rafmacalaba/data-use-mentions-extended huggingface dataset:
good track records, and who were therefore approached by branch officers about becoming an agent. As such, less than 0.1% of applications to become an agent have been rejected to this point. There were thus too few rejected applicants (less than ten) to permit meaningful analysis differentiating the characteristics of those who became agents from those who did not.<sup>16</sup>

# **3. Data and the Estimation Approach**

Our data come from 190 FINCA DRC agents who began operations at some point from 2012 to 2015. We rely on monthly transactions data for the dependent variables in the regressions that follow. Although we have some data on other transfers to FINCA accounts, those transactions occurred too infrequently to permit meaningful analysis. Similar to Jack and Suri (2013, 2014), we therefore focus on cash-in, cash-out transactions in our analysis.

As explanatory variables, we rely on the personal/business characteristics of the agents and the socio-economic characteristics of the agents’ locations. Personal and business characteristics are taken from the applications that agents submitted to FINCA. Our data are from agents located in and around Kinshasa since that was the focus of FINCA DRC in the initial phases of their agent rollout. Agents are located in four major business districts– Funa, Lukunga, Tshangu and Mont Amba –which are further subdivided into 24 so-called municipalities.<sup>17</sup> In our regressions, we rely on data from agents operating in 23 of those municipalities. These municipalities vary greatly in

> 16 In principle, it might be possible to distinguish the characteristics of agents who ceased operations from those that continued throughout our period of study to assess the viability of the agent model in different contexts. But according to FINCA agent network representatives, hardly any agents quit the business in the first few years of developing the network, precisely because those agents were picked based on ongoing business relationships with branch officers.  We acknowledge, however, that 14% of our sample is comprised
letter issued by the Ministry of Youths and Sports approving the study. The issue was resolved, and respondents were informed through text messages that they needed a valid travel document to take up migration to Dakar. According to the follow up survey, the main reasons for not migrating to Dakar included being unable to redeem the vouchers for cash (28 percent), too many responsibilities in the village (13 percent), not enough information about the offer (10 percent), unable to afford the costs of staying (10 percent), not interested in working in Dakar (7 percent), working (5 percent), not trusting the offer (4 percent), and studying (4 percent).

These logistical issues suggest that facilitating large regional international migration flows may require offering potential migrants more assistance than was needed to facilitate internal migration in Bryan et al. (2014). A more intensive intervention could also provide financial and logistical assistance for finding housing, and potentially connect migrants to employers before moving to ensure that they have jobs ready to start upon moving. Such assistance could be potentially more important when facilitating migration to places where there is not a strong existing network that migrants could otherwise use to help find jobs, housing, and other support.

The final treatment was the tuition-free vocational training program. Out of the 892 respondents assigned to this treatment, 680 (about 76 percent) of them said they were interested in participating in the training program. In collaboration with The Gambia Technical Training Institute (GTTI), training was scheduled for September 2019, and would last for six months. The training finally started on 25 November 2019. The reason for the delay of the training program was due to respondents’ preference. They requested that the training start at the end of the rainy season as the majority of them were engaged in farming activities, and were unable to leave their farms to engage in the training program. 105 participants (12 percent) enrolled in training. In feedback from monthly surveys of
household sample surveys as ona aspect of their health information systems and have conducted or are planning to conduct such an enquiry. Despite the example of the DHS and the efforts being made to improve methods by the UNHSCP, manj of these surveys are unsystematic, poorly planned and give insufficient attention to the developmnent and pretesting of questionnaires and procedures. The WHS could contribute to the development and application of more rigourous methodologies for investigating ill-health, the use of health services and factors associated with these.

# 2.3 Integration of survey investigations into existing statistical svstgms.

A more systematic approach to collecting mortality and morbidity insformation with a survey, perhaps repeated at regular intervals, could also be a useful way of demonstrating the links between the diverse sources of information on health which every country possesses. Part of the task of a WHS country study would be to demonstrate that the information to be collected can complement existing sources. In soma cases this might imply integration of the study with quite radical changes in the system of administration. For example, it may mean directing resources from the civil registration system towards more innovative services which could be better sources for the study of current morbidity and mortality trends and differentials. Where the stress is more on routinely-collected health statistics, the data from household surveys may be used to tegularly calibrate and adjust the other information. Moreover, bv stressing the need to identify trends in key indicators, particularly indicators of health status for whole communities, there is an opportunity to revise the information gathered by the reporting system itself. There is a good deal of iner,.a and redundancy in existing health information systems which tend to be multi-layered as a result of accretion over the years. Serious problems are being caused by the unintegrated, conflicting, duplicating and expensive (in time, labour and money) information-gathering activities already taking place in 10
# **5.4 Welfare Analysis**

Now, I explore the question of whether accumulating reserves is welfare improving. On one hand, the central bank accumulates international reserves to undo government over-borrowing. On the other hand, this is costly because using reserves to pay back debt reduces spreads. To quantify the welfare gains of accumulating reserves, I contrast the baseline model with the consolidated government economy (i.e. _β_<sup>_M_</sup> = _β_<sup>_F_</sup> = 0 _._ 939). Table 7 shows that, by accumulating international reserves, an independent central bank can transfer resources towards the future in a way that cannot be undone by an impatient government, reducing the net debt position of the whole economy.

Table 7: Welfare Gains

||Independent|Consolidated|
|---|---|---|
||Central Bank|Government|
|mean B/y(%)|44_._4|41_._1|
|mean A/y(%)|6_._5|0_._3|
|net debt position(%)|37_._9|40_._8|
|**change in welfare**(%)|**0**_._**1**|**0**_._**0**|

To calculate the welfare gains of having an independent central bank that accumulates reserves, I proceed as follows. First, I take as a starting point a draw from the ergodic distribution of the consolidated government economy ( _y, κ, B, A_ ). Then, I simulate a series of shocks _{_ ( _y, κ_ ) _}t_<sup>_T_</sup> =1<sup>for</sup> _T_ = 1000. Using policy functions for the consolidated government economy and the baseline model, I compute the consumption path for both economies _{
average”treatment effects of proximity to roads and electricity grids and their interaction on employment outcomes in Table 4 in the Appendix. The first two columns display the estimated effects on work status at the survey for the LSMS and DHS samples, respectively. The third column shows estimated effects on the number of hours worked during the last seven days (derived from the LSMS sample only because information on the number of hours worked in not collected in the DHS).

These estimates show positive and highly complementary effects of roads and electricity on employment outcomes. Columns 1 and 2 show that for locations close enough to an electric grid bringing road networks 10 km closer marginally increases the probability of working by 25.7 percentage points in the 20
on interest in hiring workers, in order to understand whether the average microenterprise is laborconstrained.

To attain this sample of firms, we selected Grama Niladhara (GN) divisions within Colombo, Kandy, and the Galle-Matara areas, and went door-to-door listing households from a random starting point. The listing collected information on each adult active in the labor force, and was used to screen on age, self-employment status, and sector to select firms for our sample. This was then followed by a baseline survey which collected details of the business and the owner. The first phase of this occurred in April 2008 (see Appendix 1 for a timeline) as part of a larger panel survey that also included other urban areas in Sri Lanka. We then returned in October 2008 and conducted 8
financial services within the ECA region (Brown et al., 2011; Ongena and Popov, 2010; and Popov and Udell, 2010). Brown et al. (2011), which is the most similar to our analysis, uses data from the 2005 and 2008 BEEPS to compare application for and use of credit in Eastern and Western Europe. Like us, they find that credit constraints softened in Eastern Europe prior to the crisis, but they also find that loan application rates were lower and rejection rates higher in Eastern than Western Europe and that this was associated with the presence of foreign banks. We use data from the 2002, 2005, and 2008 BEEPS, which enables us to better capture both the easing and tightening

> 2 Data in this paragraph are from World Bank (2010).

2
Administration. The product has a resolution of about 500 meters per cell and is available from 2001 onward (Friedl and others 2010).

- _GHSL‐Landsat_ is derived from images taken by sensors aboard satellites that are part of the Landsat program. As one of the first civilian satellite programs on land cover, Landsat has produced the longest continuous space‐based record of the Earth’s land surface. Data for the years 1975, 1990, 2001, and 2013/14 are available from the Global Human Settlement Layer project (Pesaresi, <mark>Ehrlich,</mark> and others 2016). The resolution of the product is about 38 meters per cell.

- _GUF‐TerraSAR_ is based on radar images taken by sensors aboard the twin satellites TerraSAR‐X and TanSAR, which are part of the more recent WorldDEM program. Reliance on radar images implies lower sensitivity to weather conditions, cloudiness and vegetation density. Data for 2011 are available from the German Aerospace Center at a resolution of about 12 meters per cell (Esch and others 2017).

After a careful comparison of the three data sources, we selected the GHSL‐Landsat product for this study. This choice is consistent with that of Burchfield and others (2006), who use the Landsat product to study urban development in the United States.

In the case of India, MODIS is limited by its coarse resolution. In particular, it does not capture built‐up cover in forest areas as well as the other two data sources. Data from both GHSL‐Landsat and GUF‐ TerraSAR yield very similar results while GUF‐TerraSAR has a higher resolution. The correlation coefficient between the two sources for the built‐up share at the town or village level is 0.96. In addition, GHSL‐ Landsat data are available for multiple years, whereas GUF‐TerraSAR data are available only for 2011. This is a constraint, because we want to estimate not only the urban extent, but also the speed of urbanization over time
and offers country specific scores along six dimensions: (i) customs, (ii) infrastructure, (iii) international shipments, (iv) logistics competence, (v) tracking and tracing, and (vi) timeliness. The infrastructure sub-indicator aggregates a quality scoring of ports, railroads, roads, and information technology. The timeliness subindicator measures reliability rather than quality per se. By scoring timeliness of shipments in reaching destination within the scheduled delivery time, this sub-indicator is a measure of unexpected transport disruptions – rather than the average performance. All LPI indicators are scored on a scale from 1 (worst) to 5 (best).

The resulting country-year panel allows us to exploit dynamic and cross-sectional variation to relate infrastructure spending to the quality of transport infrastructure, while controlling for all major confounders. Specifically, we conduct pooled Ordinary Least Squares regression analysis, including country-level fixed effects and clustering standard errors at the country level. In our main specification, the LPI Timeliness sub-indicator is regressed on the logarithm of per capita public road spending (including both maintenance and investment) and a series of control variables.<sup>4</sup> Control variables include population density, urbanization, GDP growth and governance indicators.

Annex A.1 presents estimates for the main model and variants thereof. The spending coefficient turns out significant and – like other controls -- shows the expected signs and a reasonable magnitude; the goodness of fit measure obtains as R<sup>2</sup> = 71%.<sup>5</sup> However, the key result emerges by toggling the governance control: Increasing public road spending improves transport reliability significantly –only if governance standards are strengthened at the same time. That is, fixing governance by introducing the corresponding control

> 2 Unfortunately, data on other infrastructure types are too sparse for systematic empirical analysis.

> 3 The sample is reduced to 313 country-year observations, when controlling for disaster events.

> 4 The results are robust to using alternative dependent variables,
expressed as a share of origin country population). The analysis regresses immigration on an origin country’s hurricane index (a measure of the origin country’s hurricane exposure) in a given year, including origin-country fixed effects, year fixed effects, and differential origin-country-specific linear time trends. Fixed effects for origin country in a panel data setting are crucial here, to account for an important time-invariant omitted variable: a country’s _propensity_ to be struck by hurricanes. If this analysis was done with cross-sectional data, or if country fixed effects were not included in the regression, interpretation of the coefficient on hurricanes would be confounded by the effect of the country’s general exposure to hurricanes – a geographic characteristic of a country that could itself predict the outcome of interest. For example, hurricane exposure is highly correlated with location in the tropics, which itself could have effects on many outcomes of interest, including migration.<sup>9</sup> omitted variable bias to remain. Third, these remittances could themselves be endogenous, responding to shocks to education.

> 9Another example is Abarcar (2017), who examines how return migration by Australian immigrants is affected by exchange rate shocks in immigrants’ home countries, taking advantage of exchange rate variation over time while controlling for household fixed effects. The analysis actually looks at return migration in first-differences, which is equivalent to inclusion of household fixed effects when there are only two time periods.

19
of final demand including private and government consumption, imports, exports, and investment. The database used is the Global Trade Analysis Project (GTAP) which was modified to update the data and identify subsectors of interest for Brazil. Starting from the GTAP database 9.2, the base year of 2011 was updated to 2015 and the input-output structures for Brazil were updated to reflect the latest official tables from IBGE. The sectoral dimension in GTAP was expanded to include several new sectors of interest in Mercosur countries. These include sectors such as beef, soybean, soybean products, wine, footwear, furniture, home appliances, and auto parts that are part of more aggregate GTAP sectors.

> 20 Resulting in a reduction of 15 percent in the tariff equivalents for goods and services.

> 21 It is worth highlighting there will be already free trade between Mercosur and the Pacific Alliance by 2019.

22 In this case, the trade-weighted average tariff applied by Brazil to products from the countries in the Pacific Alliance would fall from 1.3 to 0.3 percent. The average tariff faced by Brazil in the Pacific Alliance would fall from 2.9 percent to 0.8 percent.

23 For the Mercosur-Pacific Alliance scenario, NTM tariff equivalents are reduced by 15 percent and export taxes are eliminated among the parties. Moreover, a bilateral market access is assumed so that existing liberalization among partners remain. Existing tariff barriers with Pacific Alliance countries have been lowered by previous bilateral or Mercosur agreements under the ALADI framework, especially with respect to Chile and Peru. Most tariff liberalization in this scenario would be with respect to Mexico and to a lesser extent Colombia. Liberalization with the Pacific Alliance is assumed to take place both more quickly and more comprehensively. No products are excluded from liberalization and all tariffs are either removed or partially reduced for the most sensitive products.

10
sup>6</sup> The

> 5 Three years are missing from the time series presented below: 1991 and 2000 were Census years, during which PNADs are not fielded. Income data from the Censuses are based on very different questionnaires, and are not comparable with PNAD data. The survey was not fielded in 1994 either, for cost-related reasons. 1982 is included but the reader is cautioned that income questions had a different reference period on that year. They were asked with respect to a quarter, rather than a month, giving rise to different recall periods. The answers are therefore not comparable with those from other surveys.

> 6 Sample sizes rose gradually from 482,611 individuals in 1981 to 525,023 individuals in 1985. The sample size was then scaled back to 290,518 in 1986, within the same sampling frame and with care to maintain representativeness. It then rose gradually to 389,073 in 2004. The PNAD survey is not carried out in the rural areas of the old North Region of Brazil, which roughly corresponds to the Amazon rainforest. The current North Region includes the state of Tocantins, which was previously part of Goiás state. The rural areas of this state are included in the PNAD throughout the time series.
_— —_ peer characteristics including previous labor market outcomes we merged in individual-level data from multiple administrative sources for Brazil and Ecuador.

# **Brazil**

**_Higher Education Census (HEC)._** This is the universe of higher education students, programs, and HEIs in Brazil and comes from the Ministry of Education. For a given academic year, and for every student enrolled in higher education, the HEC provides demographics (age, gender, and race), initial enrollment date, and dropout or graduation status by year’s end. Focusing on the programs in our sample of effective surveys (henceforth, surveyed programs), we selected the students who entered them in 2014, and used the 2015 and 2016 HECs to establish whether they had graduated. We define a student’s peers as those who entered her same program in 2014.

**_National Educational Entrance Examination._** The _Exame Nacional de Ensino Médio_ ,

(ENEM) dataset comes from the Brazilian Ministry of Education and includes student demographic and family background variables. Although it includes individual ENEM test scores, we do not use these because they are missing for 60 percent of the sample.

**_Labor Market Outcomes._** Their source is the Annual Reports of the Social Administration ( _Relaҁão Anual de Informaҁões Sociais,_ RAIS), a matched employer-employee dataset of all workers and firms in the Brazilian formal sector. It is constructed by the Brazilian Ministry of Labor based on a mandatory annual survey filled by all firms in the formal sector. RAIS contains information on earnings (gross monthly wages), employment, occupation, and demographics for all individuals who are employed by a formal firm in a particular year. For every individual selected from the 2014 HEC, we use RAIS to measure employment status and earnings in the 12month period before she starts the program (namely, in 2013) and the 12-month period following her graduation (namely, in 2016 or 2017) provided she graduates within three years.
|Lebanon|Quarterly|Q4 2019|n/a|n/a|only census|2018-2019|
|---|---|---|---|---|---|---|
|Libya|Annually|2014|n/a|n/a|Labor force<br>survey|2013|
|Morocco|Quarterly|Q3 2021|Quarterly|Q3 2021|Quarterly|Q3 2021|
|Oman|Quarterly|Q3 2021|n/a|n/a|Annually|2020|
|Qatar|Quarterly|Q3 2021|Monthly|November 2021|Quarterly|Q2 2021|
|Saudi Arabia|Quarterly|Q3 2021|Monthly|November 2021|Quarterly|Q3 2021|
|Syrian Arab Republic|Annually|2019|n/a|n/a|Annually|2019|
|Tunisia|Quarterly|Q3 2021|Monthly|September 2021|Quarterly|Q3 2021|
|United Arab Emirates|Quarterly|Q2 2020|n/a|n/a|Annually|2020|
|West Bank and Gaza|Quarterly|Q3 2021|Monthly|November 2021|Quarterly|Q3 2021|
|Yemen, Rep.|Annually|2017|n/a|n/a|only census|2013|
|**MENA Total: 19**|**Quarterly:**<br>**15/19**||**Monthly or**<br>**Quarterly:**<br>**10/19**||**Quarterly:**<br>**8/19**||
|**Mexico**|**Quarterly**|**Q3 2021**|**Monthly**|**October 2021**|**Monthly**|**November**<br>**2021**|

Source: Gatti et
them more flexible as to allow local governments to establish service levels that reflect actual demand.16 South Africa and the US both are efficient producers at very high levels of decentralization. In the United States, resource costs are very high (before normalizing for technology) but preference costs are lower than the mean level. The responsibility for construction works rests with counties, local governments and townships. With the loss of Federal funds (GRS funds), local governments are faced with major adjustments. They have to fund construction works essentially from already limited internal revenues, generally from shared motor fuel tax receipts and license fees from state governments, property tax revenues and appropriations from the general fund of local governments. Faced with these budgetary constraints, local governments seem to have improved their delivery of road services, as the USA is well below the cross-country average in terms of total costs of road service provision. To reduce the high level of resource costs, local governments should opt for cost reduction strategies, limiting construction service programs. To improve performance, local governments could merge into larger jurisdictions to capture back economies of scale and reduce unit costs. With the average township in the Midwest having only 40 miles of road, Walzer et al (1987) emphasized that this alternative certainly deserves attention. But a survey conducted in the Midwest showed that nearly 63 percent of the farmers thought local governments were efficient providers of road services (Walzer and McWilliams, 1989). They showed relatively strong support for managing the system via the current decentralized structures. More cooperation between townships and counties is an alternative to consolidation. Cooperation purchases of materials, sharing of specialized equipment's, and cooperation in large projects are examples of the kinds of efforts that can be considered to improve performance even further (to levels close to Germany for example).

Resource costs using the eight country experience are seen to be highly variable in the [0-50] percent decentralization range, and they decline systematically (with the exception of South Africa) as we move to the