rafmacalaba/data-use-mentions-extended huggingface dataset:
# 1. **Introduction**

Bangladesh is one of the largest consumers of tobacco in the world. Approximately 4 in 10 adults (46 million adults) use some form of tobacco, whether smoked (for example, cigarettes and bidis) or smokeless (for instance, betel, betel nut, tobacco leaf, and so on).<sup>2</sup> Tobacco consumption in Bangladesh varies by gender. Men are significantly more likely than women to smoke (40 percent of men versus 1 percent of women above the age of 15).<sup>3</sup> The prevalence of smokeless tobacco use is similar among men and women (about 30 percent).<sup>4</sup> Smoking is widespread in Bangladesh, but comparable with the prevalence rates in other countries in the region (figure 1).

**Figure 1. Prevalence of Smoking Any Tobacco Product, Individuals Ages > 15 Years**

<!-- Start of picture text --> 76.2<br>39.8 41.4 41.9<br>37.1<br>31.6<br>28.4<br>20.4<br>11.1<br>6.4<br>1.9 0.4 0.7 2.3 3 3.6<br>India Sri Lanka Myanmar Nepal Bangladesh Thailand Pakistan Indonesia<br>Female Male<br><!-- End of picture text --> _Source:_ WHO 2015a.

Information on households from the Household Income and Expenditure Survey (HIES) 2016/17 confirms the high rates of tobacco consumption (both smoked and smokeless). In 2016, approximately 65 percent of households had consumed some type of tobacco product during the month previous to the interview. Comparing across time, the share of households consuming tobacco has been decreasing during the last decade. Using previous rounds of the HIES and comparable measures, one
Table 1: Estimated threshold values of the impact of the increase in the share of foreign private investors ( _푓푝푖,푡_ −1) and the expected public debt (E _푡 퐷푒푏푡푖,푡_ + _푛_ <u>) on the interest rate</u>

||Threshold composition<br>|Threshold level|
|---|---|---|
|(1) IT|_푓푝푖,푡_−1= <sup>−</sup><sup>_훽_0</sup><br>_훽_1<br>|E_푡퐷푒푏푡푖,푡_+_푛_= <sup>−</sup><sup>_훿_</sup><br>_훽_1|
|(2) PSTR|_푓푝푖,푡_−1=<br>−_훽_0<br>_훽_1(_푓푝푖,푡_−1;_훾_1_,푐_)<br>|E_푡퐷푒푏푡푖,푡_+_푛_=<br>−_훿_<br>_훽_1<br>�_휕푔_(_푓푝푖,푡_−1;_훾_1_,,푐_)<br>_휕푓푝푖,푡_−1<br>�|
|(3) GPSTR|_푓_=<br>−_훽_0|E_퐷푒푏푡_=<br>−_훿_|
||_푝푖,푡_−1 <br>_훽_1(_푓푝푖,푡_−1;_훾_1_,훾_2_,푐_)|_푡푖,푡_+_푛_<br>_훽_1<br>�_휕푔_(_푓푝푖,푡_−1;_훾_1_,훾_2_,푐_)<br>_휕푓푝푖,푡_−1<br>�|

# **2.4 Data**

## **2.4.1 Data coverage**

Based on forecast data availability, we use bi-annual data of 25 countries: 11 advanced economies and 14 emerging markets during 2006H2-2018H2. Advanced economies include Australia, Canada, the Czech Republic, Denmark, Japan, the Republic of Korea, Norway, Sweden, Switzerland, the United Kingdom, and the
probability of getting chosen, we correct for this by conditioning on the lotteries entered (Abdulkadiroglu et al, 2011).

Second, we use assignment to the treatment (winning the PAC lottery) as an instrumental variable (IV) for the actual treatment of migrating, in regressions like: where _Migratei_ is a dummy variable that equals one if person _i_ ever migrated to New Zealand and is zero otherwise. The IV estimator of <sup>ˆ</sup> is a Local Average Treatment Effect (IV‐LATE), interpreted in this case as the impact of migrating on people who would migrate only if they won the PAC lottery and not otherwise.

# **3. Impact on Risk and Time Preferences**

There are several channels through which international migration might be hypothesized to affect risk and time preferences. The very act of moving to another country to set up a new life is a risky decision, and successfully doing this may lead to more confidence in taking subsequent risks (Krueger and Dickson, 1994). To the extent that cultures differ in risk attitudes or time preferences (Falk et al., 2016), moving to a new country entails exposure to this new culture. Migration also can affect education and wealth levels, and lifespans, which in turn may affect time preferences and risk attitudes.

# **3.1 Existing Evidence of Migration Effects**

Risk is a key determinant of migration decisions and several studies have attempted to examine the association between risk preferences and migration, with an implied causal direction going from time‐ invariant risk preferences to migration. Jaeger et al. (2010) use data from the German Socio‐economic survey on an individual’s self‐reported propensity to take risks and find that individuals with higher risk propensities are more likely to migrate within Germany.<sup>6</sup> Gibson and McKenzie (2011) use the same risk measure and find that among high‐skilled people from three Pacific countries the more risk‐
operating in an_<br>_environment with relatively_<br>_low political constraints, a_<br>_utility will invest more (less)_<br>_in the presence of weak_<br>_(strong) interest group_<br>_competition, ceteris paribus._<br>_-as political constraints_<br>_increase, the positive_|-each country’s annual<br>growth rate of megawatts of<br>generation capacity per<br>capita (this is the measure<br>of investment used)<br>(PI)|**-political constraints variable**<br>(CL) – constructed from<br>information on the number of<br>independent branches of<br>government, adjusted to take into<br>account the extent of alignment<br>across different branches and of<br>preference heterogeneity within<br>each legislative branch<br>_Result: mostly significant at the_<br>_5% level_<br>**-interest group competition**<br>**variable**– simply the ratio of<br>industrial to total electricity<br>consumption (SE)<br>_Result: mostly significant at the_<br>_5% level_<br>**-existing penetration level**<br>**variable**– megawatts of<br>generation capacity per capita in<br>the previous period (PI)<br>_Result: mostly significant at the_<br>_10% level_<br>**-government’s capital**<br>**investment budget**(CL) –<br>measured as real annual gross US<br>dollar public investment on<br>capital spending per capita;<br>_Result: insignificant_|n.a.|Country-level panel data of<br>78 developed and developing<br>countries over the period<br>1970-1994
who expect weak aggregate demand to persist will be reluctant to invest; reduced investment will tend to lower asset prices which, through wealth effects, will further depress consumption (Caballero and Simsek 2017). If aggregate demand weakness is accompanied by a financial crisis, financial market frictions can restrict firms’ access to credit and start-up capital, further reducing investment and productivity growth.<sup>2</sup> Damage to potential output from short-term disruptions can also result from productivity losses due to resource misallocation (Dieppe, Kilic Celik, and Okou 2021; Furceri et al. 2021); these may be partially offset by productivity gains stemming from the exit of lowproductivity firms (Bloom et al. 2020). Finally, high unemployment that accompanies weak aggregate demand tends to lead to human capital losses and reduced job search activity among the long-term unemployed (Blanchard and Summers 1987; Lockwood 1991).

Empirical estimates have documented that some of these mechanisms were indeed at work during past recessions. An analysis of data for a large sample of countries during 19602018 found that financial crises, especially when accompanied by a rapid buildup of debt,

> 2 For details of these empirical findings involving financial markets, see Claessens and Kose (2017), Queralto (2013), and Wilms, Swank, and de Haan (2018).

3
>3</sup> In each EA, 24 maize cultivating households were selected at random from the universe of maize cultivating households identified through a full household listing in each EA. Of the sampled households, 16 were selected at random for a separate crop cutting experiment and were subject **–** to two visits (post-planting and crop cutting/post-harvest) – these comprise our treatment group while the remaining 8 households were subject to a single, post-harvest visit – they serve as our control group.

> 3 The IHS5, a nationally representative household survey, ran from April 2019 to April 2020, covering a sample of 11,472 households in 717 EAs. To access the anonymized survey data and documentation from the IHS5, please visit: <u>https://microdata.worldbank.org/index.php/catalog/3818.</u>

5
g. Collier, 1999; Barron et al., 2004; Collier and Hoeffler, 2004; Miguel et al., 2004), 1 See also Alkire and Santos (2010).

2This survey is an outcome from the cooperation between the Central Organization for Statistics of Iraq and the Kurdistan Region Statistics Organization supported by the World Bank, which initiated in 2007 with the first IHSES. The IHSES-II is an update of the first round with additional data and information such as individuals’ past migration history.

3
# **4.2 Long-run Effects on Ebola Outcomes**

Roughly one year after our endline survey, the first confirmed Ebola case was recorded in Sierra Leone. We turn now to examining the longer-run effects that the interventions had on reporting and mortality during the ensuing epidemic.

The treatment effects on reported Ebola cases are apparent in Figure 3: the left panel presents the sum of total reported cases in each week by treatment arm; the right panel is the cumulative count of cases during our study period. Between September 2014 and May 2015, we count 515 total cases in control sections; yet in sections with clinics receiving the CM and NFA interventions 735 and 795 cases are reported, respectively. This difference is even more striking for confirmed cases: only 21 confirmed cases are reported in the 54 control sections, while 248 are reported in the treated sections (see Figure E.2).

**Figure 3:** Total Cases by Treatment

<!-- Start of picture text --> C CM NFA G C CM NFA<br>C 800<br>40<br>30<br>20<br>10 600<br>0<br>CM G G<br>G G<br>40 G<br>30 400 G G<br>20 G G<br>10 G<br>0 G G G G<br>NFA 200 G G<br>40 G G G G G<br>30 G<br>2010 0 G G G G G G G G G G G G<br>0<br>Sep Oct Nov Dec Jan Feb Mar Apr May Sep Oct Nov Dec Jan Feb Mar Apr May<br>2014 2014 2014 2014 2015 2015 2015 2015 2015 2014 2014 2014 2014 2015 2015 2015 2015 2015<br>(a) Weekly Counts (b) Cumulative Counts<br>Total Cases<br>Cumulative Total Cases<br><!-- End
4 Such a response would tend to reduce the ratio of motor vehicles to roads in urban areas with high income levels.

The relative prices of motor vehicle services and roads are also likely to change with development because motor vehicles are traded goods (whose prices are reasonably constant around the world), and roads are nontraded goods (whose prices vary with wage levels across countries). The ratio of the prices of nontraded to traded goods rises with wage levels and income. Hence roads become more costly relative to vehicle services as incomes rise (assuming minimal congestion at the national level), and the ratio of motor vehicles to national roads should rise with income as traffic volumes increase on national road networks.

Finally, road quality is also an issue. Motor vehicle speeds rise and operating costs fall as road quality improves, so arguments based on economic efficiency suggest that road quality-particularly the share of roads that are paved-should increase as the value of time (related to income) and the intensity of road use rise.

Of course, these hypotheses stem from economic principles. Because public road providers face few market incentives, however, a substantial question is whether predictions based on economic reasoning have any predictive power.

# 3. **Summary Statistics**

Summary statistics on vehicle ownership and road provision at the national and urban levels for developing and high income countries, shown in table 1, provide a crude test of the hypotheses based on economic reasoning. The cross-country data set and crossurban area data set summarized in table 1 cover a wide range of country income levels, ranging in 1990 from $260 to $28,000 per capita in 1987 US dollars at market exchange rates.6 The national and urban samples summarized in table 1 do not overlap precisely in geographical terms or in time, but they provide a useful comparison of urban and national measures for developing countries and high-income countries.

Relative to countries as a whole, urban areas in both developing and high-income countries obviously have much higher population densities, higher road network densities,
sification System (NAICS6). Details of the construction of the variables can be found in appendix 2.

The empirical analysis concentrates on the Electronic sector (SIC 36). This sector was chosen because of the availability of EU product standards data. This sector consists of 36 SIC4 Industries that ranges from vehicular lighting equipment and electric lamps to semiconductors and transformers. Table 1 provides a description of the relative level of detail between industries. U.S. exports to the EU in this sector represents roughly 15.0 percent of total exports to the EU between 1992 and 2002.

Table 2 shows firms’ characteristics by exporting and non-exporting firms for 1992 and 1997.<sup>13</sup> Exporting firms are further divided into the set of firms that exports to the EU and those that export to other markets. As expected, exporters —nearly half of the firms— are bigger than nonexporters in terms of average value of shipments and average employment. Around half of exporting firms are multi-plant firms. Interestingly, and consistent with the theoretical model presented in section 2, exporters’ characteristics differ in terms of the market destinations. Exporters to the EU are bigger and export more than exporters to other markets. Finally, there is firm entry into export markets across years, which —I argue— can be partially explained by the role of European product standards harmonization.

# **3.2 Trade Costs Across Industries and Time**

Measuring the extent of product standardization across export market destinations is not an easy endeavor. I used The World Bank EU Electrotechnical Standards Database (EUESDB) to gauge this effect and to assess the degree of harmonization of EU standards with international norms. The EUESDB provides the first catalog of European standards in the electrotechnical sector<sup>14</sup> and their relationship with worldwide standards. The database provides an inventory of the “stock” of active standards<sup>15</sup> issued by the European Committee for Electrotechnical Standardization (CENELEC) and their link
For the decomposition analysis of emissions intensity, we obtain annual country-level total passenger and freight transport traveled by road and rail from the World Road Statistics and the International Transport Forum databases. Total passenger transport, measured in million passenger-kilometers, refers to the total movement of passengers using inland transportation. It represents the transport of one passenger for one kilometer. Total freight transport, measured in million tons-kilometer, refers to the total movement of goods using inland transport. It represents the transport of one ton over one kilometer. The total kilometer variable is available for 65 countries during the period from 2000 to 2018. These countries are listed in Table A2 in the appendix.

The level of urbanization, and the value added from agriculture, manufacturing, and service sectors are all obtained from the World Bank World Development Indicators database. Data on country-level BRT systems are obtained from global BRT database. Diesel and gasoline prices are compiled from fuel price documentation from the <mark>Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH (GIZ).</mark> The summary statistics of variables are presented in Table 3.

# **5. Results**

In this section, we describe the results of the decoupling analyses, the general pattern of transport-related CO2 emissions, and the results of the regression analyses. The descriptive statistics reveal that CO2 emissions of the transport sector account for 24 percent of global CO2 emissions, making the transport sector the second-largest contributor to global CO2 emissions after the energy sector, which accounts for 42 percent of such emissions. Within the transport sector, road transportation is the predominant driver, accounting for 74 percent of all CO2 emissions; international sea transport accounts for 9 percent, and international aviation accounts for 7 percent (Figure 1). The composition of global transport CO2 emissions by mode has been fairly stable over time; the shares in 1990 were about the same as those in 2018 (Figure A1). There is interesting variation between income groups and regions; the CO2 emissions from road transportation are slightly below the global average in
meantime, Tanzania (which took a $400 million loan from China in August 2009) and Zambia are seeking a credit rate in order to issue $500 million Eurobonds later this year, after they had to postpone their plans in 2008 (together with Angola, Kenya and Uganda) because of the global financial crisis. At the same time, several HIPCs, which developed local-currency bond markets, have succeeded in selling treasury bills in their own currency to foreign investors. In 2008 the share of domestic debt held by foreign investors was 13% in Zambia, about 11% in Ghana, estimated at more than $400 million, and likewise significant in Tanzania and Uganda (Delechat et al. 2010, Domeland and Kharas 2009).

On the less bright side, Côte d’Ivoire, a HIPC that has not yet reached completion point, after exchanging defaulted Brady bonds with a 22-year Eurobond worth $2300 million in April 2010, defaulted on the same bond in February 2011. Further insights into the ability of post-MDRI countries to access international financial markets can be gained from data on public and private borrowing from private creditors taken from the Global Development Finance (GDF) database of the World Bank.

Figure 1a reports the unweighted average of the ratios of yearly disbursements on long-term loans and bonds to GDP for two groups of IDA-only countries: the 21 HIPCs that received MDR in 2006 (post-MDRI countries);<sup>4</sup> and a control group of 15 countries that includes the remaining non-HIPC IDA-only countries but excludes resource rich Angola and Nigeria. The yearly amount of long-term borrowing by post-MDRI countries increased sharply from less than 0.3% of GDP in 2006 to more than 1% in 2007 to stabilize thereafter around 0.9% of GDP in the wake of the global crisis.

This performance is partly due to the low level of private inflows in the period up to 2006 when HIPC countries, being under IMF-supported programs, had to abide by stringent limits on non-concessional borrowing. However, Figure
**Figure 4.4: Outages frequency, duration, and impacts compared with average wind speeds.**

<!-- Start of picture text --> 12 16 600 16<br>14 14<br>10 500<br>12 12<br>8 400<br>10 10<br>6 8 300 8<br>4 6 200 6<br>4 4<br>2 2 100 2<br>0 0 0 0<br>160,000 16<br>140,000 14<br>120,000 12<br>100,000 10<br>80,000 8<br>60,000 6<br>40,000 4<br>20,000 2<br>0 0<br>(hours)<br>outages (2000-2017)<br>Average wind speed (km/h)<br>Average number of days with Average wind speed (km/h)<br>January February March April May June July August September October November December Average outage duration 2000-2017 January February March April May June July August September October November December<br>affected 2000-2017<br>Average number of consumers Average wind speed (km/h)<br>January March May July September November<br><!-- End of picture text --> Figure 4.4 demonstrates that outages do not match the monsoon season that is captured by average wind speeds from the ERA5 climate data (see 2.2.2). However, the significant increase in the variance of wind speed from April to May suggests the occurrence of more high intensity storm days caused by severe local convective storms (Nor’wester). As described in section 2.2.2, these storms can be proxied by data on lightning strikes. The seasonal pattern of lightning strikes resembles strongly the occurrence of power outages, thus confirming that most power outages that occur in
diagonal matrix indexed over _i, j_ with diagonal elements equal to the value added shares of output **Z** . We then use **M** to provide a breakdown of the flow of value added across activities in the form of the matrix **V** .

Similar to the Leontief inverse matrix itself, the **V** matrix identifies the inputs of value added in each sector related to a unit of final demand. If we multiply **V** by the diagonal matrix **Y**<sup>**ˆ**</sup> whose non-zero elements are the vector of final outputs, the matrix yields a breakdown of economy-wide value added (the primary component of Gross National Product on a source basis). Similarly, if we multiply **V** by the diagonal matrix **X**<sup>**ˆ**</sup> whose non-zero elements are the national export vector, we can recover the value added content of exports **X** (both direct and indirect).

The **G** matrix and the **H** matrix give us the set of linkages, both direct and indirect, between value added across sectors. We provide an example for Italy in Tables 2 and 3 below. These are based on GTAP data, aggregated to 10 sectors as indicated in the tables. Starting with Table 2, we can see that 41.76 percent of value added was located in various commercial or market service activities (including for example financial services and ICT services). This follows from reading the bottom row of column S9, which provides the value added in this sector, broken down into the various sectors where these activities fed, ultimately, into final demand. At the same time, much of this actually served as intermediate inputs, so that in terms of final demand for commercial services, this accounted for 33.2 percent of value added (the
# **INTRODUCTION**

In the past two decades, aid organizations, multilateral institutions and national governments have increasingly sought to involve local communities in development. The assumption is that mobilizing poor communities and involving them in all aspects of project design and implementation leads to more effective and equitable service delivery (WDR 2004). Billions of dollars have been spent on such community-driven anti-poverty interventions (Mansuri and Rao 2012). However, the scale and complexity of these interventions make such programs vulnerable to a number of challenges and considerable variations in local capacity (Woolcock 2009). Even if projects are able to build small islands of success in their first phase, they are unable to sustain the intensity and momentum required to induce participation as they scale-up (Mansuri and Rao 2012).

But why does the intensity of community participation decrease as projects expand? One important reason is what we might call ‘implementation failure.’ Expanding the scope of the project from a few communities to a large number puts pressure on the project’s implementation unit, which in turn weakens its intended impact. Several papers and project documents highlight this phenomenon (Mosse 2004, Barron et al. 2011, Swidler and Watkins 2015). Little scholarly attention, however, has been paid to identifying the processes and mechanisms that constitute implementation failure during scale-up, and few studies have compared ‘successful’ and less successful implementation _within_ the same project.

In this study, we draw on qualitative data that cover four years (2011–15) in the life of one of the largest World Bank-assisted, anti-poverty projects in India. We observed the project at a unique juncture in its trajectory, when it was scaling-up from its first phase (covering 400,000 households) to its second phase (covering 800,000 households). Impact evaluations found stark differences in outcomes in the first phase (Datta 2015) and the second phase (Hoffmann et al. 2017). Our own qualitative work found that the first phase was akin to a mini-social movement that