rafmacalaba/datause-encoder-data huggingface dataset:
Table B.4: Occupations at baseline of workers still employed in the construction company vs workers not employed in the company at end-line

|Variable|Not i<br>N/n|(1)<br>n company<br>Mean/(SE)|Still<br>N/n|(2)<br>in company<br>Mean/(SE)|(1)-(2)<br>Pairwise t-test<br>Mean difference|
|---|---|---|---|---|---|
|Security agent helper|179|0.307|187|0.385|-0.078|
||[55]|(0.035)|[72]|(0.036)||
|Mason|179|0.128|187|0.176|-0.048|
||[23]|(0.025)|[33]|(0.028)||
|Iron worker|179<br>[19]|0.106<br>(0.023)|187<br>[10]|0.053<br>(0.016)|0.053*|
|Form setter|179<br>[27]|0.145<br>(0.026)|187<br>[22]|0.118<br>(0.024)|0.028|
|Carpenter|179<br>[3]|0.011<br>(0.008)|187<br>[0]|0.000<br>(0.000)|0.011|
|Laborer|179|0.274|187|0.251|0.022|
||[49]|(0.033)|[47]|(0.032)||
|Driver help|179<br>[1]|0.006<br>(0.006)|187<br>[0]|0.000<br>(0.000)|0.006|
|Topographer/Topographer helper|179<br>[1]|0.006<br>(0.006)|187<br>[1]|0.005<br>(0.005)|0.000|
|Flag holder|179|0.000|187|0.005|-0.005|
||[0]|(0.000)|[1]|(0.005)||
|Specialized laborer/worker|179|0.017|187|0.000|0.017*|
||[3]|(0.010)|[0]|(0.000)||

_Notes_ : This table compares the occupations at baseline of those, at end-line, that remained in the construction company with those that did not. In square brackets, the number of individuals per occupation either still in the construction company or not.

31
12 of service, how much they spent over the period specified in the utilization question, and how much they were or would be reimbursed by an insurer (including NCMS) or a government welfare program. The household health expense and catastrophic spending variables in Table 3 are derived from the household expenditure module (all are adjusted for household size), and the remaining health spending questions are derived from the individual utilization modules. The insurance contribution variable relates to _all_ insurers. The NCMS households incurred somewhat higher health expenses in the baseline, in part because of higher spending per inpatient stay.

Table 4 reports the descriptive statistics of variables that might plausibly affect enrollment in NCMS. In general, NCMS households and households not enrolled in NCMS but living in NCMS counties are fairly similar to one another. Both tend, however, to be quite different from households living in the five non-NCMS counties. The latter are more likely to be (peasant) farmers, tend to have a lower per capita household income, are more likely to belong to one of China’s ethnic minorities, tend to live further from a health facility, are more likely to be classified as poor or to be covered by China’s new rural safety net program known as _dibao_ , more likely to have received payments from _dibao_ or another welfare program in the previous 12 months, and to be living in one of China’s (poorer) western provinces.

Finally, the paper draws on provider data from a MOH administrative database that contains annually updated data on all health care providers in China, the data being supplied by the providers themselves according to a standardized set of forms. From the database we use data on all THCs and all county hospitals in all 15 provinces and 2 municipalities that piloted NCMS in the first wave. We therefore have ‘treated’ facilities in around 200 counties, and ‘untreated’ facilities in around 1,500. Separate analyses are reported for county hospitals, central THCs and general THCs. The total number of county hospitals, central and general THCs included in the analysis is 1,746, 4,464 and 14,040 respectively (Table 5). Of these, 209, 542 and 1,831 respectively were located in NCMS counties. Roughly half of these were discarded because they were too different from facilities in non-NCMS counties to be considered close matches (i.e. they were off the ‘common support’). And only around 20% of potential control facilities were, in the event, used as controls, the others being too different (i.e. off the common support).<sup>31</sup> The dataset includes a variety of outcomes of interest, including: total revenues, as well as the share coming

> 31 Some facilities do not have data on all the outcomes, and the numbers on and off the common support vary somewhat from one indicator to the next.
<!-- Start of picture text --> (a) Married - Egypt<br>02 I<br>|I]I<br>I! |<br>01 | I ¢<br>!<br>i ot<br>I I1<br>“01 I otI<br>-.02 |i ]i<br>I<br>] I]<br>| !<br>-.03 ! H<br>1988 1993 2000 2005 2008 2014<br><!-- End of picture text -->

<!-- Start of picture text --> (b) Divorced - Egypt<br>1 1<br>05 | H<br>i |<br>! 1<br>1 ot<br>0 t<br>1 41<br>708 1<br>!I |I<br>l fl<br>|1 \I<br>I 1<br>4 ! 1<br>1988 1993 2000 2005 2008 2014<br><!-- End of picture text -->

<!-- Start of picture text --> (b) Divorced - Morocco<br>II<br>I<br>i<br>05 II | ¢<br>0<br>I<br>05<br>A<br>I<br>-15 i<br>I<br>1992 2003 2004 2007 2014<br><!-- End of picture text -->

<!-- Start of picture text --> (a) Married - Morocco<br>; I<br>|<br>3 ' .<br>I<br>8;I<br>I<br>{<br>x I<br>1992 2003 2004 2007 2014<br><!-- End of picture text -->

<!-- Start of picture text --> (a) Married - Jordan<br>01 i \<br>I I<br>1 I<br>I H<br>I I<br>005 !I Ii<br>I I<br>I i<br>I H<br>I i<br>I i<br>0 I<br>i \<br>I i<br>-.005 I i|<br>! 1<br>i<br>-01 | 1I<br>1990 1997 2002 2007 2009 2012<br><!-- End of picture text -->

<!-- Start of picture text --> (b) Divorced - Jordan<br>04 I I<br>I I<br>! !<br>I I<br>I |<br>02 II II<br>I I<br>I I<br>I I<br>I I<br>I I<br>0<br>I I<br>I I<br>-.02 Ii Ii<br>I I<br>~-04 I I<br>II II<br>1990 1997 2002 2007 2009 2012<br><!-- End of picture text -->
based on a large-scale, cross-country survey that investigates how people think about these subsidies and what other policy measures they would prefer. The survey includes a randomized experiment whereby respondents receive information treatments highlighting one of the three common arguments against energy subsidies—that they are an inefficient use of government resources (efficiency argument), that they benefit the rich more than the poor (equity argument), or that they damage the environment (environment argument). Specifically, respondents are randomly allocated into four groups; each of the first three groups is provided with information treatments about the negative impact of subsidies on efficiency, equity, or the environment, and the final group receives no information (the control group). These information treatments present recent country-specific data from the IEA and other international organizations. Before receiving the treatments, respondents provide background information on themselves, including their household energy consumption, knowledge of energy subsidies, and views about how much the government can be trusted. After the treatments, respondents were asked a series of questions about their support for subsidy reform, both in the absence of any other policy (unconditional support) and if accompanying policies such as cash transfers were immediately implemented (conditional support). Heterogeneous treatment effects are examined based on respondents’ prior beliefs (“priors”), such as their perception of their relative income; they are also examined between subsets of countries based on the main type of energy source that is subsidized. Additional analysis leveraging experimental variation is conducted to examine whether support for reform is influenced by the existing level of subsidies within countries and the level of the potential price increase from reducing subsidies.

The survey was conducted in all middle-income countries where at least 1 percent of GDP was provided in energy subsidies in 2022, at least 5 million adults have access to the internet, and fiscal incidence analysis exists based on recent estimates about the distribution of energy subsidies. The 12 countries that meet these criteria—Angola, Argentina, Bangladesh, Bolivia, Ecuador, Egypt,<sup>3</sup> Ghana, Indonesia, Kazakhstan, Nigeria, Pakistan, and Vietnam—are 3The Arab Republic of Egypt is referred to as “Egypt” for brevity throughout the rest of document.

2
- Graff Zivin, Joshua and Matthew Neidell (2014). “Temperature and the Allocation of Time: Implications for Climate Change”. _The Journal of Labor Economics_ 32.

- Hallegatte, Stephane, Jun Rentschler, and Julie Rozenberg (2019). _Lifelines: The Resilient Infrastructure Opportunity_ .

- Hauk, William and Romain Wacziarg (2009). “A Monte Carlo study of growth regressions”. _Journal of Economic Growth_ 14, 103–147.

- He, Yiyi et al. (2021). “Flood impacts on urban transit and accessibilityâA case study of Kinshasa”. _Transportation Research Part D: Transport and Environment_ 96.

- Hertin, Julia et al. (2003). “Climate change and the UK house building sector: perceptions, impacts and adaptive capacity”. _Building Research and Information_ 31, 278–290.

- Hsiang, Solomon M. and Amir S. Jina (2018). _The Causal Effect of Environmental Catastrophe on Long-Run Economic Growth: Evidence From 6,700 Cyclones_ .

- Hsiao, Allan (2023). _Sea Level Rise and Urban Adaptation in Jakarta_ .

- Huang, Henry He, Joseph Kerstein, and Chong Wang (2018). “The impact of climate risk on firm performance and financing choices: An international comparison”. _Journal of International Business Studies_ 49.

- Kassa, Woubet and Andinet Woldemichael (2024). _Hotter Planet, Hotter Factories : Uneven Impacts of Climate Change on Productivity_ .

- Keiller, Agnes Norris and John Van Reenen (2024). _Disaster Management_ .

- Kotz, Maximilian, Anders Levermann, and Leonie Wenz (2024). “The economic commitment of climate change”. _Nature_ 628, 551–557.

- La Porta, Rafael, Florencio Lopez-de-Silanes, and Andrei Shleifer (2008). “The Economic Consequences of Legal Origins”. _Journal of Economic Literature_ 46, 285–332.

- Letta, Marco, Pierluigi Montalbano, and Richard Tol (2018). “Temperature shocks, shortterm growth and poverty thresholds: Evidence from rural Tanzania”. _World Development_ 112, 13–32.

- Li, Qing et al. (2020). “Corporate Climate Risk: Measurements and Responses”.

28
_For labor or for divorce?_

# _5.4. Addressing parallel trends_

To explicitly track differential trends that underly correlations between the right to unilateral divorce and women’s labor outcomes, we use an event study. This procedure allows us to formally verify departure from parallel trends in the pre-reform periods. Consider H(t) a function mapping a given year t from the covered study period such as the event-study specification presents as follows: i = ∑ ) + ∑ ) +∝ i **(2)** −11 KK ττ=−LL ττ ii ττ=11 ττ ii ii ii YY ββ (11{HH(ii) = ττ} × p ppii ββ (11{HH(ii) = ττ} × p ppii + θθ + εε where K and L are the total number of pre-reform and post-reform periods and the year of unilateral divorce legislation is normalized to 0. Note that this equation is still similar to equation (1) in the sense that it considers a single interaction term **_post_** for post-reform measures the difference in women’s labor outcome, which ττ we consider the event-study coefficients. periods. Each coefficient term ββ ττ for the pre-reform period are different than 0 and significant suggesting that differences in We use the estimates of ββ (where ττ < 0) to test for pre-reform parallel trends. Our estimates average labor outcomes were smaller than the latter periods. F-tests of the null hypothesis that pre-reform coefficients are equal to zero result in a p-value of 0.000 for the baseline model, restricted to married women, which is strong indication of nonparallel trends in the pre-legislation reform periods. We also test the sensitivity of our results for a sample of divorced women. This is to test the hypothesis 1.b., about women increasing their labor supply in expectance of divorce. Note that for all three countries, the coefficients of the event-study for divorced women sample are close to zero with little variations between the pre and post -reform periods compared to the samples of married “treated” women. That said, the parallel trends assumption underlying a causal-inference results interpretation for the sample of divorced mothers is more conservative than the previous one (for mothers who stayed married).

29
The variable input-output ratios are a function of relative input prices conditional upon electricity production and the availability of hydroelectric, nuclear, and renewable energy resources. The full model includes the restricted variable cost function (4) and the three variable input demand equations (5).

While the model can be estimated with full information maximum likelihood, a more robust procedure is estimation with Generalized Methods of Moments with country and monthly dummy variables and lagged values of the right-hand side variables, including those involving input prices, quasi-fixed factor levels, and output.

# **5. An Overview of the Data Sample**

The above model is estimated with a pooled, monthly data sample across twelve countries in the EU from January 2002 to March 2008. The International Energy Agency (IEA) reports monthly electric power generation from nuclear, hydroelectric, geothermal and renewable resources and from the combustion of fossil fuels. The IEA does not report the types of combustible fuels but the EuroStat database does report the consumption of petroleum, coal, and natural gas in electric power generation. The EuroStat database, however, does not report data on generation from geothermal and renewable electricity generation. Given the rising importance of renewable energy in the generation portfolio, the more inclusive IEA data on generation is adopted in this study while the EuroStat data on fossil fuel use is utilized. A comparison of the generation data reported by the two agencies reveals the average differences are 3.2 percent.

An overview of the generation and net imports data appears in Table 1, which reports the sample means. The largest producers of electricity are Germany, France, United Kingdom and Spain. The mid tier includes Sweden and Poland and the other six countries have total indigenous production between 3,000 and 8,000 gigawatt hours. France and Germany are the largest producers at 47,624 and 45,666 gigawatt hours repsectively. All twelve countries in the sample produce fossil10
Section 3 outlines our methodology, followed by the presentation and discussion of results in Section 4. Section 5 covers the limitations of our study, while Section 6 explores the policy implications. Section 7 concludes the paper.

# 2. Data

The datasets used in this study are described in Table 1. These input datasets have different spatial resolution. For the subsequent sections of the article, we use the spatial resolution of the global horizontal irradiance (GHI) map, i.e., 0.2° (~22 km). We apply this resolution to all input datasets by nearest neighbour interpolation. The shape file on the type of GDEs is rasterized at this same resolution. Besides, we use irradiance data from 2020. In Figure 1, we plot the annual average of GHI for 2020, static water level, aquifer transmissivity, groundwater storage, population density, renewable groundwater resources and type of GDE across subSaharan Africa.

|**Data**|**Description**|**Unit**|**Spatial**<br>**resolution**|**Temporal resolution and**<br>**coverage**|**Year of**<br>**release**|<br>**Provider**|**Type of**<br>**data**|
|---|---|---|---|---|---|---|---|
|Global<br>Horizontal<br>Irradiance<br>(GHI)|Radiation received by a horizontal<br>plane from all directions.|W/m<sup>2</sup>|0.2°<br>~22 km|One temporal vector for each<br>location.<br>Data from 2005 to 2020 with<br>a time step of 30 min (some<br>datapoints are missing for<br>years 2005 to 2012).|<br>2021|European<br>Commission,<br>2021||
|Static water<br>level|Depth of water in the borehole when<br>there is no pumping.|m|0.0083°<br>~1 km||2013|Fan et al., 2013||
|Aquifer<br>transmissivity|Rate at which groundwater flows<br>horizontally through an aquifer|m<sup>2</sup>/day||||British<br>Geological||
|Groundwater<br>storage|Amount of water in the aquifer<br>defined as a water depth|m|0.05°<br>~6 km||2012|Survey (Bonsor<br>and<br>MacDonald,<br>2011;<br>Macdonald,<br>2012)|Raster<br>file<br>|
|Population<br>density|Number of inhabitants per square<br>kilometer|hab/km<sup>2</sup>|0.04°<br>~5 km|Dependence in time not<br>provided.|2020|Center for<br>International<br>Earth Science<br>Information<br>Network, 2018||
|Renewable<br>groundwater<br>resources|Volume of renewable groundwater<br>that can be abstracted per year|km<sup>3</sup>/yr|0.5°<br>~60 km|||||
|Type of GDE|• Inland surface waters<br>• Terrestrial springs<br>• Coastal and marine ecosystems<br>• Terrestrial vegetations|-|-||2022|World Bank|Shape<br>File|

_Table 1 – Input datasets._ 3
Figure 12. High renewable case forecasts of power Figure 13. Hourly power dispatch (MW) during peak <u>generation (LHS) and CO2 emissions (RHS) solar generation in 2025 under the high renewable case</u> <!-- Start of picture text --> 120 Wind 60<br>Solar<br>Hydro<br>100 50<br>Import<br>Gas<br>80 Coal 40<br>Oil<br>CO2 Emitted<br>60 30<br>40 20<br>20 10<br>0 0<br>2015 2017 2019 2021 2023 2025<br>TWh tCO2<br><!-- End of picture text --> <!-- Start of picture text --> 16 Fuel oil Wind<br>Solar LNG<br>14 Domestic gas Hydro<br>Import Import coal<br>12 Domestic coal<br>10<br>8<br>6<br>4<br>2<br>0<br>1 3 5 7 9 11 13 15 17 19 21 23<br>MW<br><!-- End of picture text --> The addition of solar and wind will reduce carbon emissions by 2 million tons per year by 2025, as compared to the reference case scenario. The reduction comes mainly from the displacement of fuel oil and some gas generation. Solar and wind deployment avoid 300 MW in fuel oil generation, compared to the reference case. Coal, power imports and to a large extent gas will continue to provide baseload energy in 2025 under all scenarios. Generation from natural gas will rise from 38 TWh in 2015 to 54 TWh in 2025. This increase can be met with a modest 100 MMCF/d increase in gas use by the power sector given that a number of new gas plants with high efficiency will displace some existing units that are due to retire.

# Electricity and solar cost analysis

The above scenario assumes that solar and wind costs will drop to below grid parity costs. More specifically, the cost of solar and wind generation need to be below the cost of available generation at the time and resource is scheduled to provide energy. For example, solar generation occurs during daytime hours in Bangladesh which coincides with low demand. During this period, solar effectively competes with baseload generation.

The average cost of power generated is projected to rise from USc3.35/kWh in 2015 to USc6.59/kWh in 2025 with current domestic gas prices. However, gas pricing policies pose a difficulty in assessing the cost of generation in Bangladesh. As the power sector pays only US$1/MMBtu for natural gas, average generation costs are far below the costs projected when gas is priced at import parity. The benchmark generation costs rise significantly when gas sold for power generation is priced at import LNG parity.

Due to a declining domestic gas production, the supply of low‐cost domestic gas will need to be supplemented with imports of LNG. The expected rise in imported gas accounts for much of the increase in the forecast for power prices in the Reference Case. Modeling the Reference case is based on a projected decline of domestic gas used by the power sector from 950 MMcf/d in 2015 to 480 MMcf/d in 2025 and an increase in LNG from 120 MMcf/d in 2018 (the year imports are expected to begin) to 454 MMcf/d in 2025. This translates into an increase in the average gas price for power generation to US$5.8/MMBtu.

18
<!-- Start of picture text --> oO<br>° +<br>Qa<br>£<br>wo<br>(7)<br>£cooO<br>2<br>5<br>2<br>Za<br>no<br>oN<br>i]<br>°<br>|<br>oO<br>=<br>BE<br>°<br>z<br>o<br>2007m10 2008m4 2008m10 2009m4<br>Month<br><!-- End of picture text -->
**Figure 1: Population in large cities exposed to cyclones increases from 310 to 680 million between 2000 and 2050 (World Bank 2009)**

<!-- Start of picture text --> 300<br>250<br>EAP<br>ECA<br>200<br>LAC<br>MNA<br>150<br>SAS<br>SSA<br>100<br>OECD<br>OHIE<br>50<br>0<br>1970 1980 1990 2000 2010 2020 2030 2040 2050<br>million<br><!-- End of picture text --> Exposure to earthquakes in urban areas is expected to be largest in East Asia and the Pacific at 267 million in 2050 from 83 million in 2000 (Figure 2). Exposure is also high in Latin America and the Caribbean (150 million in 2050) and OECD countries (129 million in 2050). The fastest growth of exposure to earthquakes is expected to occur in South Asia (3.5 percent) followed by Sub-Saharan Africa (2.7 percent).

**Figure 2: Population in large cities exposed to earthquakes increases from 370 to 870 million between 2000 and 2050 (World Bank 2009)**

<!-- Start of picture text --> 300<br>250<br>EAP<br>ECA<br>200<br>LAC<br>MNA<br>150<br>SAS<br>SSA<br>100<br>OECD<br>OHIE<br>50<br>0<br>1970 1980 1990 2000 2010 2020 2030 2040 2050<br>million<br><!-- End of picture text --> 7
Three key lessons emerge from this descriptive analysis of the IFLS data:

# 1. **Improved productivity in agriculture is still an important route out of poverty:**

Most of the rural agricultural poor in this sample who exit poverty do so while staying in rural agriculture, which is a sharp contrast with the SUSENAS results from the 1993-2002 analysis. Over 80% of the poor rural farmers in the sample were still working in rural agriculture in 2000, but more than half of the households from which they come still managed to exit from poverty. Most of the rest moved into non-agricultural activities in rural areas and, more often than not, out of poverty too. However, in agreement with the SUSENAS results, moving to urban areas was rare for this group – only 2.9 percent of those working as poor rural farmers in 1993 were working in urban areas in 2000. Based on SUSENAS data, just 44.6 percent of employed individuals in 2002 were primarily engaged in agriculture, but 61.3 percent of the poor and 56.9 percent of the “near poor” worked in agriculture. Raising agricultural productivity seems to be an effective way of reaching these households.

# 2. **Rural non-agricultural activities can be a stepping stone out of poverty in rural**

**areas:** Individuals from poor rural households who worked off-farm in 1993 were more mobile than those working in agriculture.  Less than a fifth stayed poor in rural non-agricultural activities—but overall they were equally likely to stay poor, as a similar number shifted back to agriculture while remaining poor.  More than half escaped poverty while staying in rural areas—a similar rate to those who started poor in agriculture. Urban migration played a more important role for this group, but still a minor one – less than 10% of this group moved to urban areas, and only half of those that did so exited poverty.<sup>14</sup> Still, this migration rate— almost 10 percent of poor, rural non-farm workers migrated to urban areas in just 7 years—is more than three times the rural-to-urban migration rate of poor farm workers (only 2.9 percent of poor farm workers moved to urban areas, and onethird of those remained in poverty).  During the same period, 15.4 percent of poor,

> 14 Note that re-classifications are very rare (3.26%) in this panel so that almost all the rural-urban movements observed involve actual physical movement of the person. This adds weight to the evidence that urbanization rather than rural-urban migration is the principle form of rural-urban transition.

26