rafmacalaba/data-use-mentions-extended huggingface dataset:
10 How does Bulgaria compare to other countries in the region? While the index based on EBRD’s scores—for obvious reasons—does not allow for precision in capturing the institutional progress, it nonetheless indicated the direction of changes and achieved progress. Bulgaria has made huge strides especially since 2001 and is much more advanced than Romania and SEE-4 economies, yet its level of institutional maturity remains well below that achieved by CEEC-8 economies. The value of structural reform index is roughly the same as achieved by a median for CEEC-8 countries around 1997-98. Hence, Bulgaria has still some way to go in order catch up with recent EU entrants.

# _B. Impact on FDI inflows_

Better governance and regulatory environment, low ‘hassle’ cost of conducting business, labor force, etc., all increase the probability of receiving higher FDI inflows. Empirical research strongly suggests that progress in second-generation reforms, i.e., with the focus on institutions supporting competitive markets, provides explanation in country variation of FDI inflows. Garibaldi et al. (2002) have shown, that the quality of institutions explains the variation in FDI flows to transition economies. In a similar vein, Broadman et al. (2004, p. 20), plotting the data on FDI per capita and EBRD’s governance and enterprise restructuring indices for all Balkan countries also find a very strong positive association between these two variables. Countries that have made the largest progress in transition to competitive markets have also attracted the largest inflows of FDI, whereas countries with weaker business climate have been less successful in attracting FDI. Ineffective protection of property rights and weaknesses in contract enforcement always discourage foreign investors.<sup>5</sup> In the analysis that follows, we focus, like in the studies mentioned above, on the institutional environment as critical to explaining variation in FDI inflows. In contrast to Broadman et al. (2004), we look at cumulative FDI inflows per capita and use an aggregate EBRD-based indicator measuring the progress in building market-supporting
sup>

# **Interventions focusing on workplace health should help increase worker retention and increase**

**mature workers’ productivity; however, there is little evidence as to what works to encourage these** . The United Kingdom targets SMEs with a specific national occupational health service to this effect.<sup>172</sup> Occupational health and safety programs have been shown to improve risk factors for injuries and chronic illnesses, many of which increase with age.<sup>173</sup> This can include both access to health care and support for healthy lifestyles – diet, exercise, and social interaction, which also affects labor market participation as well as productivity.<sup>174</sup> However, there is no conclusive evidence of positive impacts of such interventions on productivity or labor force participation, largely due to lack of quality studies.<sup>175</sup> Some positive examples on which to build do exist, however. In the United States, an occupational health intervention involving access to physicians was associated with a significant reduction in both sickness absence and early retirement .<sup>176</sup> Interventions could also be adapted to the informal sector context. A study of informal sector workers in Thailand showed that safe working practices improved older workers’ work ability, suggesting that targeted information/promotion programs could help increase their productivity.<sup>177</sup>

167 UNESCO 2022. 168 Singer and Toomet 2013. 169 Picchio 2021. 170 OECD 2019b. 171 EBRD 2020, Loch et al. 2010. 172 OECD 2019b. 173 Anger et al. 2015. 174 Prinz et al. 2018. 175 Poscia et al. 2016. 176 Crawford et al. 2009.

177 Thanapop and Thanapop 2021.

29
# **7 Bibliography**

Alonso, W. 1964. _Location and Land Use: Toward a General Theory of Land Rent_ . Harvard University Press.

- Angel, Shlomo, and Alejandro M. Blei. 2015a. “Commuting and the Productivity of American Cities.” _NYU Marron Institute of Urban Management_ , no. Working paper #19 (January): 1–44.

- ———. 2015b. “Commuting and the Spatial Structure of American Cities.” _NYU Marron Institute of Urban Management_ , no. Working paper #20 (January): 1–55.

- Bertaud, Alain. 2014. “Cities as Labor Markets.” _Marron Institute on Cities and the Urban Environment, New York University_ , no. Working paper #2 (February).

- Bright, Eddie A., Amy N. Rose, and Marie L. Urban. 2013. “Landscan 2012.” Data. Oak Ridge National Laboratory. http://www.ornl.gov/landscan/.

- Brueckner, Jan K. 1987. “Chapter 20 The Structure of Urban Equilibria: A Unified Treatment of the Muth-Mills Model.” In _Handbook of Regional and Urban Economics_ , edited by Edwin S. Mills, Volume 2:821–45. Urban Economics. Elsevier.

- http://www.sciencedirect.com/science/article/pii/S1574008087800068.

- Cervero, Robert. 2001. “Efficient Urbanisation: Economic Performance and the Shape of the Metropolis.” _Urban Studies_ 38 (10): 1651–71. doi:10.1080/00420980120084804.

- ———. 2002. “Built Environments and Mode Choice: Toward a Normative Framework.” _Transportation Research Part D: Transport and Environment_ 7 (4): 265–84.

- Cervero, Robert, and Kara Kockelman. 1997. “Travel Demand and the 3Ds: Density, Diversity, and Design.” _Transportation Research Part D: Transport and Environment_ 2 (3): 199
of the different lockdowns that were enacted to contain the COVID-19 pandemic, to help assess them.

# **3. Data**

# _3.1 Electricity Consumption_

Monthly electricity consumption in Bangladesh is available from the Bangladesh Bureau of Statistics (BBS) from 1993 until 2013, when the data series was discontinued. Daily electricity consumption data can be scraped from the Bangladesh Power Development Board and is available for all eight divisions (Dhaka, Chattogram, Mymensingh, Sylhet, Khulna, Rajshahi, Barisal, and Rangpur) from 2010 until now, with only a one-day lag. For this study, daily data from February 2010 to October 2021 has been compiled.<sup>1</sup> For 2.5 percent of all the days during this period, data are missing and were hence linearly interpolated using the day before and day after the missing one.

# _3.2 Economic Activity_

For the analysis of the relationship with economic activity, we use annual data on GDP and industrial production (2000–21) from the National Accounts statistics published in the BBS Bluebook and the monthly Quantum Index of Industrial Production data (July 2012 to April 2021) from the BBS. Monthly export data (2016–20) were taken from the Export Promotion Bureau.

# _3.3 Other Data_

We analyze the correlations between electricity consumption per capita and subnational poverty levels, the share of households with access to electricity (from the Bangladesh Household Income and Expenditure Survey), and firm density (from the Economic Census 2013). To assess the efficacy of using electricity consumption to analyze the impacts of disasters, we extract disaster data (2016–20) from the Center for Excellence in Disaster Management & Humanitarian Assistance. For estimation of the electricity consumption model, we use temperature data (2010–21) from the Bangladesh Meteorological Department and the dates of holidays and Ramadan from timeanddate.<sup>2</sup> To measure the stringency of the COVID19 containment measures Bangladesh adopted and their impact on mobility,
|Private<br>facility|26|52|18|28|16|2.2**<br>(1.3–<br>3.8)|
|---|---|---|---|---|---|---|
|Public<br>facility|44|30|43|41|12|0.5*<br>(0.3–<br>0.9)|
|Public or|70|82|61|69|4|1.6|
|private||||||(0.9–|
|facility||||||2.8)|
|PNC services|60|67|45|53|−1|1.1|
||(_N_=|(_N_=|(_N_=|(_N_=||(0.7–|
||183)|459)|779)|1281)||1.8)|

_Source:_ Population Council, Endline Household Survey 2011 a Based on differences in changes in proportions using health services: negative sign means the change was greater in the comparison group.

b Based on multilevel logit models with interaction terms; 95 percent confidence intervals are in parentheses (* _P_ <0.05; ** _P_ <0.01).

# **<<B>>** Service, Program, and Out-of-Pocket Costs

In terms of service costs, the average reimbursement for a normal delivery rose from $14 in the 2008–09 financial year to $15 in 2009–11 and $19 in 2010–11 (figure 2; Mazzilli 2011). The corresponding figures for a complicated delivery were $61, $80, and $88, respectively. The transition from an individually negotiated reimbursement fee to a program-wide fee schedule partly contributed to the rise in the
(captured by the polity index) and commercial openness exert a positive and significant effect on the returns to schooling when considering them separately (Regression 2 and 3) and simultaneously (Regression 4).

|(1)<br>**Table 5: Effect of selected v**|(2)<br>**ariables on re**|(3)<br>**turns to educa**|(4)<br>**tion**|
|---|---|---|---|
|EU accession<br>0.986|0.821|0.941|0.737|
|(0.688)|(0.652)|(0.692)|(0.667)|
|Schooling<br>-0.146|-0.105|-0.277|-0.239|
|(0.243)|(0.236)|(0.278)|(0.267)|
|Polity index|0.161**||0.169**|
||(0.079)||(0.076)|
|Trade (% GDP)||0.013**<br>(0.005)|0.014***<br>(0.005)|
|1995<br>0.984***|0.952***|0.866**|0.881**|
|(0.356)|(0.349)|(0.370)|(0.364)|
|2000<br>1.316***|1.155**|1.145**|1.026**|
|(0.498)|(0.506)|(0.509)
# _Consumption Aggregate_

We construct annual consumption aggregates (total household consumption expenditure) consistent with standard practices (see Deaton and Zaidi, 2002) but adapted to module specifications. For modules with the quantity of own-produced consumption but not shilling values (modules 1-3), monetary values were computed from unit values constructed from household information on purchases.<sup>18</sup> In each of the three modules, about 50 percent of quantities were able to be transformed by household-specific unit values (i.e., households also purchased the item), while in 25 percent of the cases the unit in which the household reported own-produced consumption was Tanzanian shillings. In the diaries, for each item we take the quantities reported minus what was recorded as sold or given away (deducted from the value as a percentage). Non-item specific (lump sum) corrections were made for changes in stocks (added consumption from stocks and adjusted for recorded acquisitions that were stocked instead of consumed), for food given to animals, and for items that were recorded as “forgotten.”<sup>19</sup> Module 3 stands out since it explicitly does not cover the same range of food as the other modules. Rather it is a subset list of the most frequently consumed foods based on the Household Budget Survey data. We scale food totals measured with this module up by 29.87 percent in order to compare with totals from the other modules, which are all covering a broader universe of foods (as stated earlier, we base this scaling up on the Household Budget Survey, where this subset of goods accounts for 77 percent [=1/(1+0.2987)] of total food consumption). For every module, we exclude expenditures on taxes and ceremonies. These consumption categories are not typically included in consumption aggregates due to their idiosyncratic nature and infrequency.

The direct comparison of results from the different survey modules sheds light on the relative influences of the various reporting errors discussed in Section 2
, the ratio of the expected marginal product of capital in North corresponds to κ=0.75. This can be justified by interpreting K as a broad concept of capital. We next retrieve estimates of A and A* as residuals from this production function, setting κ=0.75. To implement this we use data on GDP and capital stocks per worker for China and for an aggregate of the rest of the world in 1996, which is the year that maximizes our cross-country data coverage. For China, GDP per worker and capital per worker were $4,908 and $7,408 in 1996 PPP terms. For the rest of the world the corresponding figures are $17,697 and $39,137 respectively. This implies a relative 14
based trips (86 percent) than among home-based trips (14 percent), while three-quarters were round trips. The values are similar for the metropolitan region as a whole and for Bogotá.

The INDEC survey data, which indicates an increase in e-commerce adoption in 2011-2018 in CABA (from 16.9 percent to 31.4 percent) and the province of Buenos Aires (from 5.9 percent to 15.9 percent), allows identifying whether this growth translated into a reduction in the number of trips for shopping purposes. In 2018, shopping trips in AMBA accounted for 13 percent of all trips and 12.1 percent of all home-based trips. About 96 percent of all shopping trips in 2018 were home-based, which is significantly higher than in Bogota; however, this figure is again subject to the caveat that the share home-based trips in surveys tend to be overestimated. Shopping trip generation rates increased between 2011 and 2018, from 1.74 to 1.84 trips per person, suggesting that at the aggregate level there was no trip substitution (reduction) due to the growth of e-commerce.

As already noted, the potential for trip reduction by e-commerce depends on the proportion of trips for shopping purposes that are part of trip chains. If people make trips to retail outlets to and from their homes, a virtualization of shopping would lead to a reduction of two trips. Conversely, if people shop as an intermediate activity between work and home, the shift from physical shopping to the Internet would lead to a reduction of one trip instead of two trips. Moreover, if the retail outlet is already located on the route between work and home and the trip is made by car, the impact of eliminating the intermediate activity on road traffic and its externalities will be small (the number of kilometers traveled is maintained).

The total trip reduction potential as a result of e-commerce in Bogotá in 2019 on a typical working day was 9.1 percent, since a total virtualization of commercial activity would lead to a
# 3.3.1 Personal barriers

The following section presents a set of barriers identified in the qualitative data that constitute _personal_ barriers or factors related to women’s motivation to reach out for support and take action when experiencing domestic violence. It is important to note that, while these factors are framed as ‘personal,’ many relate to norms or projections society, communities, families and peers make, and women internalize (Pasinato 2014). The social context in which the interviewees live impacts on how they view themselves and what they understand to be acceptable, desirable and possible behavior. In that sense, the structural context frames the agency these women can exert. Regional studies have found widespread agreement in the Latin America region with norms that reinforce gender inequality, discourage women from seeking help, or downplay the duty of bystanders to intervene in situations of abuse, with wide variations both among and within individual countries (PAHO 2012).<sup>14</sup> This research finds that shame and guilt are the main personal barriers women had to overcome in their search for help. Because they felt shame for what others think of them, of their marriages and of their children, survivors often hid their suffering of violence from friends and family. It was common for survivors to express feelings of guilt for the offender's behavior and for wanting to leave the relationship, in addition to feelings of failure for being unable to change their partner’s behavior. Feelings of shame and self-blame are noted in other studies as barriers to seeking help (Landenburger 1989, Pasinato 2014, Garcia-Moreno et al. 2005, Kishor 1996), particularly because domestic violence strikes at the core of a woman’s social identity as a wife and mother (Ellsberg et al. 2000).

Interviewees who were mothers cited the fear of losing the relationship with their children as a significant barrier to seeking help. Women not only feared being distanced from their children but also, they feared being held responsible for ‘destroying the
(questions relating to the perception of teachers’ expectations of their students and meeting student needs, as well as the morale, enthusiasm, pride and valuation of academic achievement) to construct an index of teacher motivation. Column (2) of Table 3 shows that, consistent with the theory, the teacher motivation index is 0 _._ 591 standard deviations _higher_ among private schools than public schools, significant at the 1 percent level. Using the people management index also yields a consistent relationship, shown in Column (5). The coefficient suggests a one standard deviation higher people management score is associated with 0 _._ 238 _higher_ teacher motivation score, also significant at the 1 percent level.

**Teacher effort.** The expected effort level of a teacher hired to a high management private school is higher than the expected effort level of a teacher hired to an intermediate management public school (via teacher selection, extrinsic teacher incentives, and augmentation of teacher intrinsic motivation on-the-job)

We explore this prediction by using the school climate section of the school principal questionnaire (questions relating to how often teachers are absent, late and/or unprepared) to construct an index of teacher effort. Column (3) of Table 3 shows that, consistent with the theory, the teacher effort index is 0 _._ 792 standard deviations _higher_ among private schools than public schools, significant at the 1 percent level. Column (6) reports the same specification for the people management index, suggesting that a one standard deviation increase in the management score is associated with 0 _._ 074 _higher_ teacher effort, significant at the 5 percent level.. For completeness, Figure 8 plots the coefficients for country-level regressions using the same specifications reported in Columns (4) to (6) of Table 3. While there is some variation across countries, the results broadly hold.

# **3.4 The impact of good operations management**

In this subsection, we use the theoretical framework to give a possible explanation
The data collection had two components: a household survey and a student tracking survey. In the household survey, we gathered information on household rosters in 2010 and 2000, schooling and work histories of biological siblings (of our sample students), household income (2010) and asset holding (2010 and 2000), parents’ participation in school governance for each sibling, and public service and infrastructure access (2010 and 2000). On assets owned in 2000, only quantities were captured in each category (we asked both values and quantities of assets in 2010). To impute the value of asset holdings in 2000, we used the current prices calculated from the values and quantities of assets in 2010. The survey was supplemented by barangay (community) leader, the parent–teacher community association, and school surveys.

In the student tracking survey, we tracked our sample students to collect detailed information on their schooling and work histories as well as marriage, anthropometry, and illness. Either face-to-face or phone interviews were conducted. There were two stages in tracking activities. First, the teams tracked students residing within their original divisions. This took place immediately after the household survey. Second, in the case of out-of-division tracking, the teams attempted to schedule face-to-face interviews with students residing in National Capital Region, Baguio, and Cebu. For students residing in other provinces, we used primarily phone interviews. However, the teams tried to visit students residing near or within the province of Laguna and in between northern Luzon and Manila to conduct face-to-face interviews. We did not track overseas migrants, and we omitted several students who could not be interviewed even by phone.<sup>22</sup> Although our sample has different age groups (cohorts) due to its sampling design, the majority of our students completed four years of high school. This is as expected, since even TEEP students (grade 6 in school years 2004/05 and 2005/06) are expected to reach age 17 in the survey period
# _3.2 Exports and Non‐tariff Measures_

Studies that have examined the effect of NTMs in the MENA region are scarce at both the macro and the firm level. Ghali et al (2013) investigate the impact of NTMs on Egyptian imports, by differentiating between different categories of NTMs and find that NTMs have a significant negative effect on both the intensive and extensive margins. In the same vein, Péridy and Ghoneim (2013) calculate the average tariff equivalents (AVEs) of NTMs in selected MENA countries including Egypt, and estimate them at 39%, compared to 34% in Tunisia, 37% in Morocco and 47% in Lebanon. Their analysis of NTMs also shows that almost all NTM categories are trade reducing, especially SPS measures, quantitative restrictions, pre‐shipment inspection, and export‐ related measures with technical barriers to trade to a lesser extent.

At the firm level, the effect of technical barriers to trade (TBT), sanitary and phyto‐sanitary measures (SPS) and red tape costs (related to the trade facilitation agreement) was also examined. Enbaby et al. (2016), use firm‐level data to analyze the effects of product standards on the probability to export (firm‐product extensive margin), and on the exported value (firm‐product intensive margin). They find that SPS measures imposed on Egyptian exporters have a negative impact on the probability of exporting a new product to a new destination, while the intensive margin of exports is not significantly affected by such measures. Kamal and Zaki (2018) also examine the impact of TBT on firms’ exports in Egypt over the period 2005‐2011 and find the extensive margin and entry probability are negatively affected by technical barriers whereas exit probability is positively affected. Moreover, smaller firms are more adversely affected by TBT in their export participation and entry and exit decisions. Hendy and Zaki (2014) study the effect of red tape barriers on firms’ exports by merging the Doing Business data set with firm
2014 and 5,660 in 2017).<sup>12</sup> Our main variable of interest is which is measured in crop yields, kilograms per hectare. We define yield as the ratio of crop output reported by the household over the area occupied by the crop on the plot.

> 10The sample covers all regions and areas (urban and rural) with the exception of the region of Kidal, excluded for security reasons from the sample.

> 11The Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) provides financial and technical support to governments in Sub-Saharan Africa in the design and implementation of multi-topic, national, panel household surveys with a strong focus on agriculture. The EAC-I data and documentation are publicly available on https://www.worldbank.org/en/programs/lsms.

> 12Few observations have been omitted due to lack of information on production and/or outliers on production, yields, acreage, etc.

15