rafmacalaba/datause-encoder-data huggingface dataset:
with higher values implying that regulation is more protective of a worker. We refer to this index as EmpLaw.

Following our methodology, we need to find an industry characteristic that would make an industry most susceptible to labor regulation. We would expect labor regulations to impinge the most on industries that are most labor intensive. We calculate Labor Intensity, LabInt, from US data. It is the industry median over all Compustat firms in that industry of the number of employees divided by the amount of fixed assets (in millions of dollars), and is calculated over all firm-years in the 1998-99.<sup>37</sup> A higher score indicates higher labor intensity.<sup>38</sup> We report summary statistics and correlations for industry and country level variables in Table 9 and regression estimates in Table 10. In Table 10, column (i) the country variable is the OECD measure of the cost of dismissal. The interaction coefficient is negative and significant. In column (ii), it is the more widely available but more general index from Botero et al. (2003). The coefficient estimate remains negative and significant. Note that the Botero et al. index is a more general measure of labor protection that in addition to the ability to dismiss workers (job security) includes other aspects of labor protection, such as alternative employment contracts and conditions of employment. We conclude that there is strong evidence that labor protection hampers entry in labor intensive industries.

# _3.6.2 Regulations Protecting Property_

Lest the reader believe that we are on our way to advocating some sort of anarchical environment as being best for entry, consider regulations protecting property rights and protecting investors (another form of property protection). First, consider intellectual property.

Strong patent protection could dissuade entry because it protects incumbents and forces new entrants to carve a wide path around existing intellectual property. On the other hand, new entrants do not have the organizational structure, finance, or intellectual capital to create a

> 37 The number of employees (in thousands) is measured using Compustat item 29.

> 38 We have explored the use of other measures of labor intensity such as employees over total assets and get similar results.

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|Syrian Arab<br>Republic|1|
|---|---|
|Libya|0.9|
|**_Non-MENA Count_**|**_ries_**|
|Norway|1.1|
|Spain|1.8|
|Netherlands|1.2|
|Japan|1.4|
|United Kingdom|1.3|
|United States|1.8|

In Figure III-5 we give an impression of the relative volatilities of short- and medium-term components. These figures confirm the results of the previous volatility ratio table. The medium-term component of the credit cycle (solid line) is not more volatile than the short-term component (dashed line) in Saudi Arabia and Egypt. When we check this in the figure related to the United States, the medium and short-term cycles have the same ratio of fluctuations.

# **Figure III-5: Medium and Short-term Real Credit Cycle**

<!-- Start of picture text --> Saudi Arabia: Medium and Short-term Real Credit Cycle Egypt: Medium and Short-term Real Credit Cycle<br>(in percent) (in percent)<br>1967 1977 1987 1997 2007 2017 1967 1977 1987 1997 2007 2017<br>year year<br> Medium-term credit cycle Short-term credit cycle Medium-term credit cycle Short-term credit cycle<br>20<br>20<br>10<br>10<br>0 0<br>-10 -10<br>-20 -20<br><!-- End of picture text --> <!-- Start of picture text --> USA: Medium and Short-term Real Credit Cycle<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term credit cycle Short-term credit cycle<br>5<br>0<br>-5<br><!-- End of picture text --> 19
## REFORMING TAx SYSTEMS: **THE** WORLD BANK RECORD IN THE 1990S

Luca Barbone, Arindam Das-Gupta, Luc De Wulf and Anna Hansson

# Tax Policy and Administration Thematic Group The World Bank

_The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent._
countries are all opportunity-sensitive poorer than Mediterranean countries which, in turn, are poorer than North-Western European countries. But within these three groups of countries, dominance is harder to find.

Table 1: Dominance conditions associated with Theorem 1 <!-- Start of picture text --> AT BE CY CZ DE EE ES FI FR GR HU IT LT LU LV NL PL SK<br>AT . ><br>BE < .<br>CY .<br>CZ > > > .<br>DE < < .<br>EE > *** > *** > *** > *** > *** .<br>ES > > ** > *** > > < *** .<br>FI > < < *** < *** .<br>FR < < < < < *** < *** .<br>GR > *** > *** > *** > > *** < > > *** > *** .<br>HU > > > *** > ** > < > > * > *** < .<br>IT > > > *** > > < *** > *** > * < .<br>LT > > *** > *** > * > *** > *** > *** > > ** .<br>LU < < < < < < *** < *** < < < *** < * < *** < *** .<br>LV > *** > *** > *** > *** > *** > > *** > *** > *** > > > *** > *** .<br>NL < < *** < *** < < < *** < ** < *** < *** > < *** .<br>PL > * > *** > *** > *** > *** > > *** > *** > > < > *** < > *** .<br>SK > > *** > *** > ** > *** < > *** > *** > < > *** < > *** .<br><!-- End of picture text --> Source: Authors’ calculation from EU-SILC (2005). ” _>_ ” in row _i_ and column _j_ means that poverty is higher in country _i_ than in country _j_ . The dominance condition for each pair is obtained checking the sequential conditions in Theorem 1 (equation 1). 90%, 95%, 99% confidence intervals are based on the quantile distribution of 200 bootstrap re-sampling with replacement.

Table 2: Dominance conditions associated with Theorem 2 <!-- Start of picture text --> AT BE CY CZ DE EE ES FI FR GR HU IT LT LU LV NL PL SK<br>AT .<br>BE .<br>CY .<br>CZ > > .<br>DE .<br>EE .<br>ES .<br>FI < .<br>FR .<br>GR .<br>HU > > > .<br>IT .<br>LT > > > > * .<br>LU .<br>LV > .<br>NL < < < .<br>PL > > > *** > .<br>SK > > > .<br><!-- End of picture text --> Source: Authors’ calculation from EU-SILC (2005). ” _>_ ” in row _i_ and column _j_ means that poverty is higher in country _i_ than in country _j_ . The dominance condition for each is obtained checking the sequential conditions in Theorem 2 . 90%, 95%, 99% confidence intervals are based on the quantile distribution of 200 bootstrap re-sampling with replacement.

Significantly fewer instances of dominance are found when testing the conditions in Theorem 2, as shown in Table 2. Only 21 of the 153 possible pairwise comparisons can be ranked by these conditions, and only two of those are significant at 1% and 10%: France is less OS poor than Poland, and Lithuania is OS poorer than Italy. As expected, by relying on strong sequential requirements for both type-specific poverty incidence _and_ mean incomes among the poor, Theorem 2 yields instances of dominance much less frequently than Theorem 1, which relies on weaker, necessary and conditions.

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# **Figure 6: Deposit insurance generosity**

## **a. Number of countries with an explicit deposit insurance protection system for banks**

## **b.  Expansion of coverage since 2009 (% of respondents answering Yes)**

## **c. Increased in the amount covered since 2009 (% of respondents answering Yes)**

**d.  Deposit insurance fund used for purposes other than depositor protection (% of respondents answering Yes)**

Source: Waves 4 and 5, BRSS.

Note: Off-shore financial centers are excluded from the calculations. DEV are developing countries. The number of countries for which we have a response for the specific item/question for both rounds appears in parentheses. * next to the number of countries represents a statistically significant difference in the mean values of a regional group in the two reference years at the 10% level using a two-tailed test. In panel a, the total number of countries that have deposit insurance in LAC is higher in wave 4 than in wave 5 because Puerto Rico and Venezuela, which have a formal deposit insurance system, submitted a questionnaire in wave 4 but not in wave 5. In panels b, c and d, higher values indicate greater generosity.

Almost 90 percent of high-income OECD countries introduced creditor bail-in initiatives, which should help enhance market discipline (Figure 7, Panel A).<sup>10</sup> Relatively fewer developing countries introduced these regulations, but this is not surprising since the introduction of bail-in instruments hinges on the existence of well-developed capital markets. Developing regions such as LAC lag substantially

> 10 Bail-in regulations force banks’ creditors to bear some of the burden of bank default by having a portion of their debt written off.

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Our thesis in this paper is that demand-side empowerment matters for improving demand for a service, above and beyond the simple supply-side effects of feedback mechanisms. We posit that feedback tools can increase demand for a service not just by increasing monitoring, and therefore the quality of a service, but also by signaling to the users that the provider respects them. We set up a large field experiment to test our hypotheses, in which we formally document the impact of feedback tools on users’ demand in the context of agricultural services for 180 farmer groups in rural Rwanda. We randomly assign two types of feedback tools to farmer groups that are subscribed to an agricultural input and extension service. Our design allows us to capture two categories of treatment effects of interest. First, we capture the combined empowerment and monitoring effects of both feedback tools. Second, we separate out pure monitoring effects from user empowerment effects by announcing to extension workers that their work is being monitored, in both treatment groups (“true” announcement) and control groups (“false” announcement).

We track the impact of these different feedback and monitoring tools on a sample of 1,460 farmers. Given the nature of our interventions, we primarily rely on administrative data to establish changes in demand as a result of the different treatments and to track demand effect spillovers to farmers who were non-users at baseline. We also use survey data to document changes in farmers’ use of and satisfaction with the service, as well as empowerment to demand better service.

We find that feedback tools help sustain demand for the service among current clients and increase demand among non-users. Farmer groups offered the opportunity to provide feedback were half as likely to have members leave the service the following year as control groups. Perhaps surprisingly, this demand effect spills over to non-users in the vicinity of the treated groups, who are more likely to sign up in the following season. These effects are large and robust to multiple hypotheses testing, implying an increase in group size of up to 3 3
**Sectoral Change**

<!-- Start of picture text --> High-COD Sectors<br>Q40 ........................<br>a3 x - .<br>0Q30 | | | .intensive<br>025 L<br>020 | 111 | | | | li | | . 1:1<br>Q15 E | ll | | lll | IIIIF 1lg .<br>o_x<br>i c 0 X<br>m~ g O m Clargest<br><!-- End of picture text -->

**Figure 15: Production Share of COD-Intensive** Sectors

Figures 15 and 16 provide information on the share of production in 'dirty' sectors across our five provinces, and for China as a whole. For China, only S02sectors have exhibited a significant drop in share since 1987. In our five provinces, however, the reforms have had a more general 'clean' effect. The dirty sector share drops in all cases for water pollution and in all but one case (S02 in Liaoning) for air pollution. The changes have occurred in Beijing for COD-intensive sectors, and in Guangdong and Shanghai for both S02and TSP-intensive sectors.

<!-- Start of picture text --> High-TSP Sectors<br>0.45...7<br>0.20<br>0.15<br>0.10<br>0.05~~~~~~~~~~~~~~~~~~~~~.3<br>0.05~~~~~~~~~~~~~~~~~~~~~.0<br>0.00<br>0.25r 161:3 Prdcto.03e0fS0-ndTP<br>COCOC<br><!-- End of picture text --> <!-- Start of picture text --> High-S02 Sectors<br>0.050<br>0.020<br>0.040<br>Aa<br><!-- End of picture text -->

**Figare 16: Production Share of S02-and TSPIntensive Sectors**

# Trade Liberalization

China has opened its frontiers to more trade as the economic reforms have proceeded. From an analytical perspective, the impact of trade liberalization on the environment could be either positive or negative. Positive effects include enhanced production efficiency in export-oriented enterprises and more rapid absorption of newer, cleaner technologies from OECD sources.<sup>9</sup> On the other hand, China's comparative advantage could be in pollutionintensive industries because it has weaker environmental regulation and lower-cost heavy raw materials than many of its trading partners. We have checked for this possibility by

**9** Huq, Martin, and Wheeler (1993) have found that more open developing economies absorb clean technologies in metals and paper production much more rapidly than their less open counterparts.

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# **Following Mexican Youth: A Short-Run Study of Time Use Decisions**<sup></sup>

Juan D. Baron<sup></sup> Anna Popova _The World Bank The World Bank_ Angélica Sánchez _The World Bank_ _Keywords:_ youth, idleness, school, work, transition _JEL-Classification: J01, J24_

>  The present study is a background paper for the regional study “Out of school and out of work: Risk and opportunities for Latin America’s ninis.” (De Hoyos, Rogers, and Székely 2016). Earlier versions of this paper benefited substantially from comments and suggestions by Wendy Cunningham and Rafael de Hoyos. The authors would also like to thank Daniel Lederman, Halsey Rogers, Julian Messina, Miguel Szekely, and participants of two workshops held at the World Bank in Washington DC in October 2013 and March 2014 for their feedback. This paper benefited from a DEC’s Research Support grant given to Rafael de Hoyos under the Proposal “Youth Idleness in Latin America: Quantitative and Qualitative Evidence from Mexico”

>  Corresponding author: Juan Baron (jbaron@worldbank.org).
# **Thirty-five years of long-run energy forecasting: Lessons for climate change policy**

Jean-Charles Hourcade Franck Nadaud<sup>1</sup> CIRED (Centre International de Recherche sur l'Environnement et le Développement / International Research Center on Environment & Development, France) & SMASH (Société de Mathématiques Appliquées et de Sciences Humaines / Society of Applied Mathematics and Humanities) Background Paper for the _World Development Report 2010: Development and Climate Change_

> 1 Corresponding author : Franck Nadaud (nadaud@centre-cired.fr). Dr. Jean-Charles Hourcade is Research Director at CNRS (Centre National de la Recherche Scientifique / National Center For Scientific Research) and is the scientific director of CIRED.  Franck Nadaud is a Researcher at CIRED. Both authors are also affiliated with SMASH, a research organization closely linked to CIRED.
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4.16 The elasticities of the IMR with respect to female enrollments in 1975, population per physician and GDP per capita have been computed from Table 4 and are shown in Figures 7a and 7b. The direction of the results was similar for the 1970 equations, thus only the results with 1975 enrollments are presented here. The elasticity of IMR with respect to female secondary enrollment sharply rises to -0.92 at an enrollment level of 40 percent; whereas the elasticity of IMR with respec **t** o GDP per capita is low at -0.21 even at a per capita GDP level of $1800.'8 The elasticity of IMR with respect to population per physician is positive and decreases as the ratio decreases. It reaches 0.06 percent when the ratio of population per physician is halved (from the mean). For a typical, poor country with a GDP per capita of $300, a female secondary enrollment rate of 10 percent, and physician for every 15,000 people, let us consider alternative approaches to reducing infant deaths. A doubling of female enrollments (from 10 percent to 20 percent) would bring down the IMR from 105 to 78, whereas a doubling of GDP per capita would reduce IMR from 102 to 99. Halving the ratio of population per physician would reduce the IMR from 85 to 81. These illustrative scenarios suggest the powerful influence of mother's education in the prevention of infant deaths.

**1ThM actual** values of these elasticities are given in Appendix Table 9.
# _3.1 Domestic Variables_

The core variables used in this analysis are common throughout civil war onset studies. POLITY scores (from the POLITY dataset4) range from -10 to +10. In total, 36% of the dataset consists of democratic country-years (+5 to +10), 43% of these data are autocratic country years (-5 to -10) and 20% are anocratic country years (-4.9 to +4.9). GDP data are from the Fearon and Laitin data. This variable is logged for analysis. Low income states are defined as those in the lowest quartile (under 1000 dollars per year). Moving averages are created for both Polity and logged GDP scores to create a smoothed trend surface and reduce the impact of single year outliers. Moving averages control for the need to lag particular variables and decrease the bias of large, sudden changes in scores. Values were smoothed at three year intervals.

Ethnic fractionalization data are from Fearon’s Ethnic Fractionalization score (2002)5. This score has a high correlation (.75) with the commonly used Ethnic Fractionalization score derived from _Atlas Narodov Mira_ (1964). The differences are primarily in the typology of groups, Fearon allows for ethno linguistic and cultural groups, whereas the Russian Atlas is based solely on ethno linguistic fractionalization. Table 3.2 summarizes the primary state level independent variables (table 3.2 presented in appendix).

# _3.2 Neighbor Variables_

Spatial lag variables account for neighboring characteristics. Neighbors are first-order (direct border sharing) states and all neighborhood variables are averaged from first order values. Neighboring wars are coded positively (1) if a border sharing neighbor is at war (not limited to the year of onset but the duration of the neighboring war). This variable is further disaggregated by the size of the adjacent war. If the war intensity is coded as level 3 in the UACD it is considered a large war (similar to Fearon and Laitin’s coding of onsets). Any level 2 conflict (more than 25 and less than 1000 battle deaths over the entire conflict) or a level 1 (25 battle deaths in one year, 1000 battle deaths over the course of the conflict)6 are subsumed under the small war category. It is then possible to check whether it is the presence or the size of a neighboring conflict is the salient attribute. A total of 2207 country years are coded ‘1’ for the presence of a neighboring war. When

> 4 http://www.cidcm.umd.edu/inscr/polity/ 5 http://www.duke.edu/web/licep/6/fearon/fearon.pdf

12
# **References**

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population attitudes. Even if we could run an across-district comparison, our sample size would be restricted to six, which was the total number of districts affected.

Using these data, Figure 2a provides evidence of the tremendous diversity of organizations that came to help in the first three months after the earthquake, both by Pakistani and foreign organizations. The Pakistan Army was the dominant relief provider and served as the lead relief agency; 73 percent of all households report that somebody from the Army came to help. Other Pakistani organizations included different government departments and all types of Pakistani NGOs. We separate the foreign organizations into foreign Islamic and other foreign (labeled foreign) as they both played important roles in relief. Combining foreign and foreign Islamic organizations with the UN shows that more than 40 percent of households in our sample reported that at least one foreign organization or came to their help. Such an inflow of foreigners united to work together for a humanitarian cause in such short an interval had never been observed in this part of the world ever before.

Relating the reporting of aid to distance to the fault-line (Figure 2b) reveals a number of important relationships. First, as one might expect, there was a large presence of organizations in the hardest hit areas close to the fault-line. Virtually none of the households in the census claim that “no one came” in the immediate vicinity of the fault-line. Second, the army’s presence does go down with the distance but remains at 60 percent even at its lowest point, confirming its wide leadership in earthquake relief. Third, foreign organizations were a significant presence in the region, but more so close to the fault-line: 40 percent of the residents in villages within 10 kilometers of the fault-line report at least one foreign organization that came to their help. Similar results holds for the UN group of organizations even though many were not necessarily supposed to be in the role of first responders.

21