rafmacalaba/data-use-mentions-extended huggingface dataset:
households. Moving from indoors to an open kitchen lowers the PM10 concentration in cooking and living areas by almost the same magnitude as switching from firewood to kerosene. Switching from mud walls to other materials lowers PM10 far more than any other factor, and use of thatch roofs also has a large effect.

(5) In a representative sample of households, how serious is the indoor air quality problem for poor families in Bangladesh? For our assessment, we adopt the Indian rural PM10 exposure standard: a 24-hour average of 100 ug/m<sup>3</sup> . Our results for six Bangladeshi regions suggest that indoor PM10 concentrations are quite high for many poor families. For all rural families with per capita incomes below $1.00/day, we estimate a mean PM10 concentration of 275 ug/m<sup>3</sup> for kitchen spaces -- nearly three times the Indian standard. This falls somewhat in peri-urban and urban areas (to 226 and 193 ug/m<sup>3</sup> , respectively), but remains much higher than the standard.

(6) Are there significant geographic variations in indoor air quality? Our results suggest great geographic variation, even for households in the same per capita income group. This variation reflects local differences in fuel use and, more significantly, construction practices that affect ventilation. For the poorest households, rural PM10 concentrations vary from 410 ug/m<sup>3</sup> in Cox’s Bazar to 202 ug/m<sup>3</sup> in Faridpur. Even in urban areas, concentrations differ by almost 100 ug/m<sup>3</sup> between the highest areas, Jessore and Rajshahi, and the lowest, Sylhet. The poorest households in Rangpur face the same mean indoor concentration (198 ug/m<sup>3</sup> ) as the highest-income households in Cox’s Bazar.

(7) What are the prospects
to lower_<br>_industrial electricity prices_<br>_and industrial/residential_<br>_price ratio._|-industrial end-user price in<br>PPPs (PI)<br>-ratio of industrial to<br>residential prices in PPPs<br>(PI)<br>-utilisation rate: energy<br>production/total average<br>capacity (PI)<br>-distance of actual from<br>optimal reserve margin<br>(PI)|**-**time to liberalisation (years)<br>(RM)<br>_Results: significantly positive for_<br>_prices_<br>-time to privatisation (years)<br>(RM)<br>_Results: insignificant for prices_<br>-unbundling of generation from<br>transmission (multi-level<br>indicator) (RM)<br>_Results: insignificant for prices,_<br>_significantly positive for_<br>_utilisation rate._<br>-private ownership (multi-level<br>indicator) (RM)<br>_Results: significantly positive for_<br>_prices and for utilisation rate._<br>-third party access (dummy)<br>(RM)<br>_Results: insignificant for prices_|-GDP (US$) (CL)<br>_Result: insignificant_<br>-hydro share in generation<br>(SE)<br>_Result: significant for_<br>_prices_<br>-nuclear share in<br>generation (SE)<br>-state preference against<br>nuclear technology (SE)<br>-state preference in favour<br>of coal technology (SE)<br>-urbanisation (CL)|Panel data from International<br>Energy Agency and other<br>sources covering 19 OECD<br>countries. Number of periods<br>(1986-1996):
# **6 Conclusion**

Most empirical analyses of migration decisions and patterns are based on static models. A potential migrant is assumed to make a single decision between alternative destinations and his current place of residence based on location-specific utility differences and bilateral mobility costs. Once migration takes place, the payoff and costs are realized and, most importantly, there are no additional movements. The data indicate that this simplistic view is quite off the mark. Many migrants’ paths involve multiple countries and are likely to be the outcomes of complicated dynamic decision processes. Historical evidence and current data provide ample evidence that many migrants live in a series of countries for different lengths of time, experience repeated migration episodes, or have circular migration paths.

There is a relatively new and exciting strand of the literature that is based on innovative dynamic models. They rely on detailed survey data on individual migration histories from single origin or destination countries to estimate the determinants of return or circular migration behavior. Unfortunately, the absence of detailed individual data at the global level prevents us from shedding light on some of the other processes. Our contribution is to construct a novel dynamic model of global migration with the goal of incorporating and explaining transit migration patterns, using the available data. In our model, agents decide to stay in their current location or move to another one every period, taking instantaneous utility payoffs and bilateral mobility barriers into account. This dynamic structure leads to an option value associated with each location, making it attractive as it provides easier access to other locations in future periods. This attractiveness is exactly the basis of the transit migration behavior we observe.

Our empirical analysis relies on transit migration data from the American Community Survey, which asks migrants their place of birth and where they were living the year before they came to the United States. This data indicate that transit migration—people coming to the United States from places other than their birth countries—is actually high, especially among highskilled migrants coming from other high-income OECD countries. We combine
*****|**0.944*****|
||**(0.187)**|**(0.215)**|
|Notes: robust standard errors in parentheses, clustered at the firm level. *** deno|tes significanc|e at the|
|1 percent level. Coefficients are treatment effects post‐intervention, and control f<br>randomization strata, and|or time effects,||

# Appendix 6: Robustness of Management Improvements to Sample Attrition

Table A6.1 shows the availability of our management score data by time period and measure. The greatest data availability is for the Anexo K measure, but this still suffers from attrition, while the WMS and MOPS data are available for subsets of the same only.

57
, 19 percent of men and women had all types of ownership/rights; this figure was 23 percent for men and women in T5/IHPS.

Table 2 presents summary statistics on variables capturing exclusive versus joint ownership and rights, along with a dichotomous variable, namely _SDG_ Owner, based on the definition of the SDG indicator 5.a.1. The latter takes the value one if the individual is a documented owner, has the right to sell, or has the right to bequeath related to any parcel that the individual interview targets in each given household could have reported on. Means of ownership/rights are broken out by women and men overall, as well as women heads/non-heads of household.<sup>22</sup> Adjusted Wald tests for equality of means were also conducted across survey approaches, within the samples of women/men, with significant differences indicated in bold. Columns (9)-(12) conduct the same test on whether differences across men and women, within each survey, are statistically significant (indicated by asterisks).

> 21 Individuals, within or outside the household, who were reported to be listed on a given ownership document, if any, were identified uniquely on the questionnaire, and the enumerators requested to see the referenced ownership document to cross-check the reporting regarding the documented owners. Although our definition of documented ownership does not hinge on whether the ownership document was cross-checked, it is important to note that conditional on reporting regarding documented ownership, the respondents produced the ownership document for the enumerator less than 40 percent of the time.

> 22 As compared to women where there was greater diversity in household status by head, spouse, or other household members, nearly all of men reporting ownership were household heads; we discuss implications of this further below.

12
because data on the innovation module is available only for this category. Besides, measuring firm innovation in the services sector is intrinsically different from the innovation process in manufacturing (Ettlie and Rosenthal 2011). The sample does not include small jurisdictions that may specialize in a limited number of industries. Our final sample includes 104 countries, of which 12 are high-income countries, 33 are upper-middle countries, 36 are middle-income countries, and the remaining 23 are lowincome countries. For 52 economies we have data for 1 survey round; two survey rounds are available for 40 additional countries; and 3 survey rounds for the remaining 12 countries (see Appendix A1 for the list of countries and number of survey waves).

# _2.2 Measuring firm innovation_

Given the available data, we adopt a broad definition of innovation as the accumulation of knowledge and implementation of new ideas. Specifically, innovation is defined as the implementation of a new or significantly improved product (good or service) or process, a new marketing method, or a new organizational method in business practices, workplace organization, or external relations (OECD 2015). We consider innovation defined both as “invention” or “discovery”, i.e., those developments to push the technological frontier, and as “diffusion” or “adoption” of existing technologies and practices that lead firms to novel ways of producing or acting (in short adoption). Invention is proxied through expenditures in research and development.

We compute 5 variables to measure the extent of firm innovation and different typologies. as being innovative. Two variables capture the overall degree of a firm’s innovativeness (i.e., the intensive margin). This first variable ( _innovation score_ ) measures the intensity of innovation without considering what type of innovation is pursued. It is constructed by summing up 4 questions from the World Bank Enterprise Surveys (WB ES), weighted by the number of questions answered by each firm.<sup>3</sup> It ranges between 0 and
# **1 Introduction**

Government enforcement is a key factor determining tax compliance. The better the enforcement quality, the more it can minimize evasion (Slemrod, 2007). Transaction digitization through electronic payment technology is one such way to improve tax compliance. This is because it generates paper trails by both third parties and the government as a reliable basis for audits. Combined with well-functioning monitoring and audit systems, such paper trails can deter under-reporting behavior (Allingham and Sandmo 1972; Kleven et al. 2011, Pomeranz 2015, and Naritomi 2019). Consequently, international organizations have advised promoting the use of an electronic payment system to increase tax compliance (OECD 2018, World Bank, 2020).

There is a growing body of literature on third-party reporting and its impact on government revenue in advanced economies, as discussed by Gordon and Li (2009), and Kleven et al. (2016). However, there is little evidence on whether enacting transaction digitization programs among businesses can boost compliance and therefore increase tax revenue in developing countries, particularly in subnational settings. This study exploits unique administrative data on restaurant tax receipts in the Indonesian districts of West Manggarai and Gorontalo.<sup>1</sup> We aim to explore the variation in restaurant tax payments before and after the distribution of the Point of Sale (POS) machines among hotels and restaurants located in the districts.

Initiated by the Indonesian government and gradually implemented at sub-national levels following the recommendation of The Commission of Corruption Eradication ( _Komisi Pemberantasan Korupsi_ ), the program was meant to optimize local governments’ ownsource revenues (Komisi Pemberantasan Korupsi, 2020). Once installed, POS devices enable the direct dispatch of sales records from targeted tax subjects to the local tax authority. This mechanism enhances the accuracy of users’ taxable transaction reports and minimizes the possibility of taxpayers’ underreporting. Down the line, the produced sales records can be accessed by the tax authority for compliance monitoring.

We begin by discussing a conceptual framework to consider how surveillance enhancement may
Quantity-targeting, categorical targeting, geographical targeting, and means-tested can all be used to allocate either consumption subsidies or connection subsidies. In the remainder of this article we evaluate the targeting performance of consumption and connection subsidies that use all of these targeting methods to determine subsidy eligibility and to distribute subsidy benefits.

# **Data, measurement, and conceptual framework**

## **_Data_**

The data on which this study is based come from a variety of published and unpublished studies of subsidy performance in individual cases. All of these studies evaluate subsidy performance using a combination of household survey and utility data. In a small number of cases, we have done our own analysis of household survey data in order to generate a new subsidy case or expand the analysis done in a previous study.

The final sample includes 45 cases of existing subsidy programs in water and electricity, respectively. The majority are quantity-based subsidies, reflecting the prevalence of this subsidy mechanism. A number of simulated subsidies were also included to illustrate the differential performance of alternative subsidy mechanisms. The final sample includes 45 electricity subsidies from 27 utilities, plus 32 water subsidy programs from 13 utilities. Most cases are delimited by utility service area. Water utilities tend to serve one urban area; thus, the water cases are primarily city cases. For electricity cases, the sample includes a mix of national, state, and municipal cases. Though by no means a representative sample, this group of subsidy cases is representative of a broad cross-section of subsidy types and utilities located in different geographical areas, which permits drawing more systematic conclusions than could have been drawn from individual case studies. A list of the cases and the original sources for the case material can be found in appendices 1 and 2.

## **_Measurement of targeting performance_**

The analysis of the distributional incidence of subsidies considers two dimensions of targeting performance: the beneficiary incidence of the subsidy and the benefit incidence. Beneficiary incidence is meant to address this question, “To what extent does
|**Table 15 Fiscal Decentralization and Fiscal**|**Performance- A Summary of Empirical Results**|
|---|---|
|**Fiscal Performance Indicator**|**Impact of Fiscal Decentralization**|
|**_Central Bank Independence_**|Positive and significant|
|Growthof Money Supply|Positive butinsignificant|
|Inflation|Negative butinsignificant|
|**_Management of inflation and macroeconomic_**<br>imbalances|Positive but insignificant|
|Quality of DebtManagement|Positive butinsignificant|
|**_Quality of Fiscal Policies and Institutions_**|Positive and significant|
|Efficiencyin Revenue Collection|Mixed butinsignificant|
|**_Prudent Use of Tax Monies_**|Positive and significant|
|**_Growth of government spending_**|Negative and significant|
|Controlof fiscaldeficits|Negative butinsignificant|
|Growthof PublicDebt|Positive yetinsignificant|
|**_Public Sector Management_**- Transparency and<br>Accountability|Positive and significant|
|GDPgrowth|Positive butinsignificant|
|Source: Econometric results||

# **5.  Some Lessons for Developing Countries**

The following important lessons for reform of fiscal systems in developing countries can be distilled from a review of past experiences.

- _Monetary policy is best entrusted to an independent central bank with a sole mandate for price stability_ . Political feasibility of such an assignment improves under federal systems (decentralized fiscal systems).

- _Fiscal rules are neither necessary nor sufficient for fiscal discipline. However, fiscal rules accompanied by “gate keeper” intergovernmental councils/committees provide a useful framework for fiscal discipline and fiscal policy coordination for countries with fragmented political regimes._ In this context, one can draw upon industrial countries’ experiences with ‘golden rules’, Maastricht type guidelines and ‘common budget directives’ to develop country specific guidelines. To ensure voluntary compliance with the guidelines, an appropriate institutional framework must be developed. Transparency of the budgetary processes and institutions, accountability to the electorate and general availability of
.3837<br>(0.4863)|4866|
|TIGERMOM|Parent initiates -<br>progress discussion|52.4472<br>(38.097)|41394|62.4183<br>(41.3743)|4882|
|DUTYMOM|Teacher initiates -<br>progress discussion|66.9737<br>(36.727)|41394|68.5543<br>(37.4796)|4882|
|VOLUMOM|Parent Participation -<br>Volunteering|35.2134<br>(38.8428)|41394|38.3623<br>(39.9773)|4882|
|TEACHMOM|Parent Participation -|12.1764|41394|38.2821|4882|
||Teaching Assistance|(23.4241)||(41.5357)||
|FUNDMOM|Parent Participation -<br>Fundraising|23.0784<br>(35.2134)|41394|59.6022<br>(44.0376)|4882|
|COUNCILMOM|Parent Participation -<br>School government|36.4546<br>(37.2252)|41394|23.1174<br>(36.4406)|4882|

Notes: The variables relate to the questionnaires administered to schools. For a more detailed description of variables, please see Tables A2, A3, A4 in the Appendix. The variable means of Dev7 and Vietnam are statistically different at the 95% significance level. Figures in parenthesis represent standard deviations.

TIGERMOM refers to the reported proportion of parents who discussed their child’s behavior or the child’s progress “on their own initiative”, to differentiate from cases where parents might have done so following the initiative of the teacher, termed as DUTYMOM.

> 6[Hsin and Xie, 2014] investigate in great detail data from a set of longitudinal surveys that cover thousands of children over a long period of
- Sahoo, S, P K Gupta and S K Srivastav. 2020. “Inter-calibration of DMSP-OLS and SNPPVIIRS-DNB Annual Nighttime Light Composites using Machine Learning.” GIScience Remote Sensing 57(8):1144–1165.

- Salemi, Colette. 2021. “Refugee camps and Deforestation in Sub-Saharan Africa.” <u>Journal</u> of Development Economics 152:102682.

- The Di lomat

- The Diplomat. 2018. “Why Bangladesh Cannot Accept All the Rohingya.” <u>p</u>

- .

**URL:** _https://thediplomat.com/2018/01/why-bangladesh-cannot-accept-all-the-rohingya/_

- UNHCR. 2018. “UNHCR Submission on Bangladesh: 30th UPR Session.” UN High Commissioner for Refugees <u>(UNHCR) .</u>

**URL:** _https://www.refworld.org/policy/upr/unhcr/2018/en/120810_

- UNHCR. 2023a. “Operational Data Portal: Bangladesh.” United Nations High Commissioner for Refugees .

**URL:** _https://data.unhcr.org/en/country/bgd_

- UNHCR. 2023b. “Rohingya Refugee Crisis Explained.” United Nations High Commissioner for Refugees .

**URL:** _https://www.unrefugees.org/news/rohingya-refugee-crisis-explained/_

- USHMM. 2020. “A Timeline of Rohingya History: Burma’s Path to Genocide.” United States Holocaust Memorial Museum .

**URL:** _https://exhibitions.ushmm.org/burmas-path-to-genocide/timeline_

- Zhao, M., Y. Zhou, X. Li, C. Zhou, W. Cheng, M. Li and K. Huang. 2019. “Building a Series of Consistent Night-time Light Data (1992–2018) in Southeast Asia by Integrating DMSP-OLS and NPP-VIIRS.” IEEE Transactions on Geoscience and Remote Sensing 58(3):1843–1856.

- Zhao
substitutability between goods from different country groups. For example, we can let goods from high income countries be better substitutes for each other than for imports from low income countries in the same product category. This is accomplished by dividing countries into groups, and assigning each source country to a country group. Each country will thus be identified by a pair {l,m}, where 1 is an indicator of the country group to which it belongs and m is an indicator of the country within the country group.

The trade data on import values from a particular source country in a particular tariff line are denoted by Mijk,l at the bottom of Figure 1, where the {ijk} subscripts identify the tariff line and the {lm} subscripts identify the source country. The tariff rates imposed on imports from that product category and country can sirnilarly be identified by y-jk,,. The aggregation proceeds by first aggregating over imports within a particular country group, as indicated by node IV in Figure 1. The trade value Kw = S **Mikhn 1** represents the value of imports from country group 1 in tariff line m {i,j,k}, with Oijkl the price index for this group and Zijk the quantity index. The data on imports from country groups at node IV is then aggregated over the groups within the tariff line to yield a price index Oijk and quantity index Zijk for the tariff line (at node III). The tariff lines within each subindustry are then aggregated to yield indices for the sub-industry (node II), and the sub-industry indices are aggregated to obtain the industry aggregate at node I.

The aggregation structure is quite flexible, and can be extended to allow for any number of levels of industry and/or country groups. The application to Chile considered in this section provides an example of how the aggregation can be carried out. The trade volume data from Chile consist of 1011 tariff lines at the 4-digit (CCCN) level for the year 1986. This
Thomschke (2018) who show that housing prices are largely determined by the physical location of the property. In addition, the number of bedrooms and the size of a private property are consistently found to be positively related to the property price (e.g., Fletcher et al. 2000; Garrod and Willis, 1992; Rodriguez and Sirmans, 1994). Other attributes studied include crime rates (e.g., Ceccato & Wilhelmsson 2020), or proximity to transportation (e.g., Zhang et al. 2021; Zong & Li 2016). Other authors estimate that house prices have particularly increased since the financial crisis in 2008 due to the rise of economic activity paired with unusually low mortgage interest rates in most advanced economies (Claessens & Schanz 2019). Also, price changes in major cities are hypothesized to be driven by institutional investors trying to find high yields or safe assets in a low-interest rate environment (IMF 2018; Gauder et al. 2014).

While there is a plethora of literature on property price determinants in developed economies, studies on EMDEs are scarce. Some single-country studies focusing on EMDEs are analyzing existing house price data that are published by commercial banks such as e.g., Absa, Standard Bank, and First National Bank for South Africa (e.g., Balcilar et al. 2011; Luüs 2005). Other authors collect their own data by either surveying real estate agencies to estimate the relative importance of housing attributes to house prices (Owusu-Manu et al. (2019) for Ghana), by surveying developers (Libertun de Duren (2018) for peri-urban areas in Brazil and Mexico), or by conducting a household survey to collect data on housing costs (Uwayezu & de Vries (2020) for Kigali city in Rwanda). High property prices in EMDEs are often attributed to prohibitively high building costs due to the need to import materials, the shortage of local skills, and the absence of financial mechanisms that allow for materials to be bought