rafmacalaba/datause-encoder-data huggingface dataset:
# **List of figures**

|Figure 1: Fuel shares in global primary energy supply (EIA 2007) ................................. 93|
|---|
|Figure 2: Share of renewables in global energy supply (IEA 2006)................................. 93|
|Figure 3: Regional distribution for each renewable source (IEA 2006)........................... 94|
|Figure 4: Share of various sources of renewable in each region (IEA 2006) .................. 94|
|Figure 5: End-use sectors that consume renewable energy (IEA 2006)........................... 95|
|Figure 6: Global production of ethanol and biodiesel (Martinot 2005)............................ 95|
|Figure 7: Correlation between GDP and use of biomass energy for various countries<br>(Barnes 1996)............................................................................................................ 96|
|Figure 8: Schematic of a bioenergy production system ................................................... 96|
|Figure 9: Poverty and biomass energy use (Karekezi 2006) ............................................ 97|
|Figure 10: LCA of biomass gasification (Mann 1997)..................................................... 97|
|Figure 11: Conceptual model of supply and demand for multi-purpose crop ................. 98|
|Figure 12: Relationship between crude oil price and break-even price of corn for ethanol<br>(Tyner 2007) ............................................................................................................. 98|
|Figure 13: Impact of biofuel production on global crop prices in 2020 (Msangi 2006) .. 99|
|Figure 14: Supply curve for carbon sequestration through biomass activities (Schneider<br>2003) ......................................................................................................................... 99|

3
**_Energy demand models for policy_** **_<u>formulation</u>_**

Hunt, L. C., G. Judge and Y. Ninomiya, 2003, Underlying trends and seasonality in UK energy demand: A sectoral analysis, Energy Economics, 25(1), pp. 93-118.

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Iniyan, S., L. Suganthi and A. A. Samuel, 2006, Energy models for commercial energy prediction and substitution of renewable energy sources, Energy Policy, 34, 2640-53. Ishiguro, M. and T. Akiyama, 1995, Energy Demand in Five Major Asian Developing Countries: Structure and Prospects, World Bank Discussion Papers 277, World Bank, Washington D.C.

Jaccard, M., R. Murphy and N. Rivers, 2004, Energy-environment policy modelling of endogenous technical change with personal vehicles: combining top-down and bottom-up methods, Ecological Economics, 51, pp. 31-46.

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time periods, and data collected from weather stations. Station data has the advantage of being based only on observation and not including a modeled component. However, station networks often lack complete geographic coverage of a given area and station records may be incomplete, introducing temporal gaps in coverage as well. Re-analysis solves these problems but relies on models to do so. Despite the reliance on models, re-analysis data is broadly believed to provide a reasonable best estimate of weather variables (Auffhammer et al., 2013). As a result, it is widely used in both environmental and development economics (Schlenker and Lobell, 2010; Hsiang, 2016; Emerick, 2018). I chose re-analysis data for this project because comprehensive station data is not available. The ERA5 Land hourly product provides data at an hourly level on a grid of 0.1<sup>_◦_</sup> _×_ 0.1<sup>_◦_</sup> , which translates to a 9km resolution. I collect data on cloud cover, temperature, and precipitation over the full sample from 2003 to 2014. I aggregate these weather variables to the district level in the primary analysis.

**Agricultural Data:** I collect data on Indian agriculture from a number of sources. The first is the ICRISAT Meso data (Rao et al., 2012). This data is collected by the International Crops Research Institute for the Semi-Arid Tropics and measures the performance, structure, and behavior of the agricultural economy at the district level in India since 1966. I use data from 2003 to 2014. Because Indian district boundaries have changed over time, they provide both apportioned, where they adjust data for boundary changes, and unapportioned data in which data is not re-apportioned based on changes. I use the unapportioned data and manually re-apportion data to the district boundaries as they were recorded in the 2001 census to align with my other data sources. I use data on district level cropping patterns and land holdings from ICRISAT.

I supplement agricultural data from ICRISAT with data from the Indian Ministry of Agriculture. I scrape agricultural census data from 2001, 2005, and 2011 at the district and subdistrict levels. This provides additional data on characteristics of agricultural holdings and planting patterns at the district level to supplement ICRISAT data and provides resolution at the subdistrict level not available in the ICRISAT data. I also scrape the agricultural input survey data from 2001, 2006, and 2011 to collect data on machine inputs to production.<sup>20</sup> I also scrape data from the cost of

> 20The agricultural census can be found here: http://agcensus.dacnet.nic.in/districtsummarytype.aspx. The agricultural input survey can be found here: http://inputsurvey.dacnet.nic.in/districttables.aspx. Cost of cultivation data is here: https://eands.dacnet.nic.in/Cost_of_Cultivation.htm.

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# **Table 4 Main Factors Underlying Debt Increases, 1990-2003**

|**Country, Time Period**|**Total**<br>|**Terminal**<br>|||**Main Contri**|**buting Factors**|(% ofGDP)|||
|---|---|---|---|---|---|---|---|---|---|
||**Change**<br>(% of GDP)|**Level**<br>(% of GDP)|Primary<br>Balance|GDP<br>Growth|Real Exchange<br>Rate|Real Interest<br>Rate|Contingent<br>Liabilities|Debt<br>Indexation|Other<br>Factors|
|Argentina, 2001-2003|95.3|146.1|||**_41.7_**|**_5.5_**|||**_52.2_**|
|Brazil, 1999-2003|16.5|58.7|-18.5|-4.1||**_23.7_**|**_6.5_**|**_10.8_**|-7.2|
|India, 1997/98-2002/03|21.8|87.0|**_20.0_**|-21.2||**_20.0_**||||
|Indonesia, 1998-2000|70.8|94.6|||**_8.2_**||||**_64.4_**|
|Jamaica, 1997/98-2003/04|66.8|144.8|-56.4|-6.1|**_7.2_**|**_51.6_**|||**_70.7_**|
|Korea, 1997-1998|31.0|43.5|**_4.3_**||**_4.8_**||||**_20.6_**|
|Lebanon, 1994-2003|128.1|177.9|**_39.4_**|-29.9||**_115.5_**||||
|Malaysia, 1997-2002|19.9|70.6|-32.0|-10.6|**_8.7_**|**_17.2_**|**_26.2_**||**_10.4_**|
|México, 1994-1996|28.0|55.4|-16.8|||**_12.5_**||**_10.9_**|**_15.7_**|
|Pakistan, 1996-1999|19.1|113.5||-10.5|**_11.6_**|**_12.5_**|||**_5.0_**|
|Philippines, 1998-2002|20.9|89.1|-5.2|-11.9|**_12.1_**|**_10.3_**|||**_15.9_**|
|Russia, 1998-1999|34.0|88.7|||**_40.4_**|||||
|Turkey, 2000-2001|34.0|91.0|-8.5||**_6.4_**|**_20.4_**|**_15.4_**|||

_Based on Budina, Fiess and others (2004)_ 20
**Table 3: Robustness of results to inclusion of country-specific trends**

||(1)<br>Physical<br>Violence|(2)<br>Sexual<br>Violence|(3)<br>Any Violence|
|---|---|---|---|
|ln(U<sup>f</sup>)|-0.301***|0.050|-0.249***|
||(0.071)|(0.060)|(0.063)|
|ln(U<sup>m</sup>)|0.308**|-0.070|0.254***|
||(0.135)|(0.054)|(0.085)|
|Observations|348165|268173|266517|
|P-value F-test|0.006|0.287|0.001|
|Mean Violence|0.181|0.067|0.198|
|X controls|Y|Y|Y|
|Year FE|Y|Y|Y|
|Country FE|Y|Y|Y|
|CountrySpecific Trends|Y|Y|Y|

# _Source:_

Authors’ analysis based on data from the Demographic and Health Surveys (DHS).

# _Notes:_

* p<0.10, ** p<0.05, *** p<0.01. Standard errors are robust to arbitrary forms of heteroscedasticity and are clustered at the country level. The dependent variable in each regression is a binary dummy variable taking the value one if the individual female respondent was a victim of violence, defined as physical (I), sexual (II) or either (III) in the last twelve months. X is a vector of controls for age and education of respondent, education of her spouse, and urban location. In addition, country-specific trends are controlled for.

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- Eaton, Jonathan, and Samuel Kortum. “International technology diffusion: Theory and measurement”. _International Economic Review_ , vol. 40, no. 3, 1999, pp. 537–70.

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- ———. “Trade, knowledge spillovers, and growth”. _European economic review_ , vol. 35, nos. 2–3, 1991, pp. 517–26.

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last conclusion, in fact, is consistent with in-depth case studies reported by Hannum et al. (2009).

# **(6) Robustness Checks**

The empirical results on intergenerational mobility discussed so far are based on the sample of 18-35 years age cohorts of children in the survey year. To check robustness of the findings, we also estimated the mobility equation (14) in section (2) for alternative age ranges. The estimates for the age cohorts 18-30 years are reported in the online appendix; please see the Tables A2.IU.B, A2.IR.B, A2.CU.B, and A2.CR.B. The main conclusions regarding gender differences in intergenerational educational mobility remain intact.

We also check whether the conclusions are partly due to the effects of gender bias working through endogeneous fertility choices. To this end, we estimate the mobility equation using only the sub-sample of the first-born child, as the gender of the first born is usually not determined by parental preference. The CFPS survey on China is suitable for such an analysis. But we are unable to implement this for IHDS data in India, as the IHDS survey does not contain the information on the birth-order of the household head and spouse. The estimates based on the first-born sample for urban and rural China are reported in online appendix Tables A2.CU.C and A2.CR.C respectively for the 18-35 age cohorts; the main conclusions discussed in the text above are again robust. The estimates from the first=born sub-sample of the 18-30 age cohorts also support the main conclusions.

# **(7) Concluding Comments**

This paper provides a theoretical and empirical analysis of the implications of gender bias against girls for intergenerational educational mobility in developing countries. We develop a Becker-Tomes model where the parents self-finance children’s schooling because of credit market imperfections, and the girls may face bias in the family, the school, and the labor market. The model yields a linear conditional expectation function (CEF) under the assumptions of constant returns and separable education production function but delivers sharp predictions: parental bias against girls is irrelevant for relative mobility and for the marginal impact of parent’s education on investment in schooling. The effects of parental bias are captured by the intercepts of the investment and mobility equations, which are 32
**Figure 5. Perception of Economic Mobility 1989 – 2006**

<!-- Start of picture text --> (a) ECA (b) New EU Member States (c) Western Balkans<br> and Turkey<br><!-- End of picture text --> <!-- Start of picture text --> ECA: Type of Inter-step Mobility 1989 to 2006 EU10+1 & Turkey: Type of Inter-step Mobility 1989 to 2006 Western Balkans: Type of Inter-step Mobility 1989 to 2006<br>59.9<br>46.5 47.5<br>40.6 38.7<br>29.2<br>12.8 13.8 10.9<br>Down Same Up Down Same Up Down Same Up<br>(d) Russia, Moldova, Ukraine (e) South Caucasus (f) Central Asia<br> and Belarus<br>CIS: Type of Inter-step Mobility 1989 to 2006 South Caucasus: Type of Inter-step Mobility 1989 to 2006 Central Asia: Type of Inter-step Mobility 1989 to 2006<br>65.0<br>50.5<br>47.3<br>41.5<br>36.7<br>26.4<br>12.8 11.2<br>8.6<br>Down Same Up Down Same Up Down Same Up<br>70 70 70<br>60 60 60<br>50 50 50<br>40 40 40<br>Percentage 30 Percentage 30 Percentage 30<br>20 20 20<br>10 10 10<br>70 70 70<br>60 60 60<br>50 50 50<br>40 40 40<br>Percentage 30 Percentage 30 Percentage 30<br>20 20 20<br>10 10 10<br><!-- End of picture text -->

- (d) Russia, Moldova, Ukraine       (e) South Caucasus (f) Central Asia and Belarus

Source: 2006 LiTS (individual weights)

- **b) What is the socio-demographic profile of those with perceptions of downward mobility?**

**Perceptions of mobility vary by socio-demographic characteristics of the households, though with very few exceptions the prevalent perception is to have moved down in the income distribution after the transition.** We performed a descriptive analysis of the perceptions by income, age, location, education, and employment status. This analysis is aimed at shedding light into the postures of different socio-demographic groups, rather than to find causality or to isolate the effect of particular characteristics. The results show differences in the intensity of the perception, although in almost all cases the predominant view is to have moved down. Also, we would ideally use the characteristics before and after transition for this profiling, but the only information available about characteristics is from 2006. We prefer to report a profile based in 2006 information since this is still informative.

**Not surprisingly, poorer households in 2006 have more negative perceptions of mobility.** Using two different measures of welfare at the household level, we consistently obtain that poorest households in 2006 are more likely to report having moved down in the income distribution (Figure 6). The measures used are monthly household consumption and an index based on asset ownership. Results are roughly similar for both measures. The share of households that perceive movements down are around 55% for the lowest quintile and

12 | P a g e
resulting forward-looking investment, together with its adjustment cost function, is similar to Abel (1980), Hayashi (1982), and Summers (1981). The parsimonious structure of the model is achieved with the basic 1-2-3 model at its core. With export and import prices exogenous, there is only one endogenous price per period to be solved (the price of the domestic or nontradable good), and the simplified structure is ideal for isolating the evolution of the real exchange rate expressed as the relative price of foreign and domestic goods. The implementation allows for three types of import goods, each assessed with its own import duty. Final imports compete with the domestic good. Output is a fixed coefficient combination of intermediate imports and value added, while capital imports are fixed coefficients of investment. Value added is a CES composite of labor and installed capital. A government sector is present. Government revenue comes from import tariffs, domestic indirect tax, income tax, and foreign official grants, while public expenditures include public consumption, transfers, and subsidies, all of which are normally assumed to be exogenous. Given its structural breakdown, the model can be calibrated with national and fiscal accounts data only; it can also be used to look at trade liberalization and macroeconomic and fiscal adjustments to exogenous shocks. The framework was implemented using data from Madagascar, Mozambique, and the Philippines, which was the original country case in the 1-2-3t model.

New features of the economic framework relate to the introduction of changing or exogenous trends in the trade shares and trade substitution elasticities within the same dynamic run. Increasing the share of exports in aggregate output is akin to a change in technology, which may be brought about by greater integration and by access to the world markets. Likewise, increasing the share of imports in aggregate demand is like a change in consumer preferences that may be brought about by the availability of more types of imports through greater trade. In both cases, rising trade shares are likely to be consistent with greater capacity and flexibility in the economy, which are also linked to greater substitution elasticities between foreign and domestic goods. Modeling implementation of such flexible trade shares and elasticities is greatly simplified with the use of the share and index form of the CET and Armington functions as formulated in the previous section (see equations [40] and [45] in the annex). The calibration of the model is straightforward. One thing to note is that the reference values of the components for the 13
public or private.”<sup>12</sup> A final point in relation to the labor code worth noting is that maternity leave provisions are a right for employees. The labor code states that any agreements contrary to the provisions on maternity are void by right. Employers will be punished with a fine for violating the provision that allows a pregnant woman with health risks to terminate her contract without incurring a termination indemnity. Table 1 summarizes the restrictions and maternity leave provisions in 2012. In 2016, the family code was amended to remove marital authorization. However, some restrictions in the labor code remain.

**_Table 1: Rights and restriction of women in the DRC at the time of the survey_**

|**Democratic Republic of Congo - Laws**|**2012**|**Explanation**|
|---|---|---|
|Are wives required to obey their husbands?|Yes||
|Can a married woman get a job in the same way as a married man?|No|Authorization from spouse|
|Can a married woman sign a contract in the same way as a married<br>man?|No|Authorization from spouse|
|Can a married woman register a business in the same way as a<br>married man?|No|Authorization from spouse|
|Can a married woman open a bank account in the same way as a<br>married man?|No|Authorization from spouse|
|Can women work the same night hours as men?|No|Industrial undertakings<br>restricted|
|||For women, work should not|
|Can women do the same jobs as men?|No|exceed strength|
|Does the law mandate paid or unpaid maternity leave?|Yes||
|Who pays maternity leave benefits?|Employer<br>100%||

_Source: The World Bank Women Business and the Law_

# **3. Data and descriptive statistics**

The data used in the analysis are nationally representative firm-level data from the World Bank Enterprise Survey (ES) for the DRC; data were collected between August 2013 and May 2014. The ES have high coverage in terms of countries and topics including access to finance, corruption, infrastructure, crime,

> 12 According to the ILO definitions, "“Industrial undertakings” include “(a) mines and extractive industries of any kind (mining); (b) undertakings in which goods are manufactured (manufacturing), modified, cleaned, repaired, decorated, finished, prepared for sale, destroyed or demolished or in which the materials undergo a transformation, including shipbuilding, production, transmission of electricity (energy) and motive power in general; construction (construction) and civil engineering companies, including construction, repair, maintenance, processing and demolition.”

7
A recent report by the Independent Evaluation Unit of the UN ODC reached very pessimistic conclusions: “There is little empirical evidence that the rural development components of AD [Alternative Development] on their own reduce the amount of drug crops cultivated. Agriculture, economic and social interventions are not seen to overcome the incentive pressure exerted by the market conditions of the illicit drug trade. Where reduction in drug cropping occurs it seems other factors, including general economic growth, policing, etc., can be identified as contributors to the change that takes place.” (UNODC, 2005a) A recent study of the Chapare for the World Bank Institute (Reuter, 2006) suggests that a combination of large scale development funding and aggressive enforcement can move the locus of production. Whereas in the early 1990s the Chapare was the principal producer of coca leaf for the illicit market in Bolivia, by 2005, before the election of Evo Morales as president, only 7,000 hectares were in coca cultivation (UNODC, 2005c). The Yungas had become, as the result of heavy investment of aid by both the United States and European governments, a relatively attractive rural area, with good quality physical and social infrastructure. Production had shifted both within Bolivia (to the Yungas) and to other countries.

There are two distinct frames for assessing production controls; those of the targeted nation and of the global market. It is entirely plausible that a well-executed eradication or alternative development program could reduce production in a specific country or sub-national region; it is less plausible that successes even in a few nations could substantially reduce global production of either opium or coca. The reasoning is simple and rests largely on the fact that production costs (both cultivation and refining) constitute a trivial share of the retail price of drugs in the major Western markets. As noted earlier, the costs of the coca leaf that goes into a gram of cocaine is usually less than $0.50; the retail price of that same gram sold at retail in the West is more than $100.

Suppose that stepped-up eradication led to a doubling of the price of coca leaf, so that it cost $1 for refiners to buy the leaf that goes into one gram of cocaine. Assuming that the $0.50 per gram cost increase was passed along to traffickers and dealers, the resulting change in the retail price of cocaine would be negligible.<sup>35</sup> Indeed, leaf prices in the Andes have increased

> 35 There is a controversy as to whether price increases are additive rather than multiplicative across successive distribution levels (Caulkins, 1990).  However, the arguments for a multiplicative relationship do not apply at the pre-import level.  Since the largest proportionate increases occur at the smuggling stage, even the Caulkins model would suggest very modest retail price increases from rising leaf prices.  As a matter of historical observation there appears to be substantial variation in coca leaf prices that is not reflected in retail cocaine prices in rich nations.

20