.) In Table E.22 we find that treated sections report more cases when their treated clinic is far from the next control clinic — the opposite of what we would expect if spillovers are amplifying our effects.

30
mitigating disaster and conflict (Coskun et al. 2006). With increasing computer processing speeds and data storage, remote sensing and GIS methods to estimate runoff accommodate increasing complexity (Schultz 1994). Finally, many global models use precipitation data as a primary input, which show considerable variance and lack accuracy, as Fekete et al. (2004) demonstrated in the six monthly datasets they analyzed.

Applications of remote sensing to hydrology have improved dramatically over the past decade. Alsdorf et al. (2007) summarize the capabilities of remote sensing to calculate the spatial and temporal variability of water stored on and near the surface of all continents, illustrating this information can be measured reliably from space. A review of the literature notes several examples of specific basin runoff applications. Scipal et al. (2005) present a Basin Water Index to estimate runoff from the Advanced Scatterometer (ASCAT) ERS microwave sensor, with a 50km spatial resolution over the Zambezi River. Papa et al. (2007) apply a multi-satellite technique including Special Sensor Microwave/Imager (SSM/I) to the Ob river basin, which allows them to account for snow parameters that impact river runoff. Finally, the Basist Wetness Index (BWI), derived from SSM/I observations, is used to estimate surface wetness and runoff (Basist et al. 1998). The BWI takes advantage of the global coverage of the SSM/I satellite to observe most of the land surface twice a day, even under various weather and environmental conditions and time of day, because the wavelength of microwave radiation can penetrate cloud cover, dust, haze and rainfall. These observations are binned into 1/3 arc degree (approximately 30 km<sup>2</sup> ) monthly values.

Characteristics of hydrologic extremes vary and a variety of studies specify the distribution function in the analysis according to sample size, low or high flow, and distribution of the sample. Smakhtin (2001) and WMO (2008) provide a review of low flow hydrology, where much evidence suggests the distribution of hydrologic variables is heavy tailed (Katz et al. 2002). Among the distribution possibilities, the gamma distribution is simple and reasonably accurate for runoff models (Singh 2007).

Since future hydrologic variation is difficult to predict without an understanding of historical variation and long term normal averages, the BWI provides historical spatial and temporal coverage to quantify hydrologic variability with a globally consistent methodology and geographic coverage provided by SSM/I. Once the model is established, current observations 7
networks-- and to allow analysis of the impact of an effective system of duty exemptions for inputs used in the production of exports in India. Duty exemptions were incorporated in the GTAP model and data base following the methodology developed by Ianchovichina (2004). This duty exemption model allows for two separate activities in each industry. Production of exports is represented as an activity for which imported intermediate inputs are available duty-free. Production for the domestic market uses the same technology, but requires payment of duties on intermediate inputs. Firms engaging in production for either the domestic market or the export market purchase both imported and domestic intermediate inputs which are imperfect substitutes following the Armington structure. Ianchovichina (2004) documents the approach used to introduce duty exemptions into the GTAP model and shows that failing to account for duty exemptions introduces bias in trade liberalization outcomes in countries with such a system.

The 57 sectors and 87 regions of the GTAP 6 Data Base were aggregated into 26 sectors (Table 2) and 24 regions (Table 3) based on the importance of these sectors and regions in China’s and India’s trade patterns. To start, we used historical and projected growth rates for GDP, skilled labor, unskilled labor, capital, and population to roll the global economy forward to 2005. This pre-simulation essentially updates the database for 2001 to 2005, the starting point of our projection simulations. It also includes the removal of textile and apparel quotas on exports to Canada, USA, and EU under the Agreement of Textiles and Clothing; China’s WTO accession commitments following Ianchovichina and Martin (2004); and the remaining commitments of developing countries under the Uruguay Round using tariff data from Jean, Laborde, and Martin (2005). The efficiency gains in China’s motor vehicle sector resulting from WTO accession reforms are captured using productivity shocks as in Ianchovichina and Martin (2004).

# **Will India’s integration into global production networks intensify global competition?**

While the examination of trade data above suggests that there is surprisingly little overlap in the export mix of China and India, this might change in light of India’s move to greater integration in the world economy. We therefore look at the impact of implementing a well-functioning duty exemption/drawback system in India on the rest of the world by including the very large reductions in protection that have been undertaken in India since 2001; the further reductions in manufacturing sector protection that have been foreshadowed by the government; and measures intended to enable Indian 9
In other words, the increase in the proportion of workers with a better level of education generates an increase in the labor force with a greater probability of moving to jobs performing non-routine cognitive tasks to the detriment of manual tasks.

In this sense, the results suggest that the possibility of increasing the involvement of cognitive tasks in a sustained manner is strongly linked to the level of qualifications and skills that the workers have. In effect, from the point of view of the labor market, it is clear that space still exists in both countries to move from occupations intensive in manual tasks to those intensive in cognitive tasks if an expansion in the scope of the educational system can be achieved and if the school drop-out rate can be reduced, especially in middle and higher levels of education.

# 4.3 Changes from an intergenerational perspective

It could be suggested that it is the youngest generation of workers which has the greatest capacity to adapt to technological change, developing cognitive tasks more intensively which complement the new technologies. On the other hand, older generations could have greater difficulty in redefining tasks which are performed in their occupations, exposing themselves to greater risk of technological unemployment. In this context, a complementary approach is the analysis of the evolution of the content of tasks in the average occupation according to the cohort of birth of the workers. For that, Figure 6 shows the evolution of the relative intensity of each type of task in the average job, according the birth cohort of the workers.

Figure 6. Intensity index of the tasks in the job (��) according to the cohort. Years 1995/98 – 2015.

a) Non-routine cognitive tasks - analytical Argentina Uruguay <!-- Start of picture text --> 0.5 0.5<br>0.2 0.2<br>-0.1 -0.1<br>1998 2003 2015 1995 2003 2015<br>-0.4 -0.4<br>-0.7 -0.7<br>after 81 80-76<br>after 81 80-76<br>75-71 70-66<br>75-71 70-66<br>65-61 60-56<br>65-61 60-56<br><!-- End of picture text -->

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tNTRODUCTION Iv Table 12 Fertility and Mortality Rates by Geographic Region and by Income Group, 1985-90, 2000-05, and 2020-25

||||1985-90|||||2000-05|||||2020-25|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Region or||||||||||||||||
|income<br>group|COR|<br>**TFR**|<br>CDR|e(0)|**IHR**|<br>COR|<br>TFR|<br>**CDR**|e(O)|**IMR**|<br>CBR|<br>TFR|<br>**CDR**|e(0)|IMR|
|World|27.1|3.43|9.7|64.9|70|22.6|2.91|8.3|68.2|48|18.7|2.48|8.4|70.6|35|
|Less devetoped<br>(a)|31.0|3.92|9.6|62.1|78|25.0|3.13|7.9|66.2|52|19.9|2.55|7.8|69.3|I8|
|More developed (a)|14.7|1.90|9.9|74.1|15|12.9|1.89|10.0|76.0|10|12.5|2.03|11.6|77.0|9|
|Africa|44.4|6.14|14.2|53.4|107|38.1|5.09|10.2|58.8|73|27.8|3.36|7.6|64.0|52|
|East Africa|46.1|6.31|14.4|53.0|110|39.9|5.39|10.4|58.4|78|29.7|3.62|7.6|63.8|54|
|West Africa|47.0|6.52|16.1|50.2|113|40.8|5.52|11.5|56.1|78|29.1|3.51|8.2|62.2|55|
|North Africa|37.1|5.19|10.4|59.4|84|29.6|'80|7.4|64.6|50|20.7|2.46|6.6|u8.5|36|
|America|23.4|2.84|8.1|70.2|44|17.9|2.28|7.1|73.0|27|15.3|2.11|8.2|74.6|19|
|Latin America<br>and||||||||||||||||
|Caribbean|28.6|3.57|7.4|66.6|56|20.6|2.46|6.1|70.5|33|16.6|2.15|<br>7.1|73.0|'3|
|Northern<br>America|15.4|1.87|9.0|75.7|10|12.9|1.90|8.7|77.4|8|12.5|2.03|10.5|78.0|7|
|Asia|27.7|3.47|8.9|63.9|71|22.0|2.76|7.7|67.8|45|17.7|2.32|8.1|70.7|32|
|East and Southeast<br>Asia|22.6|2.66|7.1|68.4|40|17.5|2.21|<br>7.0|71.8|21|15.1|2.10|8.5|73.9|16|
|South Asia|34.6|4.70|11.6|57.0|102|<br>27.1|3.38|8.8|62.3|67|20.0|2.47|8.0|66.9|46|
|Southwest<br>Asia|36.6|5.11|8.8|63.4|73|30.3|4.00|6.4|67.9|43|23.8|2.94|6.0|70.8|31|
|Europe and U.S.S.R.|14.9|1.96|10.7|72.8|18|13.1|1.91|10.6|75.0|12|12.7|2.04|11.7|76.2|10|
|Oceania|20.1|2.55|<br>8.1|72.1|28|17.8|2.41|7.9|73.7|17|15.0|2.15|9.0|74.8|14|
|tncome group (b)||||||||||||||||
|Low|30.8|3.89|10.2|61.1|84|25.2|3.18|8.5|65.3|58|20.1|2.58|8.2|68.5|41|
|Lower-middle|31.5|4.05|8.4|64.0|63|24.5|3.00|6.7|68.1|38|19.2|2.41|6.9|71.0|28|
|Upper-middle|26.7|3.39|8.1|67.3|53|21.6|2.73|<br>6.9|70.9|32|18.0|2.39|7.5|73.0|23|
|High|13.9|1.79|9.4|76.1|12|12.7|1.90|9.6|77.5|10|12.6|2.10|11.6|77.9|9|
|Nonreporting<br>nonm,embe|r19.6|2.52|10.6|69.1|32|16.4|2.28|9.9|72.1|24|15.3|2.22|10.2|73.8|20|

Note: Crude birth rates (CBR) and crude death rates (CDP) are per 1000 population; total fertility rates (TFR) are per **woman** 15-49 years of age; life expectancy at birth, **e(O),** is given in years; and infant mortaLity rates (IMR) are per 1000 live births. (See definitions in World Bank 1989.) a. "More developed" comprises Europe. U.S.S.R.. Nor hern America (United States and Canada), AustraLia, New Zeatand. and Japan. "Less developed" comprises the rest of the world.

b. Criteria for income groups are given in World Bank 1989. **See** listing in Exhibit 1.
# **Acknowledgments**

Starting a Foreign Investment Across Sectors is part of the FDI Regulations database of the Global Indicators and Analysis Department (GIA) of the World Bank Group (WBG). The lead authors, Christian de la Medina Soto and Tania Ghossein are Private Sector Development Specialists working in GIA.

The authors are grateful for valuable peer review comments received from Douglas Zhihua Zeng, Asad Alam, Chunlin Zhang from the Africa region at the WBG; Alexander Pankov and Vyacheslav Zhuk from the Eastern Europe and Central Asia region; Julio Loayza and Jaime Ortega from the Latin America and the Caribbean region; Gabi George Afram, Akira Dhakwa, Onno Ruhl, Tahseen Sayed Khan, Shyamal Krishna Shrestha, and Niraj Verma from the South Asia region; Joseph Battat, Pierre Guislain, Christine Qiang, Roberto Echandi, Xavier Forneris, Peter Kusek and colleagues in the WBG Investment Climate Department. Additional valuable comments were received on earlier drafts of the paper from Melissa Johns, Augusto Lopez-Claros, Tanya Primiani, and Federica Saliola from GIA. The authors are also grateful for contributions to the analysis and writing made by Marc Reifsnyder de Chassey, Priscila Uliana and Vinesh Winodan.

Two groups of people provided pro-bono contributions without which the data and indicators could not have been gathered and developed.

The first is the group of lawyers, accountants and business regulations experts in each of the 103 economies covered by the report who responded to the _Investing Across Sectors_ and _Starting a Foreign Investment_ legal questionnaires. Their input was the fundamental source of data for the analysis. Grateful acknowledgment of these contributors is made in the Contributors section at the end of this report. Additional details about the contributors across all topics of the FDI Regulation database are provided at http://iab.worldbank.org/Local-Partners/2012.

The second is the expert consultative group formed to provide technical input to the development of both topics. This group of global experts provided input on the focus of the legal questionnaires and on the methodology to develop the indicators as well as on the analysis of the data. Members of the expert consultative group include:

- Keith Clifford Bell, World Bank Group, Washington DC

- Kim Bettcher, Center for International Private Enterprise(CIPE), Washington DC

- Thomas Farole, World Bank Group, Washington DC

- Jean-Paul Gauthier, World Economic Processing Zones Association, Delaware

- Susan Karamanian, George Washington University Law School, Washington DC

- Gerald Korngold, New York Law School, New York

- Terry Selzer, Husen Advokater, Denmark

2