rafmacalaba/data-use-mentions-extended huggingface dataset:
Survey (ESS). Basic demographic characteristics, health care utilization, school enrollment status, and labor market outcomes are available at the individual level. Others, such as consumption expenditure, other income, property taxes, business taxes, land use fee and agricultural income tax, were captured for households.

The detailed consumption module allows us to use consumption spending as a proxy for disposable income, from which market income is computed through backward calculation by adding taxes and deducting transfers. Consumption data are also used to estimate indirect (VAT and excise) taxes. To estimate the indirect effects of indirect taxes, we use the 2015/16 social accounting matrix (SAM) input-output table (Mengistu et al. 2019). For this purpose, consumption item data from the ESS is combined with tax schedules from the Ethiopian Revenue and Customs Authority with sectors in the input-output matrix (Annex 4).

In addition to survey data, we use the following administrative information: (1) national public revenue and expenditure data for the 2018/19 fiscal year, and regional education and health spending from the national income and public finance accounts of the Ministry of Finance; (2) enrollment information from the Ministry of Education; and (3) government subsidies for kerosene from the Ethiopian Petroleum Supply Enterprise and wheat from the Ethiopian Trading Businesses Corporation.

## **3.2. Assumptions**

One of the assumptions in this study is about direct taxes. Though direct taxes are borne entirely by the income earner, we assume that these taxes have an effect on the welfare of all household members, who share the burden of these taxes. Employment income tax is computed from the estimated monthly chargeable wage using official tax rates for earnings over birr 600 per month, the threshold for paying taxes (Annex 4). This simulation assumes that all eligible taxpayers do pay taxes. Though firms also pay indirect taxes, we assume that these taxes are borne 100 percent by consumers regardless of the market structure. We also assume that all
studies are successful, they can measure the share of funds that fail to reach their intended goal and identify how much is lost at different points in the system. This information is useful for focusing investigations of diverted funds but more often is used to improve public policies (e.g. reallocating resources), financial management (e.g. monitoring, reporting and controls) and administration (e.g. simplifying procedures) (Reinikka & Svensson 2006). In countries with sophisticated and reliable financial administration, the share of funds reaching facilities can be extracted from public sector data. However, such systems are lacking in most countries, leaving the PETS approach as the only viable method.

More than two dozen PETS have been conducted in a range of low- and middle-income countries and the experiences demonstrate that the instrument is useful but difficult to compare across countries, particularly in the health sector (Lindelow 2006). PETS differ from country to country precisely because they are implemented with different policy concerns and because the traceability of resource flows differs in each setting. PETS tend to be most useful where they have been repeated in the same country and can be used to monitor the effects of policy initiatives over time (Dehn 2003). For those interested in conducting a PETS, the 2004 PETS/QSDS instrument used in Chad (that was largely based on the Mozambique 2002 PETS/QSDS) is a good template to begin with.

______________________________________________________________________________________

-29-
section and specifically within the list of fully liberalized sectors, we include Water management, Land transportation, and transportation through pipelines, Warehousing and transportation support activities, Telecommunication, and Education when discussing sustainable investment.

We find that growth in FDI was mainly driven by the paper products industry and base metal industry (Table 6). Related to investments that can be linked to SDGs, only transport and logistics infrastructure in the form of warehousing and transportation support activities saw an increase in the post-policy period. There was either no statistically significant coefficient in all other sectors or a decline, most notable in Water management.

The increase in DDI of the fully liberalized sectors (as reported in autoreftab:DDI _lib_ ) Finally, the third column in Table 6 reports growth by sector in the fully liberalized sectors for planned FDI. Growth was mainly in Printing and reproduction media for recording media, Base metal industry, Water management, Food and Beverage Supply, Real Estate, Other professional, scientific and technical activities, Office administration activities, and office support activities and other business support activities. In terms of SDG related investments, there was an increase in water management but a decline in infrastructure.

Overall, the sector that saw growth in realized FDI, DDI, and announcements of FDI was the base metal industry. This is no surprise since Indonesia is a major producer of nickel in the global market and has many mines of other minerals, such as copper, bauxite, iron ore, coal, and gold. Therefore, this is an industry that will likely keep growing. Several sectors (about 50 percent) saw growth in the same direction for FDI and DDI, further signaling a possible crowding-in effect. Finally, while we can expect an increase in FDI in water management going forward due to the increase in planned FDI, and increase in some infrastructure-related sectors, other SDG-related sectors saw mixed results and even a decline (such as education). Further analysis would be needed sector by sector to observe further both the quality and quantity of
.2|22340|6.6|6.5|

Source: Authors‘ calculations based upon data from the World Bank Enterprise Survey, BEEPS data and from other sources

**Table 2: Firm status at time of Financial Crisis Survey**

||Number of firms|
|---|---|
|Group 1: Interviewed|1,686|
|_Of which: In bankruptcy or insolvency_|_26_|
|Group 2: Not interviewed but operating|545|
|Group 3: Could not be located|201|
|Group4: Confirmed as closed|69|
|Total|2,501|

Source: Authors‘ calculations based upon data from the World Bank Enterprise Survey 40
**28**

value in our sample is **Egypt** (24). The United States has a value of **71,** which is well above the mean value of **61.**

# B. **Determinants of Financial Intermediary Development and Growth**

This section examines the links between the legal environment and financial intermediary development and traces this link through to long-run growth. First, note that the legal origin variables help explain cross country differences in creditor rights, enforcement quality, and accounting standards. As shown **by** LLSV **(1998),** English legal tradition countries have laws that emphasize the rights of creditors to a greater degree than the French, German, and Scandinavian countries. French civil law countries protect creditors the least, with German and Scandinavian civil law countries falling in the middle. In terms of enforcement quality, countries with a French legal heritage have the lowest quality of law enforcement, while countries with German and Scandinavian legal traditions tend to be the best at enforcing contracts. Finally, LLSV **(1998)** show that countries with an English legal tradition tend to have much better accounting standards than French or German civil law countries.

Table **7** shows that cross-country differences in creditor rights, enforcement quality, and accounting standards help explain cross-country differences in financial intermediary development, even after controlling for the level of income per capita. Jointly, the variables CREDITOR, **ENFORCE,** and **ACCOUNT** explain a significant amount of the cross-country variation in the three financial intermediary indicators (PRIVATE CREDIT, LIQUID LIABILITIES, and **COMMERCIALCENTRAL** BANK). Each of the legal/accounting indicators, however, is not significantly correlated with all of the intermediary measures. For instance LIQUID LIABILITIES is most closely associated with **ENFORCE** and CREDITOR. In turn, **COMMERCIAL-CENTRAL** BANK and PRIVATE CREDIT are very strongly linked with **
responding to frustrations and challenges. However, the war also resulted in women becoming economically active, which in this case decreased IPV, as the pressure on men to provide for their families reduced. Also, economic independence and interventions provided women with the option of leaving a violent relationship. Thus, IPV risk is linked to both formal and informal social structures that create gender inequality, including financial dependence on men, traditional gender norms surrounding masculinity as well as the broader changes in social norms and behaviors related to interpersonal violence that occur during conflict.

# 4. Data and model specification

# 4.1 Data and sample construction

Data are drawn from the 2008 and 2013 Nigerian Demographic and Health Survey (NDHS), which include Domestic Violence (DV) modules that sample 23,752 and 27,634 women respectively.<sup>16</sup> The NDHS includes information on the location of the interview and its GPS coordinates. Observations from the 2008 NDHS provide data for the period before the BH insurgency while observations from the 2013 NDHS provide data for the period during the BH insurgency. Exposure to BH conflict is measured using the Armed Conflict Location and Event Database (ACLED), which records events whether they generate fatalities or not. The data on events are reported by date, location, agent and type.

Our sample is made up of data drawn from 664 Local Government Areas (LGAs) across all 36 states, including the Federal Capital Territory (FCT). Observations from the 2008 and 2013 NDHS are linked to the BH events recorded in ACLED using the GPS coordinates <mark>provided in both data sets. To match the timing of the NDHS surveys, we use geocoded BH events that occurred between 2009 and 2013.</mark><sup>17</sup> During this period, 799 BH events in Nigeria were recorded in ACLED. Because of the intensity of the BH insurgency, interviewers during the field work of the 2013 NDHS could not reach some conflict-affected areas (NPC 2014
**Table 4.** **<u>Correlations between actual and fitted HAZ-score</u>**

||Fitt<br>|ed HAZ-scor<br>|e_with_<br>|
|---|---|---|---|
|Actual HAZ-score|Predicted income<br>only (i)|Asset index<br>only (ii)|Predicted income<br>and asset index(iii)|
|A d ROC|0.5978|0.5985|0.6033|
|rea uner curve|**0.6627**|**0.6669**|**0.6730**|
|Searman rank correlation|0.1889|0.1861|0.1981|
|p|**0.2551**|**0.2496**|**0.2652**|

Source: Own computations using HLCS 2001 and DHS 2000 (2005 in bold)

# **3.4.Evolution of Asset-Poverty over Time**

For the purpose of the temporal comparison of assets, all of the household asset indices used in the analysis are calculated on an individual basis by dividing indices by household size. In order to factor in asset-related economies of scale within the household, indices were also calculated on a per household basis and for assets divided by the square root of household size. Results did not qualitatively change with the use of these different definitions of asset indices, so that they prove to be robust to the choice of equivalent scales. Asset-based poverty headcount (P0), poverty gap (P1), and poverty severity (P2) indices are presented in table 5 for various thresholds. The asset-based poverty lines are the 20<sup>th</sup> , 40<sup>th</sup> , 60<sup>th</sup> and 80<sup>th</sup
.06|
|Drought last year|0.0118|0.004<br>0.1159|‐0.0008|
||0.44|0.06<br>0.45|‐0.01|
|Drought last year*drought||0.1253<br>0.0179|‐0.1257|
|frequency||||
|||0.35<br>0.05|‐0.45|
|Drought last year*ln(hours) from||‐0.0212|0.0111|
|500k city||||
|||‐0.39|0.31|
|Drought frequency (proportion)||‐<br>‐2.5466***|‐1.2609***|
|||0.5241*<br>**||
|||‐4.74<br>‐5.54|‐4.25|
|Drought frequency*ln(hours)<br>from 500k city||0.5486***|0.2845***|
Figure 7 Backcasted poverty rates series using different variables to map sectoral GDP data to the 2018/19 NLSS <!-- Start of picture text --> 47 46.2<br>46.3<br>45 46.1<br>43<br>41<br>39 39.1<br>37<br>35<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br>Household head's sector of employment<br>Oldest working member of the household<br>Household member closest to 40 in age<br>Poverty rate (percent)<br><!-- End of picture text --> Note: the figure compares different backasted series that use different ways to map the sectoral GDP data to household consumption data from the 2018/19 NLSS. It compares the main specification that uses household head’s sector of employment, to one using the sector of employment of the oldest working member of the household or the member of the household closest to 40 in age.

Table 12 Annualize change in average annual household consumption for different combinations of imputed GHS data, by decile

||Annualized Difference|Annualized Difference|Annualized Difference<br>2015/16 - 2018/19,|
|---|---|---|---|
|Deciles|2010/11 - 2018/19, percent|2010/11 - 2015/16, percent|percent|
|1|0.4|0.7|-0.2|
|2|0.4|0.8|-0.3|
|3|0.4|0.8|-0.4|
|4|0.4|0.9|-0.5|
|5|0.3|0.9|-0.7|
|6|0.3|1.0|-0.
ing attitudes with measures of perceived norms (i.e. what individuals think others might approve or disapprove of) since perceptions are likely to matter for decision-making (see for example, Bernhardt et al., 2018; Bicchieri, 2016; Bursztyn and Yang, 2022).<sup>3</sup> An influential study by Bursztyn, Gonzalez and Yanagizawa-Drott (2020) documented that perceptions of peers’ opinions toward female employment outside the home influenced behavior beyond an individual’s own opinions.

In this paper, we fill data and knowledge gaps by leveraging a unique dataset that includes data both on personal attitudes and perceived norms and link them to individual-level employment outcomes on a global scale. We use data from over 150,000 individuals across 111 countries from all regions of the world collected through a survey implemented online via the Facebook platform, namely the Survey on Gender Equality at Home.<sup>4</sup> The survey has a greater coverage of low- and middle-income countries than alternative global databases that measure gender attitudes.<sup>5</sup> We designed questions for the survey to capture an individual’s personal belief and perception about others’ beliefs around traditional gender roles to study how gender norms relate to specific behaviors for women and men. We complement two levels of beliefs - what individuals think and what they think their community thinks - and correlate them with labor market, time use and decision-making behaviors. Specifically, this paper asks how do personal attitudes and perceived community norms towards a _broad norm on rights to equal opportunities_ , and the stereotypical gender roles of the _male breadwinner_ and _female caregiver_ correlate with labor force participation, intrahousehold decision-making power, and time spent on paid work, care and domestic activities for both men and women globally?

The stereotypical roles of the male breadwinner and female caregiver are hypothesized to have been born between the mid-19th and mid-20th century in many countries as industrialization gave rise to the separation of home and work. In this model of the family men
500 km of roads (table 20).

Sudanese federal and state governments and state-owned enterprises (SOEs) account for an additional third of Sudan infrastructure investments, and have focused on transport and to some extent power. The remaining capital funding comes from the private sector, which, as in most of the rest of Africa, supports the ICT boom. ODA flows have played a very small role in capital infrastructure financing.

Capital investments in Sudan are heavily skewed toward the power sector, much of this funded by China. Investment levels in ICT and transport are comparable

**Table 20. Identified infrastructure projects financed by non-OECD financiers**

|US$million|Project<br>costs|
|---|---|
|China|949|
|Construction of 300 MW gas-fired power plant in Al-Fulah|518|
|Construction of the El-Gaili Combined Cycle Power Plant, Phase 1|150|
|NEC transition line|81|
|White Nile electricity network|200|
|China and Arab countries|1,200|
|Construction of the Merowe hydroelectric dam (1,250 MW)|1,200|
|India|475|
|Setting up 4x125 MW Kosti Combined Cycle Power Plant|475|
|**Total**|**2,624**|
|_Source:_Foster, etc (2009). Calculations based on World Bank-PPIAF<br>project database. (http://ppi.worldbank.org), in current $ millions .|Chinese|

> <sup>41</sup>
This paper addresses this gap using nationally representative, geo-referenced household survey data from five African countries collected under the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) initiative. Together, these countries (Ethiopia, Malawi, Nigeria, Tanzania, and Uganda) represent 41% of the population in Sub-Saharan Africa and cover many of its agro-ecological zones. In addition to comprehensive household level information about consumption and income sources, these surveys also collected geo-referenced plot level information on the different crops and trees grown on each farm as well as the products harvested. These features of the data are exploited here to measure the prevalence and economic contribution of trees on farms in Sub-Saharan Africa.

The long time lag between planting and harvesting, insecure property rights, small plots and landholdings, and remoteness, which often characterize smallholder farming in Africa, would all seem to play against the adoption of trees. Yet case study evidence from across Africa also shows that small farmers do plant and manage trees on their farms. So, what are the key drivers? We also explore this question by developing and testing theoretically informed models of the determinants of farmer decisionmaking as regards the management of on-farm trees.

In the next section we describe first our method for measuring trees on farms. Given the multitude of possible tree-like crops, the paper first presents a brief typology of the different trees considered, followed by a description of the data and methodologies used to measure and analyze their prevalence and contribution to overall household income and welfare, as well as their key correlates. Identification of the latter is motivated by insights from the literature on the socio-economic and agro-ecological drivers of trees on farms. Section 3 presents and discusses the findings, including the spatial relationship of tree crops to forests. Concluding remarks are offered in section 4.

# 2 Materials, Methods, and Theoretical Underpinnings

## 2.1 Identifying and counting trees on farms

The nationally representative household surveys conducted under the LSMS-ISA initiative during 20103
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Robinson, Sherman, Andrea Cattaneo, and Moataz El-Said. 2001. “Updating and Estimating a Social Accounting Matrix Using Cross Entropy Methods,” Economic Systems Research, Vol. 13, No. 1, pp. 47-64.

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UBoS. 2007. The 2005/06 Uganda National Household Survey. Kampala Uganda Bureau of Statistics, Ministry of Finance, Planning and Economic Development.

van der Mensbrugghe, Dominique. 2010. The Environmental Impact and Sustainability Applied General Equilibrium (ENVISAGE) Model. Version 7.1. Technical Reference Document. December. The World Bank.

World Bank. 2007a. World Development Report 2008: Agriculture for Development. World Bank, Washington, D.C.

World Bank. 2007b. Fiscal Policy for Growth and Development: Further Analysis and Lessons <u>from Country Case Studies. Background report for the April 15, 2007, Development Committee</u> Meeting.

World Bank. 2011a. EdStats. November. Downloaded from http://data.worldbank.org/data- <u>catalog.</u> World Bank 2011b. World Development Indicators. November. Downloaded from <u>http://data.worldbank.org/data-catalog.</u>
this switch, the overall crime rate dropped by almost 10 percent over the next 20 years (Wright et al., 2014).

Shifting cash payments into accounts can also increase transparency and ensure that people receive wage or government transfer payments in full. Cash is easily pilfered by middlemen, but digital payments curb opportunities for theft by reducing the number of intermediaries between senders and recipients. Digital payments also are easier to track than cash, and when recipients have records of the amount of payments they are entitled to, it is more difficult for middlemen to seek bribes. In Argentina, moving cash payments for a national anti-poverty program into accounts was found to reduce corruption. When the payments were made in cash, 4 percent of recipients reported paying kickbacks to people or organizations that helped enroll them in the program; when the payments were made directly into accounts, that number dropped to just 0.03 percent (Duryea and Schargrodsky, 2008). In India, bribe demands for receiving social security pension payments were cut by 1.8 percentage points (or 47 percent) when the payments were made via smart cards instead of being handed out in cash by government officials (Muralidharan et al., 2014). At the same time, shifting cash payment into accounts can also help governments and businesses reduce the incidence of “ghost” or fake recipients. Payments into accounts generally require more stringent identification documentation, making it harder for ghost recipients to remain undetected. Incidence of ghost recipients fell by 1.1 percentage points when India’s social security pension payments were made digitally via smart cards rather than cash (Muralidharan et al., 2014).

Shifting payments, especially regular bill payments, from cash into accounts can also help people build a payments data history which can then be leveraged for better access to credit. Access to credit often depends on lenders being able to assess the credit risk of potential borrowers based on their credit history. However, many low-income adults lack a documented credit history, which might reduce