rafmacalaba/data-use-mentions-extended huggingface dataset:
obtain 144 million OSM routes connecting all points and compute distance-weighted local measures of network quality and continental market access (MA). Using data on trading across borders, which I translate into road distance/travel time, I characterize how cross-border trade frictions impede cell-MA. A trade across the median African border is equivalent to _∼_ 1000km of national roads or 3.2 road days. My results imply that particularly cells across the border from major markets such as Nigeria suffer _≤_ 80% MA losses due to frictions.

In the second part of the paper, I identify 447 large cities with populations above 100,000 and 52 international ports with a quarterly outflow above 100,000 TEU and obtain routes between them. I process these routes into a realistic representation of the African transport network comprising 1379 nodes and 2344 edges representing 315,000km of road segments. The network connects 444 million people or 86% of the African urban population. I parameterize the network with fresh routing information along all links and develop an algorithm to find 481 new links (104,000km), yielding route efficiency gains of _≥_ 50%. Using this network graph, I simulate partial equilibrium MA returns from extending and/or upgrading the entire network or individual links. I find that continental MA, denominated in travel time, can be improved by _≤_ 78% through eliminating crossborder frictions. Upgrading the entire network to allow travel speeds of 100km/h yields a 42% frictionless MA gain and a 27% MA gain under current frictions. MA gains from new roads are lower and also decrease under frictions. Spatially, including frictions favors investments in national links, significantly reducing the value of transnational and remote links. I also find sizeable differences in the marginal values and cost-benefit ratios of links and propose three investment packages containing high-return links with and without border frictions. These packages cost between 17 and 61 billion 2015 USD and generate MA gains between 20% and 38%. They are macroeconomically feasible if they
60%|
|**Meghalaya**|2006|6.50%|0.50%|
|**Mizoram**|2006|39.80%|-2.80%|
|**Jharkhand**|2007|2.30%|0.00%|

Notes: 1) 2009 data are budget estimates and 2010 data are revised estimates; 2) Due to limited data, Pre-FRL data of Chhattisgarh and Uttarakhand only date back 4 years before enactment of FRLs. Source: Reserve Bank of India.
service in the country‘s total service export basket. _EXPY_ s are constructed for each country and for each year with available data.

The trade data come from the IMF Balance of Payments statistics which has data available for over 190 countries from 1990 to 2007. Due to data being reported inconsistently on service exports, we end up with a sample of approximately 100 countries 1990 to 2007. GDP per capita data is taken from the World Bank Indicators database. Before describing the service EXPY we would like to discuss two of its characteristics that differ from preexisting goods index – the need for dynamic PRODYs and the high level of aggregation.

First, the _Goods EXPY_ were constructed using _static PRODY_ s (the _PRODY_ for each good is held constant at the average value 1999-2001). This means that any increase in _Goods EXPY_ measures a country‘s jump from a low _PRODY_ product to a high _PRODY_ product (that is the share of high _PRODY_ good in the export basket increased). However, in the case of _Service_

> _xij_ 7 _X i_ is hence the value-added share of commodity j in the country‘s overall export basket.

> 8 The rational for using revealed comparative advantages as weights is to control for country size when ranking the services.

12
# **Conclusions**

Over the last decade, finance has been recognized as an important driver of economic growth. More recently, access to financial services has been recognized as an important aspect of development and more emphasis is given to extending financial services to lowincome household segments. Although still early in the analysis, there is some evidence that access is improving. On the household side, there are some data on the use of microfinance that suggest there has been an expansion of access for households. Data here have to be interpreted carefully as increases may represent a better coverage over time, rather than an expansion. There is also evidence in terms of more main-streaming of access by commercial banks as competition forces and technology allow them to go to the lower segments. Examples in developing countries are ICICI bank, SHG Bank Linkage and South African banks that have made it a priority to reach out to lower segments.

For firms, the evidence on access to credit is more mixed. It appears to be increasing in some countries, but mostly in consumer finance forms, less so on the SME credit side. Some have argued that recent trends in banking systems may have adverse consequences. Consolidation of the banking system in many countries increases the distance between borrower and lender, making lending more based on hard information and reducing the role of relationship lending which can be especially useful for new and small firms. Yet, part of this increased consolidation is the consequence of increased competition, which in general helps to increase access. Indeed, while there is cross-country evidence that more concentrated banking systems could increase financing obstacles, this is more so if the system is not competitive and dominated by public banks.

But more definite interpretation on factors affecting access will have to wait for better data on access. This will require some national and international actions to develop more comparable data on access. Data on access will have to come from different sources: providers of financial services provision (using national statistics and financial institutions), users of financial services (
Overall, there are quite substantial differences in the degree of wage inequality both between country groups and within groups. This variation in wage inequality is depicted in Figure 12.<sup>11</sup>

# **Figure 12  Summary of wage distribution, 2002**

## **Summary of Wage Distribution, 2002**

<!-- Start of picture text --> 300 14<br>250<br>12<br>200<br>10<br>150<br>8<br>P10<br>100 P90<br>Decile Ratio<br>6<br>50<br>4<br>0<br>2<br>-50<br>-100 0<br>Source: UNICEF TransMONEE database; Bank staff calculation a) 2001<br>Azerbaijan RussiaKyrgyzstan SAMArmeniaEstonia a)Romania UkraineBulgaria BelarusLithnuaniaHungary a) Latvia PolandSloveniaCzech R.<br>percent<br><!-- End of picture text --> P10 = bottom decile/median P90 = top decile/median Decile ration = top decile/bottom decile In the European transition economies the wage distribution has widened especially at the upper tail, meaning the high wages have become more prevalent and also the emergence of very high wages, not seen under the previous regime. For example, in Poland before the transition a top-paid workers (top decile) was earning 60 percent more than the median worker. At present a top paid worker earns twice as much a the median worker. Thus, the earnings status of top paid workers has improved quite substantially.

Changes at the lower tail of the wage distribution were less pronounced in CEE. Sticking to the example of Poland, which is quite typical, before the transition a low-paid (bottom decile) worker was earning 35 percent less than the median worker. Currently, a low-paid worker earns

> 11  The measures of wage inequality shown in Figure 12 are defined as follows.  The decile ratio is
Burniaux, Jean-Marc, and Jean Chˆateau (2008) ‘An Overview of the OECD Env-linkages Model’

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- Dixit, Avinash K, and Robert S Pindyck (1994) _Investment Under Uncertainty_ (Princeton University Press)

ESRI (2012) www.esri.ie/irish ~~e~~ conomy/databank, accessed on 16.01.2013

- Fuss, Melvyn A. (1977) ‘The Demand for Energy in Canadian Manufacturing: An Example of the Estimation of Production Structures with Many Inputs.’ _Journal of Econometrics_ 5(1), 89–116

- Gallach ́oir, Brian P O,<sup> ́</sup> Alessandro Chiodi, Maurizio Gargiulo, Paul Deane, Denis Lavigne, and Ullash Kumar Rout (2012) ‘Irish TIMES Energy Systems Model’

- Goolsbee, Austan, and David B Gross (2000) ‘Estimating the Form of Adjustment Costs with Data on Heterogeneous Capital Goods.’ _Mimeograph, University of Chicago_

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- (1986) ‘Economic Data Issues.’ _Handbook of Econometrics_ 3, 1465
complexes that are sold in bulk as an investment project. The major issue with apartment complexes or several apartment units being sold in bulk is the mismatch between the size and price data. Often, the price reflects the price of the overall apartment complex while the size reflects that of a single unit. Since it is often impossible to infer the actual per unit price and size, we exclude these properties to avoid distortion. While we apply a context-driven data filter to maintain as many observations as feasible in the _truncated data set_ , we also apply a more rigorous winsorization to the data, common in large data sets such as ours (e.g., Bricongne et al. 2021 _for HouseLev Data_ ). We remove the first and 99<sup>th</sup> percentile of price and size and outline how this winsorization alters summary statistics (cf. Annex 3).

# Outlier Removal

Having obtained the _truncated data set_ , we perform additional outlier removal to ensure that skewed data do not drive estimations. Heavily, positively skewed property price data seem to be particularly acute in EMDEs where very luxurious properties catering to expatriates or foreign investors are transacted. To avoid analyzing severely skewed data, we employed two different approaches: First, we right censored the data to remove luxurious residential properties that are not targeted at the local housing market. In doing so, we use Numbeo, a crowd-sourced global data platform that reports consumer prices, including private property prices in most countries’ largest cities. As Numbeo data is likely to be dominated by a bias towards data entry from higher-income individuals (with internet access), we consider Numbeo’s data maximum as the “true” maximum. Hence, we consider properties within the _truncated data set_ that exceed the _maximum_ per square meter price reported in Numbeo as an outlier. Hence, we obtain the _right censored data set_ .

Second, to avoid that outliers at both tails of the distribution are distorting our estimations, we perform multivariate outlier removal on the _truncated data set_
Figures 4a and 4b display the estimates of _βk_ for both samples.<sup>40</sup> There are two important observations. First, for individuals with susceptible dependents (i.e., small children and elderly adults) the relationship between pollution and labor supply seems to be linear. The effect is negative and statistically significant even at moderate levels (i.e., around average). However, for individuals without susceptible dependents, the relationship is non-linear. At moderate levels

> 39We define the following brackets _k_ = _{_ 0 _−_ 35 _,_ 35 _−_ 45 _,_ 45 _−_ 55 _,_ 55 _−_ 75 _,_ 75+ _}_ . We define these brackets based on the breakpoints used in the U.S. Air Quality Index (U.S. EPA, 2012) and the constraint of having enough observations in each bracket.

> 40See Table B.5 in Appendix for the regression estimates.

20
the caregiver; (4) the caregiver's autonomy and control of resources; (5) the workload and time constraints of the caregiver; and (6) the social support received by the caregiver from family members and the community. Below we expand on the specific components of an ideal adequate care measure.

##  **_Caring Behavior: Breast‐feeding and Complementary Feeding, Health Seeking, Hygiene‐ Related_**

- Caregiver feeding behavior (observation of one or more eating episodes

- Caregiver responsiveness during feeding episodes

- Frequency of behavior such as feeding, number of spoonfuls, number of touches

- Breastfeeding practices (e.g., exclusive breastfeeding up to 6 months, early initiation of breastfeeding, breastfeeding at 2 years)

- Introduction of solid/semi‐solid/soft foods 6‐8 months

- Child feeding index (constructed from DHS data using the following yes/no questions: current breastfeeding; use of bottles; dietary diversity; feeding/meal frequency)

- Taking a child to a health clinic for treatment of illness

- Maternal hand‐washing with soap

- **_Maternal Education, knowledge and beliefs_**

> 9 For Indonesia the dietary diversity measures is only available for the household, not specific to each child.

> 10 Meal frequency information is not available in the Helen Keller Bangladesh survey or in the Indonesia RKD survey. 11 For milk intake we include breast milk, formula, and cow’s milk.

13
choice of being a wage earner as preferable to entering self-employment in the informal sector (often characterized by ‘defensive entrepreneurship’, see Sections 2 and 3<sup>25</sup> ).

On the other hand, evidence concerning the relationship between education and the postentry performance of new businesses in DCs is uncontroversial and consistent with what has been found using data from richer countries. For instance, Van der Sluis, Van Praag and Vijverberg (2005) conclude that an additional year of schooling raises entrepreneurial income by an average of 5.5%; by the same token, McPherson (1996) found that in Botswana and Zimbabwe business owners who have completed secondary school run faster-growing firms than those proprietors with no schooling; finally, Goedhuys and Sleuwaegen (2000 and 2010), using data respectively from Côte d’Ivoire and from eleven Sub-Saharan African countries, found unequivocal evidence that formal education of the entrepreneur positively affect a firm’s growth performance, respectively measured in terms of the growth rates of sales and employment (in both studies, the greatest effect on growth is found for entrepreneurs holding a university degree)<sup>26</sup> .

# **<u>4.2.4 Previous job</u>**

As pointed out in Section 3.1, entrepreneurship is characterized by sectoral and geographical inertia; far from being a disadvantage, persistence in entrepreneurship generates above-the-average post-entry performance, since past experience in the same sector and in the same area is often a signal of better skills and informational advantages (see, for instance, Roberts, Klepper and Hayward, 2011 **)** .

Indeed, Michelacci and Silva (2007) found that the fraction of entrepreneurs who set up their businesses in the area where they were born was significantly higher than the corresponding share for dependent workers and, more importantly, that firms created by locals were bigger, more valuable, more capital-intensive and better financed than their counterparts created by non-locals. The authors interpreted their findings
# Figure 1: Real exchange rate, Brazil, 2009-2012

Panel A: Real effective exchange rate index, Jan 2009=100 <!-- Start of picture text --> 105<br>100<br>95<br>90<br>85<br>80<br>75<br>70<br>65<br>Jan-09 Apr-09 Jul-09 Oct-09 Jan-10 Apr-10 Jul-10 Oct-10 Jan-11 Apr-11 Jul-11 Oct-11 Jan-12 Apr-12 Jul-12 Oct-12<br><!-- End of picture text --> Panel B: Selected bilateral real exchange rate indexes, Jan 2009=100 <!-- Start of picture text --> 105<br>100<br>95<br>90<br>85<br>80<br>75<br>70<br>65<br>RMB EUR USD<br>Jan-09 Apr-09 Jul-09 Oct-09 Jan-10 Apr-10 Jul-10 Oct-10 Jan-11 Apr-11 Jul-11 Oct-11 Jan-12 Apr-12 Jul-12 Oct-12<br><!-- End of picture text --> Notes: Indexes are based on data from the International Financial Statistics of the International Monetary Fund. A rise in the index denotes a depreciation of the real relative to foreign currencies.
later years and thus to make it easier for benefit payments to keep pace with inflation; to avoid situations where fluctuations in market rates cause frequent changes in regulated AIRs; and to set guaranteed benefits at a low level.

The first two reasons have some validity, although care should be taken to avoid very low AIRs that cause significant distortions in the time pattern of benefit payments. In this respect, the stipulated zero percent AIR, which was proposed in the stalled Hungarian law of 2009, was probably overly restrictive (Vittas et al 2010). In general, a regulated AIR that is close to the long-term real rate of interest will probably be appropriate for calculating initial benefit payments and will allow sufficient room for future bonuses to cover the need for inflationary adjustments. In most countries, this should be in the range between 2 and 3 percent.

The third reason, concerning the calculation of guaranteed benefits, does not seem to be valid. As shown by the Swedish PPM (see Annex B), guaranteed benefits do not need to be calculated in exactly the same way as initial benefit payments and can be based on a lower rate of interest as well as more conservative mortality tables.

**Mortality Tables.** Specifying the calculation of initial payments also requires the stipulation of the mortality tables that should be used for this purpose. In general, the tables should be based on conservative assumptions with an allowance for future increases in longevity. The same tables should be specified for all providers.<sup>21</sup> Mortality tables should be updated regularly and should be based on the pool of annuitants rather than the general population.

Historically, static period mortality tables, which did not allow for increases in longevity, were used even in advanced countries, while mortality tables were updated infrequently and were suffering from significant time lag. A common practice was to increase the age of policy holders by two years in the case of life insurance and to set it back by two years in the case of life
in child labor, by sex and age; the frequency of fatal and nonfatal occupational injuries; and the national compliance of labor rights (freedom of association and collective bargaining) based on International Labour Organization (ILO) textual sources and national legislation, by sex and migrant status. See SDG Tracker, Sustainable Development Goal 8, Decent Work and Economic Growth, Our World in Data, Global Change Data Lab, University of Oxford, Oxford, UK, https://sdg-tracker.org/economic-growth.

> 2 See “Measuring Job Quality: Difficult but Necessary,” ILOSTAT, International Labour Organization, Geneva, https://ilostat.ilo.org/measuring-job-quality-difficult-but-necessary/.

2
. One is wave 2 of the CFUWBES. The second is labor force survey data collected at around the same time as this wave. In the first instance, we predict job losses using the model estimated using CFUWBES1 (Section 4) using the data on the influence variables from wave 2 to predict cumulative job losses for wave 2. We then compare these to the results we obtain when, instead of the model, we utilize a direct computation of average sectorfirm-size job losses from CFUWBES2.<sup>38</sup> If the wave 1 coefficients were no longer valid by wave 2, one would see an important divergence in the results from these two approaches.<sup>39</sup> We do not see such a divergence. Nonetheless, this provides only an imperfect check on the ability of our estimated model to predict near-term future job losses in wave 2. This is because the job losses directly tabulated using CFUWBES2 themselves may not be a perfect measure of the “truth.” Certain sectors may be under-represented in the survey, and it may 35 We use the highest order polynomial for which the highest order term is statistically significant. 36 We compared estimates with and without this adjustment only for Jordan.

37 For Jordan, we apply the net job losses from CFUWBES1 to the employment structure in 2016. We attempted to reweight observations to match the composition by age, gender, and industry in the MENA Monitor Survey, which has a smaller sample and does not include all nationalities. We found that this does not change our results appreciably, so we prefer the simpler approach of using the sampling weights in the LMPS.

38 This approach can provide a short cut for assigning job losses to workers for situations in which such data are available and the objective is not to understand the drivers of job loss and risk or predict out of sample job losses.

39 This could be due to the well-known Lucas critique. In particular, dynamic effects may alter causal coefficients over time.

25
|(0.005)|(0.006)|(0.005)|
|FDR q-value|0.841|0.841|0.841|0.841|0.841|0.841|0.298|
|Observations|125,047|125,047|125,047|125,047|125,047|125,047|125,047|
|Mean|0.075|0.003|0.044|0.223|0.150|0.319|0.113|

Notes: Sample is restricted to females aged 19-45 in at least two IFLS rounds. We infer preschool data in-between PODES years. Each column regresses the dummy for being employed in a certain occupation category indicated in column heading, conditional on being employed, on the density of public (Panel A) or private (Panel B) preschools, defined as the number of preschools divided by the population of children aged 3-6 in each district, a dummy for having a preschool-aged eligible child, and their interaction. Comparison group includes all mothers of children of the wrong ages and non-mothers. All regressions include mother’s age fixed effect, urban dummy, district, year, and individual fixed effects. Robust standard errors clustered at district level. Stars denote statistical significance at 1, 5, and 10 percent levels based on unadjusted p-values. FDR q-values for the interaction coefficient of preschool density and eligible child are computed over all 7 outcomes and are shown in the last row of each panel. FDR q-values indicate the probability of false positives among _significant_ tests. Number of female-year observations and means of dependent variables are indicated in the last two rows. See Table 8 for a list of top-3 occupations per category.

75
own calculations|Real year-on-year GDP growth (quarterly<br>data), %|-50 to 20|
|Population with<br>immigrant<br>background|_IMMIGR_|Calculation with ESS<br>data|First generation immigrants and second<br>generation immigrants|0 to 100|
|Minority population|_MINORITY_|Calculation with ESS<br>data|Local born minority population without<br>immigrant background,%|0 to 100|
|Gini index<sup>a</sup>|_GINI_|Eurostat and World<br>Bank|Gini index of income inequality|0 to 100|
|Active labor market<br>policy expenditure<sup>a</sup>|_LMP_ACTIVE_|Calculation with OECD<br>and Eurostat data|Active labor market measures spending<br>(categories 20-70), % GDP per percentage<br>point of(LFS-based)unemployment|0 to 1|
|Passive labor market<br>policy expenditure<sup>a</sup>|_LMP_PASSIVE_|Calculation with OECD<br>and Eurostat data|Passive labor market measures spending<br>(categories 80-90), % GDP per percentage<br>point of(LFS-based)unemployment|0 to 1|
|Benefit net<br>replacement rate<br>for long-term<br>unemployed<sup>a</sup>|_UBSA_|OECD|Average unemployment benefit and social<br>assistance net replacement rate over 60<br>months of unemployment (%)|0 to 100|

_Notes_ :<sup>a</sup> Lagged 1 year in all specifications where used.<sup>b</sup> Lagged 1 year in some specifications. c In few cases, data on the closest available year or interpolation have been used to fill missing data. Relevant related data have been analyzed in these cases to assure validity of the procedure.<sup>d</sup> Lagged 1 quarter