rafmacalaba/datause-encoder-data huggingface dataset:
**Table A2. Hospital and Health Center Visits by Quintile**

||**Hospital v**<br>|**isits**<br>|**Health cen**<br>|**tre visits**<br>|
|---|---|---|---|---|
|**Quintile**|**% who made visit**|**% of total visits**|**% who made visit**|**% of total visits**|
||**Socioeconomic sta**<br>|**tus measured by consum**<br>|**ption**<br>||
|1|1.5%|**14.4%**|2.1%|**13.3%**|
|2|1.7%|**15.9%**|3.0%|**19.2%**|
|3|1.7%|**16.4%**|2.9%|**18.5%**|
|4|2.6%|**24.2%**|4.2%|**26.7%**|
|5|3.1%|**29.0%**|3.5%|**22.4%**|
||**Socioeconomic st**<br>|**atus measured by asset i**<br>|**ndex**<br>||
|1|1.0%|**10.1%**|3.0%|**20.3%**|
|2|1.5%|**13.8%**|4.0%|**24.1%**|
|3|1.9%|**18.0%**|3.5%|**22.4%**|
|4|2.6%|**24.7%**|3.3%|**21.3%**|
|5|3.5%|**33.5%**|1.9%|**11.9%**|
|**Total**|**2.1%**|**100.0%**|**3.1%**|**100.0%**|
|_N=41856_|||||

**Table A3.  Immunizations by Quintile**

|**Quintile**|**% children (1-4)**|**% of children immunized**|**% of total cases of complete**<br>**immunizations**|
|---|---|---|---|
||**Socioeconomic status**|**measured by consumption**||
|1|13.4%|39.7%|**21.4%**|
|2|13.4%|40.0%|**20.5%**|
|3|12.0%|41.4%|**19.6%**|
|4|11.6%|42.4%|**20.2%**|
|5|9.0%|50.2%|**18.3%**|
||**Socioeconomic statu**|**s measured by asset index**||
|1|12.4%|18.5%|**9.6%**|
|2|10.6%|33.1%|**13.5%**|
|3|12.8%|38.7%|**19.8%**|
|4|12.2%|53.1%|**25.0%**|
|5|11.4%|70.4%|**32.1%**|
|**Total**|**11.9%**|**42.3%**|**100.0%**|

17
Figure 1: Cluster-randomization of districts (and circles therein) <!-- Start of picture text --> Chandigarh<br>Punjab Uttarakhand C H I N A<br>Haryana<br>Delhi<br>Sikkim<br>N E PA L<br>Rajasthan<br>Uttar Pradesh<br>Bihar<br>Madhya Jharkhand<br>Pradesh West Bengal<br>Chhattisgarh<br>Odisha<br>Maharashtra<br>Bay of Bengal<br>Telangana<br>Treatment districts (ads) Control districts (no ads) IBRD 48397 | OCTOBER 2024<br><!-- End of picture text -->

This map was produced by the Cartography Unit of the World Bank Group. The boundaries, colors, denominations and any other information shown on this map do not imply, on the part of the World Bank Group, any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries.

of treatment and control districts (and circles therein) over space. While one district can contain multiple circles, all circles in a district are in the same experimental condition. Circles are identified by the coordinates of their centroids and a radius. We passed this information to the MNM advertising team, which used it to exclude the control (red) circles from their nationwide campaign, while running it on the treatment (green) circles as well as on all the remaining parts of the districts and the other targeted states. Due to an implementation issue, we dropped one district from the analysis, leaving 79 study districts in total.<sup>16</sup> We use a cluster design for the following two reasons (Hayes and Moulton, 2017). First, we acknowledge the presence of different types of spillovers.<sup>17</sup> A cluster design allows us to account for the correlation across observations within a cluster and to minimize the risk of contamination between treated and control areas. Second, related to the spillovers of the infectious disease itself, we are interested not just in the effects of the ad campaign on the direct reduction of one individual’s 16The randomization list given to the MNM advertising team included two districts named Balrampur, each in a different state. This duplication made it impossible to determine whether one of these districts belonged to the treatment or control group. Consequently, we decided to exclude it from the analysis.

> 17These may (i) occur within families, where treated users may help their family members to adopt precautions against malaria exposure; (ii) ripple across the social graph as Facebook ads and posts are shared by treated users, thereby amplifying exposure to their Facebook friends; and (iii) include health-related spillovers connected to the infectious disease itself, wherein the decrease in malaria incidence among treated individuals and their families could contribute to a reduction in malaria cases among their neighbors.

11
interaction term between bank credit and a proxy measure of the level of competition in the banking industry which equals the assets of the five largest banks in the country-year as a share of total commercial banking assets ( _5-bank asset concentration_ ). For robustness, we also report results using a similar index for the three largest banks (3 _-bank asset concentration_ ). Higher values of the concentration indices imply less competition in the banking industry. The data source is GFDD, World Bank.

In all the heterogeneity estimations, we include as controls interaction terms between bank credit and the following variables (defined above): log of GDP per capita, log of number of workers at the firm (firm size), log of age of the firm., and labor productivity of the firm (logs). These controls guard against the possibility that our main interaction term (as stated in Hypotheses 1-3) is spuriously picking up the differential effect of bank credit in countries with high vs. low GDP per capita, and firms of different size, age, and labor productivity.

# **4. Base regression results**

Base regression results are provided in table 1. Panel A in table 1 contains the estimated log odds ratios while Panel B contains the estimated marginal effects of the bank credit to GDP ratio.<sup>4</sup> Table 1 shows that regardless of the controls, the relationship between the bank credit to GDP ratio and the likelihood (log odds ratios and marginal effect) of a having a woman top manager is positive and statistically significant at the 1 percent level. Without any controls (except for country, year, and industry fixed effects), the odds of having a woman top manager increase by 2.0 log points due a unit increase in the bank credit to GDP ratio (column 1, Panel A). The corresponding increase with various baseline controls included (columns 2-5) is larger, equaling 2.7 log points for the final

> 4 Unless stated otherwise, all marginal effects throughout the paper are evaluated at the mean value of the various explanatory variables.

16
31 Data Aan ethods Panel data are preferred for investigating the effect of economic crisis and adjustment policies on health. But this type of data is rarely available. Alternatively, retrospective data are used to reach mild inferences about the effect of economic changes on child health, and how this effect is distributed among socioeconomic groups.

Data. The analysis is based on the Living Standards Measurement Surveys (LSMS) conducted in Ivory Coast in 1985 and 1986. Grootaert (1987) presents the design of the LSMS questionnaires; Ainsworth and Munoz (1986) describe the sample design and field implementation of the surveys. It is impossible to make quantitative statements about the magnitude of nonrandom errors in the LSMS mortality data.

We use two national surveys conducted in Cote d'Ivoire to check the relative quality of the LSMS mortality data: (1) the _REnquete Demographlque a Passages Repetes'_ (EPR) and (2) the Ivorian Fertility Survey (IFS). The EPR is a survey organized in 1978-79 for demographic measurement only. It was based on a national sample of 200,000 people. The survey was conducted in three rounds at six-month intervals, thus yielding two consecutive six-month periods during which demographic events, including births and deaths, were observed. The IFS, the Cote d'Ivoire country project of the World Fertility Survey, was conducted in 1981-82.

Table 3 presents infant mortality rates estimated for 1975-79 based on the different sources. The LSMS are separated into four groups: (1) data from half the sample in 1985 only; (2) the 1985 data from that half of the 1985 sample
Some definitions start from a different analytical perspective and try to answer the question: “What are the employment consequences of introducing ‘green’ policies (e.g. ‘cap and trade’) relative to a baseline case?” This approach requires implicit or explicit economic modeling of the policies. Some studies in this vein count only jobs directly created by the policies (‘direct’ employment effects) while others include jobs created in the supply chain for the products and services supported by ‘green’ policies (‘indirect’ employment effects).

Kammen at al. (2004) and Wei et al. (2010) review several studies that estimate direct employment effects of promoting renewable and other low-carbon energy supply and energy efficiency, focusing on the specific labor requirements of particular technologies (‘bottom up’ estimates, using simple spreadsheet-based analytical models in conjunction with engineering estimates). An important issue that arises is the timing and duration of job creation; there is a key distinction between construction, manufacture and installation, where jobs may be relatively short-lived, on the one hand and ongoing operation, maintenance and fuel processing on the other, where the length of jobs depends on the durability of the relevant plant.

They also consider studies that use input-output (I-O) tables to estimate both direct and indirect employment effects, taking account, for example, of the jobs created in business services provided to the renewable energy sector. These extend the scope of the estimates while sacrificing the greater granularity derived from engineering studies of specific energy projects. I-O based studies also fall prey to the usual criticisms of input-output models: that they do not allow for changes in input-output coefficients induced for example by relative price changes and technological progress; that they are often out-of-date; that they depend on industrial classifications that do not distinguish some of the key sectors of interest; and that they are highly aggregated. The meta-studies by Kammen and his associates attempt to derive standardized measures to compare estimates of jobs created per average megawatt over the life of an energy facility. The authors also explore the implications of various scenarios of exogenous energy efficiency improvements and renewable energy portfolio standards for US employment in total. As they take into account jobs destroyed when fossilfuel-based energy is displaced by low-carbon sources, their projections are for a net concept of employment change, but they do not take into account general equilibrium effects through relative wage changes.

6
<!-- Start of picture text --> <0 Debt-to-CDP Ratio (in porcent,, Net Foreign Assets /HPA/ projections from. IMF .<br>Lao country—Dasei report, d e faultw assumption on NF A interest rebe is extemal debt interest7. «=. ~~<br>— — Altemative scenstio (country-specific shock) a =?<br>12) | ~——Secondmost satreme stress test a eee<br>= = = = Most ottreme stress best eae<br>in Peai<br>a0 /, ia<br>“<br>a<br>| Zz !<br>| | a<br>la |<br>Ea c o<br><!-- End of picture text -->
# An Evaluation of the 2014 Subsidy Reforms in Morocco and a Simulation of Further Reforms

_Paolo Verme and Khalid El-Massnaoui_<sup>_1_</sup>

**Keywords** : Morocco, subsidies, energy, food, welfare, income distribution.

**JEL:** D1, D3, D4, D6, H2, H4, H6, I3, O2.

> 1 World Bank. The authors are grateful to Abdoul Gadiry-Barry for preparing the data, to Xavier Chauvot De Beauchene for useful advice on the water sector and to Jean-Pierre Chauffour for useful comments on the final draft. All simulations have been carried out with SUBSIM (see www.subsim.org).
<!-- Start of picture text --> ° 2.71 2.73<br>2.5 2.44 a) 42 g.43<br>2.08 22 2.28.2<br>1.96 1.94<br>2 1.81<br>1.58<br>1.5<br>1<br>0.5<br>(e)<br>Period 1 June- Period 2 (August Period 3 (March- Period 4(May— Period 5 (Nov.—<br>August 2013) —- October 2013) May 2014) July 2014) Dec. 2016)<br>mw 50 Birr use incentive (Incentive) m Paid 25 Birr (Pay) m Received stove for free (Free)<br><!-- End of picture text -->
Figure IV presents the geographic distribution of the intensity of migration outflows and inflows of individuals as a share of the mean population between 1995 and 2010. The upper panel of the figure presents the intensity index, which reveals that the municipalities that lost a significant portion of their population to forced displacement were mainly located on the west and between the middle and the south of Colombia. The mean intensity index for all the municipalities is 0.11, suggesting that, on average, all Colombian municipalities lost approximately 11% of their population to forced migration between 1995 and 2010. The variance, however, is high (s.e. 0.25). There are also approximately 45 municipalities that saw an intensity index of 100 percent, suggesting that their population was depleted by forced migration. The figure also shows that the forced migration that originated in the center of the country was of relatively low intensity.

The lower panel of the figure shows the pressure index, defined as the total number of forcefully displaced individuals who arrived in a municipality as a share of the municipal mean population between 1995 and 2010. The pressure index is a good proxy for the congestion of public goods and services as a consequence of forced displacement in the municipalities where these populations arrive. The pressure index has a mean value of 10 percent (s.e. 0.19). Although forced migration was substantial during the period of analysis, the figure reveals that most of the receiving municipalities did not face pressure indexes that are extremely large. In fact, only 38 municipalities have pressure indexes of more than 50 percent, suggesting that forcefully displaced migrants tend to move to urban areas with large populations. Similar results were documented by Carrillo (2009) and Lozano-Gracia et al. (2010). For instance, according to the data from RUV, Bogot ́a, the Colombian capital city, received almost 10 percent of the total number of migrants. Migrants may decide to move to urban areas in search of better economic opportunities (i.e., a larger labor market), a sense of safety due to anonymity, and to move away from conflict areas, which were predominantly rural during the period of analysis.

12
If we restrict the attention to the specific categories of NTMs A and B above, Sanitary and phytosanitary measures (SPS) and Technical barriers to trade (TBT), respectively, Table 8 provides similar type of evidence of negative correlations between the share of imported intermediate inputs under either of those categories of measures and the import outcomes of Peruvian firms in all sectors. Consistent with the results above, the evidence for agribusiness is weaker relative to the overall sector and the apparel sector. Across categories of NTMs, the relationship is stronger in both agribusiness and the apparel sectors for TBTs, which is not surprising given the type of intermediates that firms in those sectors are likely to import, such as tools and machinery as well as fertilizers (for agribusiness) and cloth (for apparel).<sup>15</sup>

**<u>Table 8. SPS and TBT Measures and Imported Intermediates</u>**

||**Log (Import**<br>**Value)**|**Log (Nb.**<br>**Imported**<br>**Products)**|**Log (Nb.**<br>**Imported**<br>**Varieties)**|**Log (Import**<br>**Value)**|**Log (Nb.**<br>**Imported**<br>**Products)**|**Log (Nb.**<br>**Imported**<br>**Varieties)**|
|---|---|---|---|---|---|---|
|||||**All**|||
|**Share of Imports under TBT**|‐2.021***<br>(0.229)|‐1.811***<br>(0.112)|‐1.860***<br>(0.131)||||
|**Share of Imports under SPS**||||‐2.261***<br>(0.262)|‐2.257***<br>(0.126)|‐2.321***<br>(0.152)|
|**Main HS 2‐Digit Fixed Effects**|Yes|Yes|Yes|Yes|Yes|Yes|
|**Observations**|1,729|1,729|1,729|1,508|1,508|1,508|
|**R‐squared**|0.206|0.266|0.237|0.214|0.346|0.310|
||||**Agrib**|**usiness**|||
|**Share of Imports under TBT**|‐2.170***<br>(0.745)|‐1.058***<br>(0.392)|‐1.123***<br>(0.422)||||
|**Share of Imports under SPS**||||‐1.291|‐1.151**|‐1.070*|
|||||(0.922)|(0.519)|(0.551)|
|**Main HS 2‐Digit Fixed Effects**|Yes|Yes|Yes|Yes|Yes|Yes|
|**Observations**|88|88|88|75|75|75|
|**R‐squared**|0.436|0.316|0.306|0.474|0.536|0.538|
||||**App**|**arel**|||
|**Share of Imports under TBT**|‐5.911***<br>(1.216)|‐3.204***<br>(0.444)|‐3.600***<br>(0.496)||||
|**Share of Imports under SPS**||||‐2.773***<br>(0.975)|‐3.121***<br>(0.574)|‐3.369***<br>(0.619)|
|**Main HS 2‐Digit Fixed Effects**|Yes|Yes|Yes|Yes|Yes|Yes|
|**Observations**|110|110|110|81|81|81|
|**R‐squared**|0.600|0.519|0.523|0.380|0.447|0.443|

Notes: Robust standard errors in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively. Only exporter-importers are included in all the regressions.

> 15 For the agribusiness sector the important distinction to make is across the SPS that we do not consider, those on imports of agribusiness sector products themselves, and the SPS that we consider, those on imports of intermediates by firms in the agribusiness sector where intermediates are fertilizers, tools and machines for which TBT measures rather than SPS measures are likely to play a role.

21
# **2 How to measure management in schools?**

Until the early 2000s, management was typically viewed as an unmeasurable productivity shifter, to be relegated to the residual in any performance regression [Bloom and Van Reenen, 2007]. Since then, improvements in survey methodology and data access have allowed for advances in measurement. The current “state of the art” approach uses a dedicated survey—the World Management Survey (WMS)—to measure establishments’ adoption of structured management best practices. While the WMS offers uniquely rich information about management practices, it costs approximately USD400 per interview and takes about 4 months to conduct a single country wave [Bloom et al., 2016]. In view of these costs, it may not be well-suited to every context.

In this section, we propose an alternative three-step approach than can, in principle, be used with any existing public dataset containing information on management practices. The first step is to use the original WMS phone survey as a benchmark, and to look for questions in the public survey that elicit information on the management practices already measured by the WMS.<sup>4</sup> The second step is to code answers in line with the WMS methodology. And the final step is to create a management index. In Section 2.1, we provide a brief overview of the WMS questions and coding. In Section 2.2, we describe our approach using two existing public datasets as examples: PISA and the Brazilian school census survey, Prova Brasil. Since Brazil and several other PISA countries are part of the Bloom et al. [2015a] sample, we can compare the (within-country) distribution of each index with the corresponding (within-country) distribution of the WMS index. Both indices are well-validated and can therefore be used by researchers interested in studying management across a wider range of countries and schools than was previously possible.

> 4Our approach follows the spirit of the re-casting of the original phone-based World Management Survey into the US Census Management and Organizational Practices Survey (MOPS) administered to the population of US manufacturing establishments as a self-reported questionnaire [Bloom et al., 2019a]. The MOPS has been replicated in a number of other countries. Its questions follow the WMS topics and look to measure similar practices, but with self-reported answers.

5
# **1. Introduction**

Agricultural growth is a critical driver for decreasing poverty in developing countries as it has a proportionately larger impact on poorer households than on the rest of society (Kilic et al. 2015). According to the World Bank (2008), GDP growth through agriculture is twice as effective in reducing poverty as growth through other sectors. Even more strikingly, investment in agriculture is 3.2 times better at decreasing extreme poverty (Christiaensen et al. 2011). Agricultural investment is especially important for countries where a large portion of farmers are smallholders. In Sub-Saharan Africa, more than 90 percent of the extreme poor are involved in agricultural activities (O'Sullivan et al. 2014). However, a lack of knowledge about new agricultural technologies and a lack of resources tend to constrain individuals from innovating in agriculture.

Gains in agricultural growth can be even more effective if the gender gap in agricultural productivity is also addressed. Women account for 43 percent of the agricultural labor force in developing countries (FAO 2011). However, they are less likely to have access to agricultural training and extension meetings (Quisumbing et al. 2014, Farnworth et al. 2015). Women also face a gap in credit (Huyer 2016). And this means lower yields: in a six-country study on agricultural productivity, O’Sullivan et al. (2014) find that women were 13 to 25 percent less productive per hectare than their male counterparts. The FAO (2011) estimates that addressing the gender gap in agriculture could increase total agricultural productivity by 2.5–4 percent, globally. At a country level, the returns from tackling the gender gap can be sizable. For example, in Malawi, it was estimated that addressing this gender gap could add USD 100 million to its GDP while lifting 238,000 people out of poverty (World Bank 2015).

This paper adds to the literature on the agricultural gender gap by looking at how addressing both financial and information constraints for agricultural households affects their agricultural productivity, livestock investments, and off-farm income. We study the Innovation Fund Project (IFP) in Ethiopia, an intervention designed to decrease the risk associated with trying new agricultural activities by increasing information access while simultaneously decreasing financial constraints. The intervention design allows us to disentangle the effect of easing these constraints for women versus men versus both. We map how men’s and women’s decisions and behaviors differ in reaction to whom the intervention has targeted within the household.

2
Figure 20. Change in exports and imports due to the NDC policies implementation, % change in 2030 relative to the post-COVID baseline <!-- Start of picture text --> 0 2 4 6 8 10 12<br>3 Exports 100<br>2 80<br>1 60<br>0 40<br>-1 20<br>-2 0<br>-3 -20<br>-4 -40<br>-5 -60<br>-6 -80<br>-7 -100<br>-8 -120<br>Total Total Agriculture Total Natural Resources Total Manufacturing Total Services<br>0 2 4 6 8 10 12<br>2 Imports 100<br>80<br>1<br>60<br>0 40<br>-1 20<br>0<br>-2<br>-20<br>-3 -40<br>-4 -60<br>-80<br>-5<br>-100<br>-6 -120<br>Total Total Agriculture Total Natural Resources Total Manufacturing Total Services<br>Change in exports, %<br>Trade Specialization Coefficient, %<br>Change in imports, %<br>Trade Specialization Coefficient, %<br><!-- End of picture text --> Notes: Trade Specialization Coefficient (TSC), plotted with circles in the figure, represents the ratio of net exports to total trade (exports + imports); TSC>0 indicates that the region is a net exporter of the specific commodity, while TCF<0 indicates a net importing commodity-region pair. Fossil fuel commodities are aggregated within the natural resources category. Source: ENVISAGE simulations.

# Climate policies and Carbon Border Adjustment Mechanism

We split the impact of the stylized EU Green Deal into two parts: more ambitious emissions reductions by the EU and implementation of the Carbon Border Adjustment Mechanism (CBAM), a charge on imports into the EU of some raw materials from “less climate ambitious countries.” The impacts of the EU Green Deal are measured relative to the scenario with NDC-consistent climate mitigation. These impacts are 33