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

In developing countries, urbanization is a key driver of economic productivity growth. Through their scale, density, and connectivity, cities offer economies of scale and agglomeration, and facilitate knowledge spillovers, which together drive efficiency gains in production and services. To create thriving cities, urban planning, infrastructure, and service provision must enhance connectivity, through productive density and strong transportation, while offering high living standards to workers. Transport system must provide workers access to the most productive jobs and places within cities.

This paper analyzes the welfare and productivity effects of transit infrastructure improvements in Amman, the capital of Jordan.

We collect rich microdata at a very granular level to characterize the city structure of Amman. We observe the neighborhoods in which people live and work and their sectors of employment. We further observe locations’ residential and commercial floor space and the legal zoning restrictions. Data on the transportation network and travel speeds allows us to calculate the travel times between all neighborhood pairs of the city. We use this data to calibrate a quantitative urban model in which workers make residential, commuting, and employment decisions (including where to live and work, what sectors to work in, and how to commute), and developers make decisions about housing supply, following Ahlfeldt, Redding, and Sturm (2016), Redding and Rossi-Hansberg (2017) and others. Structural parameters and elasticities are calibrated to values from the literature. Based on these parameters and the data described above, the model derives baseline amenities, productivities, wages, and rents for each neighborhood.

After thereby describing the city at baseline, we then use the estimated model to simulate counterfactual policy scenarios. In particular, we analyze how investments in Bus Rapid Transit (BRT) systems affect aggregate and distributional outcomes. The spatial model sim2
due on their sales. Alternatively, small retailers may be informal because they are not required to register for consumption taxes due to their size. We do not distinguish between these channels, as both imply that these retailers do not remit consumption taxes. In addition, these concepts are closely related: in countries with low enforcement capacity, the scope for evasion among small firms is such that the net revenue from bringing them into the tax net is small and outweighed by administrative and compliance costs (Ebrill et al., 2001; Keen and Mintz, 2004).

The key assumption behind our assignment method is that larger retailers are more likely to be formal. This is in part mechanically true, as a frequently used criteria for compulsory tax registration is firm size. In addition, a large literature argues that larger firms are less likely to evade taxes. Theoretically, Kleven et al. (2016) develop a model of tax evasion in which informality must be sustained by collusion between firm managers

> 7 Replication codes for the paper can be accessed here. This includes the cleaning files of each expenditure survey and the files generating the tables and figures of the paper. The data are not provided as we had to require access to the World Bank or Stat Agency for each country. Yet, these data are often accessible, allowing for a replication of the paper on a subset of countries of interest.

> 8We also use two smaller categories to classify entertainment services into (8) entertainment (e.g. restaurants) and (9) informal entertainment (e.g. food trucks).

> 9We exclude housing expenditure due to limited data on owner-occupied imputed rents.

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|YES|YES|YES|
|Grade FE|YES|YES|YES|YES|YES|YES|YES|
|Pre-program controls|YES|YES|YES|YES|YES|YES|YES|
|Observations|986|986|986|986|988|310|113|
|Outcome mean in control group|0.01|0.05|0.84|0.1|2.06|1.12|0.21|
|**_Effect size:_ In-kind - Cash**|-0.01|-0.04|0.07|-0.02|-2.21|-0.63|-0.02|
|**_H 0 :_ In-kind = Cash (p-value)**|0.33|0.05|0.09|0.54|0.07|0.30|0.91|
|**_H 0 :_ In-kind = Cash (Randomization**<br>**Inferencep-value)**|0.30|0.04|0.10|0.59|0.03|0.37|0.96|

Notes: *** p<0.01, ** p<0.05, * p<0.1 (1) Standard errors are clustered at the village level.

(2) Pre-program controls include gender, age, and indicators for whether the household head is married, whether the house has running water, and whether the closest school offers morning shift. (3) The outcomes in columns (1) to (5) are based on the information collected in the 2005 follow-up survey and refer to the week prior to the survey; the sample includes children age 12 and 13 who are reported to be enrolled in primary school. The outcomes in columns (6) and (7) are based on the Enlace de Contexto which asks
11 ―Center for Health Care Econ, U York‖ (1 publication) was not retained for cleaning and aggregation, while the appropriately Anglicized ―Centre for Health Econ, U York‖ (81 publications) was. Imposing this rule resulted in us dropping 12,500 of our 13,500 author addresses. This would be a calamity were it not for the fact that publications are highly unequally distributed across author addresses. The 980 records we retained accounted for around 21,500 (60 percent) of our 37,000 ―publications‖. We think it unlikely that our ―rule of five‖ will have had much—if any—effect on the ranking of institutions, at least at the top of the distribution: it seems likely that (say) Harvard and Yale will be just as likely to have lots of odd spellings for their various departments and research institutes; and in any case, almost 85 percent of the dropped author address records had only one publication to their name.

# **Citation data**

Like Medline, and in contrast to SSCI and SCOPUS, EconLit has no citation data. For the purpose of this study, therefore, each publication was looked up in Google Scholar<sup>16</sup> and the number of citations recorded (the numbers in the paper reflect citations as of June/July 2011). Google Scholar has some attractions over SSCI and SCOPUS as a source of citation data. It captures a very broad set of publications, including books, book chapters, established journals, new journals, open-access journals, technical reports, and even dissertations. This means that we end up finding citation data for a large fraction (85%) of our EconLit records. It also means that the citation data capture scholarly influence across a wide range of publication types, not just a narrow range of academic journals.

As a source of citation data, Google Scholar does, however, have its disadvantages. Titles are also sometimes incorrect, incomplete, ambiguous
do not find substantial evidence of impacts on migration but note that their migration data differs from the data used in previous studies.<sup>15</sup> The impact of MNREGA on education also suggests MNREGA tightened local labor markets. Shah and Steinberg (2015) show that MNREGA decreased the educational attainment of 13-16 yearolds by decreasing school enrollment. The effects are similar for boys and girls. They suggest that

> 14I describe a replication exercise of my main results using treatment in this RCT in Appendix 6.

> 15Other attempts to estimate the impact of MNREGA on migration have been limited but suggest it may have had important impacts. A government review of the impacts of MNREGA claims that the program led to a 27% decline in cross-district migration caused by economic distress. One of the few academic studies of the impact of MNREGA on migration finds a reduction in rural to urban migration of around 8% (Imbert and Papp, 2014). While not direct evidence of reductions in migration, several studies have estimated a positive spillover of MNREGA on wages in neighboring districts that is hypothesized to operate via reductions in inter-district migration (Prasann, 2016; Muralidharan et al., 2017). These estimates suggest that wages in neighboring districts may have increased by as much as 9% and that the estimated impact of MNREGA on wages in districts in which it was implemented may be substantially underestimated due to these spillovers.

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/ 0|++ / '|-- / *|-- / *|--- / **|
|Urbanization (%)|++ / **|0 / 0|-- / '|0 / *|+ / '|0 / 0|0 / 0|
|Kearney Globalization Index|++++ / ***|++++ / ***|++++ / ***|--- / ***|+++ / ***|++++ / ***|++++ / ***|
|Income per capita|++++ / ***|+++ / ***|++++ / ***|---- / ***|++++ / ***|++++ / ***|+++ / ***|
|**# of cities**|**193**|**193**|**193**|**193**|**193**|**193**|**193**|
|**Adjusted R-squared**<br>Sources: All dependent variable<br>drawn from WDI 2002; city pop<br>drawn from GAWC (Taylor); inc<br>Source: KLM city database, dra<br>Sample size: 412 cities in 134 c<br>Values for magnitude (+,0,-) an<br>the magnitude of the coefficient<br>variable. Then we assigned ma<br>“++” for any value between 0.4 a<br>rating on the significance of the<br>indicates significance at 5%, * in<br>Values for number of cities and|**0.30*
. The relationship between a bank's net interest margin and its overhead, for instance, tells us to what extent the bank can pass on its overhead cost to its depositors and lenders. The relationship between before tax profits and overhead, in turn, tells us how much of its overhead the bank can pass on to any of its customers (including, say; its brokerage customers).

· The regression analysis starts from the following basic estimating equation: (2) · _lijt_ = _ii0_ + _G:/'FSjt_ + _G:/'iBtt+ &!/}Xjt_ + _Bijt_ I where _Iijt_ = is the independent variable (either _net_ margin/ta~ _before tax profits/ta_ or _overhead/ta)_ for domestic bank _i_ in country _j_ at time _t; FSjt_ is the share of foreign banks in co~try _j_ at time _t; Btt_ are bank variables for bank _i_ at time _t; Xjt_ are country variables for C<,mntry _j_ at .time _t._ Further, _ao_ is a constant, and ~ _G:/'i,_ and ~~ are coefficients, while _f.ijt_ is an error term. All regressions inelude cotlntry and time-specific fixed effects. The set of regressors varies with the dependent variable under consideration.

> 4 See Vittas (1991) for an account of the pitfalls in interpreting international bank operating ratios.

> 5 Also, financial returns data are not available for a similarly large set of banks and countries.

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We then compute the following variables for each grid cell: (i) the average residual pollution ˆ _em,h,d_ , constructed as described above, for all the observations _m_ recorded within the cell; (ii) the median road size in the cell, where each road dummy is associated an ordinal number, as described in Section 4.4; (iii) the firm density, computed by dividing the number of firms in the cell by the cell area in _km_<sup>2</sup> . To compute (iii) we use our comprehensive initial firm listing (rather than just the firms selected for the survey).<sup>29</sup> Figures 2, 3, and 4, illustrate how our sampled parishes are split into grid cells, and provide suggestive visual evidence that firms are clustered close to major roads (Figure 2), that such roads are more polluted (Figure 3), leading to firm density being higher in grid cells with higher average pollution (Figure 4).

# **5.3 Regression Specifications and Identifying Assumptions**

Equipped with the residualized pollution observations and the grid-cell level variables, we next describe our regression specifications.

**Relationship between road size, pollution and productivity.** Our first objective is to test whether road traffic bundles a bad (pollution) with a good (access to demand). To do so, we study: (i) whether areas near major roads are more polluted and (ii) whether firms near major roads are more profitable, conditional on observables. To provide evidence on the first point, we run the following regression at the grid cell level, for grid cell _j_ in sub-county _s_ in region _r_ :

_ResidPollutionj,s,r_ = _α_ 0 + _α_ 1 _MedianRoadj_ + _δs_ + _θlog_ ( _dist_ ) _r_ + _νj,s,r,_ (3) where _ResidPollutionj,s,r_ is the average residual (log) pollution in the grid cell. _MedianRoadj_
3.5|2.3|3.9|
|_All_|7,749|4,216|3,222|5,209|6.9|3.6|3.0|5.1|

_Source:_ Author’s calculations based on 2008 ILCS

# **_Distributional impact of the gas price hike_**

The analysis suggests that the welfare losses are higher for the poorest than the richest in Yerevan, but neither progressive nor regressive for the rest of the country. Table 7 presents the results of the analysis of welfare losses, expressed in percent of household expenditure, 13
Shiny App in step #2. This downloaded dataset includes the estimates of prevalence of (i) moderate or severe and (ii) severe food insecurity among adult individuals in accordance with the “raw score” for each household (that is, the count of “yes” answers across the eight questions).</mark> <mark>6. We import the data downloaded in step #5 into Stata and using “raw score” as the linking variable, merge it with other Stata formatted data files that are defined at the householdlevel.</mark>

**<mark>Consumption quintiles:</mark>** <mark>To define pre-COVID-19 household per capita consumption quintiles, we rely on the pre-public-dissemination versions of the consumption aggregates. These are provided by the Living Standards Measurement Study (LSMS) team on an exceptional basis, with clearance from the respective NSOs, for the pre-COVID-19 LSMS-ISA-supported surveys that serve as a sampling frame for the high-frequency phone surveys on COVID-19. In the case of Ethiopia, Nigeria and Malawi, the consumption aggregates are computed by the respective NSO, with technical assistance from the LSMS. In the case of Uganda, the consumption aggregate is computed by and has been obtained from the Uganda Bureau of Statistics. In the specific case of Malawi, the consumption aggregate is for the IHPS 2016, as this information is unavailable for the IHPS 2019.</mark>

# **<mark>Econometric Analysis</mark>**

**<mark>Phone Survey Weights:</mark>** <mark>Since we rely on phone interviews with a sample of households that had been interviewed face-to-face prior to the COVID-19 pandemic as part of the LSMS-ISA-</mark>

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7 roles, much more than 55 percent of poverty reduction will have to come from rural growth if Tanzania is to make substantial progress in bringing down poverty for the nation as a whole.

# **3.3 Growth Incidence Curves**

The impact of growth on poverty can be examined graphically through a growth incidence curve (GIC), which illustrates the distribution of growth. A GIC is a plot of the growth rate for each quantile of the distribution of per adult equivalent consumption. Growth incidence curves for the nation as a whole and the three strata are shown in Figure 1. The curves were constructed based on the two household surveys, and the annual growth rates reflect average changes over the 1993-2001 period. The growth incidence curves were generated by splitting the distributions into 15 quantiles or bins.

Although the vertical scale is the same across the different curves, the horizontal scale plots percentiles within each sector, so points at the same percentile level for different sectors correspond to different levels of consumption. The vertical line in the graph indicates the poverty headcount index in 1992, while the horizontal line denotes the mean overall growth rate.

We consider growth “absolutely pro-poor” if the mean growth rate for the poor is greater than zero and “relatively pro-poor” if, in addition, the mean growth rate for the poor is at least as large as the growth rate in the overall mean. Hence, “absolute pro-poor growth” only requires that the poor be better off on average in absolute terms, while “relative pro-poor growth” requires the distributional shifts to be pro-poor as well.

In terms of Tanzania as a whole, the growth incidence curve lies entirely above zero, implying that consumption increased at every point in the distribution. In other words, growth for the country as a whole was pro-poor in absolute terms. Likewise, growth was absolutely pro-poor in Dar es Salaam and rural areas. Although mean growth rates were modest everywhere except Dar, growth did improve consumption for rich and poor alike. Only in
do not currently have access to social media, the main task of increasing people’s willingness to be vaccinated will fall to individuals delivering information in person. Health workers, both because of the nature of their job, and the high levels of trust revealed in the phone survey, will be excellent candidates to deliver information on vaccines. What needs to be learnt now is which specific types of messages are most likely to increase trust in vaccines when delivered in person by health workers. As programs are rolled out in the field, an experimental approach could be adopted to ensure as much is learnt as possible about how vaccine hesitancy can be improved in practice.

We believe there is also scope to learn more about the most effective interventions involving norms. We used one particular phrase designed to suggest that vaccine uptake was common. This had some effect, at least on a subset of the population. However, there are many ways to suggest a community norm exists. It may be the case that other phrases and approaches are more effective at this task. There is scope for further research trying to discover which approaches are most effective at evoking community norms.

Finally, from a more theoretical perspective, there is scope for further work investigating why some of our experimental treatments were quite effective in reducing vaccine hesitancy, in contrast to the treatments in many studies from OECD countries. It would be very useful to learn whether differences stem from different country contexts, different experimental treatments, or differing types of opposition to vaccines – perhaps our experiment was successful simply because vaccine hesitancy in PNG is genuine hesitancy associated with trying a new medical intervention as opposed to being an entrenched position associated with particular world views. There is a need for more learning in this area.

# **7. Reference list**

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the study was drawn from the group of successful applicants initially deemed eligible for the program and the ‘control sample’ from those initially deemed ineligible, the beneficiary status of refugees in either group were not fixed over time. As discussed above, all applicant households are checked against the eligibility criteria on a monthly basis. If ISAS identifies that the household no longer meets the eligibility criteria (for instance, because of a dependent turning the age of 18, or the expiration of a disability report), the case is removed from the ESSN program. Conversely, households that were deemed ineligible at the time of their first application to the program, can become eligible at a later date through monthly digital eligibility checks (for instance if they register a new dependent such as a newborn baby on official MERNIS records, or obtain a disability report from the designated public hospitals, which get updated on their ISAS records).<sup>23</sup> <mark>As a result, the difference between the share of those receiving cash transfers in the treatment vs. the control groups of our study declined over time. The top panel in T</mark> able <mark>5 shows the time trend in the share of the</mark>

> 22 The spillover effects are likely to be positive if we assume away general equilibrium effects on increased prices. Such an assumption is plausible as ESSN beneficiaries constitute a small share (about 2 percent) of Turkey’s population. Sävje, Aronow, and Hudgens (2019) introduce _expected average treatment effect_ , an estimand that generalizes the _average treatment effect_ to settings with interference, which is similar to VT.

> 23 In October 2018, the program initiated a complementary support, called ‘discretionary allowance,’ for households that are vulnerable but do not fit the demographic eligibility criteria. The quota for this support was capped at 5% of applications made until September 2018. Not many households fit the socio-economic assessment done to be eligible for this support based on
we observe no changes in the share of women working part-time, and no sizeable change in its impact upon likelihood to work.

We find evidence of sustained and increasing effects of childcare schemes after their implementation. Their existence enhanced the employability of women and offset the adverse effects of child dependency. The positive effect is not exclusively for mothers: childcare services expansion increased _all_ women’s likelihood to be employed. Further, for women with children under age four, the positive effect of childcare availability is doubled at the end of the decade.

Data on the availability of private childcare facilities over the 10-year period are limited, hence we might be underestimating the benefits of childcare availability on women’s employability in 2017. By mid-2017, the supply of private formal childcare institutions rose to 8,655 facilities from 3,012 in 2010 (INEGI, 2010; CONEVAL 2008; CONEVAL 2019), a number close to the 9,157 Government National Program of Childcare Services for Working Mothers institutions. Further investigation is needed related to the benefits of private childcare.

Elderly dependency rates had a positive effect on women’s employability in 2007 and 2017, likely because older adults substitute for childcare (Talamás M., 2019). However, elderly dependency rates rose in 2017. It is possible that, under traditional gender roles, more women will abandon work outside the home to care for older family members. As the population ages, elderly substitution for childcare might not compensate for rising elderly dependency rates in the long term. It is crucial to better understand this relationship going forward to inform policies to address elderly dependency, quality of retirement lifestyles, and support for women’s employability.

> 12 The first National Statistical Directory of Economic Units available in Mexico is from 2010. To our knowledge, no detailed public information about private provision of childcare services in Mexico is available before that.

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bank entry to materialize. But based on the evidence available thus far, it appears that so-called “South-South” foreign bank entry has not yet paid large dividends in Africa.

# **2.2 Economic and Financial Sector Policies and Performance during COVID-19**

The characteristics of African financial systems may have had implications for how they were affected by COVID-19 and the financial sector policy choices made by governments. The spread of COVID-19 triggered extraordinary government intervention around the world as policy makers introduced measures to stem the negative effects caused by the spread of the disease. Policies consisted of four broad types, namely fiscal, monetary, prudential, and other measures. Data on policy announcements put together by the International Monetary Fund helps in understanding the sheer size and intensity of such interventions (Kirti et al., 2022). Data is available for just three low- and lower-middle-income countries in Sub-Saharan Africa and 13 low- and lower-middleincome countries outside Africa. A total of 766 policies were announced by developing countries around the world in 2020. Figure 5 shows that developing countries in Africa introduced fewer measures compared to developing countries outside Africa. Countries made use of different types of policies. For example, Rwanda announced 14 fiscal policies (that is grants, tax reliefs, tax deferrals, equity participations, loans, and guarantees) out of a total of 21 policies, while Nigeria announced 15 monetary interventions (that is interest rates and reserve requirements changes, financing operations and asset purchases) out of a total of 25 policies.

The magnitude of the intervention also differs across countries. Figure 6 shows that India 18