rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> A<br>B<br>C<br>D<br>E<br>0 1 2 3 4 5 6 7 8 9 10<br>World Bank Data Quality Grade<br>Penn World Tables Grade<br><!-- End of picture text -->

- A. Penn World Tables versus World Bank

<!-- Start of picture text --> 0 1 2 3 4 5 6 7 8 9 10<br>World Bank Data Quality Grade<br>10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>Objective Data Quality Grade, Binned<br>1<br>0<br><!-- End of picture text --> B. Our Objective Measure (Binned to Deciles+1) versus World Bank.

Figure S7: Data Quality Grades Correspondence _Notes._ 178 countries have PWT grades (A is the highest quality) and 114 have World Bank grades (10 is the highest quality). The World Bank grades include no PWT A-graded countries. Panel A: World Bank grade averages, by-Penn World Tables Grades. Averages are E: 2.8. D: 3.5. C: 5.2. B: 7.5. A: N/A. Panel B: our objective measure orders every country by standard deviation of GDP misstatement (Table 7). We classify them into deciles+1 (11 groups) to make a direct comparison with the World Bank’s grades A-B.

S15
well with toilet coverage recorded by Government officials at the SBM baseline for these district, collected in 2012-2013, which came to around 28% for the whole of rural Nanded and Latur.

As with other contexts in India and beyond [Augsburg et al., 2015], the vast majority of households – 83 percent – reported affordability or price as the key reason for not having a toilet. This is not surprising since building a toilet requires a significant expense: for instance, low-cost toilets recommended by SBM would account for around 20 percent of annual household income in our context. Thus, policies and interventions that target these financing and price constraints might influence toilet coverage.

# **2.2 SBM subsidy program**

On 2 October 2014, the Government of India (GoI) launched its ambitious nationwide sanitation program called Swachh Bharat Mission (SBM), or ‘Clean India’ Mission. The program’s key objective is for India to become Open Defecation Free (ODF) by 2 October 2019, the 150th birth anniversary of Mahatma Gandhi. A succession of government programs targeted at improving India’s sanitation situation preceded the SBM. First efforts go back to the Central Rural Sanitation Program (CRSP) in 1986, replaced by the Total Sanitation Campaign (TSC) in 1999 and the Nirmal Bharat Abhiyan (NBA) in 2012, which in 2014 evolved into the current SBM. The programs evolved from a pure focus on constructing new private household latrines (under the CRSP), to the introduction of Information, Education and Communication (IEC) activities around sanitation and the provision of financial awards to Open Defecation Free (ODF) villages (under TSC). The NBA program further introduced the concept of community-led total sanitation (CLTS), as part of the education and communication activities on sanitation.<sup>3</sup> Much like NBA, the SBM program involves two key components: (i) Information, Education and Communication activities on sanitation; and (ii) the provision of financial
63|0.93|
|Prince Edward Island|2,988|1,595|0.65|0.93|
|Nova Scotia|3,517|1,066|0.76|0.93|
|New Brunswick|3,295|1,288|0.71|0.93|
|Quebec|3,973|610|0.86|0.93|
|Ontario|5,085|...|1.10|1.03|
|Manitoba|3,737|847|0.81|0.93|
|Saskatchewan|4,058|525|0.88|0.93|
|Alberta|6,306|...|1.36|1.2S|
|British Columbia|4,808|...|1.04|0.97|

a/ Per capita yield of tax bases at national average tax rates.

b/ Notional revenue before equalization relative to the national average.

c/ Notional revenue yield after equalization relative to the national average. Source: Broadway and Hubson (1993), p.59.

In most cases the determination of tax bases is relatively straight forward, based on provincial data. The most complex calculation involves the determination of the property tax base. Because assessment practices vary markedly from province to province, a standardized base cannot be inferred from provincial data. Instead, the value of land and capital in residential property, commercial, industrial and federal property, and farm property must be calculated by province. In the case of residential property, the value of buildings in residential use is calculated as a percentage of the value of the total residential capital stock. T1he value of land in residential use is calculated as a percentage of personal disposable income (net 7
**<u>NON-TECHNICAL SUMMARY</u>**

In this paper we focus on the consequences of skilled migration for developing countries. We first summarize the recent findings of Docquier and Marfouk (2004) on the international mobility of the highly-skilled; their estimates, based on immigration data collected from nearly all OECD countries in 1990 and 2000, show that the “brain drain” has gained in magnitude over the period covered, but that substantial differences remain across regions and income groups.

The central section presents the theoretical arguments of the ''new'' and ''old'' brain drain literatures in a fully harmonized framework. We first review the early brain drain literature, which tends to emphasize the losses to those left behind. We then ask whether the traditional detrimental effects stressed in the early literature may be offset by potentially beneficial effects emphasized in more recent contributions (remittances, return migration, creation of trade and business networks, and possible incentive effects of migration prospects on human capital formation at home), and provide empirical evidence on these different channels where available. According to most existing studies, it is unlikely that remittances, return migration or other ways through which highly-skilled emigrants continue to impact on their home country's economy are significant enough to compensate sending countries for the losses induced by the brain drain. By contrast, cross-country comparisons provide supportive evidence that migration prospects foster domestic enrollment in education. Additional investigations are clearly needed to assess the net effect of emigration on human capital formation at home.

The last section explores some of the policy implications of the analysis, with emphasis on migration policy and education policy. We show that from the perspective of developing countries, the "optimal" emigration rate for the highly-skilled is zero where liquidity constraints are strongly binding and then conforms to an inverse-U shape pattern with respect to the country's level of development. As to education policy, we discuss the role of taxes and education subsidies when human capital is mobile internationally and show how optimal policy responses
---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Drama|-0.034|-0.028|0.013|0.006|-0.025|-0.021|0.023|0.022|
||(0.041)|(0.041)|(0.048)|(0.045)|(0.042)|(0.038)|(0.032)|(0.033)|
|Documentary|-0.024|-0.023|0.001|0.006|-0.051|-0.072|0.062<sup>_∗_</sup>|0.057|
||(0.049)|(0.048)|(0.058)|(0.056)|(0.050)|(0.046)|(0.035)|(0.035)|
|Ybaseline|0.429<sup>_∗∗∗_</sup><br>(0.036)|0.256<sup>_∗∗∗_</sup><br>(0.049)|||||||
|Controls||✓||✓||✓||✓|
|R-squared|0.203|0.245|−0.004|0.144|−0.002|0.192|0.001|0.045|
|P-value equal coef.|0.826|0.911|0.839|0.993|0.586|0.248|0.235|0.296|
|Observations|567|567|542|542|
28.1). For out-of-sample municipalities, the household and sub-area models are equally accurate (correlation of 0.7), while the area-level model is moderately less accurate (correlation of 0.66).

6. All geospatial estimates are substantially less accurate than the official 2010 municipal poverty estimates when predicting 2015 municipal poverty rates, particularly for out-ofsample municipalities. The correlation between the 2010 estimates and the 2015 benchmark is 0.91 in-sample and 0.90 out of sample, as compared with 0.86 and 0.70, respectively, for the preferred household model geospatial estimates. This suggests that poverty in Mexico was relatively stable, at least between 2010 and 2015.

Overall, the results confirm that combining survey data and geospatial data can greatly improve the accuracy and precision of monetary poverty estimates. However, accuracy and precision both suffer markedly when making synthetic predictions into out-of-sample municipalities. This underscores the importance of using a statistical method that conditions predictions on the sample data and of including as many target areas as possible in the sample. In this context, householdlevel models generate more accurate and precise predictions than the basic sub-area and area models considered here.<sup>8</sup> The household-model results, however, are substantially less accurate for predicting 2015 municipal poverty than the official 2010 census-based estimates. This correlation may be overstated because data from the 2010 census are used to generate both the 2010 and 2015 estimates. Nonetheless, this finding underscores the value of regular census data collection in this context and the possible importance of designing surveys to include all target areas if successful small area estimation is one goal of the survey.

While predictions that combine survey and satellite data are not always more accurate than older census-based poverty maps for predicting current poverty rates, they greatly improve upon estimates solely based on survey data and are a viable second-best solution in settings where recent census-based estimates do not exist, which is a common situation in much of the developing world. The relative accuracy of estimates produced from survey and geospatial
The VHLSSs provide a wealth of information at both the household and individual levels. Household-level data includes details on assets, production, income, housing conditions, and participation in government programs. Meanwhile, individual-level data encompass demographics, education, and employment information. Additionally, the VHLSSs offer insights into the basic characteristics and infrastructure of the communes and villages where the sampled households reside.

In addition to the VHLSSs, we utilize two other data sources to measure local communities’ development. The first source is nighttime light data. Nighttime light intensity has been widely used as a reliable proxy for GDP (e.g., Henderson _et al._ , 2011; Hu and Yao, 2022). One advantage of using nighttime light data is its extensive temporal and spatial coverage, making it less susceptible to sampling errors compared to household surveys. We obtain nighttime light data from the Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership satellite. The annual nighttime light intensity is measured at the district level during the period 2001- 2017. The second data source is firm data at the district level. We compute the number of firms and their density using the annual Vietnam Enterprise Censuses during 2000- 2017, which were conducted by Viet Nam’s GSO. This data source provides indicators of wage and nonfarm employment opportunities at the district level.

# **3.2. Descriptive analysis**

We next present descriptive statistics on the main outcome variables of households in the treatment (program) districts participating in the 30A Programs (i.e., districts with a poverty rate greater than 50% in 2006) and the control districts (i.e., districts without the 30A Programs but with a poverty rate above 40%).

10
the likelihood that the person agrees that people can be trusted by one percentage point.

The coefficient _β_ can only be interpreted as causal under the so-called parallel trends assumption. It requires that, in absence of the refugee inflows, perceptions would have evolved the same way in high- and low-inflow areas. In other words, if we saw a gap in perceptions opening up after 2015, the assumption is that this gap is only due to the refugee inflows. This is an important advantage of a difference-in-difference design compared to cross-sectional analyses: the difference-in-difference design does require the assumption that the inflow of immigrants be as good as random.

Although the parallel trends assumption is not testable, we argue that it plausibly holds in the German context. For everyone involved, a sudden inflow of one million asylum seekers was completely unexpected. Within a few weeks, German states were forced to find accommodation, which is why many states deviated from their policy to assign asylum seekers to municipalities based on population size and often assigned them based on the availability of suitable buildings. This procedure often led to assignments that were independent of local conditions. Moreover, asylum seekers cannot choose their initial place of residence, which excludes a typical identification challenge of studies on immigration, namely that migrants deliberately choose to settle in a particular place.

# **4.2.2 Anti-immigrant violence**

To estimate the effect of the inflow of refugees and asylum seekers on violent behaviours against immigrants, we use data from the Antonio Amadeu Foundation. Our dataset allows us to follow the monthly evolution of anti-immigrant violence in Germany between 2014 and the end of 2017.

20
- 18 -

**<u>Table 6. Output elasticities with respect to transport variables, infrastructure quality and agglomeration</u>**

||SUR<br>Elasticity|Std. Err.|SFA<br>Elasticity|Std. Err.|
|---|---|---|---|---|
|Transport connectivity:|||||
|Local access|0.137|<br>(0.074) *|0.081|<br>(0.101)|
|MAI|-0.068|<br>(0.294)|-0.314|<br>(0.417)|
|Cost to access port|-0.094|<br>(0.051) *|-0.095|<br>(0.055) *|
|Infrastructure quality:|||||
|Power outage|-0.049|<br>(0.059)|-0.010|<br>(0.059)|
|Water interruption|0.063|<br>(0.077)|0.073|<br>(0.067)|
|Inventory|-0.161|<br>(0.050) ***|-0.175|<br>(0.059) ***|
|Agglomeration economies:|||||
|Number of firms in theprovince|0.103|<br>(0.162)|0.240|<br>(0.204)|

30. Another unexpected result may be that the firm production does not exhibit agglomeration economies. The estimated elasticity is positive but statistically insignificant. There are several possible reasons for this. First, the variation in firm location is very limited in the BEEP data set, leading to very
2009), MERS (2012), Ebola (2014), and Zika (2016).

A.-C. Blue bars are coefficient estimates from local projections model. Orange whiskers indicate 90 percent confidence interval. Methodological details are in annex E. Sample includes unbalanced panel of 28 advanced economies 50 EMDEs for 1998-2020.

D. Share of events associated with recessions is the share of events that coincide with a recession in a 3- year window, out of the total number of events. Sample includes unbalanced panel of 33 advanced economies and 98 EMDEs for 1981-2020.

28
households—primarily the urban poor living in slum areas—continue to rely on fuelwood.

The benefits of replacing kerosene with electricity extend beyond a higher-quality lighting source. Switching to electric lighting also means eliminating the indoor air pollution (IAP) caused by the smoke emitted by kerosene lamps, which in India accounts for about half a million premature deaths annually (ESMAP 2002b; Smith 2000).

Virtually all villages in India have access to electricity, yet only about 60 percent of rural households are connected. Low household-connection rates are often attributed to low incomes, high connection costs, poor-quality housing construction, and unreliable electricity services. Thus, for policy purposes, analyzing the determinants of electricity demand among India’s rural households is of interest.

Electricity demand combines interfuel substitution and capital stock adjustments, along with elements that affect the utilization rate of existing stocks. However, demand for electricity is much determined by whether the village has electricity, along with the price of electricity and associated household connection fees. The prices of competing or complementary fuels, such kerosene or LPG, also play an important role. In addition, the price of durable goods that complement household consumption of electricity may influence demand (Bohi 1981). Furthermore, the prices of electric equipments (e.g., irrigation pumps) as well as household wealth and durables also matter.

Household demand for electricity can be estimated by following equation: where _Eij_ represents either household access to electricity (a binary variable with value 1 when household has access and 0 otherwise) or household monthly consumption of electricity (a continuous variable), and control variables include household- and village-level exogenous variables, including a reliability measure of electricity service (average hours of electricity availability at the village level). We implement a maximum likelihood probit model with sample selection for the 11
firms. Weak control of corruption hence makes it easier for informal firms to inflict an unfair cost advantage over formal firms consistent with the parasite view.” The discussion above suggests that the impact of informal competition on the formal firms is magnified when the business environment is less conducive (more costly or less beneficial) to operating in the formal compared to the informal sector. As Distinguin et al. (2016) point it, the cost of operating in the formal sector is higher and benefits lower when there is higher corruption in the country, business regulations are more burdensome, tax rates (on formal businesses) are higher and the rule of law (enforcement) is weaker. These predictions are specific to functioning of the informal sector and therefore we can expect them to hold if our result for the relationship between informal competition and R&D activity is truly causal. However, if the result suffers from reverse causality problem or informal competition is a mere proxy for other correlated determinants of R&D activity (omitted variable bias problem), there is no reason for the observed correlation between R&D and informal competition to vary with corruption, business regulations, rule of law, etc. as predicted above. This constitutes our first endogeneity test.

We implement this test by using interaction terms between our informal competition variable and the following business environment variables: Control of Corruption variable from the Worldwide Governance Indicators, World Bank; Rule of Law variable from the Worldwide Governance Indicators, World Bank; Profit tax rate taken from Doing Business project, World Bank; Freedom from Business Regulations taken form the Heritage Foundation’s Economic Freedom of the World database; and Distance to Frontier (DTF) measure of regulatory burden on the private firms due to World Bank’s Doing Business project.<sup>11</sup> Note that except for the profit tax

> 11 The DTF measure is based on 8 sub-components for which data are available for the countries and years in our baseline sample. We focus on 5 of
# **0. Preliminaries**

Civil wars and similar large-scale violence bring death, injury, disease, displacement, and misery. The impairment of the state diminishes its provision of protection for people and property and of basic social services, such as health care and education. Private economic activity becomes difficult or impossible. People want to understand these episodes and how they can be avoided.

This essay focuses on two broad fatal flaws in the econometric literature on civil war, a large and rapidly growing body of literature that has received considerable attention (for surveys, see Collier and Hoeffler 2007; Dixon 2009; Blattman and Miguel 2010). First, rather than providing a definition of civil war, econometric studies use a coding rule based on battle deaths (or, sometimes, any deaths) associated with civil war. Second, these studies apply econometric techniques that cannot address the spatial and temporal aspects of large-scale violence. (We leave aside questions of bidirectional causality between the dependent and so-called independent variables that plague all econometric studies using country-level data.) A good definition of civil war is an essential starting point for analysis. The definition of civil war informs the universe of potential cases that can be compared with respect to their causes, dynamics, consequences, termination, and any other concerns, such as postwar reconstruction and rehabilitation. Moreover, the definition of civil war is critical for identifying trends in the nature and location of different types of warfare. After defining civil war, we critique the coding rules based on the number of deaths used by econometric studies and the properties of the data produced by such coding rules. We pay particular attention to the temporal and spatial properties of these data and their implications for econometric specification and estimation. We also develop our critique of the econometric literature on civil war through the 2
favoritism, Nguyen et al. (2011) show nepotism of public officials in their communes in Viet Nam, mis-targeting infrastructural projects. Studying the long time span from 1960 to 2010, Jedwab and Storeygard (2017) show that cities around the leader’s place of origin in Sub-Saharan Africa were growing faster than other cities because the leaders favored their cities of origin to target road infrastructure projects. Hodler and Raschky (2014) measured regional favoritism from outer space: they found that subnational regions, where current political leaders were born, have more intense nighttime lights.

This paper uses an instrumental variable (IV) strategy to account for endogeneity.<sup>15</sup> The IV is based on a map of historical road networks of Armenia obtained from a Militarytopographic map of the Caucasus region - shown on Figure 9 - prepared under the Russian Empire in 1903. The argument of exogeneity of the historical setting of roads can be motivated by several reasons. During the beginning of the 20th century Armenia was under the rule of the Russian Empire. The southernmost state of the empire, situated on the border of Ottoman and Persian empires, Armenia was an important territory for military defense. The roads maintained by the Russian Empire were mainly used to transport armies. Even though the roads could be also used for trade and economic reasons, we can argue that the government of the Russian Empire would invest in building, rehabilitating and maintaining the roads necessary for military reasons. The second argument is that, since the historical roads were mapped before the industrialization of the region, when the large majority of people were employed in agriculture, we can argue that the roads would not have been built and maintained to promote non-agricultural employment. It is very unlikely that any decisions made in different settings far back in history, motivated mainly by non-economic reasons, could have anticipated rural employment development a century later.<sup>16</sup>

> 15The literature using similar method includes: Duranton and Turner (2012), Baum-Snow et