rafmacalaba/data-use-mentions-extended huggingface dataset:
yet India’s under-5 mortality rate is 93 and Indonesia’s is 45. Indonesia has therefore not only been more effective at providing public services, it has also been far more efficient. What accounts for this stark difference in performance? I argue below that SPGs have played an important role.

# **<u>Collective Action and community development in Indonesia</u>**

Any discussion of Indonesian society has to start with the work of Geertz and, in particular, with his monumental work, _The Religion of Java_ , which laid out many of the themes that have played a central role in understandings of Javanese culture. Geertz’s work may have even shaped how Indonesia’s nationalism, with its strong Javanese flavor, has been articulated and imagined.

> 8 All data in this paragraph are from the World Development Report 2004.

11
discounts; (iii) whether the states offered females stamp duty discounts; and (iv) the feasibility and cost of data collection. Each aspect is discussed in detail below.

# 4.1.1 City size

India is both very large geographically and very diverse culturally, socially, and economically. More than 4,000 ULBs have discretion to define their own policies on recurrent property taxes. Further, India does not have a centralized repository of all legislation relating to property taxes that might provide a comprehensive review identifying cities that offer property tax incentives to females. A further complication is India’s linguistic diversity: Many of its 4,000 ULBs use languages other than Hindi or English to write their legislation, thus requiring translations for study. In view of these issues, the sample of cities was limited to India’s 20 largest, based on total population as measured by the UN World Urbanization Prospects 2018, and the ease of obtaining relevant legal information. The selected cities are listed (by urban area population) in Appendix 4.

# 4.1.2 Recurrent property tax discounts to females

The review of legislation, media reports, and telephone conversations with government officials found that only 4 of the 20 cities selected provide females with recurrent discounts on property taxes: Pune, Pimpri-Chinchwad, Delhi, and Chennai.

# 4.1.3 Stamp duty discounts to females

Unlike cities, most states publicize their Stamp Acts electronically on dedicated websites, allowing legal review of the state-level Stamp Acts of all 28 states and Delhi. This review, and telephone conversations with government officials, identified nine states and Delhi that provided stamp duty and registration charge discounts to females: Assam, Bihar, Gujarat, Haryana, Maharashtra, Odisha, Punjab, Uttarakhand, and Uttar Pradesh.

# 4.1.4 Feasibility and cost of data collection

Due to the ongoing COVID-19 pandemic, it was not feasible to do in-person meetings in the chosen cities. The necessity of relying on contacts within the government to organize remote interviews
had closed and project funding had ended – to assess whether project services had been sustained.

We define sustainability as the continued use of project components beyond their initial funding period (Scheirer and Dearing 2011; Shediac-Rizkallah and Bone 1998; Bamberger and Cheema 1990). Specifically, we measure sustainability of the early childhood education project in two ways: (i) whether there is continued delivery of early childhood education after external funding ended (i.e. whether project playgroups were still in operation) and (ii) whether there is continued employment of teachers trained under the project after external funding ended. This paper uses a unique panel data set on playgroups established under the project. Data were collected at the beginning of the project in 2010, at the end of the project in 2013, and three years after project funding ended in 2016.

We focus on the sustainability of this early childhood education project given the growing importance of increasing investments around the globe in early human capital development and the importance of early childhood education opportunities in promoting female labor force participation. Under the SDGs, the global community has committed that by 2030 “all girls and boys [will] have access to quality early childhood development, care and pre-primary education so that they are ready for primary education” (United Nations 2015). However, the global gross enrollment ratio in pre-primary education is still low at 44% (UNESCO 2015), which suggests the need for greater investments in early childhood education over the next decade. As pre-primary education is generally not included in compulsory education, much of this investment will likely come from project-type interventions given that the majority (over 53%) of global disbursements

> 1 Investing in Every Child’s Early Years: World Bank Contributions. Accessed on August 12, 2019. Available at: <u>https://results.org/blog/investing_in_every_childs_early_years_world_bank_contributions/.</u> 2 For details refer to Appendix 4 in Hasan, Hyson and Chang, 2013.

> 3 A comparison of assets
br>Very much|Yes<br>No|Defense and Police<br>Education<br>Transportation<br>Welfare<br>Health<br>Environment|

To measure tax morale in the survey, we use two outcome questions.

- 1) The tax morale question from the General Social Survey (GSS):

_If a taxpayer does not report all of their income in order to pay less income taxes do you feel it is: (Not wrong / A bit wrong / Wrong / Seriously wrong)_

# and,

- 2) A tax fine attitude question:

_If a taxpayer does not report all of their income in order to pay less income taxes, what percentage of their income should they pay as a penalty? (None (0%) / 1-10% / 11-20% / More than 20%)_ Although we think of both of these questions as tapping tax morale, note that there are important differences here between the first question, which is framed as a moral question, and the second question, which is more specific and boils down to an actual policy parameter. Also note that, while many might agree that increasing tax morale is desirable in most contexts, it is not necessarily clear that increasing punitive preferences in society is desirable in and of itself.

We also ask questions about basic covariates (age, gender, labor market status), as well as “manipulation check” and “mechanism” questions that allow us to look at whether respondents absorb the information given to them and investigate potential mechanisms. The full survey can be found in the appendix.

This survey was served to internet users in the designated countries using an online survey platform (RIWI) which delivers anonymous opt-in surveys to random Web users who are surfing online and land upon inactive domains. This means that respondents do not expect to take the 7
, the employment outcomes that we analyze in this section and in section 4.5 use retrospective data collected in May 2020 but referring to February 2020. This is to identify the bootcamp impacts on the main employment outcomes before the COVID-19 outbreak affected labor markets in Argentina and Colombia.<sup>30</sup> Panel B of Table 5 presents the estimates of the impacts of the training on these outcomes. We observe that the program increases the probability of working at a technology job by 9.2 percentage points, which represents a 38 percent (0.2 standard deviations) increase relative to the control group. While we do not observe an impact of the program on the acquisition of formal tertiary or university computer science education, we do find

> 29Being working or studying in _tech_ are not mutually exclusive, and this why, as observed in Table 5, the means of the first two dummies add up to a fraction slightly larger than the mean for the third one. We did not pre-specify this third outcome.

> 30Both countries experienced the outbreak of COVID-19 cases long after the Northern Hemisphere. The first measures of lockdowns in Buenos Aires occurred on March 20, 2020, while in Bogotá it was on March 25, 2020. These lockdowns happened still when both cities had very few cases.

19
ratio of benefits over cash pay.<sup>9</sup> These measures do not reflect all aspects of job quality. Unfortunately, we could not create a more comprehensive index of working conditions due to the data availability.

To empirically analyze working conditions, dummy variables equal to one for each good working condition are averaged across all conditions for each worker. The empirical model is presented in equation (7) where the dependent variable—the average working conditions ( ) of individual _k_ in period _t_ —is a function of (i) years of education ( ); gender (using the dummy variable that equals the value of one if the worker is female); ; _j_ industry ( ), and _i_ occupational ( ) dummies;and _p_ marital status dummies ( ); (ii) _year_ dummy equal to the value of one for the year 2005 and beyond, the _TG_ dummy that is equal to the value of one for the textile and garment sector, and their interactions with female dummy; and (iii) a remaining match-specific component that is captured in the residual term .

(7) The coefficient of interest in this regression is which shows the change in working conditions for women in the post-MFA textile and garment industry relative to men and compared to other industries.

# _3.2.Data Description_

The paper used household and labor force surveys to carry out the analysis. The Cambodian data come from the Cambodia Socio-Economic Survey (CSES) which was collected by the National Institute of Statistics. The data in this paper include data from the 1996, 1999, 2004, 2007, 2008, and 2009 surveys, which contain roughly 12,000 households each. These are crosssectional household surveys that contain detailed household and individual information including wage, education, age, marital status, gender, location, industry, occupation, some working conditions, and the hours worked.

The Sri Lankan data come from household and labor force surveys. The Sri Lanka Household Income and Expenditure Surveys (HIES) were
of about 8 points. These results also suggest that in many contexts adding deep learning estimates to existing predictions based on gradient boosting offers limited additional improvement in accuracy.

- b. Household or sub-area models are usually preferable to area-level models when sub-area data are available.

The pros and cons of different models for small area estimation in different contexts has been a subject of contention for many years and there is not yet a consensus between different statisticians and practitioners. For the purposes of this discussion, we assume that recent census data are unavailable, necessitating the use of geospatial data. In most cases, geospatial data are available in the form of zonal statistics at the “sub-area level”, where the sub-area is a geographic area such as a grid or village. A zonal statistic, for example, could be the average night-time luminosity in the village or grid. Linking survey and geospatial data at the level of the enumeration area or village is quite common in practice. The Demographic and Health Surveys publicly release jittered geocoordinates for each EA in most cases, facilitating this type of linking. Meanwhile, the target area is typically a more aggregate administrative unit, such as a district or subdistrict. Finally, we define the regional level as a level above the target area for which the survey is considered to be representative.

Many analysts use Bayesian modeling for small area estimation. We focus here, however, on empirical best predictor models (Jiang and Lahiri, 2006, Molina and Rao, 2010), rather than purely Bayesian models, for three reasons. First, when they have been compared, EBP models give similar results to hierarchical Bayesian models (Guadarrama et al, 2016). Second, national statistics offices may be more comfortable with empirical Bayesian than Bayesian methods, partly due to discomfort with assuming a prior distribution. Finally, multiple well-documented and user-friendly software packages employ EBP methods, such as the EMDI and SAE packages in R, and the SAE package in Stata (Kreutzmann et al, 2019, Molina and Marhuenda,
domestic institutions are fixed to ensure that savings equals investment spending in equilibrium. The consumer price index is chosen as the numéraire such that all prices in the model are relative to the weighted unit price of households’ initial consumption bundle. The model is also homogenous of degree zero in prices, implying that a doubling of all prices does not alter the real allocation of resources (Diao et al, 2011).

This CGE model is calibrated to the 2010/11 Social Accounting Matrix (SAM) data of the country (Ahmed et al, 2017). The SAM is a comprehensive, economy-wide data framework, typically representing the economy of the nation and consistent with macro to micro accounting framework. Its construction is based on Ethiopia’s national accounts, the Household Income Consumption and Expenditure Survey (HICES), Agricultural Sample Survey (AgSS), and other important data. Given that the SAM is a table which summarizes the economic activities of all agents in the economy, it typically includes households, enterprises, government, and the rest of the world (RoW). The relationships included in the SAM include purchase of inputs (goods and services, imports, labor, land, capital, etc.), production of commodities, and payment of wages, interest rent and taxes, savings and investment, and the rest of the world.

This 2015/16 EDRI/IFPRI SAM is disaggregated into 69 activities: 31 in agriculture sector, 26 in industry, 11 in service, and 1 mining sector; 74-commodities; 13-factors<sup>9</sup> , and 15<sup>10</sup> -households. The SAM also has government, different taxes, saving-investment, inventory and rest of the world accounts to show the interaction of different institutions within the economic system.

As briefly described above, this general equilibrium modeling involves the interactions of different actors in the economy including the activities that are linked to government income through value added and sales taxes; the households that supply and determine the
<!-- Start of picture text --> Figure 7 - Trends in taking action to enforce consumer protection laws<br>Require refund excess charges<br>Issue public notice of violations<br>Withdraw licenses to operate<br>Withdraw misleading ads<br>Impose fines and penalties<br>Issue warnings to FIs<br>0 20 40 60 80<br>Number of Countries<br>Source: Financial Access Database<br><!-- End of picture text --> <!-- Start of picture text --> Figure 8 - What do agencies do to monitor compliance with CP?<br>Mystery shopping<br>Consumer interviews<br>Receive complaint stats<br>Operate complaints hotline<br>Monitor ads/websites<br>Onsite inspections of FIs<br>0 20 40 60<br>Percent of Countries<br>Source: Financial Access Database<br><!-- End of picture text --> 24
4 MDL per month per capita); International - below the international line (5.5 USD/day in 2011 PPP or 1305.2 MDL per month per capita). Source: IMF, World Bank, National Bureau of Statistics of the Republic of Moldova.

The reduction in poverty has also been associated with a steady reduction in the degree of income inequality in Moldova. The Gini index of inequality fell from 0.35 in 2007 to 0.26 in 2017, and is at the lower end of the inequality indices among countries in the ECA region (Figure 2, Panel A). The fall in the level of inequality registered in Moldova is quite large, by international standards. Panel B of Figure 2 plots the reduction in the Gini index during the 2006-2016 period against the initial level of inequality in 2006. As one would expect, higher initial levels of inequality are associated, on average, with greater absolute declines in the Gini index. However, countries in the ECA region (orange dots) tend to cluster around the regression line, whereas Moldova (and also Kyrgyzstan) register a much higher reduction in inequality, relative to the starting point.<sup>1</sup>

> 1 This could, however, be a sign of deteriorating data quality. It would be important to compare the inequality statistics with those coming out of 2019 HBS data, as they rely on a new sample drawn from the 2014 Population Census sampling frame.

3
-  Trada Agreements Data. To account for the presence and depth of trade agreements, we use the World Bank's database on deep trade agreements (DTA), cf. Hofmann et al. (2019) and Mattoo et al., eds (2020), (https://datatopics.worldbank.org/ dta/about-the-project.html).<sup>18</sup> Capitalizing on the rich dimensionality of the DTA database, we construct and utilize several variables for our analysis. _PTAij,t_ is an indicator variable for the presence of any trade agreement between _i_ and _j_ at time _t_ . _DTAij,t_ is an indicator denoting the presence of a deep agreement between _i_ and _j_ at time _t_ . _DEPTHij,t_ is a count variable for the number of provisions in the corresponding DTA between _i_ and _j_ . _INVij,t_ is an indicator variable that takes a value of one if the DTA between _i_ and _j_ includes investment provisions. Finally, _INV_  DEPTHij,t_ is a count variable for the number of investment provisions in the corresponding DTA between _i_ and _j_ . For further details on the general features of the DTA database we refer the reader to Hofmann et al. (2019) and Mattoo et al., eds (2020). In addition, for analysis with specic focus the investment provisions in the DTAs, we refer the reader to Crawford and Kotschwar (2020).

-  Production Data. Data on GDP, employment, and capital stocks are from the Penn World Tables 8.0, cf. Feenstra et al. (2013) (http://www.rug.nl/research/ggdc/data/ pwt/). For data on GDP, we employ Output-side real GDP at current PPPs ( _CGDP_<sup>_o_</sup> ), which compares relative productive capacity across countries at a single point in time, as the initial level in our counterfactual experiments, and we use Real GDP using national-accounts growth
technologies allow observing actual itineraries and trip times. A recent example is Hernandez (2021) who uses tracking data from GPS devices located in trucks operating within Colombia. With data on locations and time stamps of 15,000 trucks in 186,000 long-haul trips, he calculates waits and delays in road freight transport. In an application to China, Alder et al. (2021) estimate congestion by road segments using real-time GPS information on 1.8 million (20% of the total) long-haul trucks in 2020. Such GPS data, however, may not be readily available for researchers and practitioners. Therefore, node-to-node least-cost path calculations remain the most viable option for a wide range of applications.

Two examples for imputing domestic transport costs are Allen and Arkolakis (2014) and Donaldson and Hornbeck (2016). Using the fast marching algorithm on a detailed map of the transport network across continental U.S. counties, together with information on trade flows from the Commodity Flow Survey, Allen and Arkolakis (2014) embed a mode-specific transport cost minimization to the estimation of general trade costs. Donaldson and Hornbeck (2016) focus on transportation costs alone. While their goal is to analyze the impact of railroads in 19th century U.S. economy, the methodology they follow in constructing a county-to-county transport cost matrix can be applied to other settings. Using information on ton-mile cost of shipping via all available modes—rail, wagon and waterways—and assumptions on transshipment costs between modes, they calculate lowest-cost freight transportation routes and the associated costs in levels between all U.S. counties before the advent of motorized travel.

In a more recent application, Combes and Lafourcade (2005) calculate transport costs between French economic zones in 1978 and 1998. Focusing attention to road transport, they distinguish various road types (tolled and free highways, secondary roads and urban roads). Additional realism comes from separately incorporating itinerary and distance-related costs (fuel, tolls, vehicle and tire depreciation and maintenance) from time-related costs (driver
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 11 Figure 4: Proposed river distributaries and surge barrier positions as illustrated for the southern Netherlands.

# **3.6 Initial protection levels**

There are no global data sets on current levels of protection and initial protection levels (taken as 2010) have to be estimated in an expert manner. Hence, current protection levels are taken from Sadoff et al. (2015) who applied current protection levels for the biggest 136 coastal cities from Hallegatte et al. (2013) and complemented these through expert judgement for coastal areas not associated to one of these cities (Table 4). Protection level zero (i.e. no protection) is assumed if the population density in the 1-in-100-years floodplain is lower than 30 people per km2 and in rural areas in countries with low and low middle incomes. As the socio-economic scenarios assume substantial economic growth, this moves people in rural areas from no protection to protection over the 21st century in these poorer countries, and the global length of protection increases with time.

Table 4: Protection standards adopted in this analysis (following Sadoff et al., 2015).

|Wealth Class (annual income per capita)<br>(2014 US$ GDP per capita (PPP))|Urban<br>(>1000<br>people/km<sup>2</sup>)|Rural<br>(30 to 1000<br>people/km<sup>2</sup>)|Uninhabited<br>(<30<br>people/km<sup>2</sup>)|
|---|---|---|---|
|Low income (<$1,035)|1:10|no protection|no protection|
|Lower middle income ($1,035 - $4,085)|1:25|no protection|no protection
15 NPLs. When the government-owned banks variable is added to the right-hand side, this latter effect loses significance (Table 2). Private monitoring has significant effects across the board in Table 1 and with the same signs found in earlier research, but with government-owned banks (Table 2) only remains significant in the regression of overhead costs.<sup>7</sup> These results differ somewhat from BCL (2006), where the addition of government ownership also decreased the size of the coefficients but for the most part they remained statistically significant. One difference here is that the sample is smaller due to differences in the construction in the indexes. In our earlier work, the use of principal components in constructing the indexes allowed us to use observations even when a few questions were not answered, and more importantly we were able to fill in a large number of missing values or clearly incorrect responses by going back to the country authorities (after the survey results had already been posted). In this paper, we took the raw data ‘as is’ from the World Bank website, and for countries with missing values, had to drop that observation as we were producing the indexes by summation, and summing responses with different numbers of replies would clearly lead to a mischaracterization of the data. This approach was dictated by the desire to be consistent with the Survey IV results; although we will attempt to fill in missing values in BCL (2012) for that survey, that work is not yet complete.

The difference in the index construction did not change materially the results on official supervisory powers, which remains insignificant with or without the addition of government-owned banks. That supervision has no impact on bank fragility, as measured by NPLs, thus remains troubling. Tighter entry requirements are linked with an increase in efficiency, even when government banks are added, and restrictions on banks’ activities lead to lower bank development and higher interest margins in both Tables 1 and 2. Interestingly, restrictions on activities are not linked with