rafmacalaba/datause-encoder-data huggingface dataset:
concentration (across producer countries) in the exports of intermediates used by a sector. To obtain (i), we calculate for each HS4 product the share of China in the product’s world exports in 2015. We average this share by ISIC revision 3 broad sector (the classification used in the inputoutput tables), after mapping HS4 products to broad sectors. Then, we use each country’s inputoutput table to construct for each given broad sector X the reliance on China as supplier of its inputs as the weighted average across all the broad sectors’ average China share, where weights are given by the shares of inputs from each broad sector used for production of broad sector X’s output. To obtain (ii), we calculate for each HS4 product the share of its largest exporter in the world in 2015. Then, we use each country’s input-output table to construct for each given broad sector X the export concentration of its inputs as the weighted average across all the broad sectors’ average share of the largest exporter, where weights are given by the shares of inputs from each broad sector used for production of broad sector X’s output. We then map these two measures at the broad sector level into the HS4 level. The resulting measures vary at the exporting country and HS4 product level. Allowing for these two dimensions of variability is important since different countries are likely to source very different inputs.

Fourth, as a proxy for technology, we use product complexity defined as the weighted average of the GDP per capita of the countries that export the HS4 product, where weights are given by countries’ revealed comparative advantage (RCA) index in that product following Hausmann and Hidalgo (2009, 2011). We compute this RCA (Balassa) index, the ratio between the share of an HS4 product in a country’s export portfolio and the share of the same product in world exports, using data from UN COMTRADE and GDP per capita data from the World Development Indicators for 2012.

12
Figure B5: Changes in APA and Government officials’ Survey indices (2016-2018) <!-- Start of picture text --> Urban planning Fiduciary responsibility<br>0 1 2 -2 0 2<br>Changes in DLI Changes in DLI<br>(a) Urban planning (b) Fiduciary responsibility<br>Infrastructure Accountability<br>-4 -2 0 2 -2 0 2 4<br>Changes in DLI Changes in DLI<br>(c) Infrastructure (d) Accountability<br>4<br>4<br>3<br>2<br>2<br>1<br>0<br>Changes in Survey Index Changes in Survey Index 0<br>-2 -1<br>3<br>4<br>2<br>2 1<br>0<br>0<br>Changes in Survey Index Changes in Survey Index -1<br>-2<br>-2<br><!-- End of picture text --> _Note:_ This figure presents a scatter of changes in DLI subcomponent scores (x-axis) and survey indices (x-axis) by LGA. DLI scores are normalized so that changes can be interpreted as standard deviations in the distribution of the 4th APA scores.

Figure B6: Correlation between APA and government officials’ survey measures <!-- Start of picture text --> Planning Baseline<br>Fiduciary Baseline<br>Infrastructure Baseline<br>Accountability Baseline<br>Planning Endline<br>Fiduciary Endline<br>Infrastructure Endline<br>Accountability Endline<br>Planning Change<br>Fiduciary Change<br>Infrastructure Change<br>Accountability Change<br>-.5 0 .5 1<br>Correlation<br><!-- End of picture text --> _Note:_ This figure presents correlations across the 18 ULGSP LGAs for baseline, endline and changes in outcomes. Endline and changes are not reported for the taxation subcomponent since it was not collected in the 6th APA.

60
Table 2: The increased incidence (RR) of disease from exposure to DDT*

||**IRS Spray**<br>**Operators**|**IRS**<br>**Exposed**<br>**Population**|**Non IRS**<br>**Exposed**<br>**Population**|
|---|---|---|---|
|Stomach Cancer|2.0|1.25|1.1|
|Liver Cancer|3.8|1.7|1.3|
|Pancreatic Cancer|4.8|1.9|1.4|
|Lung Cancer|1.8|1.2|1.08|
|Breast Cancer|3.04|1.6|1.25|
|Prostate Cancer|2.1|1.25|1.1|
|Lymphatic Cancer|1.8|1.2|1.08|
|Diabetes|2.74|1.45|1.18|
|Asthma|3.4|1.6|1.2|
|Abortion|1.6|1.15|1.06|
|Low Birth weight|3.1|1.5|1.2|

Column 2 of this table was constructed using available information from the following sources (from top to bottom): Cocco et al. 2005; McGlynn et al. 2006; Garabrant et al. 1992; Austin et al. 1989; Pavuk et al. 2003; Settimi et al. 2003; Woods et al. 1987; Everett et al. 2007; Sunyer et al. 2005; Longnecker et al. 2005; Longnecker et al. 2001. Column 3 and Column 4 present authors’ calculations.

*The Odds Ratio (OR) found in some studies has been approximated as the RR without a loss of accuracy since the incidence rates are very low.

21
# Table VII

|Effect of Ele<br>It-st|ctions on<br>atistics in<br>National|Road Con<br>parenthesi<br>Highways|structiona<br>s]<br> <br>S|tate Roads||
|---|---|---|---|---|---|
|Independent Variablesb:|[1]|[2]|[3]|[4]|[5]|
|Election year|47.06|61.48||||
||[2.22]|[2.21]||||
|1 yr before Elections|||-297.3|-196.4|-314.2|
||||[-0.33]|[-0.19]|[-0.22]|
|2 yrs before Elections|||-1275.2|-1400.4|-1249.1|
||||[-1.60]|[-1.52]|[-0.98]|
|3 yrs before Elections|||-239.4|-150.7|-237|
||||[-0.30]|[-0.16]|[-0.19]|
|4 yrs before Elections|||-1887.8|-1983.6|-2220.3|
||||[-2.34]|[-2.12]|[-1.70]|
|Spending on Roads|||||0.2|
||||||[0.72]|
|State domestic product [SDP]||0.02||0.7||
|||[0.30]||[0.32]||
|Share of agriculture in SDP||-170.34||-5613.9||
|||[-1.05]||[-1.27]||
|Proportion of rural population||115.67||-36047.9||
|||[0.10]||[-1.02]||
|Number of Observations|318|243|330|308|172|
|R-sq|0.1611|0.1850|0.2088|0.2328|0.2195|

a. State domestic product is in 1973 rupees per capita. Roads lengths are in kilometers.

47
- **Eligible non-beneficiaries** (households with at least one vulnerable individual but not benefitting from any VG sub-projects),

- **VG Beneficiaries** (households with at least one vulnerable individual and benefitting from a VG sub-project). This group was further stratified into two groups:

- **VG group leaders** (chairperson, secretary, and treasurer, who hold signatory power over group accounts), and

- **VG “rank and file”** members (the rest of the group conducting the proposed income generating activity – with no signatory powers over the group accounts)

Within each village, a short listing survey was given to every household. The short listing survey collected basic demographic information about the household (e.g. household size and age of the eldest household members), GPS data, and determined whether or not the household contained a vulnerable member. A long listing survey was given to all village elites, all households with vulnerable members (including VG beneficiaries and eligible non-beneficiaries – 38,871 households in total), and to a randomly selected sample of ineligible households. The long listing survey collected more detailed data, including household amenities, characteristics of the household head, holdings of assets, and basic consumption data. From within this sample, a smaller, random stratified (using the strata described above) sub-sample was selected to receive a detailed household survey.

In each of the 100 villages, a household survey was conducted with the two village elites, the three group leaders from each TASAF II group, three randomly sampled “rank and file” members from each TASAF II group, three randomly sampled households from all “eligible nonbeneficiaries” in each village, and three randomly selected households from all “ineligible” households in each village. Hence, in a typical village with one TASAF II group funded to run an income generating activity, the sample size was 14. There were 1,544 households that completed the household survey in the 100 villages. The household survey contained detailed consumption data at the household level, limited consumption data at the individual level, as well as collecting information on distance to the village center, the education of the household head, asset ownership, participation in village meetings, whether household members hold political office in the village, and blood relationships between household memberships and village elites.

There are 2,542 wards in 119 districts in mainland Tanzania. The poverty mapping data is unavailable for 86 of these wards. Out of these, the ward councilor elections were uncontested in 10
for export to the European Union in a much greater degree than most MENA countries is relevant here. Under just-in-time management practices, the availability of adequate service links (transport, harbor services, customs operations, telecommunications ...) is fundamental for the decision on where to outsource. Many MENA countries (particularly those around the Mediterranean) can become competitive locations for outsourcing by European companies once access to efficient producer services is made available. This requires significant changes in regulatory regimes and investment policies to enhance the contestability of markets.

**Table 6: Per Capita Incomes Relative to Germany**

|**Country**|**Value 1992**|**Relative to**<br>**Germany**<br>**(%)**|
|---|---|---|
|Egypt|650|2.8|
|Morocco|1,050|4.6|
|Jordan|1,130|4.9|
|Tunisia|1,760|7.6|
|Algeria|1,850|8.0|
|Saudi Arabia|7,640|33.2|
|Israel|13,460|58.4|
|United Arab Emirates|22,640|98.3|
|Turkey|2,040|8.9|
|Romania|1,170|5.1|
|Bulgaria|1,410|6.1|
|Poland|1,950|8.4|
|China|480|2.1|

Source: World Bank, World Development Report, 1994.

# **4. The Uruguay Round: WTO Rules and Disciplines**

The economic substance of the direction of the required changes in trade policies and institutions is clear; the question is how to create a constituency that favors reforms and makes them politically feasible and self-sustaining. Although the primary constraint in this connection is clearly the existence (or rather the absence) of internal support, external institutional mechanisms can be 22
# **Table A6: Panel A – Nested Logit Regressions, 2006**

Log Odds ratio between Renting and Owning

|**Country**|**lage**|**female**|**hhsize**|**lnincome**|**hhnodep**|**hhdep**|**house**|**secedu**|**teredu**|**fulltime**|
|---|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_|||||||||||
|Czech Republic|-0.50 ***|0.02|-0.09|0.20 ***|-0.62 ***|-0.11|-3.71 ***||2.11|-0.27|
|Estonia|-5.11 **|-0.01|-0.79 ***|1.71 ***|-3.03 **|0.39|1.09 **||7.86 *|-0.36|
|Hungary|-3.46|-0.12|-0.41|0.68|-0.03|1.67|-1.04 ***|8.06 ***|11.61|0.20|
|Latvia|-1.17|-0.11|0.05|0.35|-0.56|-0.31|-1.98|2.32|6.79 ***|-0.68|
|Lithuania|-3.34|-0.52|-0.65|0.94|-1.51|0.22|0.45||12.06|1.44|
|Poland|4.43 *|0.53|0.82 ***|-0.02|0.85|-1.71 *|-0.16|19.09 **|6.91|-2.68 *|
|Slovak Republic|-1.77|-0.29 **|0.19 **|0.89|-0.36 *|-0.12|3.64 *|6.99 ***|11.16 ***|-0.22|
|_EU-15 and Cyprus_|||||||||||
|Austria|0.44|-0.18|-0.25 ***|-0.25|0.26|1.62 ***|-3.21 ***|13.47 ***|10.36 **|1.55 *|
|Belgium|0.44|0.20 **|-0.08|0.06|-0.18|-0.09|-2.22 ***|15.10 ***|15.83 ***|-0.95 ***|
|Cyprus|-2.55|0.08|-0.20|0.60|0.01|0.23|-0.86 ***|20.33 ***|13.10 ***|1.39 *|
|Denmark|0.06|0.53 ***|-0.49 ***|0.24 ***|-0.49 ***|0.54 **|-2.42 ***||10.92 ***|-1.14 ***|
|Finland|3.90 ***|0.06|0.00|-1.33 ***|0.36|0.06|-1.60 ***|20.42 ***|17.46 ***|-1.10 ***|
|France|0.19|0.27 ***|-0.09 *|0.05|-0.21|0.01|-2.18 ***|20.00 ***|21.62 ***|-0.08|
|Greece|-9.52 *|-1.41|-1.42|1.64|3.79|6.04 **|-5.68|-27.55|-23.90|-7.96|
|Ireland|0.12|0.34 **|0.17 **|0.22|-0.40 **|-0.44|-2.67 ***|21.31 ***|22.34 ***|-1.57 ***|
|Italy|3.55 ***|0.13|0.26 *|-0.95 ***|-0.37 **|-1.15 **|0.19|9.79 ***|6.48 ***|-1.12 *|
|Luxembourg|1.09 **|-0.26|0.09|-0.35 **|0.01|-0.10|-2.37 ***|18.68 ***|8.58 ***|0.53|
|Netherlands|-1.94 ***|0.36 ***|-0.36 ***|0.74 ***|-0.97 ***|-0.53 **|-0.24||12.00 ***|-1.17 ***|
|Portugal|1.94 ***|0.10|0.22 **|-0.70 ***|0.02|-1.30 ***|1.36 ***|11.35 ***|10.92 ***|-1.16 **|
|Spain|0.34|0.19 **|0.06|-0.26|-0.46 ***|-0.60 **|-0.21 *|4.12 ***|7.17 ***|0.47 **|
|Sweden|1.10|-0.03|-0.01|-0.23|-0.29|-0.33|-2.45 ***|12.76 ***|11.76 ***|-0.65 **|
|United Kingdom|-0.87 ***|0.31 ***|-0.04|0.52 ***|-0.52 ***|0.20|-1.75 ***||18.87 ***|-1.95 ***|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

39
most directly affected by the Levant war. Thus, the results indicate that simpler linear approaches would misstate the “true” effects of war, making it difficult to determine the direction of bias.

Although widely used and comprehensive in many ways, the GTAP 8 database has insufficient information on the Levant economies. Therefore, we modify the database and add to it inputoutput, trade, and protection data on Lebanon, Syria, Iraq, Jordan, and several other Middle East and North African (MENA) economies, including West Bank and Gaza, Yemen, Algeria, and Libya. This major modification required balancing both bilateral trade flows and macroeconomic country aggregates in the global database and was warranted in order to reflect accurately the regional spillover effects of the Levant war. We also adjusted trade preferences in all MENA countries in order to reflect accurately existing global, regional, and bilateral trade agreements and avoid overestimating the trade-related effects of foregone reforms.

Simulation results reported in the paper indicate the qualitative changes likely to occur as a result of the conflict and regional trade disintegration while the magnitudes of the direct war effects reflect the intensity and scope of the conflict as of mid-2014. The results suggest that Syria and Iraq bear the brunt of the direct war costs, losing 14% and 16% in per capita welfare, respectively. The embargo on trade with Syria is a major factor behind this country’s real GDP decline, which is estimated at 30% and is much larger than its per capita output decline of 13%, due to the effect of Syrian refugees and war casualties on the population count. All other Levant economies lose in per capita terms, but not in aggregate terms because the inflows of refugees boost population numbers, and therefore aggregate consumption, investment, and labor supply. Lebanon’s per capita welfare losses are largest and reach close to 11%, while those of Turkey, Egypt, and Jordan do not surpass 1.5%. The difference between aggregate and per capita welfare effects are most pronounced in Lebanon, where the increase in the refugee-to-citizen ratio is 4
Policy Research Working Paper 6523

# **Abstract**

Theoretical accounts of compliance with court orders emphasize the importance of transparency. Most empirical studies of compliance center on high profile political cases, largely ignoring the high-volume, quotidian claims against the state for basic services that constitute the largest share of court dockets in many jurisdictions. This paper uses a unique dataset on compliance with orders from the Constitutional Chamber of the Supreme Court of Costa Rica to examine the determinants of compliance in low salience cases. It finds that orders issued just after the Court announced, in a press conference, that it was monitoring compliance were implemented roughly two months sooner than orders issued just prior to the press conference. These findings suggest that publicity can motivate compliance even in low salience cases.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at vgauri@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# TABLE OF CONTENTS

|Abu|tract|.<br>.<br>.<br>2|
|---|---|---|
|**A.**|Introduction|.<br>3|
|S.|Intornational|and Soviet Data on Services.<br>7|
|C.|International|Comparisons<br>of Service Activity .10|
|D.|Services as a|Major Source of Growth .18|
|Dib|liography|.................<br>. 22|
# The Industrial Pollution Projection Syvetem

## 1.introduction

The Industrial Pollution Projection System (IPPS) is a modeling system which can use industry data to estimate comprehensive profiles of industrial pollution for countries, regions, urban areas, or proposed new projects. It is apparent that there is a huge potential demand for IPPS among environmental and industrial planners, particularly those working on issues related to developing countries. Most developing countries have little or no reliable information about their own pollution. Rapid environmental progress in the near future will depend on estimating pollution with projection systems like IPPS.

IPPS has been developed to exploit the fact that industrial pollution is heavily affected by the scale of industrial activity, its sectoral composition, and the process technologies which are employed in production. Although most developing countries have little or no industrial pollution data, many of them have relatively detailed industry survey information on employment, value added or output. IPPS is designed to convert this information to the best possible profile of the associated pollutant output.

The prototype system has been developed from a database containing environmental and economic data for approximately 1
Table 4 reports results of a cross-sectional regression of the form, ∆ _UnorgSectorShared_ = β∆ _UnorgSectorWomenOwnershipd_ +η _s_ +γ _X d_ + ε _d_ , where d indexes districts. The outcome variable is the change in unorganized sector share of activity for district d. The key explanatory variable is the change in the share of unorganized sector firms that are owned by women. We include in regressions a vector of state fixed effects to control for broad differences across states in terms of the economic development and growth rates. These fixed effects also control the large variation that exists across states in how integrated women are into the local economy. Finally, we include in some specifications districtlevel control variables that are described below. Estimations weight district observations by an interaction of the log of district population, have 325 observations, and report robust standard errors.

The first three columns of Table 4 consider the persistence of employment levels, and we define the core regressor using employment shares for women-owned businesses as well. Panel A leaves the variables in raw shares, while Panel B considers shares expressed in unit standard deviations. We find a positive elasticity that is statistically significant at a 10% level. A 10% increase in the share of unorganized sector businesses owned by women is correlated with a 1% increase in the share of the unorganized sector overall in the district. The coefficient without the state fixed effects is 0.132 (0.074) for Panel A. Thus, the macro pattern that we observe for manufacturing is also exhibited using variations across districts within states.

The second column shows that these results hold when including as control variables a variety of district traits measured in the 2001 Census. These covariates include log population, log population density, literacy rates, age profiles, a composite infrastructure index, the strength of household banking, travel time the nearest large city, income per capita, and the urbanization rate. These covariates sharpen the estimation slightly for our core regressor, but they have fairly limited additional power, similar to persistence laid out in Ghani, Kerr, and O’Connell (2013). The third column show similar stability to including the population growth of the district.

Columns 4-6 consider instead the persistence of establishment counts. The coefficients in these columns are substantially smaller in Panel A given the reduced variation for the establishment counts in the outcome variables (i.e., the unorganized sector is a very large share 13
35 2 The Effect of the Figure

# Return to Education

i) C The Age at which Education Ceases 'ibs Is shown, as a fraction of the total period of 'youth', plotted against the Interest rate-return to education

# Ii) The Return to Education and the Fertility Rate

iii) The Return to Education Relative to the Interest Rate and the

# Fertility Rate

<!-- Start of picture text --> o__<br>0.7& .... " ....... .............. t.... .... ..0"<br>0.7 * .. ,...... ,, . , .<br>.<br>\<br>0.66. w*"""~<br>oss ........................<br>.I<br>'Z4 3 Q4 OS 0. 7 N6 3 9 t .1 12<br>t RMe- Rd= to Educatin % P..<br><!-- End of picture text --> <!-- Start of picture text --> 2i2 ... ,...,,......,..,,......................... ..<br>g S.............. .. . ...........<br>i .<br>..........<br>0j.7?........ ........................<br>O r~~. .... ...........................<br>S5 l5. 5.96 6 6.5b 6'1 6.15 82 6.25 6 6.d 5<br>RPWw to Edudimn % pa.<br>2<br>O 4 ; ..................v<br>0. 3 0.4 056 0.6 07 0.8 0d9 i I 12<br>kt. Rate -Reumrn to Education % ps.<br><!-- End of picture text -->
are drawn from the Africapolis database.<sup>6</sup> Even natural geography should not be too quickly considered to be predetermined before the drawing of the boundaries. Deforestation can influence rainfall, and even elevation or watercourses can, to some extent, be reshaped by human activity. Further, geographical discontinuities are measured on a sample of localities whose settlement is not random. However, we expect to find no or few discontinuities in rainfalls, altitude, and hydrography, and we regard this result as corroborating the quasi-randomness of the boundaries’ alignment. Conversely, constructed geography, such as population density or city distribution, is far from being independent from boundaries. Nevertheless, we want to explore the extent to which discontinuities in welfare should be linked to discontinuities in urban structures. Finally, due to differences in countries’ shape and spatial organization, distance to the capital or main city does not vary smoothly at borders. Any border effect includes a change in the capital city and a shift in the distance to it.

The major part of the border of Cˆote d’Ivoire with Ghana does not follow a natural line, except the lagoon in the extreme South and the Black Volta river in the extreme North. Given our sample distribution, these two parts contribute little to our estimates, and withdrawing them changes nothing. The border with Ghana does not exhibit any discontinuity in our geographical variables, except distance to the capital city: the Ghana border is closer to Abidjan than it is to Accra by 130 to 150 km (table 1).

> 6This database from geographers (SEDET, CNRS and University Paris-7) is probably the most complete to date. We are grateful to Eric Denis for making it available to us. We prefer not to use the urban/rural status of localities as recorded in surveys because (i) countries do not use the same definition for urban areas, and (ii) this precludes distinguishing peri-urban rural areas from more isolated ones.

16