rafmacalaba/data-use-mentions-extended huggingface dataset:
artificial lake that covers 8,502 square kilometers within Ghana. Some cells are partially outside Ghana, or partially covered in water or by the sea, something we capture with dummies. We also construct a dummy equal to one if there is a large body of water on any of the eight adjacent cells _n_ 1, and another one for the next 16 adjacent cells _n_ 2. This is done to investigate whether proximity to a lake or lagoon attracts human settlement, e.g., if the lake is use to fish or transport goods. The same thing is done for proximity to the sea and to a land border, for similar reasons.

The last piece of information that we integrate into our analysis is road data. For 1976 and 1986 we have information about the location of all roads in Ghana. These roads are classified into four categories, from 1 to 4.<sup>6</sup> Class 1 roads are the highest quality, and class 4 roads the poorest. Typically, a class 1 road is paved and hence usable all year round. In contrast, a class 4 is a dirt track and many of these roads are seasonal. Class 2 and 3 occupy an intermediate position. Road availability in 1976 is of particular interest for our purpose because these roads predate our period of analysis and thus cannot be construed as caused by buildings constructed after 1976. The same can be said of 1986 roads regarding new buildings constructed afterwards.<sup>7</sup> For each cell we construct a dummy _rkit_ equal to 1 if there is a road of class _k_ in cell _i_ in year _t_ = 1976 or _t_ = 1986 and 0 otherwise. We also calculate the proportion of immediately adjacent cells that contain a road of each particular class, which we denote _rkit_<sup>_n_1.Mirroringwhatwedidforbuilt-uparea,we</sup> construct a similar proportion of roads in the _n_ 2 neighborhood of _i_ , which we denote _rkit_<sup>_n_2.</sup>
2004; Hummels et al., 2009; Lugovskyy and Skiba, 2015). In addition, the Maritime Transport Costs (MTC) database, prepared by OECD, combines the above-mentioned data on freight expenditures with the COMTRADE dataset and other data sources to provide a comprehensive transport costs database for 43 importing countries from 218 countries at 6-digit HS product level from 1991 to 2007 (Korinek, 2011).

Another useful dataset comes from the U.S. Army Corps of Engineers(ACE). It focuses on the U.S. waterborne transport and includes freight expenditures at HS4-product level and U.S. ports of loading and unloading. These data have been used to study the effect of policy on maritime transport costs (Fink et al., 2002) and to estimate port efficiency and its impact on trade flows (Blonigen and Wilson, 2008, 2018; Clark et al., 2004).

More recently, researchers can obtain customs data from some countries with freight expenditures at the transaction level. For example, Panjiva, a part of S&P Global, provides these data for Chilean and Colombian imports from 2009 onward. These data also contain trade values, quantities, mode of transportation, port of loading and unloading, shipment weight and volume, the identity of the importing firm, information on whether the importing firm or exporting firm is responsible for arranging transportation, and the identity of the carrier or logistic firm arranging transportation. The data on freight expenditures collected from custom declarations or bills of ladings is a direct measurement of transport cost. Therefore, these datasets represent the best sources of data to study the size and nature of maritime transport costs. However, they are available only for a small number of countries.

Panjiva also provides the US import bills of lading. These data scope out a large portion of a shipment’s journey; a typical observation includes where a shipment originated, ports of lading and unlading, vessel-specific IMO Number, where the shipment is ultimately sent within the US (shipment
Figure 2: Electronic logbook data, 2017 to 2019 <!-- Start of picture text --> 4°S<br> 6°S<br> 8°S<br>10°S<br>12°S<br>14°S<br>16°S<br>83°W 81°W 79°W 77°W 75°W<br>Juvenile catch (thousands of juveniles)<br>1 20 400 8,000<br><!-- End of picture text --> Notes: Each point is a set (vessel-level fishing operation). The color of each point is the number of juvenile anchoveta caught by that set, which I calculate by matching sets to landing events and using the percentage juvenile measured by third-party inspectors at landing. There are 246,914 sets reported by 806 unique vessels in the electronic logbook data. The regulator prohibits fishing within 5 nautical miles (9.3 km) of the coast. There are 572 sets per day on average during a fishing season. The average set catches 570,103 juvenile anchoveta. The inset map in the top, right panel magnifies the sets that occur inside the orange rectangle.

regulator declares a closure (Section I).<sup>15</sup> The median anchoveta caught is between 13 and

> 158% more tons are reported in the electronic logbook data than are measured at landing. This difference is within the range at which fish can degrade or be lost between being caught at sea and landed (Getu, Misganaw and Bazezew, 2015). Vessels have little incentive to bias their estimates of tons caught in the electronic logbook data. Tons measured at landing, rather than tons estimated by vessels in the electronic logbook data, are the data that processing plants use to pay fishers and that the regulator uses to determine

16
Personnel Bureaus and tho Pl-nning Commission. Students **are** eventually assigned to jobs by the unit that owns the school.

For the SVS, an entrance examination for general and key general secondary schools, and for SVS, is prepared and administered by the provincial education bureau on a city-wide basis. Students select both the type of school and the specialty desired before taking the examination and are assigned to schools by the municipal education bureau.

Data from Anshan City in Liaoning province illustrate the pressure of applicants upon available places. As shown in Table 5, the overall ratio of secondary applicants to enrollments in 1985 was 2.5:1. For key general secondary schools, the ratio was 3.6:l; for SVS, it was 6.9:1; for STS, it was 2.6:1; and for SWS, it was 5.9:l.

**Table** 5: Applications, Intake, and Examination Cut-Off Scores, Ansebn City Secondary Education*

<!-- Start of picture text --> Year Total Key Gen. Gen. E".o. SVS STS S9S<br>sec. sec.<br>-<br>Applications 16,797 4,287 - 3,451 1,768 7,311<br>1985 Intake 8,638 1,200 3,000 500 690 1,248<br>Exaa Cut-Off 516.5 320 441.5 - na- -n-<br>-<br>Applicatiors 18,847 5,181 - 3,514 2,019 9,153<br>1988 Intake 7,538 1,000 3,200 810 905 1,821<br>bE Cut-Off 543 370 473.5 -a- -na-<br>Ratlo of 1985 2.5:1 3.6:1 1.4:1 6.9:1 2.6:1 5.9:1<br
# **What Do 50 Years of Census Records and Household Survey Data Tell Us**

**about Human Opportunities and Welfare in Latin America?**

Oscar Calvo-Gonzalez German D. Caruso R. Andres Castaneda Aguilar Eduardo A. Malasquez The World Bank, Poverty and Equity Global Practice _Keywords_ : Inequality; Human Opportunities; Latin America JEL Classification: I12, J13, O12
(i.e., the native population) and an out-group (i.e., the refugees) reduces prejudice if the following features characterise the contact situation: firstly, _equal status_ of the groups in the situation, which means that the two groups are equally engaged in the relationship and ideally have similar backgrounds and characteristics. Secondly, the groups work together on _common goals_ , which should not be achieved in competition but rather in _intergroup cooperation_ . Thirdly, _personal interaction_ with cross-group members. Lastly, _support of authorities_ , law, or custom.

This paper contributes to this extensive literature by highlighting the important role of resettlement policies in creating favourable conditions for positive intergroup contact. By providing refugees with farmland and making the self-sufficient, the League of Nations reduced the competition with natives in the labor market. By opening new schools, constructing new roads and dispensaries, the Leagues’ public utility program further helped mitigate the risk of congestion and overcrowding caused by the sudden refugee inflow. Granting the citizenship also contributed to equal status between the newcomers and the locals.

# **4 Research Design**

## **4.1 Data**

### **4.1.1 Historical data on the refugees**

The primary data source to identify the spatial distribution of the refugee inflow is the 1928 population census.<sup>15</sup> This dataset provides information on the number of refugees for reach of the 140 provinces and 5,042 municipalities that existed in Greece in 1928. While the main variable of interest is the population share of refugees in 1928, I also draw on the 1923 refugee census at the municipality level to check the robustness of the analysis to using the distribution of refugees in 1923.<sup>16</sup> The 1928 census provides a rich set of information on the socio-economic characteristics, e.g., literacy rate by age and professions, of the total Greek population and of the refugees separately. It also tabulates the characteristics
in high-income destination markets affects demand for their exports. Consumption patterns in these countries has been closely following the virus transmission, fewer COVID cases restores consumer confidence and more cases erodes it (Chetty et al., 2020; Leer, 2020). Therefore, the duration of the demand shock facing firms in developing countries is tied to the health response both domestically and in developed economies. SMEs have been more severely affected by demand shocks than large firms in general; while they are less exposed to shocks to foreign demand in particular through exports (see Figure 4, top panel). According to the surveys available, SMEs are more likely by 9 percentage points to experience a fall in sales compared to one year ago than large firms and the sales of SMEs on average are lower by 8 percentage points than one year ago. Some firms and sectors are clearly experiencing demand growth in response to COVID – think of health care, home-office equipment etc. However, SMEs are also less likely to report a rise in sales than larger firms by 6 percentage points. Thus, SMEs experience more of the downside and less of the upside. In contrast, SMEs are less likely to

> 7 Several papers have used sectoral classifications of COVID exposure often for the United States, for instance based on essential services, the ability to work from home and so on (e.g. Dingel and Neiman, 2020).  Other approaches have constructed country vulnerability measures, often using GVC or commodity trade data (e.g. IFC Country Vulnerability heat maps).

> 8 Based on the World Bank’s COVID-19 Business Pulse Surveys, expectations for the next six months range from a 60% decline to a 20% increase in sales.

7
1 if the country has laws mandating paid or unpaid paternity leave and 0 otherwise ( _Paternity_

# _Leave_

Last, the ES data we use were collected in different years across countries between 2007 and 2016. This raises the possibility that global factors that vary over time could be affect women’s employment. If these factors also vary systematically with maternity leave across countries, our main results could be spuriously affected. Thus, we control for the year the ES was conducted in the country ( _Trend_ ).<sup>7</sup> One variable that we did not mention above is the fertility rate. Higher fertility rates are known to adversely affect women’s labor force participation rates; at the same time, maternity leave policies may be systematically correlated with the fertility rate. In our sample, fertility rate is very highly correlated with GDP per capita (correlation of -0.78). Thus, we expect that the effect of fertility rate on female workers is already accounted for via the GDP per capita control. Nevertheless, we experimented with controlling for fertility rate in all the specifications discussed below.<sup>8</sup> However, controlling for fertility rate and even its interaction with maternity leave made no qualitative difference to any of the results discussed below.

# **3. Main empirical results**

All the regression results discussed below are based on the Ordinary Least Squares regression method. Sampling weights are used throughout to ensure that the results can be extrapolated to the targeted universe of the private economy.<sup>9</sup>

> 7 Our main results do not change much if we control for year dummies (year fixed effects) instead of the year trend. 8 Data source for fertility rate is WDI, World Bank. As for GDP per capita and other macro‐level controls, we use average values of fertility rate taken over 5 years prior the year the ES was conducted in the country. Results controlling for fertility rate are available on request from the
on the validation. Fifth, the custodian agency sends the validated data to the UN Statistics Department, which finally published the data. This process is fairly standard.<sup>30</sup> Given the magnitude of their role, international organizations not only curate and provide quality assurance for SDG-related data, but they are also uniquely positioned to make a significant contribution to improving their quality, as well as generating new research on the SDGs. As an example, the international financial market has become more developed and is now accessible to most, if not all, poorer countries. This may result in a more diminished role for the lending operations of international financial organizations like the World Bank. As such, there have been stronger calls for the World Bank to move more vigorously to becoming a knowledge bank that generates new data and trend-setting research (see, e.g., Clemens and Kremer (2016) and Ravallion (2016)). This would, in fact, build on its recognized strengths in data and analytics.<sup>31</sup> In this regard, international organizations may be expected to offer their vision for the development landscape in the decades to come. They may also be expected to spearhead new data initiatives to meet evolving global data needs beyond the SDGs, such as statistics on displaced population groups like refugees, who are usually not captured well in traditional surveys and censuses. Another example would be the increasingly common use of subjective well-being data, such as life satisfaction, to supplement the traditional money-metric data used to measure welfare

> 30 For an example, the World Bank often estimates a country’s poverty rate based on a joint consultation process between Bank staff and government officials (often from the national statistical office) after intensive analysis of household surveys.

> 31 Indeed, Birdsall (2015) proposes that the World Bank can invest more resources in supporting researchers, particularly in poorer countries. Clemens and Kremer (2016) even suggest that the World Bank has had more influence
nonnegative) domestic sales. Overall, the estimation dataset covers 137 countries (34 of which in the African continent) on 1-year intervals from 1986 to 2018. The counterfactual exercises are computed for year 2018 and 2014.<sup>21</sup> Trade and production data are then combined with the list of in force Preferential Trade Agreement (PTAs) as provided in Fontagn ́e et al. (2023). Using various clustering techniques, Fontagn ́e et al. 2023 show that existing PTAs can be classified into three optimal groups based on the distribution of provisions included in each of the 18 policy areas covered by the World Bank Deep Agreement database. In what follows, we use this classification and estimate the elasticity of bilateral trade flows to different types of PTAs.

> 18If the resulting value of domestic sales is negative, it is considered missing. Head & Mayer (2014) discuss an alternative options based on ratios and proposed by Romalis (2007), and Caliendo & Parro (2015) .

> 19Manufacturing data are available at `https://stat.unido.org/` and `https://comtrade.un.org/`

> 20Agricultural data are available at `http://faostat3.fao.org/home/E` .

> 21In year 2018, the dataset covers 112 and (20 of which in the African continent); while in 2014 the total number of countries is 132 (30 of which in the African continent). Importantly, these data do not include informal trade, which can be a significant part of total agricultural trade (Bouet, Pace & Glauber 2018). The likely underestimation of trade flows between African countries may also have an impact on the estimated gains from the reduction of trade barriers between them.

10
firms using the cash at hand measure. Interestingly, while firms in upper-middle income or high income countries (e.g. the Russian Federation and Greece) are less likely to fall into arrears, they do not have large cash at hand to cover their costs (Figure 12). It should be noted that cash availability among firms captures their liquidity position but does not imply insolvency because firms with no cash may still be able to survive by selling out their non-liquid assets when in need. It is possible that firms in upper-middle income countries with developed financial systems usually do not need to maintain a lot of cash at hand to handle their operations and instead rely on financial market transactions, given their efficiency during regular times.

Within country, differences between firms facing liquidity problems are larger than those between countries. For instance, the bottom 10 percentile of firms in Côte d’Ivoire can expect to cover only 14 days relative to the top 10 percentile, which can cover as much as 112 days of costs. Likewise, in Kenya, Senegal and Tanzania the bottom 10 percentile of firms can cover none, while the top 10 percentile of the firms can cover about a year of costs (Table 1). This finding is similar to Bircan et al. (2020) who use data on 16 emerging economies and three Western European countries to suggest similar within- country variation. Their work suggests that, for instance, about half of all small businesses in Ukraine have cash reserves of less than one month’s wages, while around 20% of

> 13The surveys measure liquidity constraints among firms through two questions: (i) Is it expected that this establishment will fall in arrears in any of its outstanding liabilities in the next 6 months? (ii) As of today, for how many days could this establishment continue paying all costs and payments with the cash available? See appendix for details.

9
(2017) perform both a qualitative and quantitative literature review with evidence from 14 emerging economies and reach similar results: minimum wages have only a minimal impact on employment. Interestingly, Broecke et al. (2017) find evidence of reporting bias toward statistically significant negative employment results.

However, other reviews focusing on LMICs provide less clear-cut results (Belman and Wolfson, 2016). Ten of the studies reviewed show the employment effects found using firm/establishment data vary substantially. The results range from negative employment effects—with one study reporting an employment elasticity among surviving firms of _−_ 0.13—to positive ones, with a second study showing positive employment effects for new firms and no aggregate effects at the metropolitan level. Another study reviewed reports heterogeneity across firms depending on their size, by showing evidence of a total employment decline in small firms and an employment increase in large firms. Using instead household data to study employment effects, one other study finds that a rise in the minimum wage might translate into lower employment and some employment might reallocate from big to smaller firms that are not complying with minimum wage regulations (potentially rising informal employment). The authors of that study also find that sectors more likely to comply with regulations saw a decline in employment and those less likely to comply found no employment effect. Other studies found no effects on employment at all.

Across single-country studies, most of the evidence points to minimal aggregate employment effects. Engbom and Moser (2022) find a muted aggregate employment effect of the Brazilian minimum wage (the model predicts aggregate employment to decrease by 0.7%), given that workers in affected 44
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# **Data**

The main data sources are the 2008-09 and 2010-2011 rounds of the National Panel Survey, a large multi-topic household survey conducted in Tanzania by the National Bureau of Statistics with support from the World Bank. 3,200 households were interviewed in 2008-09, and these households, plus any new households that split off from the original sample, were re-interviewed in 2010-2011. The main focus of the survey was household consumption, poverty, and agricultural production, but there was also a governance module, which was asked of one single randomly selected adult household member. Helpfully for this paper’s identification strategy, the timing of survey visits were randomized. The 2008-2009 LSMS survey documentation notes that “within each zone, each district and each region were visited at 3 separate (randomly assigned) points during the year, so as to account for seasonal fluctuations.” Communication with LSMS survey staff confirms that this randomization into early or later survey dates was maintained as much as possible in the 2010/2011 follow up survey.<sup>10</sup> This randomization, combined with the staggered roll out of the bed net campaigns, enables an “as-if” random RD design. Dates of the ITN distribution campaigns in each zone are obtained from program documents provided by one of the implementing NGOs, Mennnonite Economic Development Associates, and from Bonner et al. (2011) and Renggli et al. (2013).<sup>11</sup> I use outcome variables from this governance survey module, which asked voters for their level of approval of 8 public officials: village chairman, village executive officer, ward executive officer, ward councilor, headmaster, extension officer, police officer, and member of Parliament. I create binary outcome variables for each political leader, excluding the sectoral functionaries (extension officer, headmaster and policeman), and create standardized versions of the continuous

> 10The “cycle” variable, which denotes whether a respondent was interviewed in the earlier or later survey cycle, correlates