rafmacalaba/data-use-mentions-extended huggingface dataset:
# **5.  Outdoor vs. Indoor Air Pollution**

Policy researchers often view indoor and outdoor air pollution as separate problems in developing countries. Although a rationale is seldom provided, this distinction reflects two beliefs: that indoor pollution is a more serious problem for poor households, particularly in rural areas, and that outdoor pollution is distinct because it comes from multiple sources that may be distant from the household. Our 24-hour monitoring data for a Dhaka household with an extremely clean fuel – piped natural gas – provide us with some insight in this context, because we have also monitored the ambient concentration at five locations in urban Dhaka.

Figure 4 displays the 24-hour concentrations separately and overlaid. The kitchen readings for the natural-gas household closely resemble the ambient readings. The ambient readings, in turn, appear related to the daily cooking cycle for the great majority of households that use biomass fuels.<sup>3</sup> Because outdoor air rapidly replaces indoor air, the indoor concentration in a clean-fuel household closely reflects the ambient concentration. In urban Dhaka, our MiniVol readings at five outdoor locations yield a mean 24-hour concentration of 89 ug/m<sup>3</sup> .<sup>4</sup> The mean daily concentration for the natural gas household monitored with a PDRam unit is 101 ug/m<sup>3</sup> . For a broader group of 10 urban households monitored by our MiniVol sampler, the mean daily outdoor 24-hour concentration is also 101 ug/m<sup>3</sup> . By implication, burning piped natural gas adds negligible PM10 pollution to cooking and living spaces.

> 3 The 24-hour cycle of  ambient PM10 concentrations is very similar to the pattern of average hourly residuals from a panel regression that controls for differences in average PM10 concentrations for households that use biomass fuels.

> 4 For comparison, we cite PM10 concentration measured by the Bangladesh Air Quality Management Program monitor situation at the Parliament building in Dhaka.  From March, 2002
People can access and afford civil justice – Index (0-1, from low to high) 139 WJP Note: The sources listed above are GMD, Global Monitoring Database; AF, Afro Barometer; AB, Arab Barometer; ASB, Asian Barometer; LB, Latinobarómetro; WDI, World Development Indicators; WB, World Bank; WVS, World Values Survey; EMDAT, International Disaster Database; ACLED, the Armed Conflict Location & Event Data Project; WJP, World Justice Project; WGI, World Governance Indicators.

Source: Social Sustainability Global Database 2022; Social Sustainability Global Database 2022 Codebook.

# **4. Results**

## **4.1 Indices**

Having constructed the global database, we next analyze how each component of social sustainability relates to poverty and income levels. Even a simple non-causal analysis can provide useful insights on, for example, countries’ ability to reduce poverty levels or how variation in levels of income translates into differences in the levels of inclusion and equality. A specific question is addressed in this section: _Is social sustainability unambiguously associated with reduced poverty levels and higher per capita income?_ To address this question, we create a set of indices for each of the four social sustainability components described earlier. Each of these indices includes equally weighted indicators that reflect the level of social inclusion, resilience, social cohesion, and process legitimacy in a given country. Including the selected indicators for each component allows for the largest number of countries in the empirical exercise while meaningfully covering different aspects between and within social sustainability components. We select these indicators following three criteria: (a) indictors capture outputs or outcomes that describe the level of the social sustainability dimension; (b) indicators are either objective or capture the perceptions of vulnerable groups; (c) indicators are univariate rather than capturing multiple and complex interactions between concepts. In other words, we avoid to the extent possible indicators capturing inputs or drivers; experts’ opinions on others’ social dimensions; and the indexing of indices
The remainder of the paper is organized as follows. In section 2, we provide a detailed description of the data employed and provide summary statistics. In section 3, we present our identification strategy for examining the impacts of exporting on the share of workers trained, and present the corresponding results. In section 4, we use the worker-firm information to examine whether technical upgrading is associated with higher labor earnings for the corresponding workers within exporting firms. Section 5 concludes.

# **2 Data**

## **2.1 Data description**

The analysis in this paper links and examines three main sets of administrative data for the Brazilian manufacturing sector in the period 2009-2012. _Rela ̧c ̃ao Anual de Informa ̧c ̃oes Sociais_ (RAIS) is a longitudinal census gathering administrative social security records for employees and employers. It is collected by the Ministry of Labor in a compulsory survey of all firms and their registered workers, and covers a total of around 230 thousand formally registered firms and over 3.5 million workers each year in the manufacturing sector. RAIS provides information on workers’ demographics (age, gender, schooling, race), job characteristics (occupation, wage, hours worked), as well as hiring and termination dates. It also includes information on a number of firm-level characteristics, notably number of employees, geographical location and industry code. Unique identifiers for workers and firms make it possible to follow them over time. We have performed extensive checks to guarantee the accuracy of the worker data using information on gender, date of birth, and maximum schooling level achieved. Only full-time wage earners with complete information on demographics and job attributes were kept for the analysis.<sup>2</sup> Using the unique firm identifiers, we have merged these data with customs records on export transactions collected by _Secretaria do Com ́ercio Externo_ (SECEX) of the Ministry see Greenaway and Nelson (2000), Bastos and Silva (2009), Falvey, Greenaway and Silva (
Policy Research Working Paper 11013

# **Abstract**

This paper uses a rich set of geo-coded administrative and remotely sensed data on more than 1 million agricultural land transactions in Ukraine to explore how informality, size, and recent land reforms affect land prices. Three main findings are highlighted. First, absence of registered rights generates large negative externalities, the size of which plausibly exceeds the cost of registering all land. By contrast, informality of lease contracts is a choice that may enable owners to evade regulatory obstacles that prevent them from renegotiating contracts to obtain more favorable terms. Second, while land market liberalization generated significant indirect benefits, gains are unevenly distributed. Furthermore, competition in sales markets remains limited, pointing to scope for measures—including reducing the transaction costs of selling land and accessing mortgage finance, improving publicity of pending land sales, and use of electronic auctions—to enhance the reforms’ impact on efficiency and equity. Third, size at the parcel, field, and farm levels is associated with higher per hectare prices, pointing to scope for market-based land consolidation and growth of medium-size farms to increase land values and productivity. Achieving this potential will require measures to limit speculative land acquisition and exercise of market power by making local land markets more competitive and using market-based land valuation as a basis for taxing land on a recurrent basis and any capital gains due to land appreciation.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at kdeininger@worldbank.org and dali1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly,
Contrary to what is generally found in developing countries and to what Clay et al. (1990) have found in Rwanda, the EICV data suggest that the non-farm sector participation in rural areas is small. Clay et al. (1990) find that approximately half (47%) of farm households in Rwanda engage in some form of off-farm employment. This offfarm work is mostly carried out as agricultural wage laborers (31%) on the farms of their neighbors, while the remaining 69% of household members’ time in off-farm activities is spent in the rural non-farm sector, notably as artisans, laborers or in commerce -- generally in small businesses. Over five percent of off-farm employment is held by government functionaries. We find that in 2001 2.4 percent of women with a primary occupation (and 3.4 percent of women with a secondary occupation) are active in the non-farm sector. For men, the proportions are 7.4 of men with a primary occupation and 8.2 percent of men with a secondary occupation.. These numbers are modest, especially for women and in comparison to what Clay et al. (1990) reported in their paper at the beginning of the 1990s. Moreover, income from non-farm sector activities on average accounts for 20 percent of household income — one of the lowest shares among developing and transition countries (Ahituv and Kimhi, 2002).

**Table 1: Proportion of adult rural population by status and education (%)** **<u>and average hourly wages by education (Rwf)</u>**

||**No**<br>**education**|**Primary**|**Post-**<br>**primary**|**Secondary**|**Further**<br>**education**|_No. of Obs._|
|---|---|---|--
same individuals occupy the top of the income distribution over time – either because the majority of their income are returns to wealth that persist over time, because their successful firms are unchallenged in the market, or because opportunities for other to achieve high incomes are hampered by other structural factors.

Our panel data of individual taxpayers allow us not only to measure income levels and concentration at the top, but also to evaluate _dynamics_ at the top – given that we observe an individual at the top in a given period, how likely it is they will persist at the top in future years? Evaluating the stability of individuals at the top of the distribution is also an indirect to assess the of the administrative data - way quality while a high degree of economic mobility might lead to large changes in the identity of top earners over time, on a shorter horizon we would expect to observe some degree of stability.

Our main exercise, presented in Figure 8, is to compute the probability that indi-

> 23To be precise, we first merge survey and tax microdata without including undistributed profits in the income definition of tax records. When expanding the joint distribution from microdata to match national accounts, we follow the same procedure as in our baseline measures, matching total distributed capital income in the microdata to the sum of household property income and corporate profits in the national accounts.

17
Most of our specifications exclude Italy, which does not have a nationwide minimum wage. As a robustness check, models [7] and [8] (like models [1a]-[1c] in Table 3B) assign to Italy a minimum wage at the level of 38.5% of the average wage in industry and services (the mean level for our sample of Eastern and Southern European countries) and include a dummy for Italy. The share of non-contracted employees in Italy‘s labor force (as of 2006) was by 8 to 10 percentage points below the level predicted by the model with this hypothetical minimum wage level (this effect of absence of a minimum wage is stronger than in Table 3B because this time the Gini index is controlled).

# **4 Macro determinants of informal employment in Western and Northern Europe**

Table 5 presents seemingly unrelated regressions estimating the institutional and other macro determinants of the shares of informal dependent employment and informal selfemployment, as well as the total share of unemployed and discouraged workers in the [extended] labor force of Western European and Nordic countries. Countries covered include the UK, Ireland, Netherlands, Belgium, France, Germany, Switzerland, Austria, Norway, Sweden, Finland, and Denmark. For most countries we have three observations which refer to 2004-2005, 2006-2007, and 2008-2009 (see details in Table 1); for France, we miss the first of these biannual cycles, whilst for Austria we miss the last. Austria aside, the most recent data for each country come from a survey implemented during the economic crisis (late 2008 and/or early 2009, except for Ireland where it was a year later).

With few exceptions, the explanatory variables are the same as for Eastern and Southern Europe. The _Rule-of-Law_ indicator is not used for Western and Northern Europe. New variables (see Annex 1 for details) include:

- (i) _SAT_GOV_ – average satisfaction with the national government (on a 0 to 10 scale)
observe in the payroll account group might be the result of banking facilities not being available at times when employees need to access their account, rather than the result of learning. To test this potential alternative explanation, Appendix Table D.13 repeats the estimates in Table 4, panel A, columns (1) to (6), but excludes any transactions that occurred on weekends. Appendix Table D.14 repeats the exercise and additionally excludes transactions that occurred outside work hours. The results are similar in all three samples, which suggests that the increase in outside transactions occurs by choice rather than as a result of banking facilities and mobile agents at the workplace being unavailable when workers in the payroll account group need to access their account.

Our second learning outcome is an indicator for whether send-money transactions are made directly through a study participant’s own account, rather than a mobile money agent. Learning to make transactions through one’s account directly eliminates various consumer protection risks, including illicit fees and service charges which are widespread in our context and many similar settings. We examine treatment effects on direct versus indirect transactions using primarily data from the endline survey. This has the advantage of allowing us to observe both direct and indirect transactions (indirect transactions are typically routed through a mobile agent’s account on behalf of the customer, so that we do not observe them in administrative data).<sup>39</sup> In our analysis, we use a dummy variable equal to one if a participant made at least one send-money transaction directly from their account.

Figure 5 shows unconditional means for direct and indirect transactions at endline. Figure 5, panel (a) reports the share of workers who made at least one send-money transaction directly from their account (direct transactions) for the bank or mobile account and bank or mobile payroll treatment groups. Figure 5, panel (b) reports the share of workers who make at least one sendmoney transaction that is intermediated by a mobile money agent for the bank or
# **Appendix 3b.  Sample of a mVAM Questionnaire**

# **_Introduction:_**

Hello, my name is [name enumerator/operator]. I am calling on behalf of the United Nations World Food Programme. WFP is conducting nationwide phone surveys in Yemen to learn about its population’s food security. If you agree to participate in this survey, we will ask you some questions about the household’s food consumption and coping. Your participation in this survey is voluntary. Each survey will take maximum 10-15 minutes of your time. All your answers will remain confidential.

**<u>OPERATOR:</u>** What is your year of birth? _____ If respondent’s age is less than 18, end survey

**<u>OPERATOR:</u>** What is the sex of the head of households?  _____

# **<u>Section 1: Geographic and Demographic info</u>**

**Question 1: OPERATOR** In which Governorate has your Household been living for the past 3 months? ............ [List of Governorates] (drop-down list)

**Question 2: OPERATOR** In which District has your Household been living for the past 3 months? ............ [List of District] (drop-down list)

**Question 3: OPERATOR** How many members living in the household for the past 3 months? .... ( report the number)

# **<u>Section 2: Food Assistance</u>**

**Question 4:** **_<u>OPERATOR:</u>_** When did you receive food assistance last time?

- Never received food assistance

- Less than a month ago

- Between 1 and 3 months ago

- More than 3 months ago

**Question 5:
I use for my analysis is the normalized monsoon rainfall in the year preceding the survey.

There is considerable variation in rainfall between years as well as within a given year. Figure 5 plots the histogram of normalized rainfall for the three survey years. Within each year, the range is well over 2 standard deviations and across the three years, the range is more than 4 standard deviations with considerable overlap between the years. From this graph alone, it looks like rainfall is trending down in the 2000s, but this is driven by exceptionally high rainfall around year 2000. The trend does not hold if rainfall distribution from earlier periods (years, 1981 and 1991) is added.

# **3.3 Conflict: The Maoist insurgency**

When international migration from Nepal began to increase drastically in late 1990s and early 2000s, the ongoing Maoist insurgency took an unexpectedly sharp and violent turn. This led experts to believe that the conflict, which took over 13,000 lives and displaced many more, also pushed individuals outside the country (CBS, 2006; The World Bank, 2011, for instance). In this subsection, I discuss the context of the conflict and my data source.

The Maoist insurgency began, and gained momentum, as an anti-feudal and anti-elite movement. The insurgency, or the “People’s War” as they call it, formally began on February 13, 1996 by a splinter faction of the leftist communist parties that was dissatisfied with the lack of

> 15Author’s calculations from the historic rainfall data and historic production data for each of the districts for years 1975-2000. The estimated regression includes district fixed effects as well as year fixed effects. The identifying variation comes from geographic variation in rainfall measures within a given year and also intertemporal variation in rainfall within a given district.

11
but are not limited to, differences in consumption bundles, limited resources for dealing with rising prices (e.g., substitution across goods), absence of indexed wages broadly due to informal employment, and lower financial inclusion resulting in holding savings and wealth in cash (Kahn 1997; Erosa and Ventura, 2002; Burdick and Fisher, 2007; Cysne et al., 2005; Areosa and Areosa, 2016).

# **2. DATA AND METHODOLOGY**

Estimating household-level inflation rates depends on combining data on consumer prices with household expenditures. The monthly data on the Consumer Price Index (CPI) are collected and compiled by the Pakistan Bureau of Statistics (PBS). The current base year for price statistics is 2015-16. The data on household expenditure have been collected by the PBS through the Household Integrated Economic Survey (HIES). The latest HIES available is that of 2018-19. The HIES data (2018-19) covered 24,809 households in four provinces. The total number of expenditure items covered in the survey is 283. The expenditure items are grouped into 12 main categories according to the Classification of Individual Consumption According to Purpose (COICOP). These items across the 12 commodity groups include Food and Non-Alcoholic Beverages (98); Alcoholic Beverages, Tobacco (8); Clothing and Footwear (13); Housing, Water, Electricity, Gas and other Fuels (24); Furnishing, Household Equipment, and Routine Maintenance (41); Health (6); Transport (14); Communication (5); Recreation & Culture (14); Education (3); Restaurants and Hotels (29); Miscellaneous Goods and Services (28).

The urban CPI collects price data on 356 items, whereas the rural CPI covers 244 for price information. The Laspeyres formula computes urban and rural CPIs using weights from the Household Integrated Income and Consumption Survey (HIICS 2015/16).<sup>2</sup> These regional CPI are then used to compile the national
sup> in reality, social and cultural norms continue to undermine gender equality. Several parts of the legislation regarding family law, including divorce, are discriminatory towards women. For example, adultery alone, if committed by a man, is not grounds for divorce, however, women found guilty of adultery alone may be ordered by the court to relinquish their rights to joint marital property for the benefit of the husband or children.<sup>8</sup> The situational analysis noted that women continue to bear the major responsibility for unpaid care work in addition to their paid jobs, while men are typically the household heads. A World Bank (2020) study reports that there is a lack of women’s agency over the division of labor in the household and that this constitutes a burden to women. In the survey this was also indicated as the main impediment to accessing both paid labor and community participation. Gender segregation can also be seen in the education system. The Admission Standards page of the government operated Myanmar Maritime University states that 320, or 80 percent of students, will be male, and the remaining 20 percent will be female.<sup>9</sup> Traditional views also shape

> 4 <u>http://www.uncdf.org/download/file/127/6266/uncdf-power-country-assessment---myanmar.pdf</u>

> 5 Women Entrepreneurs in Myanmar: What does the Enterprise Survey Data Tell Us? _Working Document_ , WBG (Anne Ong Lopez, Sjamsu Rahardja) 2017. 6 Asian Development Bank, United Nations Development Program, United Nations Population Fund, and the United Nations Entity for Gender Equality and the Empowerment of Women, 2016, Gender equality and women’s rights in Myanmar: A situation analysis.

> 7 At the international level, Myanmar is a signatory to the Beijing Platform for Action and the United Nations Convention on the Elimination of All Forms of Discrimination Against Women (CEDAW), and the 2030 Agenda for Sustainable Development