rafmacalaba/data-use-mentions-extended huggingface dataset:
mechanical processes. Both primary and secondary pollutants are transported far from roads, thus worsening the air quality of whole urban areas. In addition, transport externalities soundly affect not only the environment, but also the society and economy of cities. For all these reasons, the United Nations included the creation of sustainable cities and communities as one of the 17 Sustainable Development Goals in agenda 2030, in which sustainable transport is one of the themes of several targets and goals.

Generally, studies have found that reductions in air pollution during the pandemic were most prominent for NO2, which is notably influenced by car traffic. While many other environmental indicators of air pollution (e.g., PM2.5, O3, CO) have been tied to adverse health outcomes (see Manisalidis _et al._ , 2020 for a review), a growing amount of evidence has specifically found adverse health effects from NO2 pollution. Rossi _et al._ (2020) indicated that vehicle flows significantly affect NO, NO2, and NOx concentrations, although no evidence of a relationship between traffic and PM10 was highlighted. According to this perspective, measures to limit traffic flows seem to be effective in improving air quality only in terms of reducing nitrogen oxide. Aletta _et al._ (2020) presented the results of a simulation approach using Floating Car Data (FCD) from vehicles equipped with an On-Board Unit GPS system. During the lockdown period, the traffic in Rome (Italy) was reduced by 64.6%, which had a positive impact on noise emissions reduction. On a broader scale, Chen et al. (2020) revealed that reductions in NO2 and CO were common but heterogeneous across the United States, after adjusting for temporal trends. Another USbased study by Berman & Ebisu (2020) found similar results at the county level; using two-sample aggregated 3
infrastructure provision and other social and economic affairs, can make the difference between enjoying the benefits of prosperous economies of agglomeration or suffering the consequences of the diseconomies of agglomeration (Ahrend et al., 2014).

Unfortunately, there are two aspects that make it difficult for academics to make public policy recommendations for Latin American cities (LAC). First, the existing empirical evidence is mostly concentrated on metropolitan areas in developed countries; and second, there is supporting evidence in favor of the three models of metropolitan governance: The polycentrist model, which advocates

> 1 McKinsey Global Institute Cityscape database, version 1.1

> (http://www.mckinsey.com/insights/urbanization/urbanworld).

> 2 This conclusion is based on GDP per capita data from WDI and urban population share data from the UN’s World Urbanization Prospects (2014 Revision) database <u>(http://esa.un.org/unpd/wup/).  One issue that arises with the urban</u> population data from the WUP database, however, is that it is based on official national definitions of urban areas, which differ from country to country.  An important question to address, therefore, is the extent to which the apparent underperformance of LCR countries is driven by this inconsistent measurement of urban population shares.  This will be addressed in another of the report’s background papers.
Note that these results are consistent with a number of recent studies that find greater health and cognition impacts among adult males for early life interventions such as greater nutrition (Hoynes et al., 2016; Stein et al., 2006), quality childhood care (Garc ́ıa et al., 2017; Campbell et al., 2014), and malaria eradication (Venkataramani, 2012; Cutler et al., 2010; Rosenzweig and Zhang, 2013). Furthermore, the findings of increased earnings among males also are observed for a nutritional intervention in Guatemala (Hoddinott et al., 2008) and malaria eradication programs in India and Mexico (Halim et al., 2015). Given the weak evidence of discriminatory allocation of nutrition and educational investment for children in Indonesia (Kevane and Levine, 2000; Suryadarma, 2015), one can explore the biological evidence of greater sensitivity of males to the surrounding environment _in utero_ and early childhood as a likely pathway (Kraemer, 2000; Eriksson et al., 2010; Hoynes et al., 2016). For instance, males could benefit more from the midwifery services both in dimensions of physical health and cognition which translates into better adult outcomes along a number of dimensions. However, testing these different hypotheses would require at least detailed panel data on parental investments, cognition and a host of health outcomes, and job opportunities for men and women.

32 The event time takes a value of _−_ 1 for children born one year after the arrival of midwives. Children born two years or after the arrival of midwives are grouped as less than or equal to _−_ 2.

15
method by exploiting the spatial and temporal variation in market sales of AFVs and incentive policies while controlling for vehicle model characteristics and consumer demographic variables.

The effectiveness and efficiency of subsidy programs hinge on several factors. The first is the lack of additionality: the notion that incentives do not always result in additional AFV sales, because many buyers who receive the subsidy might still have purchased AFVs without it. This problem may be pronounced during the early deployment stage because early adopters of AFVs are consumers who embrace new technologies, and who have the strongest environmental awareness and, usually, higher incomes. Therefore, their purchase decisions do not heavily rely on the provision of subsidies; these people would probably have purchased AFVs without the subsidies.

Various empirical studies document the challenge of additionality. Chandra et al. (2010) argue that the HEV tax rebates offered by Canadian provinces subsidized many consumers who would have bought HEVs in any case. Bereseteanu & Li (2011) find that HEVs sales in the United States would still be growing rapidly, even without tax incentives. Huse & Lucinda (2014) find that a substantial share of FFV consumers in Sweden would have purchased FFVs in the absence of the cash rebates because of the vehicles’ lower operational costs. Xing et al. (2019) find that federal income tax credits for purchasing plug-in electric vehicles (PEVs)<sup>20</sup> in the United States resulted in a 29 percent increase in PEV sales, but 70 percent of the credits went to households that would have purchased PEVs without the credits.<sup>21</sup> Replacing a one-size-fits-all policy with one that targets marginal buyers who are more responsive to the subsidy and would purchase AFVs only with the subsidy could improve effectiveness. Marginal buyers are those who consider the higher upfront cost the only obstacle to the adoption of AFVs, or those who view the subsidy amount as sufficient compensation for the utility loss from the other drawbacks of AFVs. Using the EV subsidy receipts data and vehicle transaction prices
>0%<br><!-- End of picture text --> _Source_ : ICA data, World Bank.

**Figure 24: Main Constraints to Business (ICA), by Size of Firms, Togo 2009. Percent of Firms perceiving a category as the main constraint.**

<!-- Start of picture text --> 40%35%30% Micro and Small Firms 40%35%30% Medium Firms 40%35%30% Large Firms<br>25% 25% 25%<br>20% 20% 20%<br>15% 15% 15%<br>10% 10% 10%<br>5% 5% 5%<br>0% 0% 0%<br><!-- End of picture text --> _Source_ : ICA data, World Bank.

> 28 <u>www.enterprisesurveys.org</u> Enterprise survey data is available for over 100,000 firms in 117 countries. The surveys include indicators that benchmark the quality of the business environment across countries. They are commonly referred to as “Investment Climate Assessment” (ICA) indicators. Togo’s business enterprise survey was concentrated in the capital city, Lomé, and includes 300 enterprises, of which 145 are micro firms (0‐4 workers); 99 are small (5‐19 workers); 41 are medium (20‐99 workers); and 15 are large (over 100 workers). These firms are also broken down into 19 firms within the Free Zone (FZ) and 281 non‐FZ. Considering the survey structure, the 300 firms in sample represent over 1,700 firms in Togo.

29 Enterprise surveys always include this type of question, providing a list of 15 candidates from the topics mentioned above. The interviewed can provide up to 3 answers: The top, second and third most serious obstacle to business. Note that we in this
Appendix Chart 5 Contributions of Shocks to Inflation (Percent of total variation) <!-- Start of picture text --> By global and domestic shocks<br>Global Domestic<br>100<br>80<br>60<br>40<br>20<br>0<br>70 70 99 70 85 99 08 20<br>-23 -98 -23 -84 -98 -07 -19 -23<br>By structural shocks<br>Oil GS GD GMP DS DD DMP<br>100<br>80<br>60<br>40<br>20<br>0<br>70 70 99 70 85 99 08 20<br>-23 -98 -23 -84 -98 -07 -19 -23<br>By types of shocks<br>Oil and supply Demand MP<br>100<br>80<br>60<br>40<br>20<br>0<br>70 70 99 70 85 99 08 20<br>-23 -98 -23 -84 -98 -07 -19 -23<br><!-- End of picture text --> Sources: Authors’ calculations based on data from January 1970 through December 2023 for the G5 economies (Canada, the euro area, Japan, the United Kingdom, and the United States). Notes: Forecast error variance decompositions of domestic CPI inflation based on FAVAR models (as explained in Section IV) that consist of four global variables (global output growth, inflation, monetary and oil prices) and three domestic variables (domestic output growth, inflation, and monetary policy rates). “OP” = oil price shock, “GS” = global supply shock, “GD” = global demand shock, “GMP” = global monetary policy shock, “DS” = domestic supply shock, “DD” = domestic demand shock,
# **Appendix D. Estimates of the privatization effects using TFP_CS**

# **Table D: Effects of privatization: Alternative TFP measures**

This table re-estimate the economic effects of privatization in Table 6 using the index function TFP using cost shares from ASIF data, i.e., _TFP_CS_ (see Appendix C for details). Specifications are the same as those in Table 6. Columns (1) and (2) reports the simple OLS regression results with and without firm fixed effects, respectively. Column (3) reports the economic effects of privatization using FE-FT model (Brown, Earle and Telegdy 2006; Brown, Earle, and Gehlbach 2009). We regress the detrended performance measures the detrended dummy variable of _Privatized_ . In column (4), we use a dummy variable, _Dum(Age≥58)_ , as the instrumental variable of privatization, which equals 1 when the age of the official is equal to or above 58 and 0 otherwise. We control for quadratic polynomials and city-level macro variables ( _GDP Per capita_ and _GDP Growth_ ). The control variables are the same as those in Table 6. We also control for firm-level fixed effects (except for column (1)), and the industry-year fixed effects, and the interaction of year dummies with initial sales, initial ROA, and initial TFP. For the lack of enough data to calculate TFP, the observations in (1) -(4) columns ends in 2007. Standard errors clustered at the firm level are reported in parentheses. *, **, and *** represent statistical significance at the 10%, 5%, and 1% level, respectively.

|Dependent variable:<br>_TFPCS_|OLS<br>(without firm FE)|Firm FE|FE-FT model|IV Regression<br>(2<sup>nd</sup>stage)|
|---|---|---|---|--
model that includes household size, location and housing infrastructure characteristics. Although the bills might not be paid directly,<sup>36</sup> the households still use electricity and water and so benefit from the subsidies, thus, we add the estimated un-paid bill to the total subsidy received.

We a cost-push model to determine the indirect tax or subsidy that consumers pay through the industrial tariffs.<sup>37</sup> For the electricity industrial and commercial tariffs, if there is an explicit tariff rate that applies to a particular sector listed in the IO table, we take this rate. For sectors where two tariffs could plausibly apply, we use a weighted average of the two, where the weight is the share of electricity consumption in the sector. For industrial sectors without a specific tariff we average the rates that apply to small, medium and large industries, weighted by share of electricity consumption for each size of firms. We apply the single water industrial tariff rate, which is marginally above the cost of production, to estimate the indirect household burden of industrial cross-subsidization of water tariffs.

# Allocating in-kind benefits: Education

The monetary value of public education is given by the unit cost per level of education (pre-school, basic, secondary, vocational, higher education) calculated from administrative budget data. This is a _government cost_ approach, in which the use-value to in-kind benefits is given by the average cost of service provision per beneficiary. This represents what the household would have to pay to use the service at the government’s cost. Budget information is taken from the Ministry of Education and the Ministry of Higher Education.<sup>38</sup> The estimated cost per beneficiary includes capital expenditure of JOD 50 million, which is allocated to each education level depending on the share of the budget spent in each level. Benefits are assigned to households who have members attending a government school, as reported in HEIS. We present benefits net of user fees, which are the self-reported household
fulfillment of obligations and the avoidance of social sanctions, such as guilt, shame or ridicule, or fear of witchcraft. It can also include altruism, which we do not attempt to distinguish from social norms.

A recent empirical literature relying on experimental design highlights the importance of these forces. Chandrasekhar, Kinnan and Larreguy (2011, 2012) find that in the presence of hidden income and limited commitment, social proximity between the risk-sharing partners increases the amounts transferred. The field experiments of Leider et al. (2009) and Ligon and Schechter (2012) show that altruism is more important than repeated interaction in determining the size of the transfer. Furthermore, social norms could weaken the constraints to risk sharing to the extent that they never bind and allow for the existence of sustained, unreciprocated transfers.<sup>9</sup> Below we will find evidence of such unilateral relations and argue that this is consistent with risk sharing motivated by social norms.

# **3. Data and descriptive analysis**

Kagera is a region in the north-western part of Tanzania. A large part of Lake Victoria is contained within this region and it shares a border with Burundi, Rwanda, and Uganda. The region is overwhelmingly rural and agricultural production is the most important source of income, with more than 80 percent of the region’s economically active population engaged in it (URT 2012). Bananas, beans, maize, and cassava comprise the main food crops while coffee, tea, and cotton are important cash crops. Recent years have seen a rise in improved banana varieties and sugar for use as cash crops. At the time of the last national census in 2002, Kagera had a population of roughly two million people.

The Kagera Health and Development Survey (KHDS) was originally designed and implemented by the World Bank and the Muhimbili University College of Health Sciences. It consisted of 915 households from 51 villages that were interviewed up to four times from autumn 1991 to January 1994.<sup>10<
**Table 4**

**Estimated Transfer Derivatives for Households with Elderly** Residents Estimated in the Neighborhood of Multiples of the Poverty Line

|_Panel A: 1995 to 1998_<br>Multiple|Income/Capita<br>Range(in RMB)|No Migrant<br>Child(ren)|Has<br>Migrant<br>Child(ren)|
|---|---|---|---|
|1⁄4 Poverty Line|119 to 319|-0.84|-1.73|
|1⁄2 Poverty Line|338 to 538|-0.73|-1.03|
|Poverty Line|775 to 975|-0.52|-0.49|
|Twice Poverty Line|1650 to 1850|-0.28|-0.4|
|Four Times PovertyLine|3400 to 3600|-0.11|-0.31|
|_Panel B: 2000 to 2003_|||Has|
||Income/Capita|No Migrant|Migrant|
|Multiple|Range (in RMB)|Child(ren)|Child(ren)|
|1⁄4 Poverty Line|119 to 319|-0.67|-0.63|
|1⁄2 Poverty Line|338 to 538|-0.57|-0.35|
|Poverty Line|775 to 975|-0.46|-0.25|
|Twice Poverty Line|1650 to 1850|-0.35|-0.21|
|Four Times PovertyLine|3400 to 3600|-0.24|-0.20|

Note: All values are in 2000 RMB Yuan per capita. The nutrition based poverty line is equal to 875 RMB Yuan per capita. We use the RCRE Rural Household Surveys for 1995 to 2003 from Anhui, Henan, Jiangsu and Shanxi and the matched RCRE 2004 Supplemental Rural Household Social
E[Y _i_ (1) _|Xi < c_ ] and _E_ [ _Yi_ (0) _|Xi > c_ ] are unobserved. Consequently, the sharp RD design exhibits an extreme case of lack of common support, as units in the control ( _Di_ = _I_ ( _Xi ≥ c_ ) = 0) and treatment ( _Di_ = _I_ ( _Xi ≥ c_ ) = 1) groups cannot have the same value of the running variable ( _Xi_ ). Thus, RD performs a local extrapolation in order to compare the control and the treatment units.

> 8It should be noted here that a bigger jump in total population is observed in the annual data series published by IBGE, but due to limited observations, the jump cannot be estimated by RD.

> 9The fuzzy RD involves a two stage estimation, where the first stage estimates the probability of treatment, while the second stage uses this estimated probability to find its effect on the outcome variable.

> 10Some evidences in support of the sharp RD design is given in the later sections of the paper.

17
to the matching grant program. Therefore the full costs are relatively high compared to the amount of matching grant funds given out. Given that the resources originate in a World Bank loan that eventually needs to be repaid, citizens of these countries could therefore be paying to subsidize relatively well-off business owners with little public benefit in return.

We also discussed the point that the purpose of the impact evaluation is not just to learn whether the program works or not, but to help improve the future operation and targeting of such programs if we can learn what types of businesses the program works for. As Biggs (1999) notes, ideally matching grant programs should select projects with large economic and social returns to the country that would not otherwise find private funding – but in his direct questions to recipients of an earlier

> 6 Note that all World Bank projects are required to include project indicators and some form of evaluation, but at most these typically measure the number of firms receiving grants and sometimes before-after changes for the recipients captured through surveys of beneficiaries.

12
the sending country. A nationally representative demographic survey, the ENADID 1997 is used to provide a broad sample of migrants in rural communities with varying levels of migrant experience. Historic migration networks formed as a result of U.S. demand conditions and the pattern of development of the railroad system in the early 1900s are used as instruments for current levels of migration. Infant mortality rates and birth weights are the two child health outcomes considered in this

> 3 See the literature review in Durand, Parrado and Massey (1996).

> 4 See, for example, Hanson and Woodruff (2003) on education and Woodruff and Zenteno (2001) on microenterprises.

- 2 -
%i i i i from country _e_ . Applying this percentage to corresponding FBS item’s carcass weight will yield ff,cc,ee<sup>indicates the percentage of a given FBS item</sup><sup>_f_consumed in Bangladesh imported</sup> all COOs from which this item was imported and its respective imported quantities.

For a given FBS item, the percentage of domestic supply that is domestically produced was used as a proxy for the percentage of consumption that is domestically produced. In this way, food loss and waste were implicitly included.

where the _f_ is a specific FBS food item and _c_ represents country, Bangladesh in this case.

# _2.3.3 Carcass weight_

Regarding quantities to be applied with GHG footprints, converting actual consumption to carcass weight requires two additional steps. We firstly converted edible portion to their raw weights using cooking yield factors (Bogna 2002 and USDA 2014). Secondly the raw weights were converted into carcass weights using extraction rates in Bangladesh (FAO 1997).

# **2.4 Data**

# _2.4.1 Data on household consumption_

Household-level consumption data are from HIES 2000 and HIES 2016,<sup>7</sup> which provide food items, quantities consumed, item values, and individuals’ basic information in a 14-day reference period. HIES 2000 includes 63 districts and 7,437 households with an average household size of 5.3 people. AME per household is 4.1 on average. HIES 2016 was designed to sample 46,076 households. But due to missing data of 94 households, the actual number of households used for calculations is 45,982 from 64 districts. The average household size was 4.1 people and AME per household was 3.3.

# _2.4.2 Data on nutrients_

In addition to macronutrients, we focused on six micronutrients, namely calcium, iron, zinc, Vit. A, Vit. B12, and folate, in terms of nutritional analysis
climate change and natural disasters on poverty often rely on household consumption surveys as a dominant source of data. This is true both for single-country (e.g., Akter and Mallick, 2013; Arouri _et al._ , 2015; Bangalore _et al._ , 2018) and cross-country analyses (Azzarri and Signorelli, 2020; Carter _et al._ , 2007). A notable advantage of most living standards measurement study (LSMS)-type household surveys is that they provide comprehensive data on household consumption levels, enabling researchers to construct poverty indicators that directly align with the study’s objectives (Wong and Brown, 2011; Jakobsen, 2012). In singlecountry analyses, the scope of the survey can be more closely tailored to specific regional or national contexts, providing nuanced understanding of the poverty situation. These studies often employ the headcount poverty rate and the Gini coefficient to measure inequality, which can be estimated using household surveys.<sup>6</sup> While single-country studies often focus on specific events or circumstances within a country, their findings are typically limited in generalizability. In contrast, cross-country studies attempt to overcome this limitation by comparing and contrasting experiences across a range of countries. This broader perspective can shed light on patterns and relationships that might not be apparent within a single-country context, thereby offering findings with greater generalizability. However, assembling comparable data across multiple countries presents a significant challenge due to variations in data collection methods, definitions of key variables, and data quality among different nations. As a result, many studies often rely on standardized databases on poverty and inequality such as the World Bank’s PovcalNet database, later transitioned into the Poverty and Inequality Platform (PIP), which offer estimates for inequality and poverty under various poverty lines or the United Nations University’s World Income

> 6 While most studies primarily rely on the Gini coefficient (Cappelli _et al._ , 2011; Tol, 2021; Paglialunga _et al._ , 2022; Pleninger, 2022),