rafmacalaba/data-use-mentions-extended huggingface dataset:
low-to middle-income consumers, and for the proportion of low-to middle-income population living in them. In Peru, audits were conducted in San Juan Lurigancho a town of 1,100,000 near Lima and the regional capitals of Puno and Piura, with populations of 140,000 and 400,000. In Ghana the audits were done in the capital Accra and the regional capitals of Kumasi and Takoradi, with population ranging from half a million to over 4 million people. In each study location, a radius of 1.5 Km was drawn from the business center, and a census of every financial institution inside the circle was conducted. We found a total of 134 distinct institutions across 3 countries. For the purposes of the analysis we differentiate between commercial or “traditional” banks and other financial institutions (retail chain banks, microfinance lenders, credit unions) that tend to target low-income households.

Credit auditors requested a 6 month loan in Mexico and Ghana and a 12 month loan in Peru for a household expenditure, preferably with monthly installments. The expenditures included house repairs, medical expenses and children’s school supplies, among others. Savings auditors were assigned an amount that they wanted to deposit. These amounts were 5,000 Pesos (350 USD) in Mexico, 2,000 Sol (780 USD) in Peru and 500 Cedi (215 USD) in Ghana.<sup>15</sup> Since we wanted to capture all the information and materials that staff would provide to prospective clients up until the signing of the credit contract or opening of savings account, a visit by the auditor was deemed completed when either the institution refused to open the account or grant the loan or when the auditor was asked to sign the contract. While all savings auditors needed only one visit to gather all the information and be offered the chance to open the account, credit auditors needed up to 4 follow-up visits, some at their residence, before they were offered the opportunity to sign the credit contract.<sup>
and erosion protection (A. Dobson et al. 2006; Song, Huang, and Townshend 2014). (See Appendix 3: _Environmental Indicator: Ecosystem Effects as Measured by Forest Cover Change in CICPEC_ .) Roads are considered one of the leading proximate causes of habitat destruction, especially tropical deforestation.<sup>4</sup> For example, a meta‐analysis by Geist and Lambin (2001) of the existing literature on the causal factors for tropical deforestation found that the extension of road infrastructure was a proximate cause of deforestation in 61% of the cases studied (93 of 152) while rail expansion was a proximate cause in 11% (17 or 152). Such evidence has led to the development of a relatively new discipline, “road ecology,” which addresses solely the impacts of road construction and operations and their mitigation (Coffin 2007; Forman 1998; Forman et al. 2003; van der Ree, Smith, and Grilo 2015). More recently, “railway ecology” has also been proposed (Popp and Boyle 2017; Wingard et al. 2014), although the general assumption has been that the environmental impacts from rail, while similar in nature, are less severe in intensity to those from roads. Perhaps for this reason, the great majority of the research on environmental impacts has focused on roads rather than railways. For example, a survey of transport‐ related impacts on wildlife found that 94% of peer‐reviewed articles were about impacts of roads, with only 6% on rail impacts (Popp and Boyle 2017).

# ii. Edge Effects

The "road effect" (also known as the “edge effect”) is the area over which the ecological effects of a road and traffic extend into the adjacent landscape due to habitat disturbance effects (van der Ree, Smith, and Grilo 2015). This is a more subtle but pervasive ecosystem effect of a road that extends well beyond the initial loss in habitat from the transportation corridor. The
sectors, and incorporates the increasing evidence that a large fraction of the employment in the informal sector is voluntary. It is developed in an efficiency wage context both because recent evidence suggests that much observed segmentation may arise endogenously rather than being imposed by labor unions or minimum wages, and because it permits explicit modeling of the determinants of turnover. Predictions can be made about how the size of the self-employed sector, the degree of segmentation in the market, and turnover should move with the development process and policy innovations.

Section 3 examines cross country data from Latin America, Europe and Asia with three objectives. The first two are straightforward: to test the predictions of the model about the size of the informal sector and rates of turnover with respect to several key labor market, productivity, and demographic variables suggested by the theoretical framework and second, to suggest the direction of possible influence of variables that are theoretically ambiguous. But somewhat speculatively, we also attempt to provide more informed estimates of the incidence of unmeasurable distortions that ration more workers into the informal sector or rigidities that decrease turnover. Since our theoretical framework abstracts from such exogenous phenomena, we tentatively measure their impact by the deviations from the model's predicted values. Though it is trivial to raise objections to this approach on either theoretical or empirical grounds, the results at once strongly coincide with the stylized facts about industrialized countries and challenge what is commonly thought about Latin America: with some predictable exceptions, regional labor markets do not appear unusually distorted or inflexible.

2
Recent work by Sheahan and Barrett (2017) also documents pesticide use in Ethiopia, Malawi, Niger, Nigeria, Tanzania, and Uganda using the LSMS-ISA data: 16% of farming households apply at least one agri-chemical to their crop during the primary season but again find considerable heterogeneity in use both across and within countries. RuLIS data on agri-chemical use among crop farming households in 13 SSA countries is illustrated in Figure 2, for small-scale food producers and larger household farms separately.<sup>9</sup> Similar to the observed patterns of fertilizer use, chemical input use is considerably lower among small-scale food producers than among larger farms. In Burkina Faso, for example, only 25% of small-holder crop-farming households apply any chemicals, while 49% of households operating larger farms report using chemical inputs. Among these countries, Ghana exhibits the highest incidence, with 63% of households applying chemical inputs. Haggblade et al. (2021) document that pesticide use has tripled in West Africa in the last twenty years, arguing that growth in use is driven by a combination of increasing labor costs, increasing pest pressures, and falling prices of generic pesticides in markets.

_Figure 2. Share of crop farming households using chemicals. Source: RuLIS._ The most commonly used pesticide in the world is glyphosate herbicide (Benbrook 2016). Glyphosate is a non-selective herbicide, often used by farmers to kill weeds before planting. It is sold in concentrated form and diluted with water before application with a sprayer. Use of agri-chemicals remains relatively low in Sub-Saharan Africa but sales of glyphosate have increased in recent years;

> 9 Data extracted from RuLIS (https://www.fao.org/in-action/rural-livelihoods-dataset-rulis/en). The year of data collection for each country in Figure 2 is as follows: Burkina Faso (2014), Cameroon (2014), Ethiopia (2016), Ghana (2013), Malawi (2017), Mali (2017), Niger (2014), Nigeria (2019)
Income Measurement for the Assessment of Public Policies” (Sutherland et al., 2009) with the objective, among others, of understanding the distributional impact of the inclusion of rents in income aggregates. Törmälehto and Sauli (2010 and 2013) analyze the implications of including rents in the income aggregate for official poverty and inequality estimates published by Eurostat and conclude that “ _the present data quality does not support adding this component to disposable income concept without major improvements in the comparability of the data between countries and within countries across time”_ (Törmälehto and Sauli 2013, 39).

By contrast, in order to guarantee meaningful international and inter-temporal comparability of countries’ welfare, observed and imputed rental prices are included in National Accounts according to internationally agreed recommendations (SNA 2008). In a sample of OECD countries, for instance, the share of observed and imputed rents in households’ total consumption expenditures range from 6.5 percent in Poland to 25 percent in Finland, according to the latest available National Accounts data (OECD 2018). For the same countries, this share may vary sensitively over time, even within a short period. For instance, in Ireland the share almost doubled from 11 percent in 1996 to 20 percent in 2016, in Spain it increased from 10 percent in 1996 to 16 percent in 2016. In some other countries the share decreased: over the period 1996-2016, in Sweden it declined from 25 to 20 percent and in Mexico from 19 to 14 percent. It is clear then that the exclusion of this large component of household consumption from National Accounts would distort country-rankings in terms of Gross Domestic Product (GDP).

The World Bank has recently started collecting information for understanding the key elements of national methodologies used to produce official poverty estimates. Preliminary results shed light on a sample of 69 country-year observations of recent poverty estimates. Only for 28 observations is it possible to understand whether the welfare aggregate included observed and imputed rents (World Bank, 2015). Nonetheless, when housing is included, researchers
more (13.3%) for non-technologically oriented firms and just under 5 times more for firms not providing worker training (17.8%) for their employees.

Despite the very modest changes in female employment post-COVID reported by our survey, looking at these stylized facts of the sector, there seem to be some interesting heterogenous effects at play that require further rigorous investigation.

# **4. Methodology**

Our aim is to assess the heterogeneous impact of COVID-19 on the gender employment gap in Egyptian manufacturing using unique firm-level data. We control for sector, location and firm level characteristics to disentangle the effect of the shock on the employment gap.

# _The Model_

To measure the impact of COVID-19 on the gender employment gap, we run the following two OLS fixed effects specifications:

> _Emp. Gapikg = α0 + Shockikg + α1Xikg + α2 Zikg + μk+ δg + ŋikg                                                     (1)_

_Percentage change in F/M emplikg = α0 + Shockikg + α1Xikg + α2 Zikg + μk+ δg + ŋikg (2)_ where the dependent variable _Emp. Gapikg_ is the change in the difference of the number of male and female employees after COVID ( _Δ in male-female employment AC_ ) in firm _i_ in sector _k_ and governorate _g_ with separate equations estimated for (i) the change in the number of male employees; and (ii) the change in the number of female employees post-COVID. δg and _μk_ are governorate and sector dummies respectively, to control for governorate and sector unobversables, and ŋikg is the error term. The sectors are classified according to the ISIC Rev. 4 at the 2-digit level. The dependent variable in equation (2) is the percentage change in male and female employment, and the difference thereof. The vectors of the independent variables are identical in equations (1) and (2).

Two sets of factors could affect the gender gap. We distinguish between ‘status variables’ or ‘innate characteristics’ and those which are shaped by the behavior of the industrial
skills and to a lesser extent thinking skills (cognitive) and personal growth (socioemotional).<sup>42</sup> The typical occupations in Malaysia requiring a high weight on these kinds of skills are highly digital occupations, such as software developers and network professionals. The third factor heavily loads on the socioemotional skills subsets of emotions and personal growth.<sup>43</sup> Typical Malaysian occupations using this skill set include occupations from all three levels of occupation digitalization, such as architects and primary school teachers. Similar factors emerge for the other three countries.<sup>44</sup>

> 40 The other three countries and the unweighted occupation profile data find similar results (Annex Table A.4.1).

> 41 Very similar results are found when not rotating factors and using oblique rotations (i.e. allowing for correlations across factors) (results available upon request).

> 42 The socioemotional skills that weighed most heavily in this factor in Vietnam was organization and in Thailand was relationship skills (Annex Table A.4.2).

> 43 The third factor is the least stable across the alternative subsamples. For example, in Cambodia, it only weighs heavily on advanced digital skills. In Thailand, the highest factor loadings are on personal growth and a lower loading on intermediate digital skills, though very low basic skills, as in the Malaysia-weighted sample. The Vietnam-weighted sample also weighs very heavily on personal growth and very low on all other skills (Annex Table A.4.2).

> 44 A notable exception is that an “advanced digital” factor emerges for Cambodia. No other skills weight positively heavily in this factor while socioemotional skills are particularly negative (Annex Table A.4.2).

28
where _m_ and _o_ denote time-sensitive and time-insensitive industries respectively. We estimate gravity regression (3) using disaggregate data of products of time-sensitive and time-insensitive industries, and do so separately for manufacturing and agricultural trade. The test is essentially whether Ecuadorian/Peruvian relative exports of time-sensitive goods are different from Ecuadorian/Peruvian relative exports of time-insensitive goods. The advantage of the double relative is that we are capturing both relative exports of time sensitive to time insensitive goods and making sure that the control group is to the same importer. A negative coefficient on relative time implies that an increase in the relative time to move goods from factory to ship reduces exports of time-sensitive goods by more than time-insensitive goods.

Table 10 presents the results for time-sensitive manufacturing and agricultural goods. The first three columns report the results for manufactures. The coefficient on exporter time is always negative and significant. Countries having longer required time for exports to take place are associated with a lower ratio of exports in time-sensitive goods to exports in time-insensitive goods to the same destination. In particular, a 10 percent increase in the ratio of time is associated with a 2.4 percent reduction in country j’s ratio of exports of time-sensitive goods to the exports of time-insensitive goods in relation to country _h_ ’s ratio.

Results for agricultural products in the difference-in-difference gravity specification are reported in columns 4-6. Because agriculture production depends on climate and land area, we also include the ratio of distance from the equator and the ratio of log land area. As expected, the coefficient on time is always negative and exhibit robustness across specifications. In particular, a 10 percent increase in the relative time of moving goods 20
this vulnerability provided an extra sense of urgency for development of a macroeconomic stimulus package that would facilitate reemployment of laid-off workers. The widespread reductions in employment that were evident by January 2009 could have placed existing institutions under considerable stress.

By the end of 2009, data from both firm and household surveys show that China had weathered the storm quite well. In spite of the ongoing slowdown elsewhere in the world, the labor market was tightening in China. Real wages of migrant workers continued to increase through the crisis, even as a significant percentage had suffered layoffs, and by late 2009 laid-off migrants looking for jobs were finding them.

The paper proceeds as follows. The next section locates the shock of the financial crisis in the context of other adjustments already under way in the Chinese economy and documents the effects of the shock on the export sector. The following section reviews evidence on the employment effects of the crisis from firm and household surveys. The paper then discusses the government’s fiscal stimulus and active labor market programs and reviews evidence of the effect of the crisis and subsequent recovery on wages and incomes. The final section offers some conclusions.

# **2. The Trajectory of China’s Economic Growth and the Global Financial Crisis**

Between 2001 and 2007, the world economy experienced steady growth (Figure 1a). China and India consistently displayed growth rates of 4–12 percent, while other major economies registered healthy annual growth rates of 2 percent or more. China’s growth rate was in fact so high by 2007 that in early 2008 the government was in the process of trying to cool off the economy and make growth more equitable. The government and the central bank had introduced contractionary macroeconomic policies in 2007, aiming to slow growth, and in January 2008 the new Labor Contract Law was put in place to provide employment protection to workers along with mandated social insurance contributions from employers.<sup>1</sup> The timing of these two events 3
report their data in all the years under analysis, leading to missing data in a non‐random way. Thus, arriving at the average performance in a year and comparing it to another year can be misleading given the difference in the composition of the service providers in the two years. The methodology to compute the trends in this paper uses a nested panel approach, such that “balanced panels” for two consecutive years are nested together across the years.<sup>1213</sup> Additionally, a survey of pourashava officials and subscribers in 80 randomly chosen pourashavas provided qualitative data on water supply services as a means to investigate the quality of services to poorer areas. The survey enquired about the number of hours of water supply per day and the number of hours elapsed between the lodging of a complaint and the response of the water supply department of the pourashava. There were 61 class A, 17 class B, and 2 class C pourashavas in the sample.

The paper qualitatively analyzes the performance of pourashavas compared to the performance of the rest of Bangladesh, i.e. the WASAs (Dhaka, Chittagong, Rajshahi, and Khulna) and five city corporations (Comilla, Rangpur, Sylhet, Barisal, and Gazipur) and global service provider performance. In addition, the paper also analyzes the performance levels among the pourashavas to provide the “aspirational” level for the lower performing pourashavas to benchmark against the better performing ones.

# Pourashava Performance Indicators

Pourashavas were measured on several indicators to benchmark their performance against that of the WASAs and city corporations and the performance of global service providers. These indicators are grouped into three categories – efficiency, availability, and financial sustainability, as described below.

**1. Efficiency**

- a. _Non‐revenue water (NRW):_ The difference between water supplied and water sold as a percentage of the former. This indicator helps assess the loss of saleable water.

- b. _Staff productivity:_ The number of connections per employee. A higher
.002)|(0.002)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Demographic|No|No|Yes|No|No|Yes|No|No|Yes|
|Education|No|No|Yes|No|No|Yes|No|No|Yes|
|Employment|No|No|Yes|No|No|Yes|No|No|Yes|
|Panel A Adj.R<sup>2</sup>|0.194|0.197|0.203|0.295|0.325|0.362|0.326|0.357|0.388|
|Panel B Adj.R<sup>2</sup>|0.170|0.177|0.189|0.282|0.318|0.359|0.280|0.330|0.374|
|Nr. of countries|19|19|19|19|19|19|19|19|19|
|Nr. of hh|418622|418622|418622|418619|418619|418619|415296|415296|415296|

Source: International Urban Poverty Database. Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors are in parentheses. WorldPop 250m is used for the DOU and DB method. The dependent variable is a dummy variable taking the value 1 if a household has access to a respective amenity. The baseline category is “Urban center” in all specifications in Panel A, and “Cores” in all specifications in Panel B. Demographic control variables include household size and household head’s age, sex, and marital status. Education is a categorical control variable that summarizes the education of the household head in seven categories. Employment is
from the World Development Indicators (WDI).<sup>2</sup> The data are then deflated to 2009 using the GDP deflator for the United States from the relevant reference fiscal year.<sup>3</sup> Note that information on the closing month of the firms’ fiscal year is used to adjust exchange rates and deflators for each firm.<sup>4</sup>

# **Treatment of Outliers**

In order to minimize sensitivity to extreme values, outlier firms are eliminated from the analysis. More specifically, outliers in d2 (capturing Y), n7a (capturing K), n2a (capturing L), n2e (capturing M), and VA (d2 minus n2e), as well as outliers in ratios n7a/VA and n2a/VA were turned into missing before estimating the production function. To find outliers in levels, we first transform variables as ln( _x_ +1), and group observations by economy and broadly defined sector (more precisely, manufacturing and services). Next, we calculate (unweighted) means and standard deviations of these transformed variables within each group. Observations that are more than three standard deviations away from the mean are then marked as outliers and turned into missing. To find outliers in ratios, we first transform variables as ln( _x_ ), and group observations by industry. The three-standard-deviation rule is then applied (unweighted) and the corresponding observations are turned into missing.

> 2WDI indicator code: PA.NUS.FCRF

> 3WDI indicator code: NY.GDP.DEFL.ZS

> 4The fiscal year and its closing month information are given in variables “ _d_ 2 _~~l~~_ 1 _~~y~~ ear perf_ _~~i~~ ndicators_ ” and “ _d_ 2 _~~n~~_ 3 _~~l~~ ast_ _~~m~~ onth_ _~~f~~ y_ _~~p~~ erf_
GDP<br>(CL)<br>_Result: significance varies_<br>_across models_<br>-degree of economic<br>freedom (based on 10-<br>point indices in ‘Economic<br>Freedom of the World:<br>2002 Annual Report’)<br>(CL)<br>_Result: significant at the_<br>_1% level_|51 LDCs from 1985-2000.<br>Generation and capacity data:<br>APERC database and World<br>Dev. Indicators.<br>Labor: Industrial Statistics<br>Yearbook, International<br>Labour Organisation.<br>Privatisation, regulation, and<br>competition: The Yearbook<br>of Privatisation, EIA, WEC,<br>and APERC.|
||_H1: Privatisation with_<br>_supportive regulation leads_<br>_to higher output and_<br>_capacity._|-output: net generation<br>(MWh) per capita<br>(PI)<br>-capacity: generation<br>capacity (MW) per capita<br>(PI)|-privatisation: existence of<br>private generation (dummy)<br>(MS/RM)<br>-regulation: existence of<br>independent regulatory agency<br>(dummy)<br>(I)<br>_Result: taken together, the_<br>_variables are significant (on_|||

48
5,000,000 Avg Footprint per Pax/Mile<br> - -<br>1 3 5 7 9 11 13 15<br>Distance Class<br>(Kg)<br>CO2 Footprint (Kg)<br>Average CO2 Footprint per Passenger Mile<br><!-- End of picture text --> Table A3.6 below shows the distribution of footprints as calculated by distance class for HQbooked trips in CY-09 and FY-12, for the sample of trips in our data set. The most interesting issue here is the substantial change in the distribution of flight activity over this period, with fewer flights in distance classes 10-11 (in the range 3,450 - 4,600 miles), and more flights in particular in class 16 (the longest flights, above 6,900 miles).

Future improvements in calculations of the carbon footprint load of WGB air travel can be made on several fronts, including the following. (a) Match the American Express data set with the purchased scheduling data published by companies such as Diio or OAL to find the equipment 34