rafmacalaba/data-use-mentions-extended huggingface dataset:
data were downloaded, and the accelerometers returned for reassignment to subsequent participants.

The participants in the study wore an accelerometer around the waist. Physical activity can be detected using accelerometers worn on various parts of the body, such as waist (hip), ankle, or wrist (Doherty et al., 2017; Menai et al., 2017; Willetts et al., 2018). Waist and wrist are the most frequently used body locations, and both have demonstrated to be valid locations for measuring and classifying physical activity intensity and sedentary time (Romazini et al., 2012; Hildebrand et al., 2014; Chandler et al., 2015). There are a few studies about how the physical activity detection using wrist and waistworn accelerometers correlate or differ. Kamada et al. (2016) and Dieu et al. (2016) report moderateto-high correlations between acceleration values from either wrist or waist. Hildebrand et al. (2014) find that wrist-worn accelerometers produce higher output as compared to waist-worn accelerometers. Shiroma et al. (2016) conclude that the waist-worn and the wrist-worn monitors may be employed for different behaviors during the day. Waist-worn devices may measure the whole-body movements while wrist-worn devices measure most of the whole-body movements in addition to the movements being specific to wrist or upper body. Zhang et al. (2012) used a wrist-worn GENEA with a waist-worn GENEA to compare classification accuracy for physical activity into walking, running, household, or sedentary activities. They report that the results from both devices are comparable. It becomes clear from these studies that while wrist worn accelerometers may offer higher accuracy for some activities, waist-worn devices offer a suitable alternative at low cost and with low profile to collect physical activity data in low-income contexts.

Complementing the activity tracking data, each participant also responded to a 24-hour recall time use module at the second and third visit (Days 9 and 17, respectively), which was modeled after the time use module of the Women’s Empowerment in Agriculture Index (WEIA
sup> These cost-share data cover 20 specific industries in the private sector and 17 industries in the public sector. A list of these industries and their intermediate inputs are found in Annex Table 1. These industries are identified by their 4-digit ISIC code (rev. 3.1).<sup>2</sup> These disaggregated industries served as the basis for estimating ad valorem equivalents of non-tariff barriers, presented in Annex 3. Note that although most of the public industries in the list continue today, they might be much reduced in importance after a decade of economic reforms. Some industries that were in the earlier 1999 list are not found in the latest CAPMAS list of public industries for 2007/2008: household appliances, carpets and rugs, containers and boxes, cutlery and hand tools, agricultural machinery, and pottery.

The other source of cost-share data is the inter-industry intermediate input costs from the latest available Egyptian input-output matrix (2006/2007), supplied by the World Bank office in Cairo. (See Annex Table 2 for a list of industries. The matrix is available upon request.) This particular input-output matrix has 23 industrial sectors, with more disaggregation of energy sectors. In his 2007 report, D. Lederman also uses an inputoutput matrix from 2005 with 21 sectors, but with more detail in some industries, such as agriculture and food, and less in others. (The accompanying Annex Table 2 also includes a list of sectors available to Lederman.)

> 1 Her Masters thesis was completed at the American University in Cairo in spring 2001

> 2 Given the current ISIC version, in some cases the industries reported in Nathan and Al Isakandarani are further disaggregated into more specific industries in the information available from CAPMAS.  See the web site http://www.msrintranet.capmas.gov.eg/pls/fdl/bkr2_e?lang=0&lname=   ECS .

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electronics and IT, life science/pharmaceutics, environmental protection technique and equipment, new materials, photo-electronics and laser-mechanic-electronics, and technology for aerospace and earth space. A large portion of these companies are manufacturing firms, while many software companies, R&D and technical service companies are also covered in this category.

Table 8 shows the indicators of high- and new-tech enterprises in the industrial parks in Beijing and Shanghai in 2001. Beijing outpaces Shanghai in the number of high-tech firms and employees, their revenues, as well as their exports; however, Shanghai leads Beijing in the average size of firms, and with regard to the revenue and exports either per firm or per employee.

**Table 8: Indicators of high-tech and new tech enterprises in industrial parks of Beijing and Shanghai, 2001**

||Beijing|Shanghai|National in total|
|---|---|---|---|
|Number of firms|7911|405|24293|
|Number of employees|282720|73652|2761433|
|Revenue (billion Yuan)|125.6|83.8|1011.7|
|Export(million US$)|2928.9|2512.6|22664.4|

_Source_ : China Statistic Yearbook 2002.

# **Knowledge intensive business services and creative content providers**

As to knowledge intensive business services and creative content providers, there is even more vagueness in the classification. We select several categories as well as subcategories relevant to our firm survey from the published statistical materials (see Table 9). We find that Beijing surpasses Shanghai in the number of firms and employees in almost all the categories and subcategories.

**Table 9: Knowledge intensive business services and creative content providers in Beijing and Shanghai, 2001**

||Beij<br>|ing<br>|Shan<br>|ghai<br>|Nationa <br>|l in total<br>|
|---
under the India Factories Act of 1948. The unorganized manufacturing sector is, by default, comprised of establishments which fall outside the scope of the Factories Act.

In the service industries, there is no simple legal distinction as in manufacturing. Service establishments, regardless of size or other characteristics, are not required to register and thus are all officially unorganized. There are various existing methodologies to comparably differentiate small-scale, autonomous establishments from larger employers which constitute the organized sector, as generally defined. We assign establishments with less than five workers and/or listed as an “own-account enterprise” (OAE) to the unorganized sector. OAE enterprises are firms that do not employ any hired worker on a regular basis. The choice of five employees as the size cutoff recognizes that average establishment size in services is significantly smaller than in manufacturing. Our results are robust to also using a ten-employee demarcation for services.

The organized manufacturing sector is surveyed by the Central Statistical Organisation every year through the Annual Survey of Industries (ASI), while unorganized manufacturing and services establishments are separately surveyed by the National Sample Survey Organisation (NSSO) at approximately five-year intervals. Establishments are surveyed with state and fourdigit National Industry Classification (NIC) stratification. We use the provided sample weights to construct population-level estimates of total establishments and employment at the state and three-digit NIC level. We focus mostly on state and industry variation in our empirical analyses, but we also consider district-level variation as a robustness check and to allow more localized traits where warranted. Districts are administrative subdivisions of Indian states or union territories.

Tables 1a and 1b document the establishment size distribution for India. In manufacturing, over 98% of establishments are unorganized, most with fewer than five workers. The last column compares this skewness to the distribution evident in the 1997 Census of Manufacturers for the United States. Only 51% of U.S. manufacturing establishments have fewer than ten employees. More broadly, about 74% of establishments have fewer than ten employees in the U.S. County
. We contribute to this literature by showing how firms’ expectations are affected by the competition of informal companies and that this effect goes beyond differences in firms’ fundamentals or realized performances. This may suggest that, even in absence of real obstacles, a firm’s lack of information or biased perception can significantly jeopardize its own growth through current investment decisions and demand for credit. Importantly, this is the first paper, to the best of our knowledge, providing evidence on expectations of firms within developing countries.

The paper proceeds as follows. Section 2 describes the data, the variables, and the sample composition. Section 3 describes the empirical analysis, presents the main results, and discusses the possible mechanisms explaining them. Section 4 summaries the analysis and discusses some policy implications of our results

# **2 Data**

Our main source of data is the WBES dataset, a large sample of privately-held companies constructed from a standardized and globally comparable survey administrated by the World Bank. Because of our research

> 1A companion literature uses randomized control trials to explore the effect of interventions alleviating micro-entrepreneurs’ financing constraints (de Mel et al., 2008; Banerjee et al., 2015; Crepon et al., 2015; Quinn and Woodruff, 2019).

> 2See Coibion et al. (2018), Enders et al. (2019b), Coibion et al. (2020), Coibion et al. (2020), and Tanaka et al. (2020).

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# **Appendix S2. Tariffs, Data and Descriptive Statistics**

The main source is the reform dataset compiled by the Research Department of the IMF (Prati, Onorato, and Papageorgiou 2013; Giuliano, Mishra, and Spilimbergo 2013) which cover an unbalanced sample of 151 countries from 1964 to 2004. We extend the data to 2014 using tariff data from the World Integrated Trade Solution (WITS) and World Development Indicators (WDI). Tariff rates at the country-level are computed by aggregating product-level tariff data using weighted averages, with weights given by the import share of each product, measured as fractions of value. We do extensive checks of the data to ensure that they do not contain spurious jumps, by checking each jump is supported by country and policy reports.

Advanced economies comprise 28 percent of the sample, emerging markets around 44 percent and low-income economies around 28 percent.

**Table S2.1.** Descriptive Statistics for Tariffs

||**Number of**<br>**observations **|**Mean**|**Standard**<br>**Deviation **|**Minimum**|**Maximum**|
|---|---|---|---|---|---|
|**Tariffs (%)**||||||
|All|5042|11.2|13.8|0.0|161.6|
|AM|1427|5.7|7.5|0.0|99.2|
|EM|2161|12.5|11.6|0.0|91.4|
|LIC|1422|15.1|19.2|0.0|161.6|
|**Annual Tariff Changes (ppt)**||||||
|All|4702|-0.3|3.6
**Figure 14: Population Pyramid in Nepal**

<!-- Start of picture text --> 95<br>Female<br>90<br>Male<br>85<br>80<br>75<br>70<br>65<br>60<br>55<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1500K 1000K 500K 0K 0K 500K 1000K 1500K<br>Male Population Female Population<br>Age Group<br><!-- End of picture text --> Source: Nepal Population Census 2011

# **6. Conclusions**

The earliest records of poverty in Nepal indicate that the country was one of the poorest in the world at least until the early 1990s. Poverty in Nepal was chronic and plagued by high population growth, sluggish economic growth, a very low level of exports and rural population dependent on subsistence agriculture. The only glimmer of hope for poverty reduction at that time was to slow down population growth and formulate effective policies focused on boosting agricultural productivity in rural areas, which accounted for 95 percent of the poor population.

Contrary to the prediction, there was a remarkable reduction in the poverty rate in Nepal in a span of a decade and a half between 1995 and 2010. While the poverty headcount rate remained virtually unchanged at approximately 40 percent until 1995, it declined to 31 percent by 2003 and as low as 12 percent by 2010. Many non-income dimensions also exhibited significant improvements, often at a pace faster than neighboring countries in South Asia. The impressive poverty reduction record led the government to revisit and raise the poverty line in order to better reflect the living standards of contemporary Nepal. Even with the revised poverty line that increased in its real value by 35 percent from the previous line, the poverty
Data on macroeconomic performance, fiscal stance, and other types of external shocks (used as controls in part of the analysis) come from various sources. Real GDP per-capita is measured in constant 2000 U.S. dollars and obtained from the Bank (2008) World Development Indicators (WDI). The terms-of-trade index is the ratio of export prices to import prices computed using the current and constant price values of exports and imports from the national accounts component of the Penn World Tables (version 6.1) and updated using the terms-oftrade data from WDI.

Data on government expenditures and revenues come from WDI, IFS, and EIU. Data on total government debt come mainly from Panizza et al. (2008), complemented with data from WDI, IFS, and EIU. Government expenses are cash payments for goods and services incurred by the government, including wages compensation and interest payments. Revenues include receipts from taxes, social contributions and fees, excluding grants. Data on a country’s CPI and inflation rate come from WDI. Official assistance and grants are not included in our analysis so that expenditure and deficit are measured before grants. This is an advantage considering that movements in them may generate movements in deficits that are not related to fiscal sustainability. Official assistance is not included separately as a variable due to its unavailability for developed countries that constitute a major part of our sample.

Finally, data on money market, discount, and deposit interest rates come from the International Monetary Fund (2010) International Financial Statistics. To increase the crosscountry coverage of our sample, we select from the three definitions the interest rate series with the longest spell during the sample period, with preference for the money market rates when two or more series had the same coverage. Summary statistics for these variables for the sample of countries during the period of analysis are presented in Table 1. To improve coverage on all macroeconomic and disaster variables, the final sample used in the econometric analysis below is restricted to the post Bretton Woods, 1975-2006
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012<br><!-- End of picture text --> Source: calculated using data from UNCTAD database. Source: calculated using data from UNCTAD database.

4. **In the past four decades, trade volume increased more than tenfold, but the share of value-added has not changed as export composition remained commodity based** . The share of value added in DRC exports remained relatively constant at around 3 percent, and has not been 3
In this context, this study aims to incorporate the gender-differentiated employment and wages for select South Asian economies across sectors to identify targeted value chains and economic activities, particularly among green trade sectors, that would benefit women in the region. The study has the potential to provide insights to policy makers in measuring the impact of trade liberalization and regional integration measures on gender employment and wages focusing on green trade sectors. The data set developed would help analyze economic activities related to green trade that could benefit female labor force participation and wages. The understanding about gender outcomes when supported by data and analysis could enable governments to design better trade liberalization and regional integration policies and programs and improve the targeting of sectors that would benefit women and reduce existing gender disparities over time in South Asian countries. The study would not only help in analyzing the gender dimensions of trade agreements but also in subsequent research on the gendered impacts of other global shocks.

While global trade liberalization is an important issue, in our study we focus mainly on regional integration for a couple of reasons; first, regional integration is a pressing issue in this region, given the mutual protectionism even in terms of tariffs: therefore it is interesting enough to focus on regional integration; second, we have focused on gender data development only in this region and not in other countries, which is beyond the scope of this study; a global trade policy shock would have required inclusion of gender information in other countries as well. We therefore focus on a couple of illustrative simulations of tariff elimination, a practical one that involves BBIN (Bangladesh, Bhutan, India and Nepal), and an ambitious one that involves all the South Asian countries.

1.1 Female Labor Force Participation and Gender Wage Gap: What is the evidence?

The importance of achieving gender equality has been increasingly recognized in the policy debate across the globe. However, the recent literature highlights that the gender gap has widened globally for women both in terms of employment and wages at work compared to men. A recent
|4|6|19|10|
|Romania|6,236|28|27|0|0|0|1|
|Solomon Islands|236|1|1|0|0|0|0|
|South Africa|28,075|90|85|0|1|3|1|
|Ukraine|4,177|41|38|0|0|0|3|
|Total|507,284|1,212|981|23|32|101|75|

_Source_ : PPI database and authors’ data collection and calculations. _Note_ : Dollar amounts are in 2020 dollars.

Brazil has the largest active portfolio in terms of both the number of projects and total investments, with 685 PPP projects and $327 billion of investment commitments. It is followed by Indonesia, Peru, Argentina, and South Africa in terms of investment commitments. The next tier of countries consists of Albania, Bulgaria, Georgia, Ghana, Kenya, Romania, and Ukraine with active portfolios of $1–$7 billion and fewer than 41 active projects. Bhutan, Burundi, Ethiopia, Kosovo, and Solomon Islands have only one to four active PPP projects.

Most active projects in the sample countries are in the electricity sector (981 out of 1,212 active PPPs). Every country in the sample has at least one active electricity PPP. Most of the active transport PPPs in our sample are in Latin America. Indonesia is the only country in our sample outside Latin America that has more than five active PPPs in transport.

In Argentina and Brazil, a substantial number of active PPPs have been implemented through equity sales (32 and 24 projects, respectively). All of them are in the electricity sector, with most of them in the distribution subsector. As the investment commitments of these projects are based on quality improvements rather than specific sets of investments, their contribution to the 18
For _malaria_ , we increase the number of malaria cases in 2030 following (Caminade et al. 2014). As with stunting, we calculate the fraction of people who are affected by malaria, based on current prevalence, and we vary this fraction using estimates of future change due to climate change in various regions. Then, based on the literature reviewed in chapter 4, we assume that these people are affected between 0.1 and 2 times per year, and lose income through the cost of treatment (between $0.7 and $6 per occurrence) and lost days of work (by the sick or caregivers, between 1 and 5 days per occurrence). Note that we consider only the monetary expenses due to the disease and do not model non‐monetary effects (like the cost of life or loss in well‐being from being sick), which would be important in a multidimensional analysis of poverty.

In Vietnam, (Caminade et al. 2014) actually project a decrease of malaria because of climate change. So we increase the income of people who would have been affected by Malaria in the absence of climate change.

For _diarrhea_ , we start from data on the number of cases per country today, the cost of treatment (between $2 and $4 per episode), and the number of days out of work (between 3 and 7 days for the sick and caregivers) (Hutton, Haller, and others 2004). We also assume that the prevalence of diarrhea will increase by 10 percent by 2030 because of climate change, using results from (Kolstad and Johansson 2010). In Vietnam, the number of cases per year is around 80% of the population, so it is increased to nearly 90% of the population in our scenarios.

To account for development, we use DHS data to explore the relationship between household income and exposure to diarrhea. We find a threshold at $15,600 per year, and we assume that only households with income below
positive perceptions of public integrity and policy credibility. For example, citizens who perceive that the GoM manages public resources in a transparent manner or that policy announcements are e↵ectively followed by actions are substantially more likely to trust institutions.

Given the nature of our dataset, which only captures a cross-section of trust-related data in Morocco, and the absence of an experimental or quasi-experimental element, the results presented in this paper have a limited capacity to identify a causal relation. To address this limitation to the maximum of our capability, we performed a range of robustness checks, checking our results against a large number of controls and alternative specifications. The findings also build on a growing body of evidence of recent policy research which argues that transparency, integrity and credibility are core drivers of trust (IADB 2022, Brezzi et al. 2021, Wang and Guan (2022), and Murtin et al 2018).

While our core finding relates to the pivotal role of policy performance and processes, our results also speak to the literature on individual-level determinants of trust, including social capital and socio-demographic characteristics. Studies have emphasized the importance of social and economic capital in explaining levels of institutional trust. Generalized social trust – the trust in individuals beyond one’s narrow community, embracing individuals for whom one has little prior information or moral claim – has been incentive to act opportunistically.

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